معرفی
Nicholas Boffi is an Assistant Professor split equally between the Machine Learning Department and the Department of Mathematical Sciences at Carnegie Mellon University. He is also a member of the Center for Nonlinear Analysis. His research focuses on the foundations of generative modeling and its applications in science and engineering, particularly addressing computational challenges through machine learning innovations. Boffi's work integrates applied mathematics disciplines including numerical analysis, partial differential equations, dynamical systems, control theory, stochastic processes, and optimization.
- Education: Ph.D. in Applied Mathematics from Harvard University (co-advised by Jean-Jacques Slotine and Chris Rycroft), B.S. in Mathematics, Physics, and Integrated Science from Northwestern University.
- Awards: DOE Computational Science Graduate Fellowship (2015-2019), Fulbright Scholar (2014-2015).
- Previous Affiliations: Courant Institute (2021-2024), Google Brain (2020).
His research explores the intersection of machine learning and computational mathematics, emphasizing generative models for scientific problems. Notable projects include developing numerical methods for PDEs, stochastic simulation of evolutionary dynamics, and model-free learning in nonequilibrium systems. Boffi's recent work investigates scalable interpolant frameworks for flow-based generative models and entropy production in active matter systems.
He advises PhD students in CMU's Machine Learning and Mathematics programs, focusing on candidates interested in applied mathematics and machine learning intersections. His lab collaborates across disciplines to advance foundational theory and practical applications in computational science.
