I am currently a postdoc in the Operations Research Department at Carnegie Mellon University. I graduated in May 2023 from the same department through the the Algorithms, Combinatorics, and Optimization (ACO) Program, 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. My e-mail is: rudyzhou1234@gmail.com. Here is my Google Scholar and DBLP.

I am on the job market for both academia and research positions in industry.

Research Interests

I am broadly interested in algorithm design with a focus on optimization under uncertainty. I design provably good algorithms for fundamental optimization problems in areas such as scheduling, resource allocation, and data analysis.

Recently, I am excited about stochastic combinatorial optimization, where we only have distributional information about the input. In this setting, I am thinking about new algorithms, hardness of approximation, adaptivity gaps, and imperfect information.

Publications

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

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 (to appear). (Link)

• 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

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

• 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)
• Teaching Assistant at Carnegie Mellon University:
  • Graph Theory (Fall 2020, Fall 2021)
• Teaching Assistant at Washington University in St. Louis:
  • Computational Geometry (Fall 2017)
  • Object-Oriented Software Development Laboratory (Spring 2017)