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Head of the Institute for Machine Learning, Johannes Kepler University

Sepp Hochreiter heads the Institute for Machine Learning, the LIT AI Lab, and the AUDI.JKU deep learning center at the Johannes Kepler University of Linz, Austria, and is director of the Institute of Advanced Research in Artificial Intelligence. He's regarded as a pioneer of deep learning, as he discovered the fundamental deep-learning problem: deep neural networks are hard to train, because they suffer from the now-famous problem of vanishing or exploding gradients. Sepp is best known for inventing the long short-term memory (LSTM) in his diploma thesis in 1991, which was published in 1997. LSTMs have emerged as the best-performing techniques in speech and language processing.

Professor, University of California, Berkeley

Ken Goldberg is an artist, inventor, and roboticist. He's also the William S. Floyd Jr Distinguished Chair in Engineering at the University of California Berkeley, and chief scientist at Ambidextrous Robotics. He co-founded the IEEE Transactions on Automation Science and Engineering. As director of UC Berkeley’s AUTOLab, Ken trains the next generation of researchers and entrepreneurs. Ken and his students have published 300 peer-reviewed papers and nine U.S. patents. Ken's artwork has been exhibited internationally and he founded the Art, Technology, and Culture public lecture series in 1997. He's an adviser to RoboGlobal ETF, and has presented over 500 invited lectures worldwide. Learn more here: http://goldberg.berkeley.edu

Assistant Professor, University of California, San Diego

Rose Yu is an assistant professor in the University of California San Diego Department of Computer Science and Engineering. Her research focuses on advancing machine learning techniques for large-scale spatiotemporal data analysis, with applications to sustainability, health, and physical sciences, and with a particular emphasis on physics-guided AI that aims to integrate first principles with data-driven models. Rose has won faculty research awards from Google, Adobe, and Amazon; the NSF CRII Award; and the Best Dissertation Award in USC. She was also nominated as one of the 'MIT Rising Stars in EECS.’ She earned her Ph.D. in computer sciences at the University of Southern California in 2017. She was subsequently a postdoctoral fellow at the California Institute of Technology.

Professor, Chemistry and Biochemistry, University of California, San Diego

Rommie E. Amaro holds the Distinguished Professorship in Theoretical and Computational Chemistry at the Department of Chemistry and Biochemistry at the University of California, San Diego. She received her B.S. in Chemical Engineering (1999) and her Ph.D. in Chemistry (2005) from the University of Illinois at Urbana-Champaign. Rommie was a NIH postdoctoral fellow with Prof. J. Andrew McCammon at UC San Diego from 2005-2009, and started her independent lab in 2009. She is the recipient of an NIH New Innovator Award, the Presidential Early Career Award for Scientists and Engineers, the ACS COMP OpenEye Outstanding Junior Faculty Award, the ACS Kavli Foundation Emerging Leader in Chemistry, the Corwin Hansch Award, and the 2019 Gordon Bell Special Prize for COVID19. Rommie’s scientific interests lie at the intersection of computer-aided drug discovery and biophysical simulation. Her scientific vision revolves around expanding the range and complexity of molecular constituents represented in such simulations, the development of novel multiscale methods for elucidating their time dependent dynamics, and the discovery of novel chemical matter controlling biological function.

VP of Applied Deep Learning Research, NVIDIA

Bryan Catanzaro is vice president of Applied Deep Learning Research at NVIDIA, where he leads a team solving problems in fields ranging from video games to chip design.
Catanzaro initially worked at NVIDIA as a research scientist in 2011. During this time, he wrote the prototype and drove the creation of CUDNN, the low-level library now used by most AI researchers to train neural networks. He has also worked at Baidu’s Silicon Valley AI Lab, where he built systems for efficiently training end-to-end deep learning based speech recognition on extremely large datasets, in both English and Chinese.

Catanzaro earned his Ph.D. from the University of California at Berkeley, where he built the Copperhead language and compiler, which allows Python programmers to use nested data parallel abstractions efficiently. He earned both his Master of Science and Bachelor of Science from Brigham Young University, where he worked on computer arithmetic for FPGAs.

Scientific Director of the Swiss AI Lab IDSIA | Prof. of AI at USI | Co-founder of NNAISENSE

Since age 15 or so, the main goal of professor Jürgen Schmidhuber has been to build a self-improving Artificial Intelligence (AI) smarter than himself, then retire. His lab's Deep Learning Neural Networks based on ideas published in the "Annus Mirabilis" 1990-1991 have revolutionised machine learning and AI. By the mid 2010s, they were on 3 billion devices, and used billions of times per day through the users of the world's most valuable public companies, e.g., for greatly improved (CTC/LSTM-based) speech recognition on all Android phones, greatly improved machine translation through Google Translate and Facebook (over 4 billion translations per day), Apple's Siri and Quicktype on all iPhones, the answers of Amazon's Alexa, and numerous other applications. In 2011, his team was the first to win official computer vision contests through deep neural nets, with superhuman performance. In 2012, they had the first deep NN to win a medical imaging contest (on cancer detection). All of this attracted enormous interest from industry. His research group also established the fields of metalearning, mathematically rigorous universal AI and recursive self-improvement in universal problem solvers that learn to learn (since 1987). In 1990, he introduced unsupervised adversarial neural networks that fight each other in a minimax game, to achieve artificial curiosity (GANs are a special case). In 1991, he introduced deep learning through unsupervised pre-training, and neural fast weight programmers formally equivalent to what’s now called linear Transformers. His formal theory of creativity & curiosity & fun explains art, science, music, and humor. He also generalized algorithmic information theory and the many-worlds theory of physics, and introduced the concept of Low-Complexity Art, the information age's extreme form of minimal art. He is recipient of numerous awards, author of over 350 peer-reviewed papers, and Chief Scientist of the company NNAISENSE, which aims at building the first practical general purpose AI. He is a frequent keynote speaker, and also advising various governments on AI strategies.