Past Events
AI x Science Seminar: Bhuv Jain
Machine Learning in Cosmology
Amy Gutman Hall, Room 414
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…
PPE, SRDA, DASA Mixer
PCPSE Forum (2nd Floor)
Interested in adding philosophy, politics, data, or survey research to your degree? Join the PPE × SRDA × DASA Mixer on October 16, 11:30 AM–1:00 PM in the PCPSE Forum (2nd Floor) to meet students and staff across the major and both minors to see if any of these programs might be…
AI x Science Seminar: René Vidal
Learning dynamics in the feature-learning regime: implicit bias, robustness, and low-rank adaptation
Amy Gutman Hall
Room 414
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…
AI Research Mixer
Amy Gutmann Hall
3317 Chestnut Street
You are invited to attend the 2025 AI Research Mixer, a full-day event featuring faculty presentations, poster sessions, and networking opportunities that highlight the breadth of artificial intelligence research taking place across Penn. This year's event is co-hosted by the…
AI x Science Seminar: Eva Dyer
Building unified models of neural data across tasks, modalities, & species
Amy Gutmann Hall, Room 414
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…
AI x Science Seminar: Nat Trask
Geometric and physics structure preservation in scientific machine learning
Amy Gutman Hall, Room 414
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…
AI × Science Seminar: Jacob Gardner
Extracting knowledge priors from scientific texts for de novo molecular design
Amy Gutman Hall
Room 414
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…
Summer Hangouts: Supranta S. Boruah
Diffusion Models in Action: From Toy Problems to Dark Matter Maps
RDDSX space outside the Collaborative Classroom
Van Pelt-Dietrich Library
Diffusion models have emerged as powerful tools in generative modeling, achieving impressive results across different domains. In this tutorial, we’ll explore how these models can be adapted to tackle a cosmological challenge: mapping dark matter in the Universe.
Summer Hangouts: Sourav Dey
Bayesian optimization, with applications in chemical reaction discovery and optimization
RDDSX space outside the Collaborative Classroom
Van Pelt-Dietrich Library
Machine learning in the low data regime. How do you find the optimal solution to a problem when the objective function is expensive to evaluate. We will also discuss how to optimize these types of black box functions in a sparse space.
Summer Hangouts: Coby Viner
Speeding up science: GNU Parallel for bioinformatics and beyond
RDDSX space outside the Collaborative Classroom
Van Pelt-Dietrich Library
This session introduces GNU Parallel—the shell utility that can launch thousands of jobs simultaneously, harnessing every CPU core (or even multiple machines) with a single command.
