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- Dario Amodei. Dario(opens in a new window) was one of the lead authors of Deep Speech 2(opens in a new window), a speech system which achieved near-human performance on many speech tasks. He is also a main co-author of “Concrete Problems in AI Safety(opens in a new window)”, which highlights issues related to accidents in machine learning systems. Prior to OpenAI, he worked at Google Brain.
- Filip Wolski. Filip’s(opens in a new window) recent background is in “practical” modeling, having spent the last few years working in the high-frequency trading space. In the past he enjoyed problem-solving in programming competitions, and won the IOI(opens in a new window) and ACM ICPC(opens in a new window).
- Jack Clark. Jack(opens in a new window) has spent the past few years writing about artificial intelligence and distributed systems, most recently at Bloomberg and BusinessWeek. His articles have covered technologies like memory networks(opens in a new window), image generation(opens in a new window), and reinforcement learning for robots(opens in a new window), and issues like diversity within AI(opens in a new window). As our Strategy and Communications Director, he will help with community outreach, policy, communications, and strategy.
- Scott Gray. Scott(opens in a new window) was previously an engineer at Nervana Systems(opens in a new window) where he focused on optimizing the performance of deep networks on GPUs. His assembly-level optimizations(opens in a new window) for dense linear algebra and convolution remain the fastest available. When not writing software(opens in a new window) he’s usually spending his time reading up on the latest research in neuroscience and related fields.
- Zain Shah. Zain(opens in a new window) previously led deep learning efforts to build a collaborative human-machine intelligence system at Clara Labs(opens in a new window). He’s also worked on speech synthesis(opens in a new window) and computational neuroscience(opens in a new window), built Mosaic(opens in a new window), and founded a mobile behavioral analytics company(opens in a new window). He most recently built a GIF search engine(opens in a new window) using deep multimodal embeddings.
We’re also pumped to be working with the following people for a more limited period of time:
- Catherine Olsson. Catherine(opens in a new window) built OpenAI Gym’s REST API(opens in a new window), which has already attracted users in Lua(opens in a new window), C++(opens in a new window), Java(opens in a new window), and Rust(opens in a new window). She graduated with a perfect GPA in CS and Brain & Cognitive Science from MIT, and has extensive research experience in computational neuroscience(opens in a new window) and psychology(opens in a new window). Catherine has taught programming and applied math(opens in a new window) for six years, including outreach to women and underrepresented minorities.
- Harri Edwards. Harri(opens in a new window) is a PhD student at the University of Edinburgh, where he is researching models that can quickly adapt to new situations by learning to represent datasets(opens in a new window).
- Igor Mordatch. Igor(opens in a new window) is interested in optimal control, machine learning, and their applications to robotics, biomechanics, and neuroscience. His PhD was in automated discovery and learning of complex movement behaviors(opens in a new window). He will join the faculty at CMU(opens in a new window) in September 2017.
- Taco Cohen. Taco(opens in a new window) is a PhD student working on applied and theoretical problems in representation learning. Most recently he invented group equivariant convolutional neural networks (G-CNNs(opens in a new window)), a generalization of CNNs that improves the statistical efficiency of these models by exploiting symmetries.
- Tambet Matiisen. Tambet(opens in a new window) is a PhD student from University of Tartu, Estonia. He previously worked as a software engineer and founded his own startup(opens in a new window). His recent projects range from making deep reinforcement learning agents cooperate(opens in a new window) to predicting a rat’s location from its brain activity. He also wrote an accessible introduction to deep Q-learning(opens in a new window).
