Invited Talks

About the speaker Jeff Dean

Jeff is the Chief Scientist for Google Research and Google DeepMind, and co-leads the Gemini project. He has worked for many years at the intersection of computer systems for machine learning, including work in ML accelerators, low-level software and frameworks for machine learning, work on sparse model architectures, algorithms like distillation and neural architecture search, training of large language and multimodal models, and applications of machine learning to areas like ASIC design, healthcare, and translation. He is a recipient of the ACM Prize in Computing, the IEEE John von Neumann medal, the Mark Weiser Award, & best paper awards at NeurIPS, OSDI, OOPSLA, PLDI, SOSP, & MLSys. He is a Fellow of the ACM, and a member of the US National Academy of Engineering, & the AAAS.

About the speaker Yejin Choi

Yejin Choi is Wissner-Slivka Professor and a MacArthur Fellow at the Paul G. Allen School of Computer Science & Engineering at the University of Washington. She is also a senior director at AI2 overseeing the project Mosaic and a Distinguished Research Fellow at the Institute for Ethics in AI at the University of Oxford. Her research investigates if (and how) AI systems can learn commonsense knowledge and reasoning, if machines can (and should) learn moral reasoning, and various other problems in NLP, AI, and Vision including neuro-symbolic integration, language grounding with vision and interactions, and AI for social good. She is a co-recipient of 2 Test of Time Awards (at ACL 2021 and ICCV 2021), 8 Best/Outstanding Paper Awards (at ACL 2023, EMNLP 2023, NAACL 2022, ICML 2022, NeurIPS 2021, AAAI 2019, and ICCV 2013), the Borg Early Career Award (BECA) in 2018, the inaugural Alexa Prize Challenge in 2017, and IEEE AI's 10 to Watch in 2016.

About the speaker J. Zico Kolter

Zico Kolter is an Associate Professor in the Computer Science Department at Carnegie Mellon University and also serves as chief scientist of AI research for the Bosch Center for Artificial Intelligence. His work spans several topics in machine learning and optimization, including work in robustness, LLM security, out-of-distribution modeling, equilibirum models, smart grid applications, and more. He is a recipient of the DARPA Young Faculty Award, a Sloan Fellowship, and best paper awards at NeurIPS, ICML (honorable mention), AISTATS (test of time), IJCAI, KDD, and PESGM.

About the speaker Kurt Keutzer

Kurt is Professor of the Graduate School in the Berkeley AI Research (BAIR) Lab of the Department of Electrical Engineering and Computer Science at University of California, Berkeley. After a distinguished career in Electronic Design Automation where he won many Best Paper awards, as well as a Most Influential Paper of the Decade award, Kurt turned his attention to efficient Machine Learning, and, later, efficient Deep Learning. Kurt is probably best known for his “Squeeze” family of Neural Nets that helped to pioneer the use of Deep Learning at the edge, but he has also collaborated to scale the training of Neural Nets to 1000’s of processors. Kurt’s research is currently moving beyond single-model optimization to efficiently harnessing the power of sophisticated systems of LLMs. He is a Life Fellow of the IEEE.