Hengzhe Zhang

Hengzhe Zhang

Victoria University of Wellington

Hi, I am Hengzhe Zhang. I am currently a Postdoctoral Research Fellow in Artificial Intelligence at the Evolutionary Computation Research Group (ECRG), School of Engineering and Computer Science, Victoria University of Wellington. I obtained my Ph.D. in Artificial Intelligence from Victoria University of Wellington in 2025 under the supervision of Prof. Mengjie Zhang and Prof. Bing Xue. My research mainly focuses on Interpretable Machine Learning, Explainable AI, and AI for Business/Healthcare, with contributions in:

  • Interpretable & Explainable AI: I develop transparent, human-readable AI models grounded in symbolic regression and automated feature engineering, with publications in top venues including ICML, ICLR (Spotlight), and IEEE TEVC (ABS/AJG 4 ×7 as first author).
  • Automated Machine Learning: I design evolutionary and LLM-based methods for automated feature construction and ensemble learning, maintaining open-source tools with 150+ community stars (Evolutionary Forest).
  • AI for Business/Healthcare: I have applied interpretable ML to financial services and healthcare, developing auditable decision support systems for business analytics and operations.

张恒哲，现为新西兰惠灵顿维多利亚大学人工智能博士后研究员，2025年获人工智能博士学位。研究方向为可解释机器学习、可解释人工智能与商业/医疗健康智能，在 ICML、ICLR Spotlight、IEEE TEVC（ABS/AJG 4，第一作者×7）等期刊与会议发表多篇论文，并维护 150+ stars 的开源项目 Evolutionary Forest。

🔥 News

  • 2026.05:  One ICML’26 paper on contrastive symbolic regression has been accepted.
  • 2026.06:  Two PPSN’26 papers on symbolic regression and genetic programming have been accepted.
  • 2026.02:  One AAAI’26 paper on pathology VLMs has been accepted (co-first author).
  • 2025.09:  Started as Postdoctoral Research Fellow at Victoria University of Wellington.
  • 2025.05:  One ICLR’25 paper has been accepted as Spotlight (RAG-SR: Retrieval-Augmented Generation for Neural Symbolic Regression).

📝 Publications

You can also find my articles on Google Scholar.

Selected Publications

  • ICML'26 Contrastive Symbolic Regression: Aligned Representations, Adaptive Prediction, and Diverse Ensembles, Hengzhe Zhang, et al.
  • IEEE TEVC'26 LLM-Meta-SR: In-Context Learning for Evolving Selection Operators in Symbolic Regression, Hengzhe Zhang, et al. (ABS/AJG 4, IF: 12)
  • PPSN'26 Adaptive Protection for Evolutionary Feature Construction in Symbolic Regression with Application to Credit Classification, Hengzhe Zhang, Qi Chen, Bing Xue, Lean Yu, Wolfgang Banzhaf, Mengjie Zhang (CORE A)
  • PPSN'26 Benchmarking Zero-Shot LLM-Generated Parent Selection in Genetic Programming for Symbolic Regression, Hengzhe Zhang, Qi Chen, Bing Xue, Wolfgang Banzhaf, Mengjie Zhang (CORE A)
  • ICLR'25 Spotlight RAG-SR: Retrieval-Augmented Generation for Neural Symbolic Regression, Hengzhe Zhang, et al.
  • AAAI'26 Patho-AgenticRAG: Towards Multimodal Agentic Retrieval-Augmented Generation for Pathology VLMs via Reinforcement Learning, Wenchuan Zhang, Jingru Guo, Hengzhe Zhang (co-first author), et al.
  • IEEE TEVC'25 LAS-GP: Harnessing Large Semantic Libraries for Evolutionary Feature Construction in Symbolic Regression, Hengzhe Zhang, et al. (ABS/AJG 4, IF: 12)
  • IEEE TEVC'25 Enhancing Generalization in Evolutionary Feature Construction for Symbolic Regression through Vicinal Jensen Gap Minimization, Hengzhe Zhang, et al. (ABS/AJG 4, IF: 12)
  • IEEE TEVC'24 Modular multi-tree genetic programming for evolutionary feature construction for regression, Hengzhe Zhang, et al. (ABS/AJG 4, IF: 12)
  • IEEE TEVC'23 SR-Forest: A Genetic Programming-based Heterogeneous Ensemble Learning Method, Hengzhe Zhang, Aimin Zhou, Qi Chen, Bing Xue, Mengjie Zhang (ABS/AJG 4, IF: 12)
  • SWEVO'22 PS-Tree: A Piecewise Symbolic Regression Tree, Hengzhe Zhang, Aimin Zhou, Hong Qian, Hu Zhang (IF: 8.2)
  • IEEE TEVC'21 An Evolutionary Forest for Regression, Hengzhe Zhang, Aimin Zhou, Hu Zhang (ABS/AJG 4, IF: 12)
  • GECCO'24 Bias-Variance Decomposition: An Effective Tool to Improve Generalization of Genetic Programming-based Evolutionary Feature Construction for Regression, Hengzhe Zhang, et al. (CORE A)
  • PPSN'24 P-Mixup: Improving Generalization Performance of Evolutionary Feature Construction with Pessimistic Vicinal Risk Minimization, Hengzhe Zhang, et al. (CORE A)

Refereed Journal Articles

  • Hengzhe Zhang, et al. “LLM-Meta-SR: In-Context Learning for Evolving Selection Operators in Symbolic Regression.” IEEE Transactions on Evolutionary Computation, 2026. (IF: 12, ABS/AJG 4)
  • Hengzhe Zhang, et al. “LAS-GP: Harnessing Large Semantic Libraries for Evolutionary Feature Construction in Symbolic Regression.” IEEE Transactions on Evolutionary Computation, 2025. (IF: 12, ABS/AJG 4)
  • Hengzhe Zhang, et al. “Enhancing Generalization in Evolutionary Feature Construction for Symbolic Regression through Vicinal Jensen Gap Minimization.” IEEE Transactions on Evolutionary Computation, 2025. (IF: 12, ABS/AJG 4)
  • Hongbo Zhao, Hengzhe Zhang, et al. “SRLinear: Lightweight Long-Term Time Series Forecasting via Symbolic Regression.” IEEE Transactions on Artificial Intelligence, 2025.
  • Hengzhe Zhang, et al. “Adaptive complexity knee point selection in multi-objective genetic programming for improving generalization of evolutionary feature construction in regression.” Genetic Programming and Evolvable Machines, 26.2 (2025): 28. (IF: 1.7)
  • Hengzhe Zhang, et al. “Modular multi-tree genetic programming for evolutionary feature construction for regression.” IEEE Transactions on Evolutionary Computation, 2024. (IF: 12, ABS/AJG 4)
  • Hengzhe Zhang, et al. “A semantic-based hoist mutation operator for evolutionary feature construction in regression.” IEEE Transactions on Evolutionary Computation, 2024. (IF: 12, ABS/AJG 4)
  • Hengzhe Zhang, et al. “A geometric semantic macro-crossover operator for evolutionary feature construction in regression.” Genetic Programming and Evolvable Machines, 25.1 (2024): 2. (IF: 1.7)
  • Hengzhe Zhang, Aimin Zhou, Qi Chen, Bing Xue, and Mengjie Zhang. “SR-Forest: A Genetic Programming-based Heterogeneous Ensemble Learning Method.” IEEE Transactions on Evolutionary Computation, 2023. (IF: 12, ABS/AJG 4)
  • Hengzhe Zhang, et al. “MAP-Elites for Genetic Programming-Based Ensemble Learning: An Interactive Approach [AI-eXplained].” IEEE Computational Intelligence Magazine, 18.4 (2023): 62-63. (IF: 11.2)
  • Hengzhe Zhang, Aimin Zhou, Hong Qian, and Hu Zhang. “PS-Tree: A Piecewise Symbolic Regression Tree.” Swarm and Evolutionary Computation, Elsevier, 2022. (IF: 8.2)
  • Hengzhe Zhang, Aimin Zhou, and Hu Zhang. “An Evolutionary Forest for Regression.” IEEE Transactions on Evolutionary Computation, 2021. (IF: 12, ABS/AJG 4)
  • Hengzhe Zhang, Aimin Zhou, and Xin Lin. “Interpretable policy derivation for reinforcement learning based on evolutionary feature synthesis.” Complex & Intelligent Systems, 6.3 (2020): 741-753. (IF: 5.0)

Refereed Conference Articles

  • Hengzhe Zhang, et al. “Contrastive Symbolic Regression: Aligned Representations, Adaptive Prediction, and Diverse Ensembles.” International Conference on Machine Learning (ICML), 2026. (CORE A*)
  • Wenchuan Zhang, Jingru Guo, Hengzhe Zhang (co-first author), et al. “Patho-AgenticRAG: Towards Multimodal Agentic Retrieval-Augmented Generation for Pathology VLMs via Reinforcement Learning.” AAAI, 2026. (CORE A*)
  • Hengzhe Zhang, et al. “RAG-SR: Retrieval-Augmented Generation for Neural Symbolic Regression.” ICLR, 2025. (Spotlight, CORE A*)
  • Hengzhe Zhang, Qi Chen, Bing Xue, Lean Yu, Wolfgang Banzhaf, and Mengjie Zhang. “Adaptive Protection for Evolutionary Feature Construction in Symbolic Regression with Application to Credit Classification.” PPSN, 2026. (CORE A)
  • Hengzhe Zhang, Qi Chen, Bing Xue, Wolfgang Banzhaf, and Mengjie Zhang. “Benchmarking Zero-Shot LLM-Generated Parent Selection in Genetic Programming for Symbolic Regression.” PPSN, 2026. (CORE A)
  • Hengzhe Zhang, et al. “Micro-Step Time-Series Regression: Insights from System Identification Using Symbolic Regression.” EuroGP, 2025. (CORE B)
  • Hengzhe Zhang, et al. “A General Feature-Informed Crossover for Two-Stage Feature Selection in Symbolic Regression.” IEEE CEC, 2025. (CORE B)
  • Hengzhe Zhang, et al. “P-Mixup: Improving Generalization Performance of Evolutionary Feature Construction with Pessimistic Vicinal Risk Minimization.” PPSN, 2024. (CORE A)
  • Hengzhe Zhang, et al. “Bias-Variance Decomposition: An Effective Tool to Improve Generalization of Genetic Programming-based Evolutionary Feature Construction for Regression.” GECCO, 2024. (CORE A)
  • Hengzhe Zhang, et al. “Improving Generalization of Evolutionary Feature Construction with Minimal Complexity Knee Points in Regression.” EuroGP, 2024. (CORE B)
  • Hengzhe Zhang, Qi Chen, Alberto Tonda, Bing Xue, Wolfgang Banzhaf, and Mengjie Zhang. “MAP-Elites with Cosine-Similarity for Evolutionary Ensemble Learning.” EuroGP, 2023. (CORE B)
  • Hengzhe Zhang, Qi Chen, Bing Xue, Wolfgang Banzhaf, and Mengjie Zhang. “A Double Lexicase Selection Operator for Bloat Control in Evolutionary Feature Construction for Regression.” GECCO, 2023. (CORE A)
  • Hengzhe Zhang, et al. “Automatically Choosing Selection Operator Based on Semantic Information in Evolutionary Feature Construction.” PRICAI, 2023. (CORE B)
  • Hengzhe Zhang, Aimin Zhou. “RL-GEP: Symbolic Regression via Gene Expression Programming and Reinforcement Learning.” IJCNN, 2021. (CORE B)

🎖 Honors and Awards

  • 2024.07 First Prize, GECCO Interpretable Control Competition
  • 2023.07 First Prize, GECCO Interpretable Symbolic Regression Competition
  • 2022.05 Honorable Mention, CVPR NAS Performance Prediction Track (4th out of 48)
  • 2022 Distinguished Graduate of Shanghai (上海市优秀毕业生)
  • 2020.10 Wisdom Scholarship, East China Normal University (华东师范大学智慧奖学金)
  • 2020.10 Second Prize, China Graduate Mathematical Modeling Contest (中国研究生数学建模竞赛, Top 15%)
  • 2020.09 First Prize, ZA Insurance Hackathon, Machine Learning Track (众安保险黑客马拉松·机器学习赛道, 1st out of 71, 报道)
  • 2018.05 National First Prize, Blue Bridge Cup JAVA Track (蓝桥杯全国软件和信息技术专业人才大赛·Java B组, Top 5%)
  • 2017.10 Bronze Award, ACM-ICPC Xi’an Regional Competition

📖 Educations

  • 2022.01 – 2025, Ph.D. in Artificial Intelligence, Victoria University of Wellington, New Zealand. Supervisors: Prof. Mengjie Zhang, Prof. Bing Xue.
  • 2019.09 – 2022.06, M.Sc. in Computer Science, East China Normal University, China. Supervisor: Prof. Aimin Zhou. Double First-Class University (QS Asian University Ranking: 64).
  • 2015.09 – 2019.06, B.Sc. in Software Engineering, Xiangtan University, China. Double First-Class University (QS Asian University Ranking: 307).

💻 Open Source Projects

  • Evolutionary Forest (150+ Stars, IEEE TEVC 2021/2023/2025, ICLR 2025) — An interpretable automated feature engineering framework using evolutionary algorithms and large language models.
  • PS-Tree (30+ Stars, SWEVO 2022) — A toolkit for interpretable, piecewise symbolic regression.
  • scikit-obliquetree (15+ Stars) — An oblique decision tree framework with three oblique decision tree algorithms.