Chaithanya Bandi
Visiting Associate Professor
Research Interests
Professor Bandi’s teaching and research bridges theoretical optimization with practical operational strategy and supply chain dynamics. His teaching spans foundational and advanced topics in operations and supply chain management. He instructs on analytical decision modeling, global operations strategy, applied analytics, optimization, forecasting, and process analysis. His primary research explores robust optimization, queueing theory, and service-system design. His funded work applies optimization to modern challenges, including power-grid and infrastructure network optimization for PNNL, and data-center load balancing via robust queueing for Google. Professor Bandi researches spot-auction design for cloud computing with Amazon, decision-flow networks for service systems, and adaptive questionnaire design for fast-fashion retail like Myntra. His active research allows him to teach emerging developments in AI computing and energy environments directly from his own primary work.
Email
Professor Bandi has extensive experience teaching at leading business schools across undergraduate, graduate, and executive levels. In addition to his position at MBZUAI, he currently teaches at NUS Business School, leading courses in Global Operations Strategy (MBA), Applied Analytics (MSBA), Introduction to Optimization (undergraduate), and executive analytics. While at Massachusetts Institute of Technology, Professor Bandi was the lead Teaching Assistant for core sequences including Introduction to Operations Management; Data, Models, and Decisions; and The Analytics Edge. Prior to his current roles, he taught operations management, supply chain courses, and Analytical Decision Modeling across Kellogg's BBA, MBA, MS, and Executive MBA programs with the Kellogg School of Management.
- Ph.D. in Operations Research, Massachusetts Institute of Technology, USA
- B.Tech. in Computer Science and Engineering, Indian Institute of Technology Madras, India
- Finalist, George Nicholson Best Paper Award
- ORC Best Paper Award
- Presidential Fellowship Award, Massachusetts Institute of Technology
- Best Presentation Award, INFORMS Annual Meeting (Financial Services Section)
- Patent: "Dynamic Pricing Model for Online Advertising" (US Patent App. 12/683,658), 2011
- Award for Academic Excellence, IIT Madras
- KVPY Fellowship Award, Department of Science and Technology, Government of India. Gold Medal, International Chemistry Olympiad Training Camp
- Bandi, C., Hertzberg, B., Boo, G., Polakam, T., Da, J., Hassaan, S., Sharma, M., Park, A., Hernandez, E., Rambado, D., et al.: "MCP-Atlas: A Large-Scale Benchmark for Tool-Use Competency with Real MCP Servers", arXiv preprint arXiv:2602.00933, 2026.
- Sharma, M., Zhang, C. B. C., Bandi, C., Wang, C., Aich, A., Nghiem, H., Rabbani, T., Htet, Y., Jang, B., Basu, S., et al.: "ResearchRubrics: A Benchmark of Prompts and Rubrics for Evaluating Deep Research Agents", arXiv preprint arXiv:2511.07685, 2025.
- Quirke, P., Oozeer, N., Bandi, C., Abdullah, A., Hoelscher-Obermaier, J., Phillips, J. M., Greaves, J., Neo, C., Barez, F., and Upadhyay, S.: "Beyond Monoliths: Expert Orchestration for More Capable, Democratic, and Safe Large Language Models", arXiv preprint arXiv:2506.00051, 2025.
- Upadhyay, S., Keigwin, B., Bandi, C., Samkharadze, L., Romanov, A., Bakuta, A., and Zverianskii, A.: "Model Mapping: Can We Turn Transformers Into Programs?", SSRN 5497818, 2025.
- Upadhyay, S., Bandi, C., Oozeer, N., and Quirke, P.: "Position: Require Frontier AI Labs To Release Small 'Analog' Models", arXiv preprint arXiv:2510.14053, 2025.
- Bandi, C., Han, E., and Proskynitopoulos, A.: "Robust Queue Inference from Waiting Times", Operations Research, 72(2):459-480, 2024.
- Bandi, C. and Harrasse, A.: "Adversarial Multi-agent Evaluation of Large Language Models through Iterative Debates", arXiv preprint arXiv:2410.04663, 2024.
- Harrasse, A., Bandi, C., and Bandi, H.: "Debate, Deliberate, Decide (D3): A Cost-Aware Adversarial Framework for Reliable and Interpretable LLM Evaluation," arXiv e-prints, 2024.
- Simonds, T., Lau, J. H., and Bandi, C.: "REL: Working Out Is All You Need", arXiv preprint arXiv:2412.04645, 2024.
- Han, E., Bandi, C., and Nohadani, O.: "On Finite Adaptability in Two-stage Distributionally Robust Optimization", Operations Research, 71(6):2307-2327, 2023.