New Faculty: Alexander Strang, Assistant Teaching Professor
The UC Berkeley Department of Statistics is excited to welcome Alexander Strang as Assistant Teaching Professor. He previously served as Kruskal Instructor at the University of Chicago after receiving his B.A and Ph.D. from Case Western Reserve in Cleaveland, Ohio.
“We are thrilled for Alex to join us,” said Chair Haiyan Huang. “His strong interdisciplinary background and experience teaching will bring a lot to our community.”
After studying Physics, Strang migrated through Applied Mathematics to Statistics. He received his graduate degree in computational mathematics and as a postdoc at the University of Chicago, where he worked in the Statistics Department. He also founded a private consulting firm, providing research advice on AI and reinforcement learning problems, broadening his research scope. He focuses on stochastic processes and modeling in biological systems, the interplay of structure and dynamics in networks, and Bayesian inference and inverse problems. Strang noted that his broad exposure to various academic disciplines has provided concrete tools to apply to diverse problems. This cross-disciplinary background also provides rich opportunities for conversations amongst a wide variety of fields.
“I am more than excited to join the Statistics Department at Berkeley. It is an enormous opportunity to contribute to such a brilliant, impactful department during a transitional time for the discipline at Berkeley and in society at large.”
Strang describes himself as an applied mathematician interested in the interaction of structure and dynamics. He is motivated by the versatility of mathematics in real-world problems. He has addressed problems involving nonequilibrium thermodynamics in active matter, selection dynamics in evolutionary game theory and reinforcement learning, and solution continuation for uncertainty quantification and estimation in Bayesian inverse problems. Active projects include the characterization information flow in neural networks, the classification of decision problems via topological data analysis, the latent space dynamics of learning processes, and the use of random polynomials to explain biodiversity in complex dynamical systems. He pulls on a wide variety of tools, including simplicial homology, random matrix theory, spectral embedding, and function approximation.
Strang received the 2022 Suzuki Postdoctoral Fellowship Award in recognition of his research. Outside academia, Strang is a competitive runner and endurance athlete. While at the University of Chicago, he served as a Cross Country and Track Coach at Kenwood Academy in Hyde Park.