Cong Zhang (张聪)

About Me:

Cong Zhang is a Ph.D. from the School of Computer Science and Engineering (SCSE) Nanyang Technological University Singapore (NTU, Singapore), jointly supervised by Prof. Zhang Jie, Dr. Tan Puay Siew, and Dr.Xu Chi. During his Ph.D. study, he held the Singapore International Graduate Award (SINGA), a full-time scholarship offered by the Singapore Government to the top international students worldwide to study at a Singapore University. Before joining NTU, Cong Zhang was fortunate to work with Dr. Krysia Broda (from Imperial College London) and Dr. Fu-lai Chung (from The Hong Kong Polytechnic University). They initialized his research career by mentoring and teaching him research skills.

Education:

  • 【2022】Ph.D., Computer Science, Nanyang Technological University, SG
  • 【2017】Master of Science, Merit, Computing Science, Imperial College London, UK
  • 【2015】Bachelor of Science, 1st-Class, Mathematics, University of Liverpool, UK
  • 【2015】Bachelor of Science, 1st-Class, Applied Mathematics, Xi'an Jiaotong-Liverpool University, CN

Cong Zhang's research interest mainly lies in the intersection of Artificial Intelligence and Operations Research. He is particularly interested in leveraging Deep Reinforcement Learning (DRL) to solve challenging combinatorial optimization problems in various application domains, such as the job-shop scheduling problem (JSSP) and the vehicle routing problem (VRP). His Ph.D. study is mainly devoted to developing DRL-based construction and improvement heuristic algorithms for solving JSSP.

Research Keywords:

  • Deep Reinforcement Learning (DRL)
  • Combinatorial Optimization Problems (COP)
  • Reinforcement Learning for LLM

Email: cong.zhang92@gmail.com

Google Scholar Github Linkedin Academic CV

News

  • 【2025.6】 One paper accepted by TMLR
  • 【2025.5】 Two papers accepted by ACL 2025
  • 【2025.3】 One paper accepted by ICLR 2025
  • 【2025.2】 One paper accepted by COLING 2025

Awards

  • 【2019】Certified to teach, Nanyang Technological University
  • 【2018】Singapore International Graduate Award (SINGA), PhD scholarship provided by the SG Government

Working Experiences

  • 【2024.08 - Present】 LLM Algorithm Research Scientist, AIIC/TikTok, Singapore
  • 【2022.11 - 2024.08】 Research Engineer, Huawei Noah's Ark Lab, Singapore
  • 【2017.11 - 2018.08】 Research Assistant, Hong Kong Polytechnic University, HongKong

Conference Papers

Click on tags to filter papers. Multiple tags can be selected to narrow down results (e.g., click [COP] then [VRP] to see only VRP papers within COP)
* denotes equal contribution, # denotes corresponding author

[NLP][LLM] CtrlA: Adaptive Retrieval-Augmented Generation via Probe-Guided Control

Huanshuo Liu, Hao Zhang, Zhijiang Guo, Kuicai Dong, Xiangyang Li, Yi Quan Lee, Cong Zhang, Yong Liu

The Annual Meeting of the Association for Computational Linguistics (ACL), 2025

paper / code

[NLP][LLM] Adaptive Tool Use in Large Language Models with Meta-Cognition Trigger

Wenjun Li, Dexun Li, Kuicai Dong, Cong Zhang, Hao Zhang, Weiwen Liu, Yasheng Wang, Ruiming Tang, Yong Liu

The Annual Meeting of the Association for Computational Linguistics (ACL), 2025

paper / code

[COP][JSSP] Graph Assisted Offline-Online Deep Reinforcement Learning for Dynamic Workflow Scheduling

Yifan Yang, Gang Chen, Hui Ma, Cong Zhang#, Zhiguang Cao, Mengjie Zhang

International Conference on Learning Representations (ICLR), 2025

paper / code

[NLP][LLM] Planning with Multi-Constraints via Collaborative Language Agents

Cong Zhang*, Derrick Goh Xin Deik*, Dexun Li, Hao Zhang, Yong Liu

The International Conference on Computational Linguistics (COLING), 2025

paper / code

[NLP][LLM] Aligning Crowd Feedback via Distributional Preference Reward Modeling

Dexun Li*, Cong Zhang#,*, Kuicai Dong, Derrick Goh Xin Deik, Ruiming Tang, Yong Liu

International Conference on Machine Learning (ICML) - MFHAIA Workshop, 2024

paper / code

[NLP][LLM] MC-indexing: Effective Long Document Retrieval via Multi-view Content-aware Indexing

Kuicai Dong, Derrick Goh Xin Deik, Yi Quan Lee, Hao Zhang, Xiangyang Li, Cong Zhang, Yong Liu

Conference on Empirical Methods in Natural Language Processing (EMNLP), 2024

paper / code

[COP][JSSP] Learning topological representations with bidirectional graph attention network for solving job shop scheduling problem

Cong Zhang, Zhiguang Cao, Yaoxin Wu, Wen Song, Jing Sun

The Conference on Uncertainty in Artificial Intelligence (UAI), 2024

paper / code

[COP][JSSP] Deep reinforcement learning guided improvement heuristic for job shop scheduling

Cong Zhang, Zhiguang Cao, Wen Song, Yaoxin Wu, Jie Zhang

International Conference on Learning Representations (ICLR), 2024

paper / code

[COP][JSSP] Learning to dispatch for job shop scheduling via deep reinforcement learning

Cong Zhang*, Wen Song*, Zhiguang Cao, Jie Zhang, Pua Siew Tan, Chi Xu

Conference on Neural Information Processing Systems (NeurIPS), 2020

paper / code

Journal Articles

Click on tags to filter papers. Multiple tags can be selected to narrow down results (e.g., click [COP] then [VRP] to see only VRP papers within COP)
* denotes equal contribution, # denotes corresponding author

[ML][DRL] Collaboration with Dynamic Open Ad Hoc Team via Team State Modelling

Jing Sun, Cong Zhang#, Zhiguang Cao

Transactions on Machine Learning Research (TMLR), 2025

paper / code

[COP][JSSP] Graph Neural Networks for Job Shop Scheduling Problems: A Survey

Igor G Smit, Jianan Zhou, Robbert Reijnen, Yaoxin Wu, Jian Chen, Cong Zhang, Zaharah Bukhsh, Wim Nuijten, Yingqian Zhang

Computers & Operations Research (CAOR), 2024

paper / code

[ML][DRL] Decision-making with speculative opponent models

Jing Sun, Shuo Chen, Cong Zhang, Yining Ma, Jie Zhang

IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2024

paper / code

[COP][JSSP] Fast pareto set approximation for multi-objective flexible job shop scheduling via parallel preference-conditioned graph reinforcement learning

Chupeng Su*, Cong Zhang*, Chuang Wang, Weihong Cen, Gang Chen, Longhan Xie

Swarm and Evolutionary Computation (SEC), 2024

paper / code

[COP][JSSP] A review on learning to solve combinatorial optimisation problems in manufacturing

Cong Zhang*, Yaoxin Wu*, Yining Ma*, Wen Song, Le Zhang, Zhiguang Cao, Jie Zhang

IET Collaborative Intelligent Manufacturing, 2023

paper / code

[COP][JSSP] Evolution strategies-based optimized graph reinforcement learning for solving dynamic job shop scheduling problem

Chupeng Su, Cong Zhang, Dan Xia, Baoan Han, Chuang Wang, Gang Chen, Longhan Xie

Applied Soft Computing (ASC), 2023

paper / code

[ML][DRL] Sampling efficient deep reinforcement learning through preference-guided stochastic exploration

Wenhui Huang, Cong Zhang, Jingda Wu, Xiangkun He, Jie Zhang, Chen Lv

IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2023

paper / code

[COP][VRP] Learning to solve multiple-TSP with time window and rejections via deep reinforcement learning

Rongkai Zhang*, Cong Zhang*, Zhiguang Cao, Wen Song, Puay Siew Tan, Jie Zhang, Bihan Wen, Justin Dauwels

IEEE Transactions on Intelligent Transportation Systems (T-ITS), 2022

paper / code