Fengxue (Alex) Zhang is a Ph.D. student in Computer Science at the University of Chicago, specializing in machine learning with a focus on decision-making under uncertainty. His research centers on developing novel deep learning and statistical learning methods, particularly in Bayesian optimization and uncertainty quantification. He works on diverse applications ranging from protein design and materials science to content distribution and in-context learning for large language models.

With experience in both academic research and industry applications, he bridges theoretical advances with practical implementations. His recent work includes optimizing in-context learning for large language models and developing innovative solutions for phosphate sensor calibration through multi-modal deep learning approaches.