Cong Zhang (张聪)
Scholar

Cong Zhang (张聪)

Google Scholar ID: lSC-K24AAAAJ
TikTok, Noah’s Ark Lab, Nanyang Technological University
Deep Reinforcement LearningCombinatorial OptimizationLLM
Citations & Impact
All-time
Citations
851
 
H-index
11
 
i10-index
11
 
Publications
20
 
Co-authors
26
list available
Publications
20 items
Browse publications on Google Scholar (top-right) ↗
Resume (English only)
Academic Achievements
  • Publications:
  • - One paper accepted as NeurIPS 2025 Spotlight
  • - One paper accepted by TMLR
  • - Two papers accepted by ACL 2025
  • - One paper accepted by ICLR 2025
  • - 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
Research Experience
  • [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
Education
  • [2022] Ph.D., Computer Science, Nanyang Technological University, SG, Supervisors: Prof. Zhang Jie, Dr. Tan Puay Siew, and Dr. Xu Chi
  • [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
Background
  • His research interest mainly lies in the intersection of Artificial Intelligence and Operations Research, particularly 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.
Miscellany
  • Personal interests and hobbies are not mentioned in the provided information.