It's crunch time near the end of the semester. For a welcome break, the Center for Statistics and Machine Learning will be holding its popular Barks, Bubbles and Brownies event. There will be therapy dogs, bubble tea and brownies for all.

RSVP not necessary but it's first come, first serve for the bubble tea!

Foundation models (ChatGPT, StableDiffusion) are transforming society: remarkable capabilities, serious risks, rampant deployment, unprecedented adoption, overflowing funding, and unending controversy. In this talk, we will center our attention on their societal impact. In the first half, we will discuss two efforts (HELM, Ecosystem Graphs) to…

Students (4th - 10th graders) and their families are invited to attend Spring Into Science on Saturday, April 22, 2023, in the Frick Atrium from 10:00 am - 12:30 pm. Students will visit tabletop activities and hands-on demonstrations, listen to research talks, and engage scientists from Astrophysical Sciences, Center for…

Join us at the CDH (B-Floor, Firestone) for a talk by Rosamond Thalken on “Quantifying Judicial Self-Fashioning: Rhetorical Depictions and Large Language Models.” Lunch will be served.

Rosamond Thalken studies Information Science at Cornell University. Her research uses large language models to identify rhetorical depictions of…

Machine learning (ML) is being deployed to a vast array of real-world applications with profound impacts on society. ML can have positive impacts, such as aiding in the discovery of new cures for diseases and improving government transparency and efficiency. But it can also be harmful: reinforcing authoritarian regimes, scaling the spread of…

Dot product embeddings take a graph and construct vectors for nodes such that dot products between two vectors give the strength of the edge. Dot products make a strong transitivity assumption, however, many important forces generating graphs in the real world are specifically non-transitive. We remove the transitivity assumption by embedding nodes into a pseudo-Euclidean space - giving each node an attract and a repel vector. The inner product between two nodes is defined by taking the dot product in attract vectors and subtracting the dot product in repel vectors. Pseudo-Euclidean embeddings can compress networks efficiently, allow for multiple notions of nearest neighbors each with their own interpretation, and can be `slotted' into existing models such as exponential family embeddings or graph neural networks for better link prediction.

Machine learning algorithms are widely used for decision making in societally high-stakes settings from child welfare and criminal justice to healthcare and consumer lending. Recent history has illuminated numerous examples where these algorithms proved unreliable or inequitable. This talk will show how causal inference enables us to more…

An open house for sophomores interested in enrolling in the undergraduate certificate program at the Center for Statistics and Machine Learning (CSML) is scheduled for April 4th. 

Ryan Adams, the director of the certificate program, and Susan Johansen, CSML's academic program coordinator, will be on hand to answer any questions…

Co-sponsored by the Department of Computer Science and the Department of Electrical and Computer Engineering

Users who wish to exercise privacy rights or make privacy choices must often rely on website or app user interfaces. However, too often, these user interfaces suffer from usability deficiencies ranging from being…

Rose Yu is an assistant professor at the University of California San Diego, Department of Computer Science and Engineering. Her research focuses on advancing machine learning techniques for large-scale spatiotemporal data analysis, with applications to sustainability, health, and physical sciences.

Lunch available at 12:00 p.m. Please RSVP to [email protected]