Upcoming Events

Fourier representations for fast Gaussian process regression

Data Science Seminar

Philip Greengard (Columbia University)

Computer vision, mostly without AI

Industrial Problems Seminar 

Elena Yudovina (CyberOptics)

On small and large scales in training physics-informed neural networks for partial differential equations

Data Science Seminar

Zhongqiang Zhang (Worcester Polytechnic Institute)

Viva la Revolución of Open Source Large Language Models: Unleashing the Dark Horse in AI Innovation

Industrial Problems Seminar

Patrick Delaney (BloomBoard)

Advancing Machine-Learned Interatomic Potentials: Enhancing Accuracy and Robustness in Materials Science Applications

Data Science Seminar

Yangshuai Wang (University of British Columbia)

Academia, to Industry, to the NBA – Navigating a Non-Academic Career with a PhD

Industrial Problems Seminar 

Daniel Martens (Minnesota Timberwolves)

Abstract

What began for me as a journey in physics through undergrad and graduate school has led me to opportunities that I could never have imagined. I’m going to step through the choices I made, the ramifications they had for me, and what I wish I knew before I had graduated. I’ll give specific examples of work I’ve done in academia, corporate America, and the NBA world, and give tips to being successful in a variety of environments.

Conditional coalescent and its applications in population genomics

Data Science Seminar

Wai-Tong (Louis) Fan (Indiana University)

Lecture: Jie Chen

Industrial Problems Seminar 

Jie Chen (IBM Research)

Lecture: Hongkai Zhao

Data Science Seminar

Hongkai Zhao (Duke University)

Lecture: Wenrui Hao

Data Science Seminar

Wenrui Hao (The Pennsylvania State University)