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.

Akanksha is a second year MS/PhD student at the College of Information and Computer Sciences at UMass Amherst. Her current research interests lie in building and using causal models to explain deep networks. She is also interested in interdisplinary applications of causal modeling, particularly security and computational social science. She graduated with a Bachelor of Science in Mathematics and Computer Science from the State University of New York at Albany in December 2016 and worked at IBM as a Software Engineer prior to joining UMass Amherst.

Kaleigh is a PhD candidate at CICS interested in causal inference and experimental design, particularly when traditional assumptions about the underlying data are not met. She focuses on applications in computational social science and those related to fairness, accountability, transparency, and ethics in machine learning. When she wants to kick back and enjoy life, she pursues interest in table top games, dinos, and diving for lost treasure.

Amanda Gentzel is a PhD candidate at the College of Information and Computer Sciences at the University of Massachusetts Amherst. Her research has focused on anomaly detection through density estimation, evaluating temporal methods for causal discovery, and empirical evaluation of causal discovery methods. She is particularly interested in testing the common underlying assumptions of both propositional and temporal causal data analysis.

Reilly is a first year MS/PhD student at the College of Information and Computer Sciences at UMass Amherst. His research interests are in finding ways to understand and explain artificial intelligence, and using causal inference methods to understand complex systems. Outside of academics he enjoys reading, running, and swing and contra dancing.

  • Emma Tosch
  • Graduate Student
  •  etosch@cs.umass.edu

Emma is a PhD candidate in CICS, hoping to defend in the next year. She is currently working on the DARPA XAI projects, building infrastructure to facilitate answering questions pertaining to explainability. Emma’s research interests lead to questions about the expressibility and correctness of data science infrastructure problems. Her dissertation work lies at the intersection of programming language design and experimental design. Emma enjoys trail running, the annual St. Patrick’s Day Holyoke Road Race, and cycling in all its incarnations.

  • Sam Witty
  • Graduate Student
  •  switty@cs.umass.edu

Sam Witty is a third year MS/PhD student in CICS. His research focus is on bridging the gap between mechanistic and machine learning models, with applications in AI-assisted scientific discovery and explainable AI. Before joining the Knowledge Discovery Lab, Sam spent three years as an energy efficiency policy consultant, where he led efforts in experimental design, statistical analysis, simulation modeling, and machine learning. When he’s not working you’re likely to find him lost in the woods with his puppy Mira.


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