Posts in category: Microsoft AI

Transcript

ANIRUDDH VASHISTH: From the computation point of view, we always thought that if somebody gave us, like, a hundred different chemistries, we can do a bunch of simulations; tell you, like, 10 of these actually work. What we’ve been able to do specifically for vitrimers is that we’re able to look at the problem from the other side, and we are able to say that if you tell me a particular application, this particular chemistry would work best for you. In essence, what we were thinking of is that if aliens abducted all the chemists from the world, can we actually come up with a framework? [LAUGHTER]

JAKE SMITH: If all of this work is successful, in 10 years, maybe our materials design process looks completely different, where we’ve gone from this kind of brute-force screening to an approach where you start with the properties that you care about—they’re defined by the application that you have in mind—and we use this, like, “need space” to define the material that we would like, and we can use machine learning, artificial intelligence, in order to get us to the structure that we need to make in order to actually achieve this design space.

GRETCHEN HUIZINGA: You’re listening to Collaborators, a Microsoft Research Podcast showcasing the range of expertise that goes into transforming mind-blowing ideas into world-changing technologies. I’m Dr. Gretchen Huizinga.

I’m thrilled to be in the booth today, IRL, with Dr. Jake Smith, a senior researcher at Microsoft Research and part of the Microsoft Climate Research Initiative, or MCRI. And with him is Dr. Aniruddh Vashisth. He’s an assistant professor of mechanical engineering at the University of Washington and director of the Vashisth Research Lab. Jake and Aniruddh are working on a project that uses machine learning to help scientists design sustainable polymers with a particularly exciting application in the field of the ubiquitous printed circuit board, or PCB. But before we get all sustainable, let’s meet our collaborators!

Jake, I’ll start with you. You’re a self-described “chemist with relatively broad interests across applications” and you’ve done some pretty cool things in your career. Tell us about those interests and where they’ve led you and how they’ve contributed to the work you’re doing now in MCRI, or the Microsoft Climate Research Initiative.

JAKE SMITH: Yes. Thank you very much for having me. So I started, like most chemists, poking things around in the lab and learning really fundamentally about how atoms interact with one another and how this affects what we do or what we see at our microscopic level. And so after I left grad school doing this super-basic research, I wanted to do something more applied, and so I did a couple of postdocs, first, looking at how we can more effectively modify proteins after we’ve synthesized them so they might have a property that we care about and then later doing similar work on small molecules in a more traditional drug-design sense. But after I finished that, I wound up here at Microsoft. We were very interested in one molecule in particular, one family of molecules, which is DNA, and we wanted to know, how do we make DNA at just gigantic scale so that we can take that DNA and we could store digital data in it? And because DNA has this nice property that it kind of lasts forever, …

HUIZINGA: Yeah.

SMITH: … at least on our, you know, human scale, it makes a very, you know, nice archival storage medium. So we worked on this project for a while, and at some point, we determined we can, kind of, watch it blossom and find the next challenge to go work on.

HUIZINGA: Interesting …

SMITH: And the challenge that we, you know, wound up at I’ll describe as the Microsoft Climate Research Initiative, the MCRI. We were a group of applied scientists from, like, natural scientist backgrounds within Microsoft, and we said, how can we make a difference for Microsoft? And the difference that we thought was Microsoft has climate goals.

HUIZINGA: Oh, yeah!

SMITH: Microsoft wants to be carbon negative, it wants to be water positive, and it wants to be zero waste. And in order to make this happen, we need novel materials, which really are a macroscopic view of, once again, atomic behavior. And we said, hey, we understand atomic behavior. We’re interested in this.

HUIZINGA: [LAUGHS] We can help! We’re from the government …

SMITH: Yeah, maybe this is something we could help on. Yeah. And so here we are. We wound up with Aniruddh, and we’ll go into that later, I’m sure.

HUIZINGA: Yeah, yeah. So just quickly back to the DNA thing. Was that another collaboration? I had Karin Strauss on the podcast a while ago, and she talked about that.

SMITH: Oh, absolutely. Yeah, this was with Karin, and we had great collaborators, also at the University of Washington in the Molecular Information Systems Lab, or MISL, who did a lot of work with us on the practicalities of working with DNA once it’s synthesized and how would you do things like retrieve information from a big pool of DNA.

HUIZINGA: Right. Right. They could … people could go back to that podcast because she does unpack that quite a bit. Well, Aniruddh, you describe yourself as a “trained mechanician who hangs out with chemists,” hence your friendship with Jake here, but for your day job, you’re a professor and you have your own lab that conducts interdisciplinary research at the intersection, as you say, of mechanics and material science. So what made you want to move to that neighborhood, and what goes on there?

ANIRUDDH VASHISTH: Yeah. Well, again, thank you so much for having me here. I’m super excited about this. Yeah, just a little bit of background about me. So I started off with my undergrad in civil and mechanics from IIT BHU, did a PhD in mechanics at Penn State, and moved to Texas …

HUIZINGA: Go back … go back to, what’s the first one?

VASHISTH: It’s Indian Institute of Technology, in India, so that’s …

HUIZINGA: IIT …

VASHISTH: … IIT. I did my undergrad there and then straight away came to the US to do my PhD in mechanics at Penn State and then ended up going to Texas, to Texas A&M University, and postdoc-ed in a chemical engineering lab, and that’s how I became, like, super familiar and fond of chemical engineers and chemists! [LAUGHTER] And we moved to Seattle, when I got the job at University of Washington in 2021, with my wife and my daughter. And what we do in our lab is we make and break things now! [LAUGHS] We try to see, like, you know, when we are making and breaking these things, we try to see them from an experimental and a simulation point of view and try to gain some understanding of the mechanics of these different types of materials. Especially, we are very interested in polymers. I always joke with my students and my class that go about one day without touching a polymer, and I’m always surprised by the smiles or the smirks that I get! But in general, like, we have been super, super excited and interested about sustainable polymers, making sustainable composites. Particularly, we are very excited and interested in vitrimer polymers. So let me just take, like, a step back. I’ll probably wear my professor hat straight away here.

HUIZINGA: Yeah. Let’s do! Let’s go. [LAUGHTER]

VASHISTH: And I’ll tell you, just, like, taking a step back, what are the different types of polymers. So in general, you can think of polymers as thermosets or thermoplastics. So to Jake’s point, let’s just go to the molecular scale there, and you can think of polymers as bunch of these pasta noodles which can slide over each other, right. Or these bunch of pasta noodles which are packed together. So thermoset, as the name suggests, it’s a set network. The pasta noodles are kind of, like, set in their place. Thermoplastics is when these pasta noodles can slide over each other. So you’ve probably put too much sauce in there! [LAUGHTER] Yeah, so a good analogy there would be a lot of the adhesives that we use are thermosets because they set after a while. Thermoplastic … we use plastics for 3D printing a lot, so those are thermoplastics. So they’re solid. You can heat them up, you can make them flow, print something, and they solidify. Vitrimers are very exciting because, just like thermoplastics, they have this flowability associated to them but more at a molecular scale. Like, if you think of a single pasta noodle, it can unclick and re-click back again. So it’s like, you know, it’s made up of these small LEGO blocks that can unclick and re-click back again …

HUIZINGA: LEGO pasta …

VASHISTH: LEGO pasta …

HUIZINGA: I like that! [LAUGHS]

VASHISTH: Exactly. So this unclicking and re-clicking can make them re-processable, reusable, recyclable. Gives them, like, much longer life because you can heal them. And then vitrimers basically become the vampires of the polymer universe!

HUIZINGA: Meaning they don’t die?

VASHISTH: Well …

HUIZINGA: Or …

VASHISTH: They have like much longer life! [LAUGHTER]

SMITH: They sleep every now and then to regenerate! Yes … [LAUGHS]

HUIZINGA: Aniruddh, sticking with you for a minute, before we get into the collaboration, let’s do a quick level set on what we might call “The Secret Life of Circuit Boards.” For this, I’d like you to channel David Attenborough and narrate this PCB documentary. Where do we find printed circuit boards in their natural habitat? How many species are there? What do they do during the day? How long do they live? And what happens when they die?

VASHISTH: OK, so do I have to speak like David … ?

HUIZINGA: Yes, I’d appreciate it if you’d try. [LAUGHTER] … No. Just be your voice.

VASHISTH: Yeah. Yeah. So PCBs are, if you think about it, they are everywhere. PCBs are in these laptops that we have in front of us. Probably there are PCBs in these mics. Automobiles. Medical devices. So PCBs are, they’re just, like, everywhere. And depending upon, like, what is their end applications, they have a composite part of it, where you have, like, some sort of a stiff inclusion in a polymeric matrix, which is holding this part together and has bunch of electronics on top of it. And depending on the end application, it might come in different flavors: something that can sustain much higher temperatures; something which is flexible. Things of that sort. And they live as long as we use the material for, like, you know, as long as we are using these laptops or as long as we end up using our cars. And unfortunately, there is a lot of e-waste which is created at the end.

HUIZINGA: Right …

VASHISTH: There’s been a lot of effort in recycling and reusing these materials, but I’m confident we can do more.

HUIZINGA: Right.

VASHISTH: I think there’s like close to 50 million metric tons of …

HUIZINGA: Wow!

VASHISTH: … of e-waste which is generated—more than that actually—every year, so …

HUIZINGA: OK.

VASHISTH: … a lot of scope for us to work there.

HUIZINGA: Um, so right now, are they sort of uniform? The printed circuit board? I know we’re going to talk about vitrimer-based ones, but I mean, other than that, are there already multiple materials used for these PCBs? Jake, you can even address that.

SMITH: Yeah. Of course. So there are, like, kind of, graded ranks of circuit board materials …

HUIZINGA: OK.

SMITH: … that as Aniruddh said, you know, might be for specialty applications where you need higher-temperature tolerance than normal or you need lower noise out of your circuit board.

HUIZINGA: Gotcha.

SMITH: But, kind of, the bog-standard circuit board, the green one that you think about if you’ve ever seen a circuit board, this is like anti-flammability coating on a material called FR-4. So FR-4—which is an industrial name for a class of polymers that are flame-retardant, thus FR, and 4 gives you the general class—this is the circuit board material …

HUIZINGA: OK …

SMITH: … that, you know, we really targeted with this effort.

HUIZINGA: Interesting. So, Jake, let’s zoom out for a minute and talk about the big picture and why this is interesting to Microsoft Research. I keep hearing two phrases: sustainable electronics and a circular economy. So talk about how the one feeds into the other and what an ultimate success story would look like here.

SMITH: Yeah, absolutely. So I’ll start with the latter. When we set out to start the Microsoft Climate Research Initiative, we started with this vision of a circular economy that would do things that avoid what we, you know, can avoid using. But there are many cases where you can’t avoid using something that is nonrenewable. And there, what we really want to do is we want to recapture what we can’t avoid. And this project, you know, falls in the latter. There’s a lot of things that fall in the latter case. So, you know, we were looking at this at a very carbon dioxide-centric viewpoint where CO₂ is ultimately the thing that we’re thinking about in the circle, although you can draw a circular economy diagram with a lot of things in the circle. But from the CO₂ viewpoint, you know, what led us to this project with Aniruddh is we thought, we need to capture CO₂, but once you capture CO₂, you know, what do you do with it? [LAUGHTER] You can pump some of it back into the ground, but this is, you know, an economically non-productive activity. And so it’s something we have to do. It’s not something we want to do.

HUIZINGA: Right.

SMITH: And so what could we want to do with the CO₂ that we’ve captured? And the thought was we do something economically viable with it. We, you know, upcycle the CO₂ into something interesting, and what we really want, and what we still really want, is to be able to take that CO₂, convert it down into a useful chemical feedstock—and there are great laboratories …

HUIZINGA: Oh, interesting …

SMITH: … doing work on this—and then we could, you know, look at our plastic design problem and say, hey, we have all this FR-4 in the world. How could we replace the FR-4—the, you know, explicit atoms that are in the FR-4—with atoms that have come from CO₂ that we pulled out of the air? And so this is, you know, the circular economy portion. We come down to, you know, the specific problem here. Aniruddh talked a lot about e-waste.

HUIZINGA: Yeah.

SMITH: And I have great colleagues who also collaborated with us on this project—Bichlien Nguyen, Kali Frost—who have been doing work with our product teams here at Microsoft on, you know, what can we do to reduce the amount of e-waste that they put out towards Microsoft’s climate goals?

HUIZINGA: Right.

SMITH: And Microsoft, as a producer of consumer electronics and a consumer of, you know, industrial electronics, has a big e-waste problem itself that we need to, you know, actually take research steps in order to ultimately address, and so what we thought was, you know, we have this end-of-life electronic. We can do things like desolder the components. We can recapture those ICs, which have a lot of embedded carbon in them in the silicon that’s actually there. We can take and we can etch out the copper that has been put over this to form the traces, and we can precipitate out that electrochemically to recapture the copper, but at the end of the day, we’re left with this big chunk of plastic, and it’s got some glass inside of it, too, for completeness sake, and the thought was, you know, how do we do this? You can’t recapture this with FR-4. FR-4, to go back to the spaghetti thing, …

HUIZINGA: Right … [LAUGHS]

SMITH: … spaghetti is glued to itself. It doesn’t come apart. It rips apart if you try and take it apart. And so we wanted to say, you know, what could we do and, you know, what could we do with Aniruddh and his lab in order to get at this problem and to get us at a FR-4 replacement that we could actually reach this complete circularity with.

HUIZINGA: Interesting! Well, Jake, that is an absolutely perfect segue into “how I met your mother,” which is, you know, how you all started working together. Who thought of who first, and so on. I’m always interested to hear both sides of the meet-up. So, Aniruddh, why don’t you take the baton from Jake right there and talk about, from your perspective, how you saw this coming together, who approached who, what happened—and then Jake can confirm or deny the story! [LAUGHTER]

VASHISTH: Yeah, yeah. So it actually started off, I have a fantastic colleague and a very good friend in CS department, Professor Vikram Iyer, and he actually introduced me to Bichlien Nguyen from Microsoft, and we got a coffee together and we were talking about vitrimers, like the work that we do in our lab, and I had this one schematic—I forget if it was on my phone or I was carrying around one paper in my pocket—and I showed them. I was like, you know, if we can actually do a bunch of simulations, guide an ML model, we can create, for lack of a better word, like a ChatGPT-type of model where instead of telling like, “This is the chemistry; tell me what the properties are,” we can go from the other side. You can ask the model, “Hey, I want a vitrimer chemistry which is recyclable, re-processable, that I can make airplanes out of or I can make glasses out of. Tell me what that chemistry would look like.” And I think, you know, Bichlien was excited about this idea, and she connected me with Jake, and I think I’ve been enjoying this collaboration for the last couple of years, …

HUIZINGA: Right …

VASHISTH: … working on that.

HUIZINGA: Was there a paper that started the talk, or was it just this napkin drawing? [LAUGHS]

VASHISTH: I think, to give myself a little bit of credit there, I think there was a paper with a nice drawing on it.

HUIZINGA: Right?

VASHISTH: Yeah. There was a white paper. Yeah.

HUIZINGA: That’s good. Well, Jake, what’s your side of this story?

SMITH: Ah, this is awesome! We got the first half that I didn’t know, so …

HUIZINGA: Oh—filling in gaps!

SMITH: This was the Bichlien-mediated half! [LAUGHTER] I was sharing an office with Bichlien, who apparently came up from this meeting, and, you know, I saw the mythical paper! She put this on my desk. And I’ll plug another MCRI project that we were working on there where—or at the time—where we were attempting to do reverse design, or inverse design, of metal organic frameworks, which are these really interesting molecules that have the possibility to actually serve as carbon capture absorbents, …

HUIZINGA: Oh, wow.

SMITH: … but the approach there was to use machine learning to help us, you know, sample this giant space of metal organic frameworks and find ones that had the property that we cared about. I mean, you draw this diagram that’s much like Aniruddh just described, where you’ve got this model that you train and out the other side comes what you want, and so this paper came down on my desk, and I looked at it and I said, “Hey, that’s what we’re doing!” [LAUGHTER] And it, kind of, you know, went from there. We had a chat. We determined, hey, we’re both interested in, you know, this general approach to getting to novel materials.

HUIZINGA: Right.

SMITH: And then, you know, we’ve already talked about the synergy between our interests and Microsoft’s interests and the, you know, great work or the great particular applications that are possible with the type of polymer work that Aniruddh does.

HUIZINGA: Yeah. So the University of Washington and Microsoft meet again. [LAUGHTER] Well, Jake, let’s do another zoom out question because I know there’s more than just the Microsoft Climate Research Initiative. This project is a perfect example of another broader initiative within Microsoft which has the potential to quote “accelerate and enhance current research,” and that’s AI for Science. So talk about the vision behind AI for Science, and then if you have any success stories—maybe including this one—tell us how it’s working out.

SMITH: Yeah, absolutely. We are—and by we, I mean myself and my immediate colleagues—are certainly not the only ones interested in applying AI to scientific discovery at Microsoft. And it turned out, a year or two after we started this collaboration, a bigger organization named AI for Science arose, and we became part of it. And it’s, you know, generally a group of people who—along with our kind of sister organization in research called Health Futures, who work more on the biology side—are interested in how AI can help us do science in (a) a faster way, but (b) maybe a smarter, better-use-of-resources way, or the ultimate goal, or the ultimate dream, is (c) a way that we just can’t think of doing right now. A way that, you know, it just is fundamentally incompatible with the way that research has historically been done in, you know, small groups of grad students directed by a professor who are themselves, you know, the actual engine behind the work that happens. And so the AI for Science vision, you know, it’s got a couple of parts that really map very well onto this project. The first part is we want to be able to simulate bigger systems. We want to be able to run simulations for longer, and we want to be able to do simulations at higher accuracy. When we get into the details of, you know, the particulars of the vitrimer project, you’ll see that one of the fundamental blocks here is the ability to run simulations, and Aniruddh’s excellent grad student Yiwen, you know, spent a ton of time trying to identify the appropriate simulation parameters in order to capture the behavior that we care about here. And so, the first AI for Science vision says we don’t need Yiwen to do that, you know, we’re going to have a drop-in solution or we’re going to have, you know, a set of drop-in solutions that can, you know, take this work away from you and make it much easier for you to go straight to running the simulations that you care about.

HUIZINGA: Yeah. A couple questions. Not on the list here, but you prompted them. No pun intended. Are these specialized models with the kinds of information … I mean, if I go to ChatGPT and ask it to do what you guys are doing, I’m not going to get the same return am I?

SMITH: Absolutely.

HUIZINGA: Am I?

SMITH: Oh, no, no, no, no! [LAUGHTER] I was saying you were absolutely correct. [LAUGHS] You can ask ChatGPT, and it will tell you all sorts of things that are very interesting. It can tell you, probably, a vitrimer. It could give you Aniruddh’s spiel about the spaghetti, I’m sure, if you prompted it in the correct way. But what it can’t tell you is, you know, “Hey, I have this particular vitrimer composition, and I would like to know at what temperature it’s going to melt when I heat it up.”

HUIZINGA: Right. OK, so I have one more question. You talk about the simulations. Those take a lot of compute. Am I right? Am I right?

SMITH: You’re absolutely right.

VASHISTH: Yeah.

HUIZINGA: So is that something that Microsoft brings to the party in terms of … I mean, does the University of Washington have the same access to that compute, or what’s the deal?

VASHISTH: I think especially on the scale, we were super happy and excited that we were collaborating with Microsoft. I think one of these simulations took, like, close to a couple of weeks, and we ended up doing, I would say, like, close to more than 30,000 simulations. So that’s a lot of compute time if you think about it.

HUIZINGA: To put that in perspective, how long would it take a human to do those simulations? [LAUGHS]

SMITH: [LAUGHS] Oh, man, to try and actually, like, go do all this in the lab …

HUIZINGA: Right!

SMITH: First, you got to make these 30,000, like, starting materials. This in itself … let’s say you could buy those. Then to actually run the experiments, how long does it take to do one …

HUIZINGA: And how much money?

VASHISTH: That’s … that’s like you’re talking about like one PhD student there.

HUIZINGA: Right?

VASHISTH: That’s like, you know, it takes like a couple of years just to synthesize something properly and then characterize it, and it’s …

HUIZINGA: Yeah …

VASHISTH: Yeah, no, I think the virtual world does have some pluses to it.

HUIZINGA: So this is a really good argument for AI for Science, meaning the things that it can do, artificial intelligence can do, at a scale that’s much smaller than what it would take a human to do.

SMITH: Yeah, absolutely. And I’ll plug the other big benefit now, which is, hey, we can run simulations. This is fantastic. But the other thing that I think all of us really hope AI can do is it can help us determine which simulations to run …

HUIZINGA: Ooh …

SMITH: … so we need less compute overall, we need less experiments if we have to go do the experiments, and this is …

HUIZINGA: So it’s the winnowing process.

SMITH: Exactly.

HUIZINGA: OK. That’s actually really interesting.

SMITH: And this is, like, the second, or maybe even the largest, vector for acceleration that we could see.

HUIZINGA: Cool. Well, every show I ask, what could possibly go wrong if you got everything right? And, Aniruddh, I want to call this the “Defense Against the Dark Arts” question for you. You’re using generative AI to propose what you call novel chemistries, which can sound really cool or really scary, depending on how you look at it. But you can’t just take advice from a chatbot and apply it directly to aerospace. You have to kind of go through some processes before. So what role do people, particularly experts in other disciplines, play here, and what other things do you need to be mindful of to ensure the outputs you get from this research are valid?

VASHISTH: Yeah, yeah. That’s a fantastic question. And I’ll actually piggyback on what Jake just said here, about Yiwen Zheng, who’s like a fantastic graduate student that we have in our lab. He figured out how to run these simulations at the first point. It was like six months of … like, really long ordeal. How to make sure that in the virtual world, we are synthesizing these polymers correctly and we are testing them correctly. So that human touch is essential, I feel like, at every step of this research, not just like doing virtual characterization or virtual synthesis of these materials, training the models, but eventually, when you train the models also and the model tells you that, well, these are, like, the 10 best polymers that would work out, there you need people like Jake who are like chemists, you know. They come in [LAUGHTER] and they’re like, hey, you know what? Like, out of these 10 chemistries, this one you can actually synthesize. It’s a one-step reaction or things of that sort. So we have a chemist in our lab also, Dr. Agni Biswal, who’s a postdoc. So we actually show him all these chemistries, apart from Jake and Bichlien. We show the chemistries to all the chemists and say, like, OK, what do you think about this? How do these look like? Are they totally insane, or can we actually make them? [LAUGHTER]

SMITH: Yeah, we still need that, like, human evaluation step at the end, at this point.

HUIZINGA: Yeah … VASHISTH: Exactly.

HUIZINGA: Ask a chemist! Well, and I would imagine it would be further than just, “This would be the best one,” or something like, “You better not do that one.” Are there ever like crazy responses or replies from the model?

SMITH: [LAUGHS] It’s fascinating. Models are very good—and particularly we’ll talk about models that generate small organic structures—at generating things that look reasonable. They follow all the rules. But there’s this next step beyond that. And you see this when you talk to people who’ve worked in med chem for, you know, 30 years of their life. Well, they’ll look at a structure and they’ll, like, get this gut feeling like, you know, a storm is coming in and their knee hurts, and they really don’t like that molecule. [LAUGHTER] And if you push them a little bit, you know, sometimes they can figure out why. They’ll be like, oh, I worked on, you know, a molecule that looked like that 20 years ago, and it, you know, turned out to have this toxicity, and so I don’t want to touch that again. But oftentimes, people can’t even tell you. They’ve just got this instinct …

HUIZINGA: Really?

SMITH: … that they’ve built up, and trying to, you know, capture that intuition is a really interesting next frontier for this sort of research.

HUIZINGA: Wow. You know, you guys are just making my brain fry because it’s like so many other questions I want to ask, but we’re actually getting there to some of them, and I’m hoping we’ll address those questions with the other things I have. So, Jake, I want to come … Well, first of all, Aniruddh, have you finished your defense against the dark arts? [LAUGHS]

VASHISTH: I think I can point out one more thing very quickly there, and as Jake said, like, we are learning a lot, particularly about these materials, like, the vitrimer materials. These are new chemistries, and we are still learning about, like, the mechanical, thermorheological properties; how to handle these materials. So I think there’s a lot that we don’t know right now. So it’s like a bunch of, like, unknown unknowns that are there. So …

HUIZINGA: Well, and that’s research, right? The unknown unknowns. Jake, I want to come back to the vision of the climate research initiative for a minute. One goal is to develop technologies that reduce the raw tonnage of e-waste, obviously. But if we’re honest, advances in technology have almost encouraged us to throw stuff away. It’s like before it even wears out. And I think we talked earlier about, you know, this will last as long as my car lasts or whatever, but I don’t like my car in five years. I want a different one, right? So I wonder if you’ve given any thought to what things, in addition to the work on reusable and recyclable components, we might do to reverse engineer the larger throwaway culture?

SMITH: This was interesting. I feel like this gets into real questions about social psychology and our own behaviors …

HUIZINGA: Yeah …

SMITH: … with individual things. Why do I have this can of carbonated water here when I could have a glass of carbonated water? But I want to, kind of, completely sidestep that because …

HUIZINGA: Yeah … Well, we know why! Because it’s convenient, and you can take it in your car and not spill.

SMITH: Agreed. Yes. All right. [LAUGHTER] I also have this cup, and it could not spill, as well.

HUIZINGA: True! Recyclable—reusable.

SMITH: Ahhh … no, no … this is like a—it’s an ingrained consumer behavior that I’ve developed that might … I’ll slip into “Jake’s Personal Perspectives” here, which is that it should not be on the individual consumer behavior changes to ultimately drive a shift towards reusable and recyclable things. And so one of the fundamental, like, hypotheses that we had with the, you know, design of the projects we put together with the MCRI was that if we put appropriate economic incentives in place, then we can naturally guide behavior at a much bigger scale than the individual consumer. And maybe we’ll see that trickle down to the consumer. Or maybe this means that the actual actors, the large-scale actors, then have the economic incentive to follow it themselves.

HUIZINGA: Right.

SMITH: And so with the e-waste question in particular, we talked a lot about FR-4 and, you know, it’s the part of the circuit board that you’re left over with at the end that there’s just nothing to do with …

HUIZINGA: Right.

SMITH: … and so you toss into landfill, you burn it, you do something like this. But, you know, with a project like this, where our goal was to take that material and now make it reusable, we can add this actual economic value to the waste there.

HUIZINGA: Yeah. I realized even as I asked that question, that I had the answer embedded in the question because, in part, how we design technologies drives how people use things.

SMITH: Yeah, absolutely. VASHISTH: Yeah.

HUIZINGA: And usually, the drivers are convenience and economics. So if upstream of consumer … consumption? [LAUGHTER] Upstream of that, the design drives environmental health and so on, that’s actually … that’s up to you guys! So let’s get out of this booth and get back to work! [LAUGHTER] Well, Jake, to that point, talk about the economics. We talk about a circular economy. And I know that recycling is expensive. Can you talk a little bit about how that could be impacted by work that you guys do?

SMITH: Recycling absolutely is expensive relative to landfilling or a similar alternative.

HUIZINGA: Right …

SMITH: One of the things that makes us target e-waste is that there are things of value in e-waste that are, like, innately valuable. When you go recollect that copper or the gold that you’ve put into this, when you recollect the integrated circuits, you know, they had value, and so a lot of the economic drive is already there to get you to the point where you have these circuit boards. And then, you know, the question was, how do we get that next bit of economic value so that you’ve taken steps this far, you have this pile of circuit boards, so you’ve already been incentivized to get to here and it will be easy to make this—even if it’s not a completely economically productive material—versus synthesizing a circuit board from virgin plastic, but it’s offset enough. We’ve taken enough of that penalty for reuse out that it can be justifiable to go do.

HUIZINGA: Right. OK. So talk—again, off script a little bit—but talk a little bit about how vitrimers help take it to the last mile.

VASHISTH: Yeah, I think the inherent property of the polymer to kind of unclick and re-click back again, the heal-ability of the polymer, that’s something that, kind of, drives this reusability and re-processability of the material. I’ll just, like, point out, like, you know, particularly to the PCB case, where we recently published a collaborative paper where we showed that we can actually make PCB boards using vitrimers. We can unassemble everything. We can take out the electronics, and even the composite, the glass fiber and the polymer composite, we can actually separate that, as well, which is, in my mind, like, a pretty big success.

HUIZINGA: Yeah.

VASHISTH: And then we can actually put everything back together and remake a PCB board, and, you know, keep on doing that. So …

HUIZINGA: OK, so you had talked to me before about “Ring Around the Rosie” and the hands and the feet. Can you … ?

SMITH: [LAUGHS] His favorite analogy!

HUIZINGA: Do that one just for our audience because it’s good.

VASHISTH: OK. So I’ll talk a little bit about thermoset/thermoplastic again, and then I’ll just give you a much broader perspective there.

HUIZINGA: Yeah.

VASHISTH: So the FR-4 PCBs that are made, they are usually made with thermosetting polymers. So if you think about thermosetting polymers, just think of kids playing “Ring of Roses,” right? Like their hands are fixed and their feet are fixed. Once the network is formed, there’s no way you can actually destroy that network. The nice thing about vitrimers is that when you provide an external stimulus, like, just think about these kids playing “Ring of Roses” again. Their feet can move and their handshakes can change, but the number of handshakes remain the same. So the polymer is kind of, like, unclicking and re-clicking back again.

HUIZINGA: OK.

VASHISTH: And if you can cleverly use this mechanism, you can actually recycle, reprocess the polymer itself. But what we showed, particularly for the PCB paper, was that you can actually separate all the other constituents that are associated with this composite, yeah.

HUIZINGA: OK. That’s … I love that. Well, sticking with you for a second, Aniruddh, talking about mechanical reality—not just chemical reality, but mechanical reality—even the best composites wear out, from wear and tear. Talk about the goal of this work on novel polymers from an engineering perspective. How do you think about designing for reality in this way?

VASHISTH: Yeah, yeah. That’s a fantastic question. So we were really motivated by what type of mechanical or thermal loadings materials see in day-to-day life. You know, I sit in my car, I drive it, it drives over the road, there is some fatigue loadings, there’s dynamic loading, and that dynamic loading actually leads to some mechanical flaws in the material, which damages it. And the thought was always that, can we restrict that flaw, or can we go a step further? Can we actually reverse that damage in these composites? And that’s where, you know, that unclicking/re-clicking behavior of vitrimer becomes, like, really powerful. So actually, the first work that we did on these type of materials was that we took a vitrimer composite and we applied fatigue loading on it, cyclic loading on it, mechanical loading. And then we saw that when there was enough damage accumulated in the system, we healed the system. And then we did this again. And we were able to do it again and again until I was like, I’ve spent too much money on this test frame! [LAUGHS] But it was really exciting because for a particular loading case that we were looking at, traditional composites were able to sustain that for 10,000 cycles, but for vitrimers, if we did periodic healing in the material, we were able to go up to a million cycles. So I think that’s really powerful.

HUIZINGA: Orders of magnitude.

VASHISTH: Yeah, exactly.

HUIZINGA: Wow. Jake, I want to broaden the conversation right now, beyond just you and Aniruddh, and talk about the larger teams you need to assemble to ensure success of projects like this. Do you have any stories you could share about how you go about building a team? You kind of alluded to it at the beginning. There’s sort of a pickup basketball metaphor there. Hey, he’s doing that. We’re doing this. But you have some intentionality about people you bring in. So what strengths do each institution bring, and how do you build a team?

SMITH: Yeah, absolutely. We’ve tried a bunch of these collaborations, and we’ve definitely got some learnings about which ones work better than others. This has been a super productive one. I think it’s because it has that right mix of skills and the right mix of things that each side are bringing. So what we want from a Microsoft side for a successful collaboration is we want a collaborator who is really a domain expert in, you know, something that we don’t necessarily understand but who can tell us, in great detail, these are the actual design criteria; these are, you know, where I run into trouble with my traditional research; this is the area that, you know, I’d like to do faster, but I don’t necessarily know how. And this was the critical part, I think, you know, from the get-go. They need to, themselves, be an extremely, you know, capable subject matter expert. Otherwise, we’re just kind of chatting. We don’t have anyone that really knows what the problem truly is and you make no progress or you … worse, you spend a whole lot of resources to make “progress”—I’m doing air quotes …

HUIZINGA: Yeah. I love air quotes on a podcast!

SMITH: [LAUGHS]—that is actually just completely tangential to what the field needs or what the actual device needs. So this was, you know, the fundamental ingredient. And then on top of that, we need to find a problem that’s of joint interest where, in particular, …

HUIZINGA: Right …

SMITH: … computation can help. You talked about the amount of computation that we have at our disposal as researchers at Microsoft, which is a tremendous strength. And so we want to be able to leverage that. And so for a collaboration like this, where running a large number of simulations was a fundamental ingredient to doing it, this was, you know, a really good fit, that we could come in and we could enable them to have more data to train the models that we build together.

HUIZINGA: Mm-hm. Well, as researchers, are you each kind of always scanning the horizon for who else is doing things in your field that—or tangential to your field but necessary? How does that work for recruiting, I would say?

VASHISTH: Yeah, that’s a good question. I think … I mean, that’s kind of like the job, right. For the machine learning work we did, we saw a lot of inspiration from biology, where people have been designing biomolecules. The challenges are different for us. Like, we are designing much larger chains. But we saw some inspiration from there. So always, like, looking out for, like, who is doing what is super helpful, and it leads to, like, really nice collaborations, as well. We’ve had, like, really fruitful collaborations with the professor Sid Kumar at TU Delft, and we always get his wisdom on some of these things, as well. But yeah, recruiting students also becomes, like, very interesting and how, like, people who can help us achieve our idea …

HUIZINGA: Yeah. Jake, what’s your take on it from the other seat? I mean, do you look actively at universities around the world—and even in your backyard—to … like U Dub … ? [LAUGHTER]

SMITH: My perspective on, like, how collaborations come in to be is they’re really serendipitous. You know, we talked about how this one came in to be, and it was because we all happen to know Vikram, and Vikram happened to connect Bichlien with Aniruddh, and it kind of rolled from there. But you can have serendipitous, you know, meetings at a conference, where you happen to, you know, sit next to someone at a talk and you both share the same perspective on, you know, how a research problem should be tackled, and something could come out of that. Or in some cases, you go actually shopping for a collaborator.

HUIZINGA: Right. [LAUGHTER]

SMITH: You know, you need to talk to 10 people to find the one that has that same research perspective as you. I’ll second Aniruddh’s, you know, observation that you get a very different perspective if you go find someone who, they may have the same, like, perspective on how research should be tackled, but they have a different perspective on what the ultimate output of that research would be. But, you know, they can often point you in areas where your research could be helpful that you can’t necessarily see because you lack the domain knowledge or you lack that particular angle on it.

HUIZINGA: Which is another interesting thing in my mind is, you know, the role that papers, published papers, play—that’s a lot of p’s in a sentence [LAUGHTER] … alliteration—that you would be reading or hearing about either in a lightning talk or a presentation at a conference. Does that broaden your perspective, as well? And how do you … like, do you call people up? “I read your paper … ”?

SMITH: [LAUGHS] I have cold-emailed people. You know, this works sometimes! Sometimes this is just the introduction that you need. But the interesting thing in my mind is how much the computer science conferences and things like ChemRxiv and arXiv have really replaced, for me, the traditional chemistry literature or the traditional publishing literature where you can have a conversation with this person while they’re still actively doing the work because they put their initial draft up there and it still needs revision, and there’s opportunities even earlier on in the research process than we’ve had in the past.

HUIZINGA: Huh. And to your earlier point, I’m envisioning an Amazon shopping cart for research collaborators. [LAUGHTER] “Oh, he looks good. Into my cart.” Aniruddh, I always like to know where a project is on the spectrum from what I call lab to life, and I know there are different development stages when it comes to technology finding its way into production and then into broader use. So to use another analogy I like, pretend this is a relay race and research is the first leg. Who else has to run, and who brings it across the line?

VASHISTH: Yeah, yeah. So I think the initial work that we have done, I think it’s been super fruitful, and to Jake’s point, like, converging to, like, a nice output. It took a bunch of chemists, mechanical engineers, simulation folks, machine learning scientists to get where we are. And, as Jake mentioned, we’ve actually put some of our publications on arXiv, and it’s getting traction now. So we’ve had some excitement from startups and companies which make polymers asking us, “Oh, can you actually … can we get a slice of this framework that you’re developing for designing vitrimers?” Which is very promising. So we have done very fundamental work, but now, like, what’s called “the valley of death” in research, [LAUGHTER] like taking it from lab to like production scale, …

HUIZINGA: Yeah.

VASHISTH: … it’s usually a very tightly knit collaboration between industry, labs, and sometimes national labs, too. So we’re excited that, actually, a couple of national labs have been interested in the work that we have been doing, so super optimistic about it.

HUIZINGA: So would you say that the vitrimer-based printed circuit board is a proof of concept right now? Or have you made prototypes? Where is that now?

SMITH: Yeah, absolutely. We’ve mentioned our other collaborator, Vikram Iyer, a couple of times. And in collaboration with his lab, we did actually make a prototype circuit board. We showed that it works as you expect. We showed that it can be disassembled. It can be put back together, and it still works as expected …

HUIZINGA: The “break stuff/make stuff back” thing …

VASHISTH: Yeah, exactly.

SMITH: But, you know, I think to the spirit of the question, it’s still individual kind of one-off experiments being run in a lab, and Aniruddh is right. There’s a long way to go from, like, Technology Readiness Level 3, where we’re doing it ourselves on bench scale, up to, you know, the 7, 8, 9, where it’s actually commercially viable and someone has been able to reproduce this at scale.

HUIZINGA: Right. … So that’s when you bring investors in or labs that can make stuff in and scale.

VASHISTH: Yeah. Yeah, I think once you’re, like, close to 7, I think that’s where you’re pretty much ready for the big show.

HUIZINGA: So where are you now? 2? 3?

VASHISTH: I would say, like, 2 or 3 …

SMITH: 2, 3, somewhere in that range.

VASHISTH: Yeah.

HUIZINGA: OK.

SMITH: The scales, kind of, differ depending on which agencies you see put it out.

HUIZINGA: So, Jake, before we close, I want to talk briefly about other applications of recyclable vitrimer-based polymers, in light of their importance to the climate research initiative and AI for Science. So what other industries have polymer components that have nowhere to go after they die but the landfill, and will this research transfer across to those industries?

SMITH: An excellent question. So my personal view on this is that there’s a couple of classes of polymers. There’s these very high-value application uses of polymers where we’re talking about the printed circuit boards; we’re talking about aerospace composite; we’re talking about the panels on your car; we’re talking about things like wind turbines …

HUIZINGA: Oh, yeah.

SMITH: … where there’s a long life cycle. You have this device that’s going to be in use for five years, 50 years, and at the end of that, the polymer itself is still probably pretty good. You could still use it and regenerate it. And so Aniruddh’s lab has done great work showing that you can take things like the side panel of a plane and actually disassemble this thing, heal it, keep it in use longer, and use it at the end of its lifetime. There’s this other class of polymers, which I think are the ones that most people think about—your Coke bottle—and vitrimers seem like a much harder sell there. I think this is more the domain of, you know, biodegradable polymers in the long run to really tackle the issues there. But I’m very excited in this, you know, high-value polymer, this long-lifetime polymer, this, like, permanent install polymer, however you want to think about it, for work like this to have an impact.

HUIZINGA: Yeah. From your lab’s perspective, Aniruddh, where do you see other applications with great promise?

VASHISTH: Yeah. So as Jake said, places where we need high-performance polymers is where we can go. So PCBs is one, aerospace and automotive industry is one, and maybe medical industry is, …

HUIZINGA: Oh, interesting…

VASHISTH: … yeah, is another one where we can actually … if you can make prosthetics out of vitrimers … prosthetics actually lose a little bit of their stiffness, you know, as you use them, and that’s because of localized damage. It’s the fatigue cycle, right. So what if you can actually heal your prosthetics and reuse them? So, yeah, I feel like, you know, there’s so many different applications, so many different routes that we can go down.

HUIZINGA: Yeah. Well, I like to end our Collaborators shows with a little vision casting, and I feel like this whole podcast is that. I should also say, you know, back in the ’50s, there was the big push to make plastics! Your word is vitrimers! So let’s do a little vision casting for vitrimer-based polymers. Assuming your research is wildly successful and becomes a truly game-changing technology, what does the future look like—I mean, specified future, not general future—and how has your work disrupted this field and made the world a better place? I’ll let you each have the last word. Who’d like to go first?

VASHISTH: Sure, I can go first. I’ll try to make sure that I break it up into computation and experiments …

HUIZINGA: Good.

VASHISTH: … so that once I go back, like, my lab does not, like, pounce on me. [LAUGHS] Yeah, so I think from the computation point of view, we always thought that if somebody gave us, like, a hundred different chemistries, we can actually bottle it down to, like, we can do a bunch of simulations; tell you, like, 10 of these actually work. What we’ve been able to do specifically for vitrimers is that we’re able to look at the problem from the other side, and we are able to say that if you tell me a particular application, this particular chemistry would work best for you. In essence, what we were thinking of is that if aliens abducted all the chemists from the world, can we actually come up with a framework? [LAUGHS] So I think it’ll be difficult to get there because as I said earlier that, you know, you need that human touch. But I think we are happy that that we are getting there. And I think what remains to be seen now is, like, you know, now that we have this type of a framework, like what are the next challenges? Like, we are going from the lab to the large scale; like, what challenges are associated there? And I think similarly for the experimental side of things also, we know a lot—we have developed frameworks—but there’s a lot of work that still needs to be done in understanding and translating these technologies to real-life applications.

HUIZINGA: I like that you’re kind of hedging your bets there, saying, I’m not going to paint a picture of the perfect world because my lab is going to be responsible for delivering it. [LAUGHTER] Jake, assuming you haven’t been abducted by aliens, what’s your take on this?

SMITH: I view, kind of, the goal of this work and the ideal impact of this work as an acceleration of getting us to these polymers being deployed in all these other applications that we’ve talked about, and we can go broader than this.

HUIZINGA: Yeah …

SMITH: I think that there’s a lot of work, both within the MCRI, within Microsoft, and outside of Microsoft in the bigger field, focused on acceleration towards a specific goal. And if all of this work is successful, in 10 years, maybe our materials design process looks completely different, where we’ve gone from this kind of brute-force screening that Aniruddh has talked about to an approach where you start with the properties that you care about; they’re defined by the application that you have in mind. You want to make your vitrimer PCB, it needs to have, you know, a specific temperature where it becomes gummy; it needs to have a specific resistance to burning; it needs to be able to effectively serve as the dielectric for your bigger circuits. And we use this, like, “need space” to define the material that we would like, and we can use machine learning, artificial intelligence, in order to get us to the structure that we need to make in order to actually achieve this design space. And so, this was, you know, our big bet within AI for Science. This is the big bet of this project. And with this project, you know, we take one step towards showing that you can do this in one case. And the future casting would be we can do this in every materials design case that you can think about.

HUIZINGA: Hmmm. You know, I’m thinking of lanes—track analogy again—but, you know, you’ve got mechanical engineering, you’ve got chemistry, and you’ve got artificial intelligence, and each of those sciences is advancing, and they’re using each other to, sort of, help advance in various ways, so this is an exciting, exciting project and collaboration.

Jake, Aniruddh, thanks for joining us today on Collaborators. This has been really fun for me. [LAUGHTER] So thanks for coming in and sharing your stories today.

VASHISTH: Thank you so much.

SMITH: Yeah. Of course. Thank you.

Transcript

BEHNAZ ARZANI: I guess the thing I’m seeing is that we are freed up to dream more—in a way. Maybe that’s me being too … I’m a little bit of a romantic, so this is that coming out a little bit, but it’s, like, because of all this, we have the time to think bigger, to dream bigger, to look at problems where maybe five years ago, we wouldn’t even dare to think about.

GRETCHEN HUIZINGA: You’re listening to Ideas, a Microsoft Research Podcast that dives deep into the world of technology research and the profound questions behind the code. I’m Dr. Gretchen Huizinga. In this series, we’ll explore the technologies that are shaping our future and the big ideas that propel them forward.

My guest today is Behnaz Arzani. Behnaz is a principal researcher at Microsoft Research, and she’s passionate about the systems and networks that provide the backbone to nearly all our technologies today. Like many in her field, you may not know her, but you know her work: when your networks function flawlessly, you can thank people like Behnaz Arzani. Behnaz, it’s been a while. I am so excited to catch up with you today. Welcome to Ideas!

BEHNAZ ARZANI: Thank you. And I’m also excited to be here.

HUIZINGA: So since the show is about ideas and leans more philosophical, I like to start with a little personal story and try to tease out anything that might have been an inflection point in your life, a sort of aha moment, or a pivotal event, or an animating “what if,” we could call it. What captured your imagination and got you inspired to do what you’re doing today?

ARZANI: I think that it was a little bit of an accident and a little bit of just chance, I guess, but for me, this happened because I don’t like being told what to do! [LAUGHTER] I really hate being told what to do. And so, I got into research by accident, mostly because it felt like a job where that wouldn’t happen. I could pick what I wanted to do. So, you know, a lot of people come talking about how they were the most curious kids and they all—I wasn’t that. I was a nerd, but I wasn’t the most curious kid. But then I found that I’m attracted to puzzles and hard puzzles and things that I don’t know how to answer, and so that gravitated me more towards what I’m doing today. Things that are basically difficult to solve … I think are difficult to solve.

HUIZINGA: So that’s your inspiring moment? “I’m a bit of a rebel, and …”

ARZANI: Yup!

HUIZINGA: … I like puzzles … ”?

ARZANI: Yup! [LAUGHTER] Which is not really a moment. Yeah, I can’t point to a moment. It’s just been a journey, and it’s just, like, been something that has gradually happened to me, and I love where I am …

HUIZINGA: Yeah …

ARZANI: … but I can’t really pinpoint to like this, like this inspiring awe-drop—no.

HUIZINGA: OK. So let me ask you this: is there nobody in this building that tells you what to do? [LAUGHS]

ARZANI: There are people who have tried, [LAUGHS] but …

HUIZINGA: Oh my gosh!

ARZANI: No, it doesn’t work. And I think if you ask them, they will tell you it hasn’t worked.

HUIZINGA: OK. The other side question is, have you encountered a puzzle that has confounded you?

ARZANI: Have I encountered a puzzle? Yes. Incident management. [LAUGHTER]

HUIZINGA: And we’ll get there in the next couple of questions. Before we do, though, I want to know about who might have influenced you earlier. I mean, it’s interesting. Usually if you don’t have a what, there might not be a who attached to it …

ARZANI: No. But I have a who. I have multiple “whos” actually.

HUIZINGA: OK! Wonderful. So tell us a little bit about the influential people in your life.

ARZANI: I think the first and foremost is my mom. I have a necklace I’m holding right now. This is something my dad gave my mom on their wedding day. On one side of it is a picture of my mom and dad; on the other side is both their names on it. And I have it on every day. To my mom’s chagrin. [LAUGHTER] She is like, why? But it’s, like, it helps me stay grounded. And my mom is a person that … she had me while she was an undergrad. She got her master’s. She got into three different PhD programs in her lifetime. Every time, she gave it up for my sake and for my brother’s sake. But she’s a woman that taught me you can do anything you set your mind to and that you should always be eager to learn. She was a chemistry teacher, and even though she was a chemistry teacher, she kept reading new books. She came to the US to visit me in 2017, went to a Philadelphia high school, and asked, can I see your chemistry books? I want to see what you’re teaching your kids. [LAUGHTER] So that’s how dedicated she is to what she does. She loves what she does. And I could see it on her face on a daily basis. And at some point in my life a couple of years ago, I was talking to my mom about something, and she said, tell yourself, “I’m stronger than my mom.”

HUIZINGA: Oh my gosh.

ARZANI: And that has been, like, the most amazing thing to have in the back of my head because I view my mom as one of the strongest people I’ve ever met, and she’s my inspiration for everything I do.

HUIZINGA: Tell yourself you’re stronger than your mom. … Did you?

ARZANI: I’m not stronger than my mom, I don’t think … [LAUGHS]

HUIZINGA: [LAUGHS] You got to change that narrative!

ARZANI: But, yes, I think it’s just this thing of, like, “What would Mom do?” is a great thing to ask yourself, I think.

HUIZINGA: I love that. Well, and so I would imagine, though, that post-, you know, getting out of the house, you’ve had instructors, you’ve had professors, you’ve had other researchers. I mean, anyone else that’s … ?

ARZANI: Many! And in different stages of your life, different people step into that role, I feel like. One of the first people for me was Jen Rexford, and she is just an amazing human being. She’s an amazing researcher, hands down. Her work is awesome, but also, she’s an amazing human being, as well. And that just makes it better.

HUIZINGA: Yeah.

ARZANI: And then another person is Mohammad Alizadeh, who’s at MIT. And actually, let’s see, I’m going to keep going …

HUIZINGA: Good.

ARZANI: … a little with people—Mark Handley. When I was a PhD student, I would read their papers, and I’d be like, wow! And, I want to be like you!

HUIZINGA: So linking that back to your love of puzzles, were these people that you admired good problem solvers or … ?

ARZANI: Oh, yeah! I think Jen is one of those who … a lot of her work is also practical, like, you know, straddles a line between both solving the puzzle and being practical and being creative and working with theorists and working with PL people. So she’s also collaborative, which is, kind of, my style of work, as well. Mohammad is more of a theorist, and I love … like more the theoretical aspect of problems that I solve. And so, like, just the fact that he was able to look at those problems and thinks about those problems in those ways. And then Mark Handley’s intuition about problems—yeah, I can’t even speak to that!

HUIZINGA: That’s so fascinating because you’ve identified three really key things for a researcher. And each one is embodied in a person. I love that. And because I know who you are, I know we’re going to get to each of those things probably in the course of all these questions that I’ll ask you. [LAUGHTER] So we just spent a little time talking about what got you here and who influenced you along the way. But your life isn’t static. And at each stage of accomplishment, you get a chance to reflect and, sort of, think about what you got right, what you got wrong, and where you want to go next. So I wonder if you could take a minute to talk about the evolution of your values as a researcher, collaborator, and colleague and then a sort of “how it started/how it’s going” thing.

ARZANI: Hmm … For me, I think what I’ve learned is to be more mindful—about all of it. But I think if I talk about the evolution, when you’re a PhD student, especially if you’re a PhD student from a place that’s not MIT, that’s not Berkeley, which is where I was from, my main focus was proving myself. I mean, for women, always, we have to prove ourselves. But, like, I think if you’re not from one of those schools, it’s even more so. At least that’s how I felt. That might not be the reality, but that’s how you feel. And so you’re always running to show this about yourself. And so you don’t stop to think how you’re showing up as a person, as a researcher, as a collaborator. You’re not even, like, necessarily reflecting on, are these the problems that I enjoy solving? It’s more of, will solving this problem help me establish myself in this world that requires proving yourself and is so critical and all of that stuff? I think now I stop more. I think more, is this a problem that I would enjoy solving? I think that’s the most important thing. Would other people find it useful? Is it solving a hard technical question? And then, in collaborations, I’m being more mindful that I show up in a way that basically allows me to be a good person the way I want to be in my collaboration. So as researchers, we have to be critical because that’s how science evolves. Not all work is perfect. Not all ideas are the best ideas. That’s just fundamental truth. Because we iterate on each other’s ideas until we find the perfect solution to something. But you can do all of these things in a way that’s kind, in a way that’s mindful, in a way that respects other people and what they bring to the table. And I think what I’ve learned is to be more mindful about those things.

HUIZINGA: How would you define mindful? That’s an interesting word. It has a lot of baggage around it, you know, in terms of how people do mindfulness training. Is that what you’re talking about, or is it more, sort of, intentional?

ARZANI: I think it’s both. So I think one of the things I said—I think when I got into this booth even—was, I’m going to take a breath before I answer each question. And I think that’s part of it, is just taking a breath to make sure you’re present is part of it. But I think there is more to it than that, which is I don’t think we even think about it. I think if I … when you asked me about the evolution of how I evolved, I never thought about it.

HUIZINGA: No.

ARZANI: I was just, like, running to get things done, running to solve the question, running to, you know, find the next big thing, and then you’re not paying attention to how you’re impacting the world in the process.

HUIZINGA: Right.

ARZANI: And once you start paying attention, then you’re like, oh, I could do this better. I can do that better. If I say this to this person in that way, that allows them to do so much more, that encourages them to do so much more.

HUIZINGA: Yeah, yeah.

ARZANI: So …

HUIZINGA: You know, when you started out, you said, is this a problem I would enjoy solving? And then you said, is this a problem that somebody else needs to have solved? Which is sort of like “do I like it?”—it goes back to Behnaz at the beginning: don’t tell me what to do; I want to do what I want to do. Versus—or and is this useful to the world? And I feel like those two threads are really key to you.

ARZANI: Yes. Basically, I feel like that defines me as a researcher, pretty much. [LAUGHS] Which is, you know, I was one of the, you know, early people … I wouldn’t say first. I’m not the first, I don’t think, but I was one of the early people who was talking about using machine learning in networking. And after a while, I stopped because I wasn’t finding it fun anymore, even though there was so much hype about, you know, let’s do machine learning in networking. And it’s not because there’s not a lot of technical stuff left to do. You can do a lot of other things there. There’s room to innovate. It’s just that I got bored.

HUIZINGA: I was just going to say, it’s still cool, but Behnaz is bored! [LAUGHTER] OK, well, let’s start to talk a little bit about some of the things that you’re doing. And I like this idea of a researcher, even a person, having a North Star goal. It sounds like you’ve got them in a lot of areas of your life, and you’ve said your North Star goal, your research goal, is to make the life of a network operator as painless as possible. So I want to know who this person is. Walk us through a day in the life of a network operator and tell us what prompted you to want to help them.

ARZANI: OK, so it’s been years since I actually, like, sat right next to one of them for a long extended period of time because now we’re in different buildings, but back when I was an intern, I was actually, like, kind of, like right in the middle of a bunch of, you know, actual network operators. And what I observed … and see, this was not, like, I’ve never lived that experience, so I’m talking about somebody else’s experience, so bear that in mind …

HUIZINGA: Sure, but at least you saw it …

ARZANI: Yeah. What they do is, there’s a lot of, “OK, we design the network, configure it.” A lot of it goes into building new systems to manage it. Building new systems to basically make it better, more efficient, all of that. And then they also have to be on call so that when any of those things break, they’re the ones who have to look at their monitoring systems and figure out what happened and try to fix it. So they do all of this in their day-to-day lives.

HUIZINGA: That’s tough …

ARZANI: Yeah.

HUIZINGA: OK. So I know you have a story about what prompted you, at the very beginning, to want to help this person. And it had some personal implications. [LAUGHS]

ARZANI: Yeah! So my internship mentor, who’s an amazing person, I thought—and this is, again, my perception as an intern—the day after he was on call, he was so tired, I felt. And so grumpy … grumpier than normal! [LAUGHTER] And, like, my main motivation initially for working in this space was just, like, make his life better!

HUIZINGA: Make him not grumpy.

ARZANI: Yeah. Pretty much. [LAUGHS]

HUIZINGA: Did you have success at that point in your life? Or was this just, like, setting a North Star goal that I’m going to go for that?

ARZANI: I mean, I had done a lot of work in monitoring space, but back then—again, going back to the talk we were having about how to be mindful about problems you pick—back then it was just like, oh, this was a problem to solve, and we’ll go solve it, and then what’s the next thing? So there was not an overarching vision, if you will. It was just, like, going after the next, after the next. I think that’s a point where, like, it all came together of like, oh, all of the stuff that I’m doing can help me achieve this bigger thing.

HUIZINGA: Right. OK, Behnaz, I want to drop anchor, to use a seafaring analogy, for a second and contextualize the language that these operators use. Give us a “networking for neophytes” overview of the tools they rely on and the terminology they use in their day-to-day work so we’re not lost when we start to unpack the problems, projects, and papers that are central to your work.

ARZANI: OK. So I’m going to focus on my pieces of this just because of the context of this question. But a lot of operators … just because a lot of the problems that we work on these days to be able to manage our network, the optimal form of these problems tend to be really, really hard. So a lot of the times, we use algorithms and solutions that are approximate forms of those optimal solutions in order to just solve those problems faster. And a lot of these heuristics, some of them focus on our wide area network, which we call a WAN. Our WANs, basically what they do is they move traffic between datacenters in a way that basically fits the capacity of our network. And, yeah, I think for my work, my current work, to understand it, that’s, I think, enough networking terminology.

HUIZINGA: OK. Well, so you’ve used the term heuristic and optimal. Not with an “s” on the end of it. Or you do say “optimals,” but it’s a noun …

ARZANI: Well, so for each problem definition, usually, there’s one way to formulate an optimal solution. There might be multiple optima that you find, but the algorithm that finds the optimum usually is one. But there might be many, I guess. The ones that I’ve worked on generally have been one.

HUIZINGA: Yeah, yeah. And so in terms of how things work on a network, can you give us just a little picture of how something moves from A to B that might be a problem?

ARZANI: So, for example, we have these datacenters that generate terabytes of traffic and—terabytes per second of traffic—that wants to move from point A to point B, right. And we only have finite network capacity, and these, what we call, “demands” between these datacenters—and you didn’t see me do the air quotes, but I did the air quotes—so they go from point A to point B, and so in order to fit this demand in the pipes that we have—and these pipes are basically links in our network—we have to figure out how to send them. And there’s variations in them. So, like, it might be the case that at a certain time of the day, East US would want to send more traffic to West US, and then suddenly, it flips. And that’s why we solve this problem every five minutes! Now assume one of these links suddenly goes down. What do I do? I have to resolve this problem because maybe the path that I initially picked for traffic to go through goes exactly through that failed link. And now that it’s disappeared, all of that traffic is going to fall on the floor. So I have to re-solve that problem really quickly to be able to re-move my traffic and move it to somewhere else so that I can still route it and my customers aren’t impacted. What we’re talking about here is a controller, essentially, that the network operators built. And this controller solves this optimization problem that figures out how traffic should move. When it’s failed, then the same controller kicks in and reroutes traffic. The people who built that controller are the network operators.

HUIZINGA: And so who does the problem-solving or the troubleshooting on the fly?

ARZANI: So hopefully—and this, most of the times, is the case—is we have monitoring systems in place that the operators have built that, like, kind of, signal to this controller that, oh, OK, this link is down; you need to do something.

HUIZINGA: Much of your recent work represents an effort to reify the idea of automated network management and to try to understand the performance of deployed algorithms. So talk about the main topics of interest here in this space and how your work has evolved in an era of generative AI and large language models.

ARZANI: So if you think about it, what generative AI is going to enable, and I’m using the term “going to enable” a little bit deliberately because I don’t think it has yet. We still have to build on top of what we have to get that to work. And maybe I’ll reconsider my stance on ML now that, you know, we have these tools. Haven’t yet but might. But essentially, what they enable us to do is take automated action on our networks. But if we’re allowing AI to do this, we need to be mindful of the risks because AI in my, at least in my head of how I view it, is a probabilistic machine, which, what that means is that there is some probability, maybe a teeny tiny probability, it might get things wrong. And the thing that you don’t want is when it gets things wrong, it gets things catastrophically wrong. And so you need to put guardrails in place, ensure safety, figure out, like, for each action be able to evaluate that action and the risks it imposes long term on your network and whether you’re able to tolerate that risk. And I think there is a whole room of innovation there to basically just figure out the interaction between the AI and the network and where … and actually strategic places to put AI, even.

HUIZINGA: Right.

ARZANI: The thing that for me has evolved is I used to think we just want to take the human out of the equation of network management. The way I think about it now is there is a place for the human in the network management operation because sometimes human has context and that context matters. And so I think what the, like, for example, we have this paper in HotNets 2023 where we talk about how to put an LLM in the incident management loop, and then there, we carefully talk about, OK, these are the places a human needs to be involved, at least given where LLMs are right now, to be able to ensure that everything happens in a safe way.

HUIZINGA: So go back to this “automated network management” thing. This sounds to me like you’re in a space where it could be, but it isn’t ready yet …

ARZANI: Yeah.

HUIZINGA: … and without, sort of, asking you to read a crystal ball about it, do you feel like this is something that could be eventually?

ARZANI: I hope so. This is the best thing about research. You get to be like, yeah!

HUIZINGA: Yeah, why not?

ARZANI: Why not? And, you know, maybe somebody will prove me wrong, but until they do, that’s what I’m working towards!

HUIZINGA: Well, right now it’s an animating “what if?”

ARZANI: Yeah.

HUIZINGA: Right?

ARZANI: Yeah.

HUIZINGA: This is a problem Behnaz is interested in right now. Let’s go!

ARZANI: Yeah. Pretty much. [LAUGHTER]

HUIZINGA: OK. Behnaz, the systems and networks that we’ve come to depend on are actually incredibly complex. But for most of us, most of the time, they just work. There’s only drama when they don’t work, right? But there’s a lot going on behind the scenes. So I want you to talk a little bit about how the cycle of configuring, managing, reconfiguring, etc., helps keep the drama at bay.

ARZANI: Well … you reminded me of something! So when I was preparing my job … I’m going to tell this story really, really quickly. But when I was preparing my job talk, somebody showed me a tweet. In 2014, I think, people started calling 911 when Facebook was down! Because of a networking problem! [LAUGHS] Yeah. So that’s a thing. But, yeah, so network availability matters, and we don’t notice it until it’s actually down. But that aside, back to your question. So I think what operators do is they build systems in a way that tries to avoid that drama as much as possible. So, for example, they try to build systems that these systems configure the network. And one of my dear friends, Ryan Beckett, works on intent-driven networking that essentially tries to ensure that what the operators intend with their configurations matches what they actually push into the network. They also monitor the network to ensure that as soon as something bad happens, automation gets notified. And there’s automation also that tries to fix these problems when they happen as much as possible. There’s a couple of problems that happen in the middle of this. One of them is our networks continuously change, and what we use in our networks changes. And there’s so many different pieces and components of this, and sometimes what happens is, for example, a team decides to switch from one protocol to a different protocol, and by doing that, it impacts another team’s systems and monitoring and what expectations they had for their systems, and then suddenly it causes things to go bad …

HUIZINGA: Right.

ARZANI: And they have to develop new solutions taking into account the changes that happened. And so one of the things that we need to account for in this whole process is how evolution is happening. And like evolution-friendly, I guess, systems, maybe, is how you should be calling it.

HUIZINGA: Right.

ARZANI: But that’s one. The other part of it that goes into play is, most of the time you expect a particular traffic characteristic, and then suddenly, you have one fluke event that, kind of, throws all of your assumptions out the window, so …

HUIZINGA: Right. So it’s a never-ending job …

ARZANI: Pretty much.

HUIZINGA: It’s about now that I ask all my guests what could possibly go wrong if, in fact, you got everything right. And so for you, I’d like to earth this question in the broader context of automation and the concerns inherent in designing machines to do our work for us. So at an earlier point in your career—we talked about this already—you said you believed you could automate everything. Cool. Now you’re not so much on that. Talk about what changed your thinking and how you’re thinking now.

ARZANI: OK, so the shallow answer to that question—there’s a shallow answer, and there’s a deeper answer—the shallow answer to that question is I watched way too many movies where robots took over the world. And honestly speaking, there’s a scenario that you can imagine where automation starts to get things wrong and then keeps getting things wrong, and wrong, not by the definition of automation. Maybe they’re doing things perfectly by the objectives and metrics that you used to design them …

HUIZINGA: Sure.

ARZANI: … but they’re screwing things up in terms of what you actually want them to do.

HUIZINGA: Interesting.

ARZANI: And if everything is automated and you don’t leave yourself an intervention plan, how are you going to take control back?

HUIZINGA: Right. So this goes back to the humans-in-the-loop/humans-out-of-the-loop. And if I remember in our last podcast, we were talking about humans out of the loop.

ARZANI: Yeah.

HUIZINGA: And you’ve already talked a bit about what the optimal place for a human to be is. Is the human always going to have to be in the loop, in your opinion?

ARZANI: I think it’s a scenario where you always give yourself a way to interrupt. Like, always put a back door somewhere. When we notice things go bad, we have a way that’s foolproof that allows us to shut everything down and take control back to ourselves. Maybe that’s where we go.

HUIZINGA: How do you approach the idea of corner cases?

ARZANI: That’s essentially what my research right now is, actually! And I love it, which is essentially figuring out, in a foolproof way, all the corner cases.

HUIZINGA: Yeah?

ARZANI: Can you build a tool that will tell you what the corner cases are? Now, granted, what we focus on is performance corner cases. Nikolaj Bjørner, in RiSE—so RiSE is Research in Software Engineering—is working on, how do you do verification corner cases? But all of them, kind of, have a hand-in-hand type of, you know, Holy Grail goal, which is …

HUIZINGA: Sure.

ARZANI: … how do you find all the corner cases?

HUIZINGA: Right. And that, kind of, is the essence of this “What could possibly go wrong?” question, is looking in every corner …

ARZANI: Correct.

HUIZINGA: … for anything that could go wrong. So many people in the research community have observed that the speed of innovation in generative AI has shrunk the traditional research-to-product timeline, and some people have even said everyone’s an applied researcher now. Or everyone’s a PM. [LAUGHS] Depends on who you are! But you have an interesting take on this Behnaz, and it reminds me of a line from the movie Nanny McPhee: “When you need me but do not want me, then I will stay. When you want me but no longer need me, I have to go.” So let’s talk a little bit about your perspective on this idea-to-ideation pipeline. How and where are researchers in your orbit operating these days, and how does that impact what we might call “planned obsolescence” in research?

ARZANI: I guess the thing I’m seeing is that we are freed up to dream more—in a way. Maybe that’s me being too … I’m a little bit of a romantic, so this is that coming out a little bit, but it’s, like, because of all this, we have the time to think bigger, to dream bigger, to look at problems where maybe five years ago, we wouldn’t even dare to think about. We have amazingly, amazingly smart, competent people in our product teams. Some of them are actually researchers. So there’s, for example, the Azure systems research group that has a lot of people that are focused on problems in our production systems. And then you have equivalents of those spread out in the networking sphere, as well. And so a lot of complex problems that maybe like 10 years ago Microsoft Research would look at nowadays they can handle themselves. They don’t need us. And that’s part of what has allowed us to now go and be like, OK, I’m going to think about other things. Maybe things that, you know, aren’t relevant to you today, but maybe in five years, you’ll come in and thank me for thinking about this!

HUIZINGA: OK. Shifting gears here! In a recent conversation, I heard a colleague refer to you as an “idea machine.” To me, that’s one of the greatest compliments you could get. But it got me wondering, so I’ll ask you: how does your brain work, Behnaz, and how do you get ideas?

ARZANI: Well, this has been, to my chagrin, one of the realities of life about my brain apparently. So I never thought of this as a strength. I always thought about it as a weakness. But nowadays, I’m like, oh, OK, I’m just going to embrace this now! So I have a random brain. It’s completely ran—so, like, it actually happens, like, you’re talking, and then suddenly, I say something that seems to other people like it came out of left field. I know how I got there. It’s essentially kind of like a Markov chain. [LAUGHTER] So a Markov chain is essentially a number of states, and there’s a certain probability you can go from one state to the other state. And, actually, one of the things I found out about myself is I think through talking for this exact reason. Because people see this random Markov chain by what they say, and it suddenly goes into different places, and that’s how ideas come about. Most of my ideas have actually come through when I’ve been talking to someone.

HUIZINGA: Really?

ARZANI: Yeah.

HUIZINGA: Them talking or you talking?

ARZANI: Both.

HUIZINGA: Really?

ARZANI: So it’s, like, basically, I think the thing that has recently … like, I’ve just noticed more—again, being more mindful does that to you—it’s like I’m talking to someone. I’m like, I have an idea. And it’s usually they said something, or I was saying something that triggered that thought coming up. Which doesn’t happen when … I’m not one of those people that you can put in a room for three days—somebody actually once told me this— [LAUGHTER] like, I’m not one of those people you can put in a room for three days and I come out with these brilliant ideas. It’s like you put me in a room with five other people, then I come out with interesting ideas.

HUIZINGA: Right. … It’s the interaction.

ARZANI: Yeah.

HUIZINGA: I want to link this idea of the ideas that you get to the conversations you have and maybe go back to linking it to the work you’ve recently done. Talk about some of the projects, how they came from idea to paper to product even …

ARZANI: Mm-hm. So like one of the works that we were doing was this work on, like, max-min fair resource allocation that recently got published in NSDI and is actually in production. So the way that came out is I was working with a bunch of other researchers on risk estimation, actually, for incident management of all things, which was, how do you figure out if you want to mitigate a particular problem in a certain way, how much risk it induces as a problem. And so one of the people who was originally … one of the original researchers who built our wide-area traffic engineering controller, which we were talking about earlier, he said, “You’re solving the max-min fair problem.” We’re like, really? And then this caused a whole, like, one-year collaboration where we all sat and evolved this initial algorithm we had into a … So initially it was not a multipath problem. It had a lot of things that didn’t fully solve the problem of max-min fair resource allocation, but it evolved into that. Then we deployed it, and it improved the SWAN solver by a factor of three in terms of how fast it solved the problem and didn’t have any performance impact, or at least very little. And so, yeah, that’s how it got born.

HUIZINGA: OK. So for those of us who don’t know, what is max-min fair resource allocation, and why is it such a problem?

ARZANI: Well, so remember I said that in our wide area network, we route traffic from one place to the other in a way that meets capacity. So one of the objectives we try to meet is we try to be fair in a very specific metric. So max-min is just the metric of fairness we use. And that basically means you cannot improve what you allocated to one piece of traffic in a way that would hurt anybody who has gotten less. So there’s a little bit of a, like, … it’s a mind bend to wrap your head a little bit around the max-min fair definition. But the reason making it faster is important is if something fails, we need to quickly recompute what the paths are and how we route traffic. So the faster we can solve this problem, the better we can adapt to failures.

HUIZINGA: So talk a little bit about some of the work that started as an idea and you didn’t even maybe know that it was going to end up in production.

ARZANI: There was this person from Azure Networking came and gave a talk in our group. And he’s a person I’ve known for years, so I was like, hey, do you want to jump on a meeting and talk? So he came into that meeting, and I was like, OK, what are some of the things you’re curious about these days? You want to answer these days? And it was like, yeah, we have this heuristic we’re using in our traffic engineering solution, and essentially what it does is to make the optimization problem we solve smaller. If a piece of traffic is smaller than a particular, like, arbitrary threshold, we just send it on a shortest path and don’t worry about it. And then we optimize everything else. And I just want to know, like, what is the optimality gap of this heuristic? How bad can this heuristic be? And then I had worked on Stackelberg games before, in my PhD. It never went anywhere, but it was an idea I played around with, and it just immediately clicked in my head that this is the same problem. So Stackelberg games are a leader-follower game where in this scenario a leader has an objective function that they’re trying to maximize, and they control one or multiple of the inputs that their followers get to operate over. The followers, on the other hand, don’t get to control anything about this input. They have their own objective that they’re trying to maximize or minimize, but they have other variables in their control, as well. And what their objective is, is going to control the leader’s payoff. And so this game is happening where the leader has more control in this game because it’s, kind of, like the followers are operating in subject to whatever the leader says, … right. But the leader is impacted by what the followers do. And so this dynamic is what they call a Stackelberg game. And the way we map the MetaOpt problem to this is the leader in our problem wants to maximize the difference between the optimal and the heuristic. It controls the inputs to both the optimal and the heuristic. And now this optimal and heuristic algorithms are the followers in that game. They don’t get to control the inputs, but they have other variables they control, and they have objectives that they want to maximize or minimize.

HUIZINGA: Right.

ARZANI: And so that’s how the Stackelberg-game dynamic comes about. And then we got other researchers in the team involved, and then we started talking, and then it just evolved into this beast right now that is a tool, MetaOpt, that we released, I think, a couple of months ago. And another piece that was really cool was people from ETH Zürich came to us and were like, oh, you guys analyzed our heuristic! We have a better one! Can you analyze this one? And that was a whole fun thing we did where we analyzed their heuristics for them. And, then, yeah …

HUIZINGA: Yeah. So all these things that you’re mentioning, are they findable as papers? Were they presented …

ARZANI: Yes.

HUIZINGA: … at conferences, and where are they in anybody’s usability scenario?

ARZANI: So the MetaOpt tool that I just mentioned, that one is in … it’s an open-source tool. You can go online and search for MetaOpt. You’ll find the tool. We’re here to support anything you need; if you run into issues, we’ll help you fix it.

HUIZINGA: Great. You can probably find all of these papers under publications …

ARZANI: Yes.

HUIZINGA: … on your bio page on the website, Microsoft Research website.

ARZANI: Correct.

HUIZINGA: Cool. If anyone wants to do that. So, Behnaz, the idea of having ideas is cool to me, but of course, part of the research problem is identifying which ones you should go after [LAUGHS] and which ones you shouldn’t. So, ironically, you’ve said you’re not that good at that part of it, but you’re working at getting better.

ARZANI: Yes.

HUIZINGA: So first of all, why do you say that you’re not very good at it? And second of all, what are you doing about it?

ARZANI: So I, as I said, get attracted to puzzles, to hard problems. So most of the problems that I go after are problems I have no idea how to solve. And that tends to be a risk.

HUIZINGA: Yeah.

ARZANI: Where I think people who are better at selecting problems are those who actually have an idea of whether they’ll be able to solve this problem or not. And I never actually asked myself that question before this year. [LAUGHTER] So now I’m trying to get a better sense of, how do I figure out if a problem is solvable or not before I try to solve it? And also, just what makes a good research problem? So what I’m doing is, I’m going back to the era that I thought had the best networking papers, and I’m just trying to dissect what makes those papers good, just to understand better for myself, to be like, OK, what do I want to replicate? Replicate, not in terms of techniques, but in terms of philosophy.

HUIZINGA: So what you’re looking at is how people solve problems through the work that they did in this arena. So what are you finding? Have you gotten any nuggets of …

ARZANI: So a couple. So one of my favorite papers is Van Jacobson’s TCP paper. The intuition is amazing to me. It’s almost like he has a vision of what’s happening, is the best I can describe it. And another example of this is also early-on papers by people like Ratul Mahajan, Srikanth Kandula, those guys, where you see that they start with a smaller example that, kind of, shows how this problem is going to happen and how they’re going to solve it. I mean, I did this in my work all the time, too, but it was never conscious. It’s more of like that goes to that mindfulness thing that I said before, too. It’s like you might be doing some of these already, but you don’t notice what you’re doing. It more of is, kind of, like putting of like, oh, this is what they did. And I do this, too. And this might be a good habit to keep but cultivate into a habit as opposed to an unconscious thing that you’re just doing.

HUIZINGA: Right. You know, this whole idea of going back to what’s been done before, I think that’s a lesson about looking at history, as well, and to say, you know, what can we learn from that? What are we trying to reinvent …

ARZANI: Yeah.

HUIZINGA: … that maybe doesn’t need to be reinvented? Has it helped you to get more targeted on the kinds of problems that you say, “I’m not going to work on that. I am going to work on that”?

ARZANI: To be very, very, very fair, I haven’t done this for a long time yet! This has been …

HUIZINGA: A new thing.

ARZANI: I started this this month, yeah.

HUIZINGA: Oh my goodness!

ARZANI: So we’ll see how far I get and how useful it ends up being! [LAUGHS] [MUSIC BREAK]

HUIZINGA: One of my favorite things to talk about on this show is what my colleague Kristina calls “outrageous” lines of research. And so I’ve been asking all my guests about their most outrageous ideas and how they turned out. So sometimes these ideas never got off the ground. Sometimes they turned out great. And other times, they’ve failed spectacularly. Do you have a story for the “Microsoft Research Outrageous Ideas” file?

ARZANI: I had this question of, if language has grammar, and grammar is what LLMs are learning, which, to my understanding of what people who are experts in this field say, this maybe isn’t that, but if it is the case that grammar is what allows these LLMs to learn how language works, then in networking, we have the equivalent of that, and the equivalent of that is essentially network protocols. And everything that happens in a network, you can define it as an event that happens in a network. You can think of those, like, the events are words in a language. And so, is it going to be the case, and this is a question which is, if you take an event abstraction and encode everything that happens in a network in that event abstraction, can you build an equivalent of an LLM for networks? Now what you would use it for—this is another reason I’ve never worked on this problem—I have no idea! [LAUGHTER] But what this would allow you to do is build the equivalent of an LLM for networking, where actually you just translate that network’s events into, like, this event abstraction, and then the two understand each other. So like a universal language of networking, maybe. It could be cool. Never tried it. Probably a dumb idea! But it’s an idea.

HUIZINGA: What would it take to try it?

ARZANI: Um … I feel like bravery is, I think, one because with any risky idea, there’s a probability that you will fail.

HUIZINGA: As a researcher here at Microsoft Research, when you have this idea, um … and you say, well, I’m not brave enough … even if you were brave enough, who would you have to convince that they should let you do it?

ARZANI: I don’t think anybody!

HUIZINGA: Really?

ARZANI: That’s the whole … that’s the whole point of me being here! I don’t like being told what to do! [LAUGHS]

HUIZINGA: Back to the beginning!

ARZANI: Yeah. The only thing is that, maybe, like, people would be like, what have you been doing in the past six months? And I wouldn’t have … that’s the risk. That’s where bravery comes in.

HUIZINGA: Sure.

ARZANI: The bravery is more of there is a possibility that I have to devote three years of my life into this, to figuring out how to make that work, and I might not be able to.

HUIZINGA: Yes …

ARZANI: And there’s other things. So it’s a tradeoff also of where you put your time.

HUIZINGA: Sure.

ARZANI: So there. Yeah.

HUIZINGA: And if, but … part of it would be explaining it in a way to convince people: if it worked, it would be amazing!

ARZANI: And that’s the other problem with this idea. I don’t know what you would use it for. If I knew what you would use it for, maybe then it would make it worth it.

HUIZINGA: All right. Sounds like you need to spend some more time …

ARZANI: Yeah.

HUIZINGA: …ruminating on it. Um, yeah. The whole cliché of the solution in search of a problem.

ARZANI: Yeah.

HUIZINGA: [LAUGHS] As we close, I want to talk a little bit about some fun things. And so, aside from your research life, I was intrigued by the fact, on your bio page, that you have a rich artistic life, as well, and that includes painting, music, writing, along with some big ideas about the value of storytelling. So I’ll take a second to plug the bio page. People, go look at it because she’s got paintings and cool things that you can link to. As we close, I wonder if you could use this time to share your thoughts on this particular creative pursuit of storytelling and how it can enhance our relationships with our colleagues and ultimately make us better researchers and better people?

ARZANI: I think it’s not an understatement to say I had a life-changing experience through storytelling. The first time I encountered it, it was the most horrific thing I had ever seen! I had gone on Meetup—this was during COVID—to just, like, find places to meet people, build connections and all that, and I saw this event called “Storytelling Workshop,” and I was like, good! I’m good at making up stories, and, you know, that’s what I thought it was. Turns out it’s, you go and tell personal stories about your life that only involve you, that make you deeply vulnerable. And, by the way, I’m Iranian. We don’t do vulnerability. It’s just not a thing. So it was the most scary thing I’ve ever done in my life. But you go on stage and basically talk about your life. And the thing it taught me by both telling my own stories and listening to other people’s stories is that it showed me that you can connect to people through stories, first of all. The best ideas come when you’re actually in it together. Like one of the things that now I say that I didn’t used to say, we, we’re all human. And being human essentially means we have good things about ourselves and bad things about ourselves. And as researchers, we have our strengths as researchers, and we have our weaknesses as researchers. And so when we collaborate with other people, we bring all of that. And collaboration is a sacred thing that we do where we’re basically trusting each other with bringing all of that to the table and being that vulnerable. And so our job as collaborators is essentially to protect that, in a way, and make it safe for everybody to come as they are. And so I think that’s what it taught me, which is, like, basically holding space for that.

HUIZINGA: Yeah. How’s that working?

ARZANI: First of all, I stumbled into it, but there are people who are already “that” in this building …

HUIZINGA: Really?

ARZANI: … that have been for years. It’s just that now I can see them for what they bring, as opposed to before, I didn’t have the vocabulary for it.

HUIZINGA: Gotcha …

ARZANI: But people who don’t, it’s like what I’ve seen is almost like they initially look at you with skepticism, and then they think it’s a gimmick, and then they are like, what is that? And then they become curious, and then they, too, kind of join you, which is very, very interesting to see. But, like, again, it’s something that already existed. It’s just me not being privileged enough to know about it or, kind of, recognize it before.

HUIZINGA: Yeah. Can that become part of a culture, or do you feel like it is part of the culture here at Microsoft Research, or … ?

ARZANI: I think this depends on how people individually choose to show up. And I think we’re all, at the end of the day, individuals. And a lot of people are that way without knowing they are that way. So maybe it is already part of the culture. I haven’t necessarily sat down and thought about it deeply, so I can’t say.

HUIZINGA: Yeah, yeah. But it would be a dream to have the ability to be that vulnerable through storytelling as part of the research process?

ARZANI: I think so. We had a storytelling coach that would say, “Tell your story, change the world.” And as researchers, we are attempting to change the world, and part of that is our stories. And so maybe, yeah! And basically, what we’re doing here is, I’m telling my story. So …

HUIZINGA: Yeah.

ARZANI: … maybe you’re changing the world!

HUIZINGA: You know, I’m all in! I’m here for it, as they say. Behnaz Arzani. It is such a pleasure—always a pleasure—to talk to you. Thanks for sharing your story with us today on Ideas.

ARZANI: Thank you.