Organizors

Wen is a fourth-year Ph.D. student in Information Systems at Carnegie Mellon University’s Heinz College of Information Systems and Public Policy. Her research interests lie in the intersection of machine learning and business/management. Specifically, she is interested in Multi-modal Machine Learning, Reinforcement Learning, and their application in digital marketing, healthcare and education. She has also spent two summers time as a research scientist intern at Amazon AWS AI lab NLP group.

Han is a machine learning researcher at the D.E. Shaw group and an adjunct assistant professor at the Department of Computer Science, the University of Illinois at Urbana-Champaign. He completed his Ph.D. from the Machine Learning Department, Carnegie Mellon University, where he was advised by Geoff Gordon. His research interests are broadly in machine learning, with a focus on representation learning, probabilistic circuits, transfer learning and algorithmic fairness.

DK is an assistant professor of Business Analytics at Tepper School, CMU. He studies the application, development, impact of AI in e-commerce and the digital economy. Two current streams of research are 1) developing and applying interpretable machine learning (transparent algorithm) in different business settings and 2) measuring the economic impact of unstructured data. He runs the Business Insights through Text Lab (BITLAB).

George Chen is an Assistant Professor of Information Systems at Carnegie Mellon University's Heinz College of Information Systems and Public Policy, and an affiliated faculty member of the Machine Learning Department. He primarily works on machine learning for healthcare, with an emphasis on forecasting problems involving survival analysis as well as time series data. A recurring theme in his work is the use of nonparametric prediction methods that aim to make few assumptions on the underlying data. Since these methods inform interventions that can be costly and affect people’s well-being, ensuring that predictions are reliable is essential. To this end, in addition to developing nonparametric predictors, George also produces theory to understand when and why they work, and identifies forecast evidence to help practitioners make decisions. George received his PhD from MIT in Electrical Engineering and Computer Science, where he won the George Sprowls Award for outstanding computer science thesis as well as the top graduate student teaching award, the Goodwin Medal.