IEEE Annual Symposium on Foundations of Computer Science · 2023
Cited
2
Resume (English only)
Academic Achievements
Journal: INFORMS Journal of Computing (Minor Revision) – 'Informative Path Planning with Limited Adaptivity'
Journal: Operations Research (Articles in Advance) – 'Non-Adaptive Stochastic Score Classification and Explainable Halfspace Evaluation'
Journal: Operations Research, 72(3):1156–1176, 2024 – 'The Power of Adaptivity for Stochastic Submodular Cover'
Journal: Operations Research, 70(2):786–804, 2022 – 'Constrained Assortment Optimization under the Paired Combinatorial Logit Model'
Journal: Mathematics of Operations Research, 47(2):1612–1630, 2022 – 'Quasi-Polynomial Algorithms for Submodular Tree Orienteering and Other Directed Network Design Problems'
Conference: ICML 2025 – 'Improved and Oracle-Efficient Online l₁-Multicalibration'
Conference: STOC 2025 – 'Single-Sample and Robust Online Resource Allocation'
Conference: FOCS 2024 – 'Semi-Bandit Learning for Monotone Stochastic Optimization'
Background
Assistant Professor of Decision Science in the Department of Information, Risk, and Operations Management (IROM), McCombs School of Business, The University of Texas at Austin
Research focuses on designing data-driven algorithms for decision-making under uncertainty
Applies data-driven methods and machine learning techniques to stochastic optimization where the input distribution is known but individual instances are uncertain
Key research themes: the power of adaptivity (achieving near-optimal solutions with limited feedback rounds), robust online decision-making under noisy or uncertain data, and simultaneous parameter learning and decision optimization with convergence rate analysis
Applications include healthcare diagnostics, online preference elicitation, ad placement, and web search ranking