Navigating industry after academia: Q&A with Software Engineering Manager Hyojeong Kim

Academics come to work at Facebook at various stages of their career. Some join right after graduating; others join after long and established careers as professors. Somewhere in between is Hyojeong Kim, who joined Facebook after spending some time in industry.

Kim is a Software Engineering Manager within network routing at Facebook. She owns routing protocols (BGP and Open/R) and related software services running in Facebook’s production network, which consists of data center, backbone, and edge networks. Her team focuses on tackling routing challenges to support next-generation production networks while ensuring production reliability.

We reached out to Kim to learn more about her academic background, her journey at Facebook so far, and her recent projects on the Networking team. She also offers advice for PhDs looking to follow a similar path.

Q: Tell us about your experience in academia before joining Facebook.

Hyojeong Kim: During my undergraduate career in South Korea in the late ’90s, I saw how the internet connected people around the world. This inspired me to want to study the core technologies that enable the internet and contribute to it, so I came to the United States and joined the PhD program in computer science at Purdue University.

During my time at Purdue, I had the opportunity to learn about Border Gateway Protocol (BGP)-based internet routing and its problems. I built an internet-scale BGP simulation test bed over a distributed Linux cluster. I was very excited to learn so many new things. As I tried to experiment with the latest measurement-based internet topologies, I encountered many distributed systems challenges due to the scale of the input, and I spent lots of time and effort creating software tools to support the distributed simulation.

These were interesting problems by themselves, but they were also very challenging. As a graduate student, I didn’t have the context for how my work could be applied to real-world problems. However, my learnings from PhD study turned out to be very useful for solving real-world problems throughout my career in various ways. It helped me to extend BGP to support fast response to internet distributed denial-of-service attacks, to build a system to improve how Facebook serves user traffic, and to build and run Facebook data center network routing software stack. In retrospect, all my learnings and experiences during my PhD study were very valuable. I just did not have perspective at that time.

Q: What brought you to Facebook?

HK: I started my career at Cisco, working on BGP routing software running on core routers used by internet service providers. I felt proud contributing to the foundation of the internet. After gaining some industry experience, when I was thinking about the next chapter of my career, I had a chance to attend the Grace Hopper Conference. I was so inspired, meeting so many women in various stages of their career and getting advice from them on helping women have successful careers in tech. I also met engineers from Facebook and heard about their experiences. It led me to join Facebook eventually.

Q: What has your journey with Facebook been like so far?

HK: I first joined Facebook as a software engineer. Coming to Facebook made it possible to pursue big research questions and be creative, which I was very excited about. I learned how Facebook’s production network is connected to the internet, and I had an exciting opportunity to build and run a software-defined networking controller called Edge Fabric. This was a research collaboration with a PhD intern and his advisers. We enhanced the system significantly and shared our operational experience with the academic community at SIGCOMM 2017.

On the Facebook Networking team, we study our own production network, identify problems, build solutions, deploy them to production, and keep iterating on solutions, receiving signals from operations. I really have enjoyed the opportunity of owning the full problem-solving cycle. At Facebook, engineers are empowered to innovate and to be bold and creative in their solutions. This encouraged me to take ownership of big challenges.

Within Facebook, changing teams or trying out different job roles is common and very much encouraged. This keeps the work exciting and challenging, and it ensures that we’re always learning new things. As a software engineer, I had the opportunity to lead a team of engineers for a couple of major initiatives. Then, I became interested in learning how to grow other engineers and how to support a team to solve multiple challenging projects. Eventually, I became a software engineering manager, and now I lead a team of software engineers within network routing.

Q: What are some of your most recent projects?

HK: I changed my focus to data center network routing a few years ago. This was the time when the team was scaling Facebook’s in-house network switch and software stack, FBOSS. The goal was to transition the data center network to FBOSS. During this time, I learned and improved the BGP-based data center routing design. I led building a scalable, performant BGP software and its testing/deployment pipeline. These allow us to treat BGP like any other software component, enabling fast incremental updates.

Using what I’ve learned over the years, I co-authored the NSDI 2021 paper “Running BGP in data centers at scale.” BGP was designed for the internet, but big web-scale companies often use it in data centers. This paper describes how we build, test, deploy, and use BGP in Facebook’s data centers, which has never been thoroughly discussed in academia before. This paper was a collaboration with our past PhD interns, Anubhavnidhi Abhashkumar and Kausik Subramanian from the University of Wisconsin, and their adviser, Aditya Akella. They helped capture our operational experience from an academic point of view.

Q: What advice would you give to current PhD candidates looking to transition to industry?

HK: If you’re a PhD candidate who’s having a similar experience as I did, where you feel unsure about how your current work would make an impact on real-world problems, I recommend looking for internship opportunities in the industry you’re interested in. When you have only academic experience, it’s difficult to know how research is applied in industry without actually having industry experience. Internships can help you contextualize your research and give you a new perspective on it, which will help you think about it in relation to solving practical problems. Additionally, you’ll make connections that could potentially result in future research collaborations. Internships also allow you to experience and explore different company cultures, which may help you find the right place to work after graduation.

Also, I recommend that PhDs attend as many networking events as possible. Attending Grace Hopper was a pivotal moment in my career, and it opened my eyes to all the places I could work.

Q: Where can people learn more about what the Facebook Networking team is up to?

HK: Check out the Networking team page for all our most recent publications, news, programs, and job openings. We are also launching an RFP at NSDI ’21. Sign up here for email notifications about new RFPs.

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New Analytics API for researchers studying Facebook Page data

Today we are launching the Facebook Open Research & Transparency (FORT) Analytics API for researchers. This release includes a collection of API endpoints that helps academics identify trends on Facebook Pages and how they’ve evolved over time. You can leverage these insights to focus on specific Pages that are of interest.

We designed this API specifically for the academic community to conduct longitudinal analyses with time series data. With the launch of FORT Pages API in 2020 and now the Analytics API, we will continue to develop a product roadmap focused on sharing new types of Facebook and Instagram data with the academic research community, as well as additional analytics endpoints.

Analytics API features

With the FORT Analytics API, we are offering three new endpoints. Each of these are aggregated, time-series data, captured at daily intervals and include:

  1. Lifetime follower count (number of users who have ever followed a Page) by country per Page.
  2. Page admin Post count (applies only to Posts made by Page admins and not to user posts)
  3. Page engagement count, where engagement is defined as total number of likes, comments, clicks and shares on Posts created on that Page

Unlike other research-related APIs, these endpoints facilitate queries across the entirety of public Facebook Pages. (You can learn more about Facebook data sets that we make available to researchers here.)

For technical documentation, click here.

Important considerations

Current Analytics data: These endpoints provide aggregate metrics on Facebook Pages. They are designed to help researchers observe and analyze engagement patterns of Pages and leverage that analysis to decide which Pages to focus on, saving time and effort. Our systems take a snapshot of Page activity once per day and provide that information to you via the Pages API. Consequently, these aggregations are directionally accurate (as opposed to data which is dynamically generated for each query).

Historical Page data: When using these endpoints you may encounter small, historical inaccuracies with the counts. For instance, the Center for Disease Control Page seems to have lost a few hundred followers in January, most likely due to user account deletions etc. For now, we consider this an acceptable amount of inaccuracy to still deliver high quality research, but we continue to monitor these to incorporate into future updates. Note that a user deleting a Post, unliking a Post, or deleting a Comment will not be reflected in the analytics. In other words, the data does not account for content deletions.

How to access the FORT Analytics API

If you are a grantee of Social Science One, you will have default access to this API through the FORT platform. If you are not an SS1 researcher but are interested in using this API, please apply to join the Social Science One community here.

Technical limitations
While we won’t apply any formal limit on query size, we recommend the following for best performance:

  • No more than 250 page ids in a single API call
  • Request no more than 10,000 records in a single API call

See this documentation for more details.

About the Facebook Open Research and Transparency Platform

The Facebook Open Research and Transparency (FORT) Platform facilitates responsible research by providing flexible access to valuable data. The platform is built with validated privacy and security protections, such as data access controls, and has been penetration-tested by internal and external experts.

The FORT platform runs on an opinionated version of JupyterHub, an open source tool that is widely used by the academic community. The FORT platform supports multiple standard programs, including SQL, Python, and R, and a specialized bridge to specific Facebook Graph APIs.

Publication guidelines

Researchers may publish research conducted using this data without Facebook’s permission. As with other research conducted under the Research Data Agreement, Facebook is entitled to review (not approve or reject) research prior to publication, and remove any confidential or personally identifiable information.

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Core Data Science researchers discuss research award opportunity in adaptive experimentation

On February 24, Facebook launched a request for proposals (RFP) on sample-efficient sequential Bayesian decision making, which closes on April 21. With this RFP, the Facebook Core Data Science (CDS) team hopes to deepen its ties to the academic research community by seeking out innovative ideas and applications of Bayesian optimization that further advance the field. To provide an inside look from the team behind the RFP, we reached out to Eytan Bakshy and Max Balandat, who are leading the effort within CDS.
View RFPBakshy leads the Adaptive Experimentation team, which seeks to improve the throughput of experimentation with the help of machine learning and statistical learning. Balandat supports the team’s efforts on modeling and optimization, which is primarily focused on probabilistic models and Bayesian optimization. In this Q&A, Bakshy and Balandat contextualize the RFP by sharing more information about how the work of their team relates to the areas of interest for the call.

Q: What’s the goal of this RFP?

A: Primarily, we are keen to learn more about all the great research that is going on in this area. Conversely, we are also able to share a number of really interesting real-world use cases that we hope can help inspire additional applied research, and increase interest and research activity into sample-efficient sequential Bayesian decision making. Lastly, we aim to further strengthen our ties to academia and our collaborations with academics who are at the forefront of this area.

We are both excited to dive in and learn about creative applications and approaches to Bayesian optimization that researchers come up with in their proposals.

Q: What inspired you to launch this RFP?

A: We publish quite a bit in top-tier AI/ML venues, and all our papers are informed by very practical problems we face every day in our work. The need for exploring large design spaces via experiments with a limited budget is widespread across Facebook, Instagram, and Facebook Reality Labs. Much of our team’s work focuses on applied problems to help support the company and use-inspired basic research, but it is clear that there are plenty of ideas out there that can advance the area of sample-efficient sequential decision making, such as Bayesian optimization and related techniques.

In academia, it can sometimes be challenging to understand what exactly the most relevant and impactful “real-world” problems are. Conversely, academics may have an easier time taking a step back, looking at the bigger picture, and doing more exploratory research. With this RFP, we hope to help bridge this gap and foster increased collaboration and cross-pollination between industry and academia.

Q: What is Bayesian optimization and how is it applied at Facebook?

A: Bayesian optimization is a set of methodologies for exploring large design spaces on a limited budget. While Bayesian optimization is frequently used for hyperparameter optimization in machine learning (AutoML), our team’s work had originally been motivated by the use of online experiments (A/B tests) for optimizing software and recommender systems.

Since then, the applications for Bayesian optimization have expanded tremendously in scope, with applications ranging from the design of next-generation AR/VR hardware, to bridging the gap between simulations and real-world experiments, to efforts around providing affordable connectivity solutions to developing countries.

The main idea behind Bayesian optimization is to fit a probabilistic surrogate model to the “black-box” function one is trying to optimize, and then use this model to inform at which new parameters to evaluate the function next. Doing so allows for a principled way of trading off reducing uncertainty with exploring promising regions of the parameter space. As described earlier, we apply this approach to a large variety of problems in different domains at Facebook.

Q: What is BoTorch, and how does it relate to the RFP?

A: The adaptive experimentation team has been investing in methodological development and tooling for Bayesian optimization for over five years. A few years ago, we found that our current tooling was slowing down researchers’ ability to generate new ideas and engineers’ ability to scale out Bayesian optimization use cases.

To address these problems, we developed BoTorch, a framework for Bayesian optimization research, and Ax, a robust platform for adaptive experimentation. BoTorch follows the same modular design philosophy as PyTorch, which makes it very easy for users to swap out or rearrange individual components in order to customize all aspects of their algorithm, thereby empowering researchers to do state-of-the art research on modern Bayesian optimization methods. By exploiting modern parallel computing paradigms on both CPUs and GPUs, it is also fast.

BoTorch has really changed the way we approach Bayesian optimization research and accelerates our ability to tackle new problems. With the RFP, we hope to attract more widespread interest in this area and raise awareness of our open source tools.

Q: Where can people stay updated and learn more?

A: We actively engage with researchers on Twitter, so follow @eytan and @maxbalandat for the latest research, and always feel free to reach out to us via Twitter, email, or GitHub Issues if you have any questions or ideas.

You can find the latest and greatest of what we are working on in our open source projects, BoTorch and Ax. It also helps to keep an eye out for our papers in machine learning conferences, such as NeurIPS, ICML, and AISTATS.


Applications for the RFP on sample-efficient sequential Bayesian decision making close on April 21, 2021, and winners will be announced the following month. To receive updates about new research award opportunities and deadline notifications, subscribe to our RFP email list.

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Q&A with Brown University’s Anna Lysyanskaya, two-time winner of Facebook research awards in cryptography

In this monthly interview series, we turn the spotlight on members of the academic community and the important research they do — as partners, collaborators, consultants, and independent contributors.

For March, we nominated Anna Lysyanskaya, a professor at Brown University. Lysyanskaya is a two-time Facebook research award recipient in cryptography and is most known for her work in digital signatures and anonymous credentials. In this Q&A, Lysyanskaya shares more about her background, her two winning research proposals, her recent talk at the Real World Cryptography Symposium, and the topics she’s currently focusing on.

Q: Tell us about your role at Brown and the type of research you specialize in.

Anna Lysyanskaya: I am a professor of computer science, and my area of expertise is cryptography, specifically privacy-preserving authentication and anonymous credentials. I’ve had a long career in academia and finished my PhD 19 years ago, so this particular area is something that I started working on basically since I started doing research as a PhD student.

I got into this field mostly by chance, and honestly, I could have ended up anywhere. At the time, everything was new and interesting to me, but I remember I had a chance encounter with the person who would eventually become my adviser. At the time, he had a couple of papers he wanted to take a closer look at, so I started reading them and meeting with him to discuss them.

At the beginning, I was attracted to cryptography because I was interested in the math aspect, as well as the social aspect of solving math problems with interesting people who made everything fun. That initial fascination, paired with being in a great place to study it, led me to where I am today.

Eventually, I learned that it’s not just fun and math, and that there are actually interesting applications of what I’m working on. This is actually why I’m still working on it all this time later, because I just haven’t run out of interesting places to apply this stuff.

Q: You were a winner of two Facebook requests for proposals: the Role of Applied Cryptography in a Privacy-Focused Advertising Ecosystem RFP and the Privacy Preserving Technologies RFP. What were your winning proposals about?

AL: My ads-focused proposal was entitled “Know your anonymous customer.” Let’s start with how a website — say, — turns content into money: by showing ads. When you click on an ad and buy something, the website that sent you there gets a small payment. At scale, these payments are what pays for the content you find online. The main issue here is that the websites you visit track your activities, and by tracking what you do, they are able to reward the sites that successfully showed you an ad.

My project is about finding a privacy-preserving approach to reward ad publishers — an approach that would not involve tracking a user’s activities but that would still allow reliable accountability when it comes to rewarding a website responsible for sending a customer to, say, a retailer that closed a sale with that customer. The idea is to use anonymous credentials: When you purchase something, your browser obtains a credential from the retailer that just received money from you. Your browser then communicates this credential, transformed in a special way, to whichever website sent you to the original retailer. The crux of the matter is that the transformed credential cannot be linked to the data issued by the retailer, so even if the website and retailer collude, they cannot tell that it was the same user.

My other proposal, which I coauthored with Foteini Baldimtsi from George Mason University, was about private user authentication and anonymous credentials on Facebook’s Libra blockchain. The nature of a blockchain is that it’s very public, but you also want to protect everyone’s privacy, so our goal was to build cryptographic tools for maintaining privacy on the blockchain. Having the opportunity to work with Libra researchers on this project is very exciting.

The tools for both research projects are very similar in spirit, but the stories are different. Because the applications are different enough, you still need to do some original research to solve the problems. The motivations for both projects are achieving user privacy and protecting users.

Q: You recently spoke at Real World Cryptography (RWC). What was your presentation about?

AL: Anonymous credentials have been central to my entire research career. They are what I am most known for, and they were the subject of my talk. An anonymous credential allows you to provide proof that you’re a credentialed user without disclosing any other information. In the aforementioned advertising example, a retail website you visit gives an anonymous credential to your browser that allows you to prove that you have purchased something at this retailer, without revealing who you are or any information that would allow anyone to infer who you are or what you purchased.

Of course, anonymous credentials can be used much more broadly. An especially timely potential application would be vaccination credentials. Suppose that everyone who receives a vaccination also receives a credential attesting to their vaccination status. Then, once you’re vaccinated, you can return to pre-pandemic activities, such as attending concerts and sports events, air travel, and even taking vacation cruises. To gain access to such venues, you’d have to show your vaccination credential. But unless anonymous credentials are used, this is potentially a privacy-invasive proposition, so anonymous credentials are a better approach.

Q: What are some of the biggest research questions you’re interested in?

AL: This talk that I gave at RWC is kind of about this. In a technical field, it’s hard to communicate what you’re doing to people who can actually potentially apply it, mostly because it’s not easy to explain mathematical concepts. Anonymous credentials are especially hard to explain to somebody who hasn’t studied cryptography for at least a few years.

Right now, my focus is to recast this problem in a way that’s a little bit more intuitive. My current attempt is to have an intermediate primitive called a mercurial signature. This is just like a digital signature, but it’s mercurial as in you can transform it in a way that’s still meaningfully signing a statement — just in a way that’s not recognizable to what it looked like when it was first issued.

There are several reasons why I think mercurial signatures are a good building block to study:

  • First, we actually do have a candidate construction, so it’s not completely far-fetched, and we know that we can do it. Now, that construction has some shortcomings, but it isn’t a completely crazy idea.
  • Second, mercurial signatures are an accessible concept to somebody who has just a basic undergraduate understanding of cryptography. You can actually explain what a mercurial signature is to somebody who knows what a digital signature is in just a few minutes.
  • Also, mercurial signatures have very rich applications, and they allow us to build anonymous credentials that have some nice features. One example is delegation. Let’s say I anonymously give a credential to you and then you give a credential to someone else. When they use their credential, it doesn’t reveal what the chain of command is — just that they’re authorized.

This is actually the bulk of my RWC talk, and it’s what I think is the next thing to do.

Q: Where can people learn more about your research?

AL: People can learn more about my research on my Google Scholar profile.

The post Q&A with Brown University’s Anna Lysyanskaya, two-time winner of Facebook research awards in cryptography appeared first on Facebook Research.

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Diverse representation in advertising: Q&A with Creative Shop Researcher Fernanda de Lima Alcantara

International Women’s Day celebrates the social, economic, cultural, and political achievements of women. To highlight the impactful work that women researchers are doing at Facebook, we reached out to Fernanda de Lima Alcantara, Marketing Science Researcher at Facebook’s Creative Shop.

The Creative Shop is an internal team of creative strategists, designers, writers, producers, and data experts who collaborate with advertisers to help them run effective campaigns on Facebook’s apps and services. Within this team, De Lima focuses on helping businesses succeed by providing them with marketing and advertising insights, with a current focus in representation in online ads.

In this Q&A, we ask De Lima about her journey at Facebook, her background, and her current research projects. She shares insights from her recent white paper, “Diverse and inclusive representation in online advertising: An exploration of the current landscape and people’s expectations,” and explains what marketers should take away from this research.

Q: Tell us about your experience in academia before joining Facebook.

Fernanda de Lima Alcantara: I first started my career in Brazil as a telecommunications technician, but soon I found my passion for data analysis and earned an undergraduate degree in computer science. For almost six years, I worked with data mining and decision science in the finance sector. I also obtained many certifications in analytical and statistical tools. To continue growing my skills in quantitative and qualitative analysis, I moved to Europe to pursue a master’s in machine learning at University College London.

What excited me about machine learning is that it can be applied to multiple domains (like neuroscience, bioinformatics, machine vision, and so on) to solve real-world problems using a data-driven approach. I learned to design, develop, and evaluate appropriate algorithms and methods for new applications, as well as some new techniques to analyze data. I felt the machine learning master’s program was strongly aligned with my business experience and my field of interest.

Q: What has your journey with Facebook been like so far?

FDLA: I joined Facebook in 2012 in the São Paulo office. In Brazil, I helped many businesses grow by transforming current marketing practices and developing new strategies, always grounded in our foundational measurement practices. Over the years, I worked on projects using simple aggregation, descriptive analysis, or more advanced analyses using data models and causal inference.

I officially joined the research team five years ago, when I moved to the United States to work from the Facebook Menlo Park office in California. In the first two years, I was dedicated to consumer insights and spent time studying the intersection of advertiser value and consumer behavior within ads products. I worked on a range of projects, some focused on the consumer journey and others focused on understanding how people feel about our products. It was very exciting to work with a breadth of methodologies like behavioral lab and consumer neuroscience, passive measurement in sales touchpoints, surveys, focus groups, and in-depth interviews.

For the last three years, I’ve been working in New York as a Marketing Science Researcher in Creative Shop. Every day, I’m provided with the unique opportunity to explore the creative potential of Facebook platforms and help businesses connect with people in meaningful ways and succeed. In my day-to-day, I use experimental design, online surveys, and Facebook data to build tools for statistical, qualitative, and quantitative analysis. My goal is to learn, share, and inspire business with new possibilities through data, creativity, and storytelling. And I love working at the intersection of art and science.

Q: What are you currently working on?

FDLA: Every half we are presented with exciting challenges to advance the industry. Currently, I have two projects that are top of mind: The first one promotes diverse and inclusive representation in online advertising, and the second explores the creative opportunities in emerging platforms.

The first project is very close to my heart because it promotes social justice and business equality. The objective is to identify opportunities to better represent people in online ads, inspire more inclusive and authentic advertising content, and uncover the positive impact of inclusive portrayal — for people and businesses.

The second project investigates the new ways people are connecting online and the new creative potential for people and businesses. In this project, I explore creative ideas to help businesses succeed in AR, VR, and other immersive experiences.

Both projects bring me a sense of community and meaningfulness because they aim to create a positive social impact by improving people’s representation in ads and their experience with Facebook, and they support business growth.

Q: Social impact, diversity, and inclusion continue to play a big role in the advertising industry. What should marketers take away from this research?

FDLA: Our research in diverse and inclusive representation in online advertising showed that stereotypes and bias still exist within advertising, with some groups practically absent or portrayed in stereotypical ways. In contrast, people expect the advertising industry to ensure diverse voices and experiences are represented authentically, and they want to see ads that reflect their lived experiences and communities more accurately.

While there’s no single path to progress, part of this process involves getting more comfortable having conversations around inclusivity and ensuring diversity of people both building and leading creative development. It is also part of the challenge to support creative development with mechanisms to spot bias and track progress with data.

Fundamentally, people expect brands to get involved and promote better representation and portrayal of people in advertising. In doing so, they might see a range of positive effects on business outcomes.

Advertising aims to tell stories, evoke emotions, and compel actions. But to improve the representation and portrayal of people in advertising, we must close the gap between what people want to see in advertising and what the ad creative — that is, characters and storyline — is actually showing them. This is how we can better reflect the full breadth of people we serve and make progress.

More details can be found in the white paper.

Q: Where can people learn more about your research?

FDLA: You can find an article about this research at

To learn more about how Facebook is celebrating the achievements of women during Women’s History Month, visit Newsroom.

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Inside Magma: A look at the team behind the software

Facebook Connectivity’s mission is to bring more people online to a faster internet. Together with partners around the world, we’re developing programs and technologies that increase the availability, affordability, and awareness of high-quality internet access. In this blog, we take a closer look at Magma, an open source software platform that enables operators and internet service providers to deploy mobile networks in hard-to-reach areas.

To help tell the story of Magma, we reached out to two members of the Facebook Magma team, Brian Barritt (Software Engineering Manager) and Ulas Kozat (Software Engineer). As experts in this space, they provide more details about Magma, its use cases, as well as its growing community of academics, developers, and industry partners.

About Magma

Every mobile network needs a high-performance packet core at the center of its network. But the market has made it difficult for communications providers to buy, deploy, and maintain the latest technologies at a reasonable cost. According to Kozat, Magma is an open-source, enhanced packet core solution that delivers flexibility, openness, and lower costs to communications service providers. This ultimately means people can experience better connectivity, whether through 4G, 5G, Wi-Fi, or other wireless access technologies.

Kozat names the following potential use cases for Magma:

  • Providing connectivity solutions for smaller populations (such as remote locations, enterprises, and factories) that need more localized, self-managed networking
  • Providing regional or national operators with a solution to fill gaps in coverage or capacity in both rural and urban areas
  • Providing low-latency, high-bandwidth access to edge cloud (like AR/VR applications), to proliferate the next generation of applications and services

Facebook Connectivity’s work by nature is highly collaborative and spans several fields of expertise, and Magma is no exception. “Facebook Connectivity open-sourced Magma in 2019, and we continue to be major contributors to the code base,” says Kozat. “Our partner engineers, marketing team, and management team build partnerships with vendors, system integrators, academics, and service providers to accelerate market adoption and bring millions of real users online powered by Magma.”

Partnerships, collaborations, and community

The Magma team actively solicits researchers to join its advanced research arm through the Magma Academic Partnership Program, which was launched in 2020. “The program aims to foster strong participation from academic researchers to advance edge connectivity over open wireless research testbeds and platforms. The program also supports research projects that more directly explore advanced use cases using the Magma platform,” says Kozat.

In line with this vision, Magma and others within Facebook Connectivity have been part of the organization committee for the academic program, and have been speakers for the first OpenWireless Workshop, which was organized as part of ACM Mobisys in June 2020.

The Magma team further fosters collaboration and community among industry and academic partners through events like the Magma Developers Conference, which took place this year on February 3, 2021. The annual event brings together developers, communications service providers, field experts, academia, and technology leaders to discuss opportunities, challenges, and new ways to improve and expand global connectivity. “The major theme of the event this year echoes Facebook Connectivity’s mission and underscores Magma’s role in connecting people to a faster internet,” says Barritt.

This year’s conference featured three talks led by academic collaborators: Sylvia Ratnasamy (University of California, Berkeley), Kurtis Heimerl (University of Washington), and Rahman Doost-Mohammady (Rice University). For those interested in learning more about the event, all the sessions are on the Open Infra Foundation’s YouTube channel.

Magma is expanding its community of developers through an open-source industry collaboration with the Linux Foundation. The Linux Foundation will provide a neutral governance framework for Magma, and is joined by other open-source communities including the Open Infrastructure Foundation and the OpenAirInterface Software Alliance. Many other partner companies of varying sizes have also joined the project.

“The sustainability of open-source projects depends on a healthy ecosystem. For Magma, there are many partners actively contributing to the codebase and are actively deploying Magma. Their business success is intertwined with the success of Magma,” Kozat says. More information about the collaboration is available on the Linux Foundation blog.

Next steps

For 2021, the Magma team will continue to emphasize the importance of collaboration. “Our efforts to include the research community in the Magma ecosystem will continue in 2021 with full thrust,” says Kozat. “New funding opportunities and support mechanisms for universities will be offered to push the envelope further than the near-term industry needs.”

To get involved with the Magma developer community, check out Magma’s GitHub page. Here you will find information about Slack channels and mailing lists. For marketing and industry-related news and announcements, visit the Magma website and subscribe to receive updates. For general updates from the Facebook research community, follow our Facebook page.

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