Yining MA (马一宁)

Yining MA (马一宁)

Postdoctoral Associate, MIT

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 innovations in domains including LLMs, transportation, advanced manufacturing, robotics, and beyond. I have published 35+ papers in top-tier conferences and journals, with multiple spotlight/oral presentations. I serve as Area Chair (AC) for NeurIPS and Senior Program Committee (SPC) for AAAI, 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!
  • [07/2026] I will be serving as an Senior Program Committee (SPC) for AAAI 2027!
  • [06/2026] AlphaOPT and DyNACO are accepted as Oral presentations at KDD 2026.
  • [05/2026] 🔥 Our LLM RFT method, METIS, is officially deployed in the Amazon Nova Forge AWS service! 🎉 Our METIS bypasses handcrafted heuristics, establishing a simple, closed-loop, and highly efficient curriculum internalization paradigm.
  • [05/2026] Two papers got accepted by KDD 2026! 1) DyNACO, learning to provide dynamic guidance for large-scale ant colony optimization; and 2) AlphaOPT, pioneering a self-improving experience library that dramatically boosts LLMs for COP auto-formulation!
  • [04/2026] One paper regarding MILP representation has been accepted by ICML 2026.
  • [04/2026] I was invited to give a Guest Lecture to MIT 1.041 Transportation Foundations and Methods class with the topic “Optimization in the Era of AI” (see slides here).
  • [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 RL4CO 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 RL4CO - 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 July 2026.)