David Jensen is Professor of Computer Science and Director of the Knowledge Discovery Laboratory, which he founded in 2000. He also serves as the Associate Director of the Computational Social Science Institute, an interdisciplinary effort at UMass to study social phenomena using computational tools and concepts. His current research focuses on machine learning and data science for analyzing large social, technological, and computational systems. In particular, his work focuses on methods for constructing accurate causal models from observational and experimental data, with applications in explainable AI, social science, fraud detection, security, and systems management.

Professor Grabowicz's research contributes statistical methods to understand and augment fundamental social processes in systems of our information society. The ambition of his research is to design fair and representative social computing systems, such as rating systems in social media, predictive models of human decision-making, and recommender systems. Important questions concerning the design of these systems include: how to train non-discriminatory machine learning models and how to prevent biases in social evaluations? His research focuses on machine learning, causal inference, computational social science, data science, and network science.

David Westbrook is a Senior Research Fellow at the University of Massachusetts in Amherst. He helps folks design and build complex software systems. He has built or worked on a simulation of fire-fighting robots in Yellowstone Park, a control system to tell a network of radars how to scan the atmosphere, a military planning and simulation system that plays computer games, a system that collects and disseminates severe weather alerts to the public, a testbed for investigating distributed problem solving networks, a simulation of air tasking order execution, a global name service for a highly mobile internetwork, a bunch of robots that ran around bumping into things until they learned how not to and many other interesting projects.

Pracheta is a first-year Ph.D. student in CICS, co-advised by Peter Haas. Her research focuses on the intersection of causal inference and simulation, with a particular interest in explaining emergent phenomenon in simulation models through causal reasoning. Before joining KDL, Pracheta pursued her masters in CICS, and previously worked as a systems engineer in Cisco Systems designing solutions for routing, switching and data center architectures. Away from work, she enjoys reading, playing board games and cooking! 

Kate Avery is a first-year PhD student in CICS. Her research focuses on the intersection of causal inference and reinforcement learning (RL), specifically using causal reasoning to learn better RL policies. Before joining KDL, she completed a BS and MS in computer science at the University of Oklahoma (OU) and spent four years working in Amy McGovern’s lab at the National Weather Center/OU. Outside of work, she enjoys writing science fiction, playing video games, and doing needlework. 

Erica is a first year MS/PhD student in CICS. Her research is in designing methods for computing a conditional dependence measure, in structure learning for causal models, and in explainable AI. Before this, she completed her undergraduate studies at Rutgers University focusing on research in theoretical computer science. Away from work, she likes to explore new places and play tennis. 

Kaleigh is a PhD candidate interested in using causal reasoning to develop experimental methodologies over complex distributions. Her recent work has focused on designing interventions for counterfactual explanations and evaluations of deep learning systems and has applications in reinforcement learning, fairness, and computational social science. Outside CICS, she enjoys rock climbing, tabletop games, and scuba diving with weird fish.

Jack Kenney is an M.S. Bay State Fellow at the College of Information and Computer Sciences, UMass Amherst. Their research has focused on causal inference and explainable artificial intelligence. Jack previously completed a Bachelor of Science in Computer Science at UMass and went on to work at MathWorks, Inc. before joining KDL in 2021. They enjoy making instrumental music, ceramic art, and surfing.

Purva is a third year MS/Ph.D. student in CICS. Her research focuses on explaining the behavior of complex systems using causal inference. Her long-term research goal is to design agents that can do causal reasoning, deal with uncertainty, and can adapt to new situations using prior knowledge. Before joining KDL, Purva spent three years working with Goldman Sachs where she designed statistical models for systematic investment strategies and root-cause analysis of undesirable behavior in trade settlement procedures. Away from work, she enjoys reading books, writing prose-style poetry, cooking, playing piano, and taking long walks appreciating nature.

Sankaran is a second year Ph.D. student at UMass CICS. His research focuses on inferring causal mechanisms that underlie the behaviour of complex black-box AI systems, and reasoning about their performance under changes in their environments. Outside of AI research, he enjoys theater, music production, and travel.

Sam Witty is a PhD candidate in CICS. His research is on Bayesian nonparametric and probabilistic programming approaches to causal inference with observational, experimental, and quasi-experimental data. Before joining the Knowledge Discovery Lab, Sam spent three years as an energy efficiency policy consultant, where he led efforts in program evaluation, simulation modeling, and experimental design. When he’s not working you’re likely to find him lost in the woods with his puppy dog Mira.

Andy is a second year MS/PhD student in CICS. His current research involves using causality to create generalizable, transferable, and adaptive models, and is often motivated by approaches in philosophy and philosophy of science. Before joining KDL, he worked in the natural resource industry, leading his team in the construction of mobile, IoT, and computer vision applications; Andy remains peripherally involved in these efforts. Away from work, Andy enjoys travel, vocal performance, the outdoors, and really any kind of game.

Justin Clarke is a third year MS/Phd student at CICS. HIs research focuses on causal inference, explainability, and generalization. Before joining KDL Justin worked as a systems analyst at the University of California, Berkeley. Outside of work Justin enjoys reading, photography, playing guitar, and hiking with his family.