جست‌وجوی استاد و پژوهشگر

David Choiمشاهده پروفایل

دانشیار

• Network Analysis
• Causal Inference
• Machine Learning
• +۳ مورد دیگر

David Choi is an Associate Professor at the Heinz College, part of Carnegie Mellon University's Dietrich College of Humanities and Social Sciences, with a courtesy appointment in the Department of Statistics. His expertise lies in statistics and machine learning applied to network data, including community detection and causal inference in social networks. He holds a PhD in Electrical Engineering from Stanford University (2004) and has held roles at MIT Lincoln Laboratory, Harvard University, and UC Berkeley. Research focuses on network models involving latent variables, exploratory data analysis, and interference effects in network experiments. His work bridges statistical methodology with practical applications in public policy and social sciences. Recent studies include analyzing Medicaid expansion impacts, single-cell network construction, and Alzheimer's disease microglia regulation. Prominent publications address network clustering, dynamic network analysis, and causal inference in experimental settings. Technical reports explore structured blockmodels and exposure mappings in experiments. Contact: Hamburg Hall 2118B, davidch@andrew.cmu.edu.

Christopher Harshawمشاهده پروفایل

استادیار

• Causal Inference
• Algorithm Design
• Randomized Experiments
• +۳ مورد دیگر

Christopher Harshaw is an Assistant Professor in the Statistics Department at Columbia University. His research focuses on causal inference and algorithm design, particularly in improving the design and analysis of randomized experiments, including experiments with interference and sequential experiments. He holds a PhD in Computer Science from Yale University and completed postdoctoral fellowships at MIT/UC Berkeley (via FODSI) and the Simons Institute for the Theory of Computing. Education: PhD in Computer Science (Yale University), Postdoctoral Fellowships at MIT/UC Berkeley and the Simons Institute. Research Interests: At the intersection of computation and statistics. Key areas include causal inference methodologies, algorithmic tools for experimental design, and submodular optimization. His work emphasizes balancing covariates, handling interference in experiments, and developing efficient experimental frameworks. Notable Achievements: Received the Best Paper Award at the CML4Impact NeurIPS 2022 Workshop and the Best Paper Award at CISRC 2016. His contributions include the Gram-Schmidt Walk Design for covariate balancing and the Conflict Graph Design for causal effect estimation under interference. Labs/Teams: Collaborations include work on software packages such as GSWDesign.jl and SubmodularGreedy.jl, advancing tools for statistical experimentation and submodular optimization.