I am a Data and Applied Scientist at Microsoft. I got my PhD from the Algorithms, Combinatorics, and Optimization (ACO) Program at Carnegie Mellon, where I was advised by Ben Moseley. My thesis On Combinatorial and Stochastic Optimization won the 2023 Gerald L. Thompson Doctoral Dissertation Award in Management Science.

In Summer 2022, I was an intern at Microsoft Research Redmond in the Cloud Operations Research (CORE) group, where my mentor was Konstantina Mellou. Previously, I obtained a MS in Computer Science and BS in Mathematics from Washington University in St. Louis, where I was advised by Brendan Juba.

Here is my CV, Google Scholar, and DBLP.

Research Interests

I am broadly interested in algorithms and optimization in both theory and practice. On the theory side, recently I am interested in stochastic models in combinatorial optimization. On the applied side, I build end-to-end optimization systems for cloud computing applications

Publications

Author order is alphabetical by last name unless otherwise noted by (*).

Preprints

• Benjamin Moseley, Kirk Pruhs, Marc Uetz, Rudy Zhou
  Minimizing Completion Times of Stochastic Jobs on Parallel Machines is Hard
  Workshop on Approximation and Online Algorithms (WAOA) 2026 (To appear). (Link)

Journal Publications

• Konstantina Mellou, Marco Molinaro, Rudy Zhou
  The Power of Migrations in Dynamic Bin Packing
  Proceedings of the ACM on Measurement and Analysis of Computing Systems (POMACS) 2024. (Link) (arXiv)

• Franziska Eberle, Anupam Gupta, Nicole Megow, Benjamin Moseley, Rudy Zhou
  Configuration Balancing for Stochastic Requests
  Mathematical Programming B 2024. (Link)

• Anupam Gupta, Benjamin Moseley, Rudy Zhou
  Structural Iterative Rounding for Generalized k-Median Problems
  Mathematical Programming A 2024. (Link)

• Benjamin Moseley, Kirk Pruhs, Clifford Stein, Rudy Zhou
  A Competitive Algorithm for Throughput Maximization on Identical Machines
  Mathematical Programming B 2024. (Link)

• Sungjin Im, Benjamin Moseley, Rudy Zhou
  The Matroid Cup Game
  Operations Research Letters, 2021. (Link)

• Rudy Zhou, Han Liu, Tao Ju, Ram Dixit (*)
  Quantifying the polymerization dynamics of plant cortical microtubules using kymograph analysis
  Methods in Cell Biology, 2020. (Link) (Pdf) (GitHub)

Conference Publications

• Anupam Gupta, Benjamin Moseley, Rudy Zhou
  Bayesian Probing on Graphs
  Integer Programming and Combinatorial Optimization (IPCO) 2026. (Link) (arXiv) (Slides)

• Benjamin Moseley, Heather Newman, Kirk Pruhs, Rudy Zhou
  Robust Gittins for Stochastic Scheduling
  Sigmetrics 2025. (Link) (arXiv)

• Konstantina Mellou, Marco Molinaro, Rudy Zhou
  The Power of Migrations in Dynamic Bin Packing
  Sigmetrics 2025. (Link) (arXiv) (Slides)

• Konstantina Mellou, Marco Molinaro, Rudy Zhou
  Online Demand Scheduling with Failovers
  International Colloquium on Automata, Languages, and Programming (ICALP) 2023. (Link) (arXiv) (Slides)

• Franziska Eberle, Anupam Gupta, Nicole Megow, Benjamin Moseley, Rudy Zhou
  Configuration Balancing for Stochastic Requests
  Integer Programming and Combinatorial Optimization (IPCO) 2023. (Link) (arXiv) (Slides)

• Anupam Gupta, Benjamin Moseley, Rudy Zhou
  Minimizing Completion Times for Stochastic Jobs via Batched Free Times
  Symposium on Discrete Algorithms (SODA) 2023. (Link) (arXiv) (Slides)

• Benjamin Moseley, Kirk Pruhs, Clifford Stein, Rudy Zhou
  A Competitive Algorithm for Throughput Maximization on Identical Machines
  Integer Programming and Combinatorial Optimization (IPCO) 2022. (Link) (arXiv) (Slides)

• Silvio Lattanzi, Benjamin Moseley, Sergei Vassilvitskii, Yuyan Wang, Rudy Zhou
  Robust Online Correlation Clustering
  Neural Information Processing Systems (NeurIPS) 2021. (Link) (Full Version) (Slides)

• Anupam Gupta, Benjamin Moseley, Rudy Zhou
  Structural Iterative Rounding for Generalized k-Median Problems
  International Colloquium on Automata, Languages and Programming (ICALP) 2021. (Link) (arXiv) (Slides)

• Sungjin Im, Mahshid Montazer Qaem, Benjamin Moseley, Xiaorui Sun, Rudy Zhou
  Fast Noise Removal for k-Means Clustering
  Artificial Intelligence and Statistics (AISTATS) 2020. (Link) (arXiv) (Slides)

Teaching

• Main Instructor at Carnegie Mellon University:
  • MSBA Machine Learning Fundamentals (Course Designer, Spring 2024)
  • MBA Calculus Fundamentals (Spring 2022, Spring 2023)