Team++

• Yura Burda. Yura(opens in a new window) finished a math PhD at the age of 24, and switched into machine learning a year and a half ago. He’s focusing on generative models(opens in a new window). He discovered(opens in a new window) a simple but fundamental improvement to the variational lower bound that had evaded notice since its original discovery decades ago.

• Ian Goodfellow. Ian(opens in a new window) is well known for his many contributions to machine learning, including the MaxOut Network(opens in a new window) and the Generative Adversarial Network(opens in a new window), the latter of which is a driver of excitement in generative modeling research. In addition, he’s the lead author of the book(opens in a new window) on deep learning.

• Alec Radford. Alec(opens in a new window) created DCGAN(opens in a new window), a neural model that could generate large, coherent images containing an unprecedented level of global coherence and detail. In addition, his model has learned to do image analogies(opens in a new window) in an entirely unsupervised way.

• Tim Salimans. Tim(opens in a new window) is an expert on variational methods. The author of the first paper(opens in a new window) on stochastic gradient variational inference (for which he won the Lindley prize(opens in a new window)), he was at one point ranked number 2 overall(opens in a new window) on Kaggle.

Also joining us for the summer (and in some cases, to continue the collaboration once they return to their home institution):

• Peter Chen. Peter(opens in a new window) previously co-founded a startup(opens in a new window) and did research on parallel computing and cognitive science. Recently, he’s been working on reinforcement learning, from theory to applications with deep neural nets.

• Rocky Duan. Rocky(opens in a new window) is a PhD student who previously co-founded a startup(opens in a new window) (with Peter) and worked on a number of projects in robotics. He’s now transitioned to the field of deep reinforcement learning, and recently TA’d Berkeley’s Deep Reinforcement Learning(opens in a new window) class.

• Linxi Fan. Although Linxi(opens in a new window) is just finishing up his undergrad, he has already worked on a variety(opens in a new window) of projects in deep learning, including Deep Speech 2(opens in a new window).

• Jon Gauthier. Jon(opens in a new window) is an undergraduate student whose achievements include, among others, a method(opens in a new window) that makes it possible to efficiently train large recursive neural networks on large datasets.

• Jonathan Ho. Jonathan(opens in a new window) is a PhD student at Stanford, where he is developing a new approach to imitation learning. Earlier in his research career, he had success teaching robots how to tie knots(opens in a new window).

• Rein Houthooft. Rein(opens in a new window) hails from Belgium, started his research career in the field of computer networking, and now is developing novel ways to incorporate uncertainty into deep reinforcement learning algorithms.

• Eric Price. Eric(opens in a new window) is a professor of theoretical computer science at UT Austin. His achievements include the development and the analysis of a faster-than-n log n sparse Fourier transform(opens in a new window). In his past life, he achieved a perfect score(opens in a new window) at the IOI.

As a closing note, we get a lot of questions about what we’re working on, how we work, and what we’re trying to achieve. We’re not being intentionally mysterious; we’ve just been busy launching the organization (and finding awesome people to help us do so!).

We’re currently focused on unsupervised learning and reinforcement learning. We should have interesting results to share over the next month or two. A bunch of us will be around ICLR(opens in a new window), where we’ll likely hold an event of some form. I’ll also host a Quora Session(opens in a new window) in May or June to answer any questions for people we don’t meet in Puerto Rico.