Our workshop unfolds in 3 sessions: Diagnose, Fix, and Future paths. We list the speakers' bios in order of their talks below.

Session 1: Diagnose. Moderator: Priyanka Nanayakkara

Dr. Gilles Vandewiele (@Gillesvdwiele): Dr. Gilles Vandewiele obtained his PhD in Computer Science Engineering from Ghent University, where he worked at the Internet and Data Science Lab research group in the Department of Information Technology. He conducts research in the domain of white-box machine learning for critical domains and (semantic) knowledge models. His other research interests are bio-inspired algorithms and sport-related data science.

Dr. Michael Roberts: Dr. Michael Roberts is Senior Research Associate of Applied Mathematics at DAMTP and member of the Cambridge Image Analysis group (CIA) and leads the algorithm development team for the global COVID-19 AIX-COVNET collaboration. Dr. Roberts' research interests focus on variational methods for image processing—in particular image segmentation and registration, machine learning for image and data analysis, image processing and data analysis. He has active interdisciplinary collaborations with other applied mathematicians, computer scientists and clinicians focussing on medical imaging problems. He has vast experience in studying medical imaging problems for lung diseases including lung cancer, idiopathic lung fibrosis, mesothelioma and drug induced interstitial lung disease.

Prof. Odd Erik Gundersen: Prof. Odd Erik Gundersen is an adjunct associate professor at the Norwegian University of Science and Technology in Trondheim, Norway, where he teaches courses and supervises master students in AI. He received his PhD from the Norwegian University of Science and Technology. Gundersen has applied AI in the industry, mostly for startups, since 2006. He has conducted several analyses of reproducibility in the artificial intelligence and machine learning literature, and has developed guidelines for reproducibility in data science. Currently, he investigates how AI can be applied in the renewable energy sector and for driver training.

Session 2: Fix. Moderator: Sayash Kapoor

Prof. Michael Lones (@michael_lones): Dr. Michael Lones is an Associate Professor of Computer Science at Heriot-Watt University in Edinburgh, Scotland. He received his PhD from the University of York in 2003, and has spent the last 20 years or so working in the areas of machine learning and data science, authoring around 80 publications. His early research focused on evolutionary algorithms and biomedicine, but his interests have since broadened out to include other methodological and application areas, including neural networks, complex systems, robotics, and computer security. He is on the editorial boards for the journals BioSystems and Genetic Programming and Evolvable Machines, and is a Senior Member of the IEEE and a member of the IEEE Technical Committee on Bioinformatics and Bioengineering.

Inioluwa Deborah Raji (@rajiinio): Inioluwa Deborah Raji is a PhD student at UC Berkeley. She was previously a Mozilla fellow, and worked on algorithmic auditing. She also works closely with the Algorithmic Justice League initiative to highlight gender and racial bias in facial recognition technology. She was named to Forbes 30 Under 30 and MIT Tech Review 35 Under 35 Innovators. She has also worked with Google’s Ethical AI team and been a research fellow at the Partnership on AI and AI Now Institute at New York University working on how to operationalize ethical considerations in machine learning engineering practice.

Dr. Momin Malik (@MominMMalik): Dr. Momin Malik is a multidisciplinary researcher who brings statistical modeling to bear on critical, reflexive questions with and about large-scale digital trace data. He is broadly concerned with issues of algorithmic power and control, and of validity and rigor in computational social science and applications of machine learning to areas of social science and policy. He has an undergraduate degree in history of science from Harvard, a master's from the Oxford Internet Institute, and a PhD from Carnegie Mellon University's School of Computer Science. He is currently a Senior Data Science Analyst - AI Ethics at the Mayo Clinic’s Center for Digital Health. He is also a fellow at the Institute in Critical Quantitative, Computational, & Mixed Methodologies, and an instructor at the University of Pennsylvania’s School of Social Policy and Practice.

Prof. Marta Serra-Garcia (@m_serra_garcia): Prof. Marta Serra-Garcia conducts research in behavioral and experimental economics. Her research focuses on how individuals acquire and transmit information and how this in turn affects their preferences and behavior. Among others, her research studies how the desire to preserve a positive self-image shapes individuals’ ethical decision-making, such as lying and charitable giving. Serra-Garcia’s research has been published in numerous journals including American Economic Review, Management Science, and Psychological Science. She has also been recognized as the 2020 Best 40 under 40 MBA Professors. Prior to coming to the Rady School, Prof. Serra-Garcia was an assistant professor at the University of Munich. Prof. Serra-Garcia earned her Ph.D. and M.Sc. in Economics at Tilburg University. She earned a B.A. in Business Administration from the Universitat Pompeu Fabra, Barcelona.

Session 3: Future paths

Dr. Jake Hofman (@jakehofman): Jake Hofman is a Senior Principal Researcher at Microsoft Research in New York City, where his work in computational social science involves applications of statistics and machine learning to large-scale social data. Prior to joining Microsoft, he was a member of the Microeconomics and Social Systems group at Yahoo! Research. Jake is also an Adjunct Assistant Professor of Applied Mathematics at Columbia University, where he has designed and taught classes on a number of topics ranging from biological physics to applied machine learning. He holds a B.S. in Electrical Engineering from Boston University and a PhD in Physics from Columbia University.

Prof. Jessica Hullman (@JessicaHullman): Dr. Jessica Hullman is the Ginni Rometty Associate Professor of Computer Science at Northwestern University. Her research contributes techniques and empirical results related to interactive data analysis and visualization. Much of her research program focuses on addressing challenges associated with surfacing and communicating uncertainty in analysis and decision-making scenarios. Recent contributions of her work include new uncertainty visualization techniques and applications that are understandable to broad audiences, including applications to privacy algorithm decisions, and Bayesian and decision-theoretic methods for evaluating data representations. Dr. Hullman is a frequent contributor to the Statistical Modeling, Causal Inference, and Social Science blog, where she writes on topics at the intersection of computer science and data analysis. She is the recipient of a 2019 Microsoft Faculty Fellowship, NSF CAREER Award, and multiple best papers at top visualization and human-computer interaction conferences, among other awards.

Prof. Brandon Stewart (@b_m_stewart): Brandon Stewart is an Associate Professor in the Department of Sociology and the Office of Population Research at Princeton University. He develops new methods for applications across the social sciences with a particular focus on text analysis and causal inference. He is a developer of the Structural Topic Model, a popular framework for topic modeling. One of his recent pieces of work focused on the role that unspecified estimands play in quantitative research practice in sociology. His work has won best paper awards from the Society of Political Methodology and the American Sociological Associations Methodology section. He holds a masters degree in statistics and PhD in government both from Harvard University.