We created Facebook AI Research over four years ago to focus on advancing the science and technology of AI, and we’ve always done this by collaborating with local academic communities. FAIR relies on open partnerships to help drive AI forward, where researchers have the freedom to control their own agenda. Ours frequently collaborate with academics from other institutions, and we often provide financial and hardware resources to specific universities. It’s through working together and openly publishing research that we’ll make progress. Today, we’re announcing new additions to FAIR who are helping us build new AI-specific labs and strengthen existing offices:
This dual affiliation model is common across FAIR, with many of our researchers around the world splitting their time between FAIR and a university. Rob Fergus and I do this with NYU, Joelle Pineau with McGill, Devi Parikh and Dhruv Batra with Georgia Tech, Pascal Vincent with Université de Montréal, Iasonas Kokkinos with University College London, and Lior Wolf with Tel Aviv University. This model allows people within FAIR to continue teaching classes and advising graduate students and postdoctoral researchers, while publishing papers regularly. This co-employment appointment concept is similar to how many professors in medicine, law, and business operate.
Part of our commitment to academia and local ecosystems is also investing in them and providing tools they need to thrive. As we’ve done in the past, we plan to support a number of PhD students who will conduct research in collaboration with researchers at FAIR and their university faculty, or on topics of interest to FAIR under the direction of their faculty. We’re also providing millions in funding to the schools from which we’ve hired. This allows the professors to spend less time fundraising for their labs and more time working with their students.
For students, association with FAIR can provide collaboration opportunities with researchers who have a broad set of expertise and the computational resources to pursue large-scale learning research. It also provides a platform for students to showcase their research and ground it in real-world problems at scale. Beyond collaborations, we also offer fellowships and emerging scholars programs to support promising doctoral students. We’re constantly evaluating what opportunities we can offer students, and most recently increased the number of PhD fellows with FAIR Paris’ CIFRE program from 15 students to 40, granted new scholarships to students, and funded 10 servers for French public institutions. We will assess how to bring similar investments to other FAIR offices around the world.
We’re excited to continue investing in academia, educating the next generation of researchers and engineers, and strengthening interaction across AI disciplines that can traditionally become siloed. Thank you to all the academics around the world who are collaborating with FAIR to advance AI.