Yian Ma is an Assistant Professor at the UC San Diego, Halıcıoğlu Data Science Institute. He earned his Ph.D. in applied mathematics at University of Washington. He was subsequently a Postdoctoral Researcher at University of California Berkeley, Department of Electrical Engineering and Computer Sciences. He spent a year as a visiting faculty at Google Research before joining UC San Diego.
His research primarily involves scalable inference methods and their theoretical guarantees. He has designed a number of novel Bayesian inference algorithms with a focus on applying them to time series data and sequential decision making.
Dr. Rose Yu is an Assistant Professor at the UC San Diego, Department of Computer Science and Engineering. She earned her Ph.D. in Computer Sciences at the University of Southern California in 2017. She was subsequently a Postdoctoral Fellow at the California Institute of Technology. She was an assistant professor at Northeastern University prior to her appointment at UC San Diego.
Her research focuses on advancing machine learning techniques for large-scale spatiotemporal data analysis, with applications to sustainability, health, and physical sciences. A particular emphasis of her research is on physics-guided AI which aims to integrate first-principles with data-driven models. Among her awards, she has won Google Faculty Research Award, Adobe Data Science Research Award, NSF CRII Award, Best Dissertation Award in USC, and was nominated as one of the ’MIT Rising Stars in EECS’.
Liyao (Mars) Gao
Liyao Gao is a graduate student at the University of Washington, Department of Statistics. He received his B.S. in Math with Computer Sciences at Purdue University 2020.
His research focuses are primarily on Bayesian inference algorithms with their applications and theoretical guarantee.
Dongxia (Allen) Wu
Dongxia Wu is a graduate student at the UC San Diego, Department of Electrical and Computer Engineering. He earned his B.S. in Math, Physics, and Computer Sciences at the University of Wisconsin-Madison in 2020.
His research focuses are primarily on machine learning and its applications in spatiotemporal data analysis, such as COVID-19 forecasts.