Bio: Suchi Saria is the John C. Malone Assistant Professor at Johns Hopkins University where she directs the Machine Learning and Healthcare Lab. Her work with the lab enables new classes of diagnostic and treatment planning tools for healthcare—tools that use statistical machine learning techniques to tease out subtle information from “messy” observational datasets, and provide reliable inferences for individualizing care decisions. Saria’s methodological work spans Bayesian and probabilistic approaches for addressing challenges associated with inference and prediction in complex, real-world temporal systems, with a focus in reliable ML, methods for counterfactual reasoning, and Bayesian nonparametrics for tackling sample heterogeneity and time-series data. Her work has received recognition in numerous forms including best paper awards at machine learning, informatics, and medical venues, a Rambus Fellowship (2004-2010), an NSF Computing Innovation Fellowship (2011), selection by IEEE Intelligent Systems to Artificial Intelligence’s “10 to Watch” (2015), the DARPA Young Faculty Award (2016), MIT Technology Review’s ‘35 Innovators under 35’ (2017), the Sloan Research Fellowship in CS (2018), the World Economic Forum Young Global Leader (2018), and the National Academies of Medicine (NAM) Emerging Leader in Health and Medicine (2018). In 2017, her work was among four research contributions presented by Dr. France Córdova, Director of the National Science Foundation to Congress’ Commerce, Justice Science Appropriations Committee. Saria received her PhD from Stanford University working with Prof. Daph
Keynote talk 1: Safety Challenges with Deep Learning and Novel Approaches for Failure Proofing.
Bio: Shiri Azenkot is an Assistant Professor of Information Science at Cornell Tech, the new Cornell University campus in New York City. Her research lies in the intersection of technology, disability, and interaction. She likes building things and discussing their sociocultural implications. In particular, Shiri’s research focuses on designing intelligent interactive systems for people with visual impairments. She has published at top-tier human-computer interaction and accessibility venues such as ACM CHI, ACM ASSETS, and ACM UIST, receiving multiple best paper awards and nominations. Shiri is also the founder of the XR Access Initiative (xraccess.org), a broad academic-industry partnership to make augmented and virtual reality accessible from the ground up. She received her PhD in computer science from the University of Washington and her BA, also in computer science, from Pomona College.
Keynote talk 2: AI-Powered Access: Intelligent Interactive Systems to Support People with Visual Impairments
As artificial intelligence advances, it presents opportunities to address human needs in new ways. I aim to leverage advances in AI to solve problems of equity for people with diverse abilities. The US Census Bureau estimates that about 20 percent of Americans have a disability, meaning that they face significant barriers in their daily lives because their needs and abilities differ from what is typically considered “mainstream.” In my research, I conduct studies to understand these specific barriers and design intelligent interactive systems that help people overcome them. In my talk, I will describe several recent projects involving people with visual impairments, both blind and low vision. The projects aim to help people with visual impairments learn STEM concepts, navigate, and engage with others on social networking sites. I will conclude with open questions for the community on how to ensure that advances in AI empower (instead of further marginalize) all people, regardless of (dis)ability.
Bio: Doina Precup splits her time between McGill University, where she co-directs the Reasoning and Learning Lab in the School of Computer Science, and DeepMind Montreal, where she has led the research team since its formation in October 2017. Her research interests are in the areas of reinforcement learning, deep learning, time series analysis, and diverse applications of machine learning in health care, automated control, and other fields. She became a senior member of the Association for the Advancement of Artificial Intelligence in 2015, Canada Research Chair in Machine Learning in 2016, Senior Fellow of the Canadian Institute for Advanced Research in 2017, and received a Canada CIFAR AI (CCAI) Chair in 2018. Dr. Precup is also involved in activities supporting the organization of Mila and the wider Montreal and Quebec AI ecosystem.
Pablo Samuel Castro
Bio: Pablo Samuel was born and raised in Quito, Ecuador, and moved to Montreal after high school to study at McGill, eventually obtaining his masters and PhD at McGill, focusing on Reinforcement Learning. He is currently a staff research Software Developer in Google Research (Brain team) in Montreal, focusing on fundamental Reinforcement Learning research, Machine Learning and Creativity, and being a regular advocate for increasing the LatinX representation in the research community. He is also an active musician.
Bio: Fenwick McKelvey is Assistant Professor in Information and Communication Technology Policy in the Department of Communication Studies at Concordia University. To understand the influences, controls, nudges, and optimizations of the Internet as things, he draws on a range of scholarly work in communication studies, media studies, science and technology studies, and political economy. He is co-author of The Permanent Campaign: New Media, New Politics (Peter Lang, 2012) with Greg Elmer and Ganaele Langlois. He holds a PhD in the joint program of Communication and Culture between York University and Ryerson University.