BeSMART combines academic rigor with the social experience of college. Students are expected to attend all classes, which will be held at UC Berkeley’s College of Engineering lecture halls and lab facilities. Each day will consist of classes divided into two parts: morning lecture + afternoon lab.

The daily schedule will look something like:

Xin Guo

Currently teaching:  Applied Stochastic Process II, Financial Engineering Systems II

Professor Xin Guo is the Chair of the Department of Industrial Engineering and Operations Research at UC Berkeley, where she also holds the Coleman Fung Chair in Financial Modeling. A faculty member since 2006, she is an internationally recognized leader in stochastic processes, game theory, and the theoretical foundations of machine learning, with research that bridges mathematics and engineering to address complex real-world challenges. She earned her Ph.D. in Mathematics from Rutgers University (1999), following an M.S. in Mathematics from the Graduate School of Academia Sinica (1995) and a B.S. in Mathematics from the University of Science and Technology of China (1992). Her interdisciplinary work has led to significant breakthroughs, including an FDA-approved machine learning technique for early cancer detection and innovative optimization strategies for Amazon’s transportation systems.

Phillip Kerger

Currently teaching:  Introduction to Machine Learning and Data Analytics, Machine Learning and Data Analytics II

Phillip Kerger earned his bachelor’s degree in mathematics from Fordham University, where he conducted undergraduate research on parameter estimation for heavy-tailed stochastic processes. He then received his PhD from Johns Hopkins University in Applied Mathematics, researching optimization, complexity, and classical and quantum algorithms. Dr. Kerger continued his work in NASA’s Quantum Artificial Intelligence Laboratory (QuAIL), in collaboration with USRA, looking into distributed quantum algorithms. Currently an Assistant Teaching Professor at UC Berkeley’s Department of Industrial Engineering and Operations Research department, Dr. Kerger is deeply interested in sharing his knowledge through teaching.

Ying Cui

Currently teaching:  Introduction Optimization Modeling, Special Topics in Industrial Engineering and Operations Research

Ying Cui earned her bachelor’s degree in Mathematics at Zhejiang University (China), and completed her PhD in Mathematics at the National University of Singapore, Singapore. She is currently an assistant professor in the Department of Industrial Engineering and Operations Research at the University of California, Berkeley. Her research expands on the mathematical foundations of data science with an emphasis on optimization techniques for operations research. The optimization models and computational methods include decision making under uncertainty, variational analysis for nonsmooth and semidefinite optimization. Outside of UC Berkeley, Professor Cui serves as an assistant editor for Mathematical Programming and the vice chair of Nonlinear Optimization for INFORMS Optimization Society.

Thibaut Mastrolia

Currently teaching:  Financial Engineering Systems I, Group Studies, Seminars, or Group Research

Thibaut Mastrolia is an Assistant Professor in the Department of Industrial Engineering and Operations Research at the University of California, Berkeley. His research centers on the development of stochastic control, game-theoretic, and contract-theoretic frameworks to design resilient financial and cyber systems under conditions of risk and uncertainty.

Huiwen Jia

Currently teaching:  Service Operations Design and Analysis

Huiwen Jia received her bachelor’s degree in Industrial Engineering from Tsinghua University and a PhD in Operations Research from the University of Michigan, Ann Arbor. She is currently an Assistant Professor in the Department of Industrial Engineering and Operations Research at the University of California, Berkeley.  Prior to joining the team, she spent two years as an Applied Scientist at Amazon Marketplace, where she and her team worked on two-sided marketplace design. Professor Jia focuses her research on stochastic and robust optimization, machine learning, and online learning algorithms with applications in transportation and revenue management.

Anil Aswani

Currently teaching:  Development Engineering Research and Practice Seminar, Healthcare Analytics, Engineering Statistics, Quality Control, and Forecasting

Anil Aswani is an associate professor and the head undergraduate advisor in the Department of Industrial Engineering and Operations Research at the University of California, Berkeley. Dr. Aswani completed his bachelor’s degree in Electrical Engineering at the University of Michigan, in Ann Arbor, and received his PhD in Electrical Engineering and Computer Sciences at UC Berkeley, where his research focused on computational and genomic biology. Not only has Professor Aswani  been awarded an NSF CAREER award through the Operations Engineering program for his work on personalized healthcare, but he has a Hellman Fellowship for his research on food insecurity, and the Leon O. Chua award from Berkeley for his outstanding achievements in nonlinear science. His current research interests include data-driven decision making with emphasis on inefficiencies and inequities in health systems and physical infrastructure.

Students will gain fluency in working with, manipulating, and extracting insights from datasets using Python. Through real-world examples, participants will learn to identify opportunities for data analytics, effectively communicate insights through visualization, and develop collaboration and leadership skills in a problem-solving context.

Yes, housing is included in the program price. Students will reside in a double occupancy dormitory rooms with other program participants of the same sex. Resident Assistant and Mentors (RAMs) will be on duty throughout the evening to monitor participants and provide emergency assistance, if needed.

All our RAMs are UC Berkeley undergraduate students and undergo special training to work with students under 18 years old. They are also resources for participants to better understand life and academia at UC Berkeley.