Shihao Yang

Shihao Yang serves as a Harold E. Smalley Early Career Professor and tenure-track Assistant Professor in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Institute of Technology. He maintains critical affiliations with the Institute for Data Engineering and Science (IDEaS), Center for Machine Learning at Georgia Tech (ML@GT), and Institute of People and Technology (IPaT).

His academic credentials include:

• Ph.D. in Statistics (2019), Harvard University
• A.M. in Statistics (2016), Harvard University
• B.Sc. in Actuarial Science (2014), University of Hong Kong

Professor Yang's research program centers on harnessing big data through methodological development, computational tools, and probabilistic modeling to solve real-world problems. His expertise spans statistics, machine learning, and data science with concentrated applications in infectious disease modeling, dynamic system inference (parameter estimation in differential equations), and electronic medical records mining. His work consistently bridges theoretical innovation with practical healthcare implementations.

Analysis of his recent publications reveals dominant themes in time series forecasting, causal inference, and physics-informed machine learning. His research integrates deep learning with statistical methods to enhance dynamic system analysis and healthcare applications, particularly in electronic health records interpretation and infectious disease prediction using internet search data. This synthesis drives methodological advances in handling noisy, sparse real-world datasets.

His notable recognition includes:

• Harold E. Smalley Early Career Professorship

Professor Yang actively mentors undergraduate, master's, and PhD students, encouraging prospective candidates to contact him with CVs and transcripts. His research receives substantial institutional support through Georgia Tech's interdisciplinary centers. He leads methodological development for real-world data challenges while maintaining strong industry and healthcare partnerships.

As a core member of IDEaS, ML@GT, and IPaT, he drives cross-disciplinary collaboration between engineering, computing, and social sciences to advance data-driven solutions for complex societal problems.