Jack Baskin Engineering

Presenter: Denis Titov, Assistant Professor, University of California, Berkeley Description: Cells run thousands of chemical reactions simultaneously, and these reactions must be precisely controlled—like a thermostat that prevents overheating. When this control fails, diseases including diabetes, cardiovascular disease, and fatty liver disease result. One key control mechanism is allosteric regulation, where a small molecule binds […]

Presenter: Dr. David A. Knowles, New York Genome Center & Columbia University Description: Many human diseases have a substantial genetic component, which association studies are increasingly capable of characterizing, empowered by ever-growing sample sizes. These associations have the potential to elucidate complex disease biology and prioritize therapeutic interventions. However, it is challenging to determine the […]

Presenter: Peng Ding, Associate Professor, UC Berkeley Description: I will present some recent results on unifying regression-based and design-based causal inference in time-series experiments and crossover experiments. Part I: Time-series experiments, also called switchback experiments or N-of-1 trials, play increasingly important roles in modern applications in medical and industrial areas. Under the potential outcomes framework, […]

Presenter: Yu Zhang, Associate Professor, ECE Department of UC, Santa Cruz Description: Modern cyber-physical systems present statistical learning problems that deviate significantly from standard i.i.d. supervised settings. In particular, two challenges frequently arise: (i) learning under hard structural constraints, and (ii) learning under severe distributional imbalance and rare events. In this talk, I present two […]

Presenter: Anil Damle, Associate Professor, Cornell University Description: The column subset selection problem is a classical topic in numerical linear algebra, with renewed interest driven by applications in computational quantum chemistry, integral equations, model reduction, and model compression in machine learning. This talk surveys recent advances that clarify how structural properties of a matrix influence […]

Presenter: Dr. Pulkit Dubey, Postdoc, UC Santa Cruz Description: Neural climate emulators such as NeuralGCM and LUCIE offer efficient, differentiable alternatives to General Circulation Models (GCMs), producing climate predictions at a fraction of the cost. While work to date has focused largely on predictive accuracy, we leverage differentiability to study control of long-horizon climatological targets. […]

Presenter: Marcos Calegari Andrade, Assistant Professor, Chemistry and Biochemistry, UC Santa Cruz Description: In this talk, I will demonstrate how neural networks can represent the high-dimensional potential energy surfaces of many-body systems. By achieving the accuracy of first-principles quantum calculations at a fraction of the computational cost, these models enable atomistic simulations of condensed matter […]

Presenter: Riddhiman Bhattacharya, Postdoc, UCSC Description: We propose an active learning approach for dynamic fair resource allocation problems. In contrast to prior work that assumes full feedback from all agents on their allocations, we focus on scenarios where feedback is available only from a carefully select subset of agents at each epoch of the online […]