معرفی
Jorge Nocedal is the Walter P. Murphy Professor of Industrial Engineering and Management Sciences at Northwestern University, with a courtesy appointment in Engineering Sciences and Applied Mathematics. He directs the Center for Optimization and Statistical Learning. His research focuses on optimization methods, particularly in large-scale, noisy, and uncertain environments, with applications in machine learning, engineering design, and physical sciences. Nocedal co-authored the influential textbook Numerical Optimization and developed widely used optimization software including L-BFGS and KNITRO.
Education: B.S. in Physics from the National University of Mexico (UNAM) and Ph.D. in Mathematical Sciences from Rice University.
Research emphasizes creating efficient algorithms for complex optimization problems, including derivative-free methods, constrained optimization under noise, and parallel computing. His work bridges theory, algorithm design, and software implementation, aiming to reduce computational costs in machine learning and AI.
Scientific awards include the 2012 George B. Dantzig Prize and 2017 Von Neumann Theory Prize, alongside being a SIAM Fellow and US National Academy of Engineering member.
Advising: Nocedal has mentored numerous students, including recent advisees Shigeng Sun and Yuchen Lou. His former students hold prominent roles in academia and industry. Research grants and collaborations are extensive but not detailed here.
Labs/Teams: Leads the Center for Optimization and Statistical Learning, fostering interdisciplinary research in optimization and data science.
