Rohan Ghuge
Scholar

Rohan Ghuge

Google Scholar ID: A_lYbWQAAAAJ
H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology
Citations & Impact
All-time
Citations
104
 
H-index
5
 
i10-index
4
 
Publications
14
 
Co-authors
3
list available
Contact
Publications
2 items
Semi-Bandit Learning for Monotone Stochastic Optimization*
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