Kaleigh Clary PhD candidate Knowledge Discovery Laboratory College of Information & Computer Sciences University of Massachusetts Amherst

I am a member of the Knowledge Discovery Laboratory, advised by David Jensen. My research interests include causal inference and experimental design in complicated distributions, particularly for applications in fairness, computational social science, and reinforcement learning. Feel free to check out my CV or send me mail at . Recent events: I was accepted as a 2018 Data Science for Social Good Fellow. For 3 months, I worked with a small team of fellows in partnership with AllianceChicago, a national network of 44 community health centers serving the least resourced members of their communities, on a project to support early diabetes screenings by identifying patients at risk of developing the disease in the next 3 years. Our code will be made publicly available. You can read an excerpt of my application in the UMass announcement. (August 2018) Completed a Masters degree and accepted into candidacy. My Masters thesis on A/B Testing in Networks with Adversarial Nodes received a 2017 CICS Outstanding Synthesis Project Award. (February 2018) My work with David Jensen on A/B Testing in Networks with Adversarial Members (paper / code) was accepted at the 2017 Workshop on Mining and Learning with Graphs at KDD 2017. You can check out the slides from my workshop talk or watch the video. (August 2017) Presented a poster, "Does Topology Matter? The Impact of Network Structure on Graph Cluster Randomization", at the 2016 Data Science Mixer hosted by the UMass Amherst Center for Data Science. I also gave a lightning talk. (October 2016) Participated in the 2015 Hack the Dinos challenge at the American Museum of Natural History, and featured in a New York Times article. Check out our project on GitHub! (November 2015) Mentored young girls age 12-15 in introductory programming using the Scratch language through the Girls, Inc. of Holyoke Eureka! program. (July 2015) Presented a poster on "Alternative approaches to discovering causality with additive noise models" at New England Machine Learning Day 2015. (May 2015)