CV

Education

• Ph.D. in Computer Science, University of Illinois Chicago, 2025 – present
  • Advisor: Prof. Lu Cheng; Collaborator: Prof. Kun Zhang (CMU)
  • Research: Causal Representation Learning, Reinforcement Learning, LLM Alignment, AI for Science
• B.Eng. in Automation (Control & Robotics), Zhejiang University, Chu Kochen Honors College, 2021 – 2025
  • GPA: 3.98/4.0, Rank: 6/121 (Top 5%), Outstanding Graduate

Publications

• Shicheng Fan, Kun Zhang, Lu Cheng. “TRACE: Trajectory Recovery for Continuous Mechanism Evolution in Causal Representation Learning.” In Proceedings of the International Conference on Machine Learning (ICML), 2026. arXiv:2601.21135
• Hanrong Zhang, Shicheng Fan, Henry Peng Zou, Yankai Chen, Zhenting Wang, Jiayu Zhou, Chengze Li, Wei-Chieh Huang, Yifei Yao, Kening Zheng, Xue Liu, Xiaoxiao Li, Philip S. Yu. “CoEvoSkills: Self-Evolving Agent Skills via Co-Evolutionary Verification.” Under Review; manuscript arXiv:2604.01687. Co-first author.

Research Experience

• CoEvoSkills: Self-Evolving Agent Skills · Co-first author · 2026
  • Studied autonomous skill generation with a co-evolutionary framework coupling a Skill Generator and a Surrogate Verifier
  • Contributed to SkillsBench evaluation and cross-model generalization analysis
• TRACE: CRL under Continuous Mechanism Evolution · First Author · 2025–2026
  • Proposed the first theoretical framework extending causal representation learning from discrete mechanism shifts to continuous transitions; proved joint identifiability via nonlinear ICA and variational inference
  • Designed the TRACE framework based on Mixture-of-Experts, recovering mechanism trajectories via time-varying mixture coefficients, generalizable to unseen intermediate states
• Knowledge-Enhanced RL for LLM Factuality Alignment · Co-1st Author · 2025–present
  • Designing an end-to-end LLM post-training pipeline combining SFT and RL for improved factuality alignment
• Causal Representation Learning for Protein Conformational Transitions · 1st Author · 2025–present
  • Applying causal representation learning to molecular dynamics for identifying key residues driving protein conformational transitions
• Semantic and Goal-Driven Dexterous Hand Manipulation · B.Sc. Thesis · 2024–2025
  • Reproduced the Text2HOI three-stage framework, matching reported performance on H2O, GRAB, and ARCTIC datasets
  • Proposed a Shadow Hand → MANO cross-embodiment retargeting algorithm; joint training with GraspM3 dataset improved Physical Realism to 0.897 and reduced penetration volume by ~50%

Competitions

• ASC Student Supercomputer Challenge, National Second Prize, 2024
• ACM-IPCC International Parallel Computing Challenge, National Second Prize, 2022

Skills

• Programming: Python, C/C++
• Frameworks & Tools: PyTorch, HuggingFace Transformers, verl/TRL, Git, LaTeX, Linux/HPC (NCSA Delta, NRP Nautilus), CUDA, MPI
• Research Areas: Causal Inference & Causal Representation Learning, LLM Alignment & Anti-Hallucination, Diffusion Models, Embodied Intelligence
• Languages: Chinese (native), English (fluent)

Awards & Honors

• Outstanding Graduate, Zhejiang University, 2025
• Academic Excellence Model, Zhejiang University, 2022, 2023, 2024
• Outstanding Student in AI&ML Summer Project, University of Cambridge, 2023
• National Second Prize, Advanced Mathematics Competition of China, 2022

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