I am a Ph.D. Candidate in Computer Science both affliated with The Kahlert School of Computing (SoC) and Scientific Computing and Imaging Institute (SCI) at The University of Utah. I received my M.S. degree at University of Pittsburgh and my B.E. degree at South China University of Technology(SCUT). I also spent time at Schlumberger-Doll Research as robotics research intern and at Amazon as applied scientist intern working on privacy-preserving learning algorihtms and in-context few-shots learning with pretrained large language/multi-modality models.

My research vision is deeply rooted in AI for Science — by leveraging state-of-the-art learning techniques to facilitate the foundamental and classical studies of scientific priciples from complex, structural, and high-dimensional data. I am advised by Dr. Shandian Zhe.

My interests include but not limit to

• Probabilistic Learning : Bayesian modeling, Approximate Inference, Uncertainty quantification of deep models
• AI for Science : Surrogate Modeling, Operator Learning, Physical-Informed Machine Learning
• Multi-Objective Learning : Multi-Task Learning, Multi-Fidelity Learning, Transfer Learning, Meta-Learning
• Interactive Machine Learning : Bayesian Optimisation, Active Learning, Multi-Armed Bandits, Reinforcement Learning

I am on job market right now! I am open for academic and industrial poisitions starting summer/fall/winter 2024. If you are interested in my background, do not hesitate to contact me via shiboli.cs@gmail.com