Stochastic machine
learning group

Stochastic Machine Learning Group

Our research focuses on fundamental challenges in the theory of Probabilistic Machine Learning using novel stochastic techniques and Hilbert space methods. Our mission is to strengthen the theoretical underpinnings that contribute to the effectiveness of these methodologies and to broaden their applicability. By leveraging advanced mathematical approaches, we aim to drive innovation and explore new frontiers in the field.

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Julio Enrique Castrillon Candas

Mark Kon

Research

The Stochastic Machine Learning Group at Boston University, housed in the Department of Mathematics and Statistics, conducts research at the intersection of machine learning, statistical modeling, and computational mathematics. Led by Professors Julio Castrillon and Mark Kon, the group includes graduate and undergraduate students working on advanced techniques to address complex theoretical and applied problems in data analysis and prediction.

With the rise of large-scale data and high-dimensional stochastic systems, our research focuses on developing principled and robust methods for prediction, uncertainty quantification, and optimization. By integrating statistical theory, stochastic processes, applied mathematics, and high-performance computing, we aim to create novel solutions for real-world applications in fields such as disaster management, drug discovery, remote sensing, and medical diagnostics.

Our approach emphasizes mathematically rigorous analysis of random systems and machine learning models, ensuring that our solutions are both reliable and adaptable to uncertain environments. Through a combination of theoretical research and high-impact practical applications, we are advancing the understanding of machine learning under uncertainty and contributing to the development of next-generation analytical tools.

Meet The Team

of the Stochastic Machine Learning Research Group

Research Group Leader

Profile

Publications

Research Group Leader

Mark Kon

holds a PhD in Mathematics from MIT and Bachelor's degrees in Mathematics, Physics, and Psychology from Cornell University. His research spans across quantum probability, machine learning, computational biology, and mathematical physics, with recent work focusing on quantum computation and its applications in bioinformatics and is an investigator on a number of grants in these areas. He has published extensively, held academic appointments at Columbia, Harvard, and MIT, and is currently affiliated with Boston University's Bioinformatics Graduate Program in the Faculty of Computing and Data Sciences.