About Me
Hi, I am a Postdoctoral Associate at MIT’s Laboratory for Information and Decision Systems (LIDS), mentored by Prof. Cathy Wu. Previously, I was a Research Fellow at Nanyang Technological University (NTU), supervised by Prof. Jie Zhang. I obtained my Ph.D. in Industrial Systems Engineering at National University of Singapore (NUS) in 2024, where I was mentored by Prof. Zhiguang Cao and honored to be advised by Prof. Yeow Meng Chee. I received my B.E. from South China University of Technology (SCUT) in 2019, advised by Prof. Yuejiao Gong. I am also working closely with NVIDIA, Amazon, Microsoft Research, Symbotic and Grab (SG).
My background unites AI, Operations Research (OR), and system design. I develop principled, scalable, and trustworthy decision intelligence systems, aiming for high-impact deployments in transportation, advanced manufacturing, robotics, and beyond. I have published 35+ papers in top-tier conferences and journals. I am an Area Chair (AC) for NeurIPS and recognized with multiple best reviewer awards.
Welcome to see my publications, fundings, services, experience, and honors & awards. Drop me an email if you’d like to collaborate or discuss with me! You may approach me at:
- Office: 45-611, 51 Vassar St, Cambridge, MA 02139
- E-mail: yiningma [at] mit [dot] edu
💡 Research Interests
My research focuses on AI for Optimization and Decision Intelligence, which asks: how can AI learn to optimize complex decision-making at scale?
Research Directions 🔥 - Complete list available here and on Google Scholar
- Neural Combinatorial Optimization (NCO) - a methodological frontier developing novel AI paradigms for hard optimization problems that are combinatorial, large-scale, constrained, and demand reliable generalization;
- Learning-Guided Optimization (LGO) - the near-term path to industrial killer applications and real impact, leveraging AI and NCO to accelerate classical operations research (OR) algorithms, uniting OR’s reliability with AI’s adaptivity;
- Agentic AI and Trustworthy Decision-Making - building next-generation decision systems where AI/LLM agents assist in formulating, solving, and interpreting the decision-making pipeline, while ensuring these systems remain interpretable, robust, and safe to deploy and fine-tune with principled guarantees.
- Survey, Benchmark & Open-source Library - IET Review (survey of NCO through 2023), MetaBox-v2 (benchmark of learning-guided MetaBBO), RL4CO (library of RL for COP)
Research Keywords
- Machine Learning: Reinforcement Learning, Deep Learning, LLM, LLM agent, Foundation Model, Multi-Agent Systems, Interpretability
- Optimization: Neural Combinatorial Optimization (NCO), Learning-guided Optimization (LGO), Black-Box Optimization (BBO), GPU-Accelerated Optimization
- Application: Transportation, Planning, Scheduling, Robotics, Manufacturing
🎉 News
I'm on the job market! Please contact me if you know of any suitable positions!- [03/2026] Our work regarding learning guided optimization in warehouse robot automation is featured on the MIT.edu (see MIT News 🎊)!
- [03/2026] I will be serving as an Area Chair for NeurIPS 2026!
- [03/2026] We are hosting the MIT 2026 Summer Research Camp! Welcome to apply!
- [02/2026] Our L2Seg has been accepted as an Oral presentation at ICLR 2026!!
- [01/2026] Three L2Opt papers got accepted by ICLR’26, including 1) L2Seg, accelerating SoTA solvers by 2x to 7x by learning to prun redundant search; 2) CaR, enabling efficient constraint handling via jointly learned feasibility refinement and awareness; and 3) RADAR, promoting asymmetry-aware representations to boost NCO practicality!
- [01/2026] Awarded the MIT Kaufman Teaching Certificate in recognition of dedication to teaching excellence! See the reference letter from the MIT Vice Chancellor!
- [01/2026] One paper on L2Opt got accepted by JAIR, where we introduce L-RH-PP, the first work of NCO for warehouse automation to coordinate multiple robots in Symbotic!
- [01/2026] One paper on L2Opt got accepted by TEVC, where we introduce LLaMoCo, a very first attempt of finetuning LLM for optimization code generation!
- [11/2025] I will organize and serve as a session chair of “AI for Planning and Scheduling” in the International Federation of Operational Research Societies (IFORS), 2026.
- [10/2025] 🎉 Awarded AI Singapore grant “Trustworthy Human-AI Combinatorial Optimization with Language Feedback” (with MIT, NTU, and SMU) see LinkedIn news!
- [09/2025] Three papers on L2Opt got accepted by NeurIPS’25, including 1) the very first paper studying the internal mechanism and intepretability of NCO models (Spotlight); 2) DesignX, a bi-agent learning system tailored for automated algorithm design in black-box optimization; and 3) MetaBox-v2, an up-to-date benchmark platform for MetaBBO!
- [09/2025] We have organized the 1st workshop on Learning-assisted Evolutionary Algorithm Design (LEAD 2025). Watch recordings here; see you all next year!
- [06/2025] Checkout MetaBox-v2🚀! A major update offering broader optimization scenarios (single-objective, multi-objective, multi-modal, multi-task, etc), extensive benchmarks, even faster parallel training/inference, and more support for MetaBBO.
- [05/2025] Our
benchmark library got accepted by KDD (Oral) 2025! - [05/2025] One paper on L2Opt got accepted by ICML’25, where we introduce SHIELD, a novel NCO solver for Multi-Task Multi-Distribution Vehicle Routing Problem (MTMDVRP).
- [04/2025] I will be serving as an Area Chair for NeurIPS 2025!
- [04/2025] Our L-RHO is featured on the Front Page of MIT.edu (see MIT News 🎊)!
- [12/2024] One paper on L2Opt got accepted by ICLR’25, where we introduce L-RHO, leveraging ML to accelerate RHO by up to 54% for Long-Horizon COPs such as FJSP.
- [12/2024] One paper on L2Opt got accepted by AAAI’25 (Oral), where we propose ConfigX, a unified configuration agent that learns to boost diverse evolutionary algorithms.
- [12/2024] We hosted an unofficial L2Opt workshop at NeurIPS 2024 (see photos)!
- [12/2024] ✈️ I will be attending NeurIPS’24 in person. See you in Vancouver, Canada! 🇨🇦
- [11/2024] One paper on L2Opt got accepted by KDD’25, where we propose a neural approach for optimizing diverse, high-quality solutions to Multi-Solution TSP (MSTSP).
- [10/2024] I am selected as NeurIPS’24 Top Reviewer (10%) for both main and DB track!
- [09/2024] One paper on L2Opt got accepted by NeurIPS’24, where we propose Proactive Infeasibility Prevention (PIP) to elevate neural solvers for complex VRP constraints.
- [08/2024] One paper on L2Opt got accepted by TPAMI, where we promoted a neural collaborative framework integrating learning-to-search and learning-to-construct solvers.
- [08/2024] I’m so excited to join Prof. Cathy Wu’s group in MIT!
- [07/2024] I am selected as ICML’24 Best Reviewer (< 3%)!
- [06/2024] Check out - our latest extensive benchmark to unify frameworks and facilitate research in RL-based CO algorithms 🚀.
- [05/2024] One paper on L2Opt got accepted by KDD’24, where we propose a hierarchical neural solver for realistic TSPs under real-world customer distributions.
- [05/2024] One paper on L2Opt got accepted by ICML’24, where we introduce MVMoE, a step towards multi-task domain foundation models for VRPs based on mixture of experts.
- [05/2024] Promoted to the Research Fellow in NTU.
- [03/2024] One paper on MARL got accepted by TNNLS, where we introduce DOMAC for opponent modelling in multi-agent systems using only local information.
- [03/2024] One paper on L2Opt got accepted by SMCA, where we introduce RL-DAS for dynamic algorithm selection based on deep reinforcement learning.
- [02/2024] Gave a talk at MIT, hosted by Prof. Cathy Wu.
- [02/2024] Successfully defended my PhD thesis at NUS 🎓!
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(Last updated April 2026.)