Bosch AI CON 2021

Dr.-Ing. Michael Bolle was a member of the board of management of Robert Bosch GmbH.

He was chief digital officer and chief technology officer, he was responsible for the corporate sector for research and advance engineering as well as the corporate IT sector. His responsibilities also included the Bosch Software Innovations GmbH subsidiary and corporate technology coordination and engineering methods.

Raquel Urtasun was the Chief Scientist of Uber ATG and the Head of Uber ATG R&D. In addition, she is a Full Professor in the Department of Computer Science at the University of Toronto, a Canada Research Chair in Machine Learning and Computer Vision and a co-founder of the Vector Institute for AI. 

Her research interests include machine learning, computer vision, robotics, AI, remote sensing and she is a world leading expert in AI for self-driving cars.

Dr. Feiyu Xu is Senior Vice President, Global Head of Artificial Intelligence, at SAP.

Feiyu has in-depth knowledge and extensive experience in the areas of AI platform development, conversational AI, knowledge graphs, information extraction, business intelligence, and big-data text analytics. Through her various functions, she has obtained broad and in-depth experience of the total cycle of innovation in her expert areas – ranging from basic research via AI development all the way to products and their commercialization.

Zico Kolter is an Associate Professor in the Computer Science Department at Carnegie Mellon University, and Chief Scientist of AI Research at the Bosch Center for AI (BCAI) in Pittsburgh.

His work focuses on machine learning and optimization, with an emphasis on making these method more robust, reliable, and explainable.

Prof. Jun Zhu is the Director of the Tsinghua-Bosch Joint Center on Machine Learning. He is also the Director of the Basic Theory Research Center at Tsinghua Institute for AI.

His research focuses on developing statistical machine learning methods to understand complex scientific and engineering data, including Bayesian methods, adversarial machine learning, and decision-making under uncertainty.

George Karniadakis is the Charles Pitts Robinson and John Palmer Barstow Professor of Applied Mathematics at Brown University and an MIT research scientist. He is also an AAAS Fellow (2018-), Fellow of the Society for Industrial and Applied Mathematics (SIAM, 2010-), Fellow of the American Physical Society (APS, 2004-), Fellow of the American Society of Mechanical Engineers (ASME, 2003-) and Associate Fellow of the American Institute of Aeronautics and Astronautics (AIAA, 2006-).

His current research interests are on machine learning for scientific computing (Scientific Machine Learning), that is how to solve and discover new PDEs via deep learning, hence removing the tyranny of grids and using gappy data only.
His broad research interests focus on stochastic multiscale mathematics and modeling of physical and biological systems.

Nuria Oliver is Chief Data Scientist at Data-Pop Alliance, Chief Scientific Advisor at the Vodafone Institute, co-founder and vice-president of ELLIS (The European Laboratory for Learning and Intelligent Systems) and co-founder of the Alicante ELLIS Unit, devoted to research on "Human(ity)-centric Artificial Intelligence". She is a Telecommunications Engineer from the Universidad Politécnica de Madrid and holds a PhD in Artificial Intelligence from the Massachusetts Institute of Technology (MIT).

Her work focuses on the areas of human behavior modeling and prediction from data and human-computer interaction, as well as computational modeling of human behavior using Artificial Intelligence techniques, human-computer interaction, mobile computing and Big Data analysis - especially for the Social Good.

Nuria firmly believes in the value of technology to improve the quality of people, both individually and collectively, and dedicates her professional life to achieving it.

Karen E. Willcox is Director of the Oden Institute for Computational Engineering and Sciences, Associate Vice President for Research, and Professor of Aerospace Engineering and Engineering Mechanics at the University of Texas at Austin. She holds the W. A. “Tex” Moncrief, Jr. Chair in Simulation-Based Engineering and Sciences and the Peter O'Donnell, Jr. Centennial Chair in Computing Systems.

Her research focuses on Multi-fidelity uncertainty quantification, multi-fidelity optimization under uncertainty, adaptive reduced models, predictive digital twins, scientific machine learning, and data to decisions in aerospace engineering.

Luis Ceze is a Professor in the Paul G. Allen School of Computer Science and Engineering at the University of Washington, Co-founder and CEO at OctoML and Venture Partner at Madrona Venture Group.

His research focuses on the intersection between computer architecture, programming languages, machine learning and biology. His current research focus is on approximate computing for efficient machine learning and DNA-based data storage. He co-directs the Molecular Information Systems Lab and the Systems and Architecture for Machine Learning Lab.