Parnian Barekatain - Director
Parnian founded SolveCity, a company focused on knowledge mapping to solve complex problems, which was acquired. She interned at OpenAI as a researcher and organized the first OpenAI Hackathon.
Aaron Lin graduated from MIT in mathematics and computer science. His favorite questions pertain to how underlying self-organizing natural systems work and how those governing principles can inform the design of artificially intelligent systems.
Jason is a ML research scientist at Uber AI Lab. Jason Yonsini is a machine learning scientist, founding member of Uber AI Labs (previously Geometric Intelligence), and scientific advisor to Recursion Pharmaceuticals. His research focuses on training and understanding neural networks and figuring out how to make them better. Jason completed my Ph.D. at Cornell, where at various times he worked with Hod Lipson (at the Creative Machines Lab), Jeff Clune, Yoshua Bengio (at U. Montreal's MILA), Thomas Fuchs (at Caltech JPL), and Google DeepMind.
Joscha is cognitive scientist at Harvard Program for Evolutionary Dynamics. He is interested in conceptual frameworks to understand the mind and universe in new ways, building artificially intelligent plants, computational psychology, and computational models of consciousness. He is also curious about many other domains, including physics, technology, politics and macroeconomics. Previously, he was research scientist at MIT Media Lab and Humanity Plus. He was an A.I. lecturer at Universität Osnabrück. At MIT Media Lab, he co-taught a course, Cognitive Integration - the nature of mind, with Adam Marblestone (see below).
Adam is the Chief Strategy Officer of Kernel, and a part-time research affiliate with the Synthetic Neurobiology group at MIT. Adam got his PhD Biophysics at Harvard, with George Church and colleagues, where he co-authored experimental and theoretical papers on molecular recording devices and road-mapped approaches for whole-brain mapping. More recently, he co-authored papers that propose new designs for neural interfaces and analyze our understanding of cortical computation, seeking strategies to integrate deep learning and neuroscience.
Scott is currently working at OpenAI, he was previously an engineer at Nervana Systems where he focused on optimizing the performance of deep networks on GPUs. His assembly-level optimizations for dense linear algebra and convolution remain the fastest available. When not writing software he’s usually spending his time reading up on the latest research in neuroscience and related fields.
Blake is assistant professor and associate fellow of the Canadian Institute for Advanced Research. He is interested in understanding how learning works in a variety of animal species and neural circuits. In particular, he interested in developing an integrated picture that links cellular-level circuit properties to the information processing systems that determines how animals perceive, act and remember.