Scholar
Daksh Mittal
Google Scholar ID: ug78g5YAAAAJ
Columbia University
Citations & Impact
Citations
18
H-index
2
i10-index
0
Publications
12
Co-authors
0
Publications
Resume (English only)
Academic Achievements
- Publications: - Data-driven Stochastic Modeling Using Autoregressive Sequence Models (with Shunri Zheng, Jing Dong, and Hongseok Namkoong), finalist for the 2025 Service Science Best Student Paper Award, journal version submitted, preliminary version accepted at NeurIPS 2025 MLxOR Workshop; - Architectural and Inferential Inductive Biases for Exchangeable Sequence Modeling (with Leon Li, Thomson Yen, Daniel Guetta, and Hongseok Namkoong), to appear in NeurIPS 2025; - A Planning Framework for Adaptive Labeling (with Yuanzhe Ma, Shalmali Joshi, and Hongseok Namkoong), journal version submitted, conference version accepted at NeurIPS 2024; - Agent-based simulators for epidemic modeling (with Sandeep Juneja and Shubhada Agrawal), journal version submitted, accepted as an extended abstract in ACM SIGMETRICS Performance Evaluation Review 2023, accepted for oral presentation at Workshop on Autonomous Agents for Social Good (AASG) 2023; - Modelling the Delta Covid-19 Wave in Mumbai (with Sandeep Juneja), accepted at Winter Simulation Conference 2022.
Research Experience
- Worked as a Research Associate at Tata Institute of Fundamental Research Mumbai under the guidance of Prof. Sandeep Juneja; Currently pursuing doctoral research at Columbia Business School, advised by Prof. Hongseok Namkoong and Prof. Jing Dong.
Education
- PhD: Columbia Business School, advised by Prof. Hongseok Namkoong and Prof. Jing Dong; Research Associate: Tata Institute of Fundamental Research Mumbai, advised by Prof. Sandeep Juneja; B.Tech: Indian Institute of Technology, Delhi, in Mechanical Engineering.
Background
- Research Interests: Intersection of artificial intelligence and operations, focusing on developing AI methodologies for solving large-scale operational problems. Brief Introduction: Currently a fourth-year PhD student in the Decision, Risk and Operations Division at Columbia Business School.
Co-authors