I am an associate professor in Computer Science at Cornell University. I work on computer vision and machine learning, in particular on important problems that defy the "Big Data" label. I enjoy problems that require marrying advances in machine learning with insights from computer vision, geometry and domain-specific knowledge

Aleksandra Faust is a Director of Research at Google DeepMind, where she leads Frontier AI Health efforts. Her research focuses on foundation models and world models for complex adaptive systems, treating the AI design pipeline as a learnable, sequential, and self-improving decision-making process. This methodology has driven state-of-the-art improvements across drug discovery, robotics, autonomous driving, and web agents, and led to her founding the field of Automated Reinforcement Learning (AutoRL). Notably, she co-authored the seminal "Levels of AGI" framework and led the Gemini Self-improvement research team, developing the reinforcement learning methods behind the Gemini model family. Previously, Aleksandra served as Chief AI Officer at Genesis Molecular AI and held foundational leadership roles at Google Brain, Google Robotics, and Waymo/X. Earlier in her career, she was a Senior R&D Engineer at Sandia National Laboratories. Faust holds a Ph.D. in Computer Science with distinction from the University of New Mexico and an M.S. from the University of Illinois at Urbana-Champaign. She is an IEEE Fellow and a recipient of the IEEE RAS Early Career Award for Industry and the Tom L. Popejoy Dissertation Award, and was named a Distinguished Alumna of the UNM School of Engineering. Her work has been featured in …

Yanan Sui is a tenured associate professor at Tsinghua University working on Machine Learning, Neural Engineering, and Robotics. He received undergraduate degree from Tsinghua and Ph.D. degree from Caltech, working with Profs. Mu-ming Poo, Joel Burdick, and Yisong Yue. He was a postdoc with Prof. Fei-Fei Li at Stanford before joining Tsinghua. Yanan is dedicated to the research of neuro-musculo-skeletal modeling, learning and control, with applications in embodied intelligence and brain-machine interaction. His work on safe optimization has been included in textbooks at Stanford and other universities. He co-won best paper awards at robotics conference and workshop. His work has been successfully applied to the clinical treatment of neural injuries in China and the United States. He serves as committee member / senior area chair / area chair for leading AI conferences. His contributions have been recognized by MIT Technology Review's Innovators Under 35 in China, AI 100 Young Pioneer Award etc.

I am a Senior Staff Research Scientist / Tech Lead Manager @ Google DeepMind. My current work focuses on computer vision and machine learning. I graduated with a Ph.D. from Stanford University in 2016.

Pablo was born and raised in Quito, Ecuador and obtained his PhD at McGill University. He has worked at Google since 2012, mostly focusing on fundamental Reinforcement Learning and Neuroscience research. Aside from his interest in coding/AI/math, Pablo is an active musician and loves running (7 marathons so far, including Boston!).