Gaurav Koley
Gaurav Koley is interested in Computational Economics and Social Network Analysis. He earned his Master’s degree in Computer Science from the International Institute of Information Technology (IIIT), Bangalore. Previously, he worked at Microsoft R&D India where he used performance engineer products for the Dynamics 365 team. In his free time, Gaurav works on Gratia with […]
Lingyi Xu
Lingyi Xu is a Ph.D. student in the Faculty of Computing & Data Sciences at Boston University. She is currently working with Professor Vijaya B. Kolachalama on computation-assisted methods that help with cancer diagnosis and treatment. Her research focuses on graph representation learning, especially in clinical settings, to improve diagnostic accuracy, efficiency, and interpretability. Learn […]
Andrew Roberts
Andrew Roberts is a PhD student in Computing and Data Sciences at Boston University, working with Professor Jonathan Huggins and Professor Michael Dietze. He is broadly interested in scientific machine learning, Bayesian modeling, and uncertainty quantification, with the goal of developing new methodologies for environmental and ecological applications. Andrew’s current work focuses on developing statistical […]
Gabe McDonnell-Maayan
Gabe McDonnell-Maayan is a PhD candidate in Boston University’s Faculty of Computing and Data Sciences whose work bridges computational innovation and pressing societal challenges. His research applies tools from complexity science—such as system dynamics modeling, agent-based modeling, and machine learning—to understand and influence the behavior of complex social systems. Gabe’s primary focus is advancing suicide […]
Kevin Quinn
Kevin is a PhD student at Boston University working with Professor Mark Crovella and Professor Evimaria Terzi. Kevin’s research focuses on the design and application of interpretable machine learning models, specifically for unsupervised clustering problems. He previously completed a BA in mathematics and computer science at BU. Research focus: Interpretable machine learning