Medical diagnosis can be naturally framed as a classification problem: inferring an underlying pathology from observed (e.g., imaging) data. A common failure mode in classification is shortcut learning, where models exploit spurious or confounding correlations. Shifts in patient…

Capturing path- and rate-dependent behaviors of solids, such as creeping, plastic deformation, damage, and fracture, often requires interpreting and quantifying relationships among the histories of variables, such as dislocation density and porosity. This relational information…

The Chatterjee Lab at the University of Pennsylvania develops generative algorithms to design functional molecules directly from sequence. Our work begins with a question: can short peptides be de novo designed to bind undruggable targets like disordered oncogenic fusions…

Professor Jain will describe how we map dark matter from large surveys of galaxies via gravitational lensing. Testing theories of cosmology with maps of galaxies and dark matter is a rich area for machine learning. He will describe the ‘old-fashioned’ physics-based approaches and…

This talk will present new insights into the learning dynamics of gradient flow in the feature-learning regime. For separable data, we show that gradient flow learns a simple ReLU network with two neurons corresponding to the two classes. For data drawn from an orthogonal…

Neural activity is complex, dynamic, nonlinear, and high-dimensional, and the datasets we collect from the brain are equally varied. This richness makes analysis difficult, and models trained on narrow conditions rarely generalize. Rather than seeking to simplify the problem, we…

In scientific machine learning, researchers construct simulators of physical systems which are either computationally expensive or intractable to model with conventional simulation tools. Despite impressive steps in the last couple years, these techniques generally fail to meet…

This talk will explore a large-scale effort to exploit the natural-language understanding capabilities of large language models in order to unlock information about the structure, function, and biological activity for proteins, small molecules, genetic variants, and other…

Reliable forecasts of the Earth system are crucial for human progress and safety from natural disasters. Artificial intelligence offers substantial potential to improve prediction accuracy and computational efficiency in this field, however this remains underexplored in many…

This talk will explore how concepts from statistical and soft matter physics can be leveraged to analyze neural manifold packing dynamics under stochastic gradient descent and related optimization algorithms.