Dr. Oren Etzioni has served as the Chief Executive Officer of the Allen Institute for AI (AI2) since its inception in 2014. He has been a Professor at the University of Washington’s Computer Science department since 1991, and a Venture Partner at the Madrona Venture Group since 2000. He has garnered several awards including Seattle’s Geek of the Year (2013), the Robert Engelmore Memorial Award (2007), the IJCAI Distinguished Paper Award (2005), AAAI Fellow (2003), and a National Young Investigator Award (1993). He has been the founder or co-founder of several companies, including Farecast (sold to Microsoft in 2008) and Decide (sold to eBay in 2013). He has written commentary on AI for The New York Times, Nature, Wired, and the MIT Technology Review. He helped to pioneer meta-search (1994), online comparison shopping (1996), machine reading (2006), and Open Information Extraction (2007). He has authored over 100 technical papers that have garnered over 2,000 highly influential citations on Semantic Scholar. He received his Ph.D. from Carnegie Mellon in 1991 and his B.A. from Harvard in 1986.
Semantic Scholar, NLP, and the Fight Against COVID-19(Track Keynote)
Mark Weber is an applied researcher and Strategy & Operations Lead at the MIT-IBM Watson AI Lab, a $250 million partnership funding over 200 scientists making fundamental breakthroughs in AI. Through the lab’s corporate membership program, which he runs, Mark works closely with global leaders across multiple sectors on the creative challenge of bridging fundamental science to real-world impact. Mark’s current applied research includes neuro-symbolic generative modeling for construction monitoring, graph deep learning for anti-money laundering, and supply chain demand forecasting. Mark also oversees strategic engagements with IDEO, the International Monetary Fund, and the Internal Revenue Service. Prior to IBM Research, Mark was a graduate researcher at the MIT Media Lab and a fellow at the MIT Legatum Center for Development & Entrepreneurship while he earned his M.B.A in finance from MIT Sloan. There he led the development of an open-source protocol called b_verify for verifiable records in supply chain finance. Before his foray into technology, Mark spent the first chapters of his career focused on political economy and development. He produced three documentary films on these subjects, most notably the critically acclaimed film Poverty, Inc
Sebastian Raschka is a machine learning researcher developing new deep learning architectures to solve problems in the field of biometrics with a focus on face recognition and privacy protection. Among others, his research activities include applications of machine learning to solve problems in (computational) biology. After receiving his doctorate from Michigan State University, Sebastian recently joined the University of Wisconsin-Madison as Assistant Professor of Statistics. Sebastian Raschka is also the author of the bestselling book “Python Machine Learning”, which received the ACM Best of Computing award in 2016 and was translated into many different languages, including German, Korean, Chinese, Japanese, Russian, Polish, and Italian. In his free time, Sebastian loves to contribute to open source projects, and methods that he implemented are now successfully used in machine learning competitions such as Kaggle.
Christopher Kanan is an Assistant Professor at the Rochester Institute of Technology (RIT), a Visiting Assistant Professor at Cornell Tech, and a Senior AI Scientist at Paige. At RIT, his lab works on lifelong machine learning and language driven computer vision, which has been supported by awards from NSF, AFOSR, ONR, DARPA, Adobe Research, and other industrial partners. He is also Associate Director of RIT’s Center for Human-aware AI and he is a member of RIT’s McNair Scholars advisory board. At Paige, a startup that has raised $95M to improve the diagnosis of cancer, he led the AI R&D team during its first 1.5 years and continues to advise its AI teams. He received a PhD in computer science from the University of California at San Diego, where he worked on brain-inspired algorithms for object recognition, neural networks, active vision, and cognitive modeling. He received an MS in computer science from the University of Southern California. Before joining RIT, he was a postdoctoral scholar at the California Institute of Technology, and later worked as a Research Technologist at NASA’s Jet Propulsion Laboratory, where he used deep learning to develop vision systems for autonomous ships. He is the recipient of the 2016 Rising Star Award and the 2019 Distinguished Scholarship Award in RIT’s College of Science. He is an IEEE Senior Member and has published over 50 refereed papers, many of which are in top venues across AI such as CVPR, ICCV, NeurIPS, AAAI, ICLR, ACL, etc.
Dr. Gerald Friedland is the CTO of Brainome, Inc and is also teaching as an adjunct professor in the Electrical Engineering and Computer Sciences department of UC Berkeley. Before that, Dr. Friedland was at Lawrence Livermore National Lab and the International Computer Science Institute in Berkeley. Dr. Friedland’s work is primarily in the areas of signal processing and machine learning. Dr. Friedland has published more than 250 peer-reviewed articles in conferences, journals, and 3 books. Dr. Friedland received his doctorate (summa cum laude) in computer science from Freie Universitaet Berlin, Germany, in 2006.
Thomas Chen is an early-career machine learning researcher from New Jersey that is passionate about machine learning, computer vision, and artificial intelligence. He is highly involved in science research, especially in applying ML and AI to real-world issues that face society (e.g. deep learning-based computer vision for damage assessment post-natural disaster). He has presented his work at workshop sessions at high-level conferences such as NeurIPS, and is an invited speaker at numerous conferences like the IEEE Conference on Technologies for Sustainability and the Energy Anthropology Network.
Mike serves as Chief ML Scientist and Head of Machine Learning for SIG, UC Berkeley Data Science faculty, and Director of Phronesis ML Labs. He has led teams of Data Scientists in the bay area as Head of Data Science at Uber ATG, Chief Data Scientist for InterTrust and Takt, Director of Data Science for MetaScale/Sears, and CSO for Galvanize where he founded the galvanizeU-UNH accredited Masters in Data Science degree and oversaw the company’s transformation from co-working space to Data Science organization. Mike began his career in academia serving as a mathematics teaching fellow for Columbia University before teaching at the University of Pittsburgh.
As clinical research director and principal research scientist at the MIT Laboratory for Computational Physiology (LCP), and as a practicing intensive care unit (ICU) physician at the Beth Israel Deaconess Medical Center (BIDMC), Leo brings together clinicians and data scientists to support research using data routinely collected in the process of care. His group built and maintains the publicly-available Medical Information Mart for Intensive Care (MIMIC) database and the Philips-MIT eICU Collaborative Research Database, with more than 15,000 users from around the world. The MIMIC-III paper has been cited more than 2000 times since 2016. In addition, Leo is one of the course directors for HST.936 – global health informatics to improve quality of care, and HST.953 – collaborative data science in medicine, both at MIT. He is an editor of the textbook for each course, both released under an open access license. “Secondary Analysis of Electronic Health Records” has been downloaded more than 900,000 times, and has been translated to Mandarin, Spanish and Korean. Leo has spoken in more than 35 countries across 6 continents about the value of data and learning in health systems. His publications have been cited more than 2500 times during the pandemic.
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