👋 About Me

Hello! This is Enneng Yang. I’m currently a Postdoctoral Researcher at Shenzhen Campus of Sun Yat-sen University, China, advised by Assoc. Prof. Li Shen. Before that, I received the PhD degree (June 2025) from Northeastern University, China, advised by Prof. Guibing Guo. From March 2024 to March 2025, I am a visiting Ph.D. student in Prof. Jie Zhang’s group at Nanyang Technological University, Singapore.

My research interests lie in large language models, machine learning, and recommender systems. More specifically, I focus on:

• Large Language Model: continual pretraining/finetuning, LLM self-evolution, knowledge editing
• Machine Learning: model merging, multi-task learning, continual/incremental learning, data-free learning, dataset/knowledge distillation
• Recommendation System: multi-task/multi-scenario recommendation, sequential recommendation, OOD recommendation

🔥 Our team is seeking self-motivated students (including remote internships, undergraduates, graduate students, and other candidates) to join research on LLMs, continual learning, and model merging, with the goal of publishing high-quality academic papers. If interested, please email me your resume.

👏 News

• 2026.05: Several of our papers have been accepted by ICML 2026.
• 2026.05: Our survey paper on data augmentation has been accepted by TKDE 2026.
• 2026.04: Our paper on POI recommendation has been accepted by SIGIR 2026.
• 2026.02: Our paper on sequential recommendation has been accepted by TPAMI 2026.
• 2026.01: Two of our papers on model merging have been accepted by ICLR 2026.
• 2025.12: Our survey on model merging has been accepted by ACM Computing Surveys (CSUR) 2026.
• 2025.11: Our paper on model merging benchmark has been accepted by JMLR 2025.
• 2025.11: Our paper on long-sequence recommendation has been accepted by AAAI 2026.
• 2025.10: Our paper on model merging has been accepted by TPAMI 2025.
• 2025.09: Two of our papers on continual model merging have been accepted by NeurIPS 2025.
• 2025.05: Our paper on knowledge editing has been accepted by ACL (Main) 2025.
• 2025.05: Our paper on model merging has been accepted by ICML 2025.
• 2025.04: Our paper on explainable recommendation has been accepted by TOIS 2025.
• 2025.04: Two of our papers on sequential recommendation have been accepted by SIGIR 2025.
• 2025.02: Our paper on out-of-distribution recommendation has been accepted by TOIS 2025.
• 2025.01: Our paper on flatness-aware continual learning has been accepted by TPAMI 2025.
• 2025.01: Two of our papers on out-of-distribution recommendation have been accepted by WWW 2025.
• 2024.12: Two of our papers on LLM fine-tuning and sequential recommendation have been accepted by AAAI 2025.
• 2024.11: Our paper on recommendation unlearning has been accepted by TOIS 2024.
• 2024.11: Our survey on forgetting in deep learning has been accepted by TPAMI 2024.
• 2024.09: Our paper on continual learning has been accepted by TPAMI 2024.
• 2024.05: Our paper on model merging has been accepted by ICML 2024.
• 2024.01: Our paper on model merging has been accepted by ICLR 2024.
• 2023.10: Our paper on sequential recommendation has been accepted by TKDE 2023.
• 2023.09: Our paper on dataset condensation has been accepted by NeurIPS 2023.
• 2023.07: Our paper on flatness-aware continual learning has been accepted by ICCV 2023.

More

✨ Repositories

Comments and contributions are welcome.

• Awesome-Model-Merging-Methods-Theories-Applications
  This repository collects the latest research on Model Merging in Machine Learning.
• Awesome-Forgetting-in-Deep-Learning
  This repository collects the latest research on Catastrophic Forgetting in Deep Learning.
• FusionBench
  This repository integrates the latest model merging algorithm.

📝 Selected Preprints and Publications

Survey or Benchmark Papers

• Model Merging in LLMs, MLLMs, and Beyond: Methods, Theories, Applications and Opportunities
  ACM Computing Surveys 2026 | Repository
  Enneng Yang, Li Shen, Guibing Guo, Xingwei Wang, Xiaochun Cao, Jie Zhang, Dacheng Tao.

• Data Augmentation for Sequential Recommendation: A Survey
  TKDE 2026 | Repository
  Yizhou Dang, Enneng Yang, Yuting Liu, Guibing Guo, Linying Jiang, Jianzhe Zhao, Xingwei Wang.

• FusionBench: A Unified Library and Comprehensive Benchmark for Deep Model Fusion
  JMLR 2025 | Code
  Anke Tang, Li Shen, Yong Luo, Enneng Yang, Han Hu, Lefei Zhang, Bo Du, Dacheng Tao.

• A Comprehensive Survey of Forgetting in Deep Learning Beyond Continual Learning
  TPAMI 2024 | Repository
  Zhenyi Wang, Enneng Yang, Li Shen, Heng Huang.

Conference Papers

• MergOPT: A Merge-Aware Optimizer for Robust Model Merging
  ICLR 2026| Code
  Enneng Yang, Qun Yang, Peng Wang, Anke Tang, Guibing Guo, Xiaochun Cao, Li Shen.

• OptMerge: Unifying Multimodal LLM Capabilities and Modalities via Model Merging
  ICLR 2026 | Code
  Yongxian Wei, Runxi Cheng, Weike Jin, Enneng Yang, Li Shen, Lu Hou, Sinan Du, Chun Yuan, Xiaochun Cao, Dacheng Tao.

• Multi-Perspective Driven Expected Location Preferences for Next POI Recommendations
  SIGIR 2026 | Code
  Pengxiang Lan, Enneng Yang, Yuliang Liang, Jianzhe Zhao, Guibing Guo, Hai Zhao.

• Interest-Shift-Aware Logical Reasoning for Efficient Long-Sequence Recommendation
  AAAI 2026 | Code
  Fei Li, Qingyun Gao, Enneng Yang, Jianzhe Zhao, Guibing Guo.

• Continual Model Merging without Data: Dual Projections for Balancing Stability and Plasticity
  NeurIPS 2025 | Code
  Enneng Yang, Anke Tang, Li Shen, Guibing Guo, Xingwei Wang, Xiaochun Cao, Jie Zhang.

• Merging on the Fly Without Retraining: A Sequential Approach to Scalable Continual Model Merging
  NeurIPS 2025 | Code
  Anke Tang, Enneng Yang, Li Shen, Yong Luo, Han Hu, Lefei Zhang, Bo Du, Dacheng Tao.

• Knowledge Decoupling via Orthogonal Projection for Lifelong Editing of Large Language Models
  ACL 2025
  Haoyu Xu, Pengxiang Lan, Enneng Yang, Guibing Guo, Jianzhe Zhao, Linying Jiang, Xingwei Wang.

• Representation Surgery in Model Merging with Probabilistic Modeling
  ICML 2025 | Code
  Qi Wei, Shuo He, Enneng Yang, Tingcong Liu, Haobo Wang, Lei Feng, Bo An.

• Denoising Multi-Interest-Aware Logical Reasoning for Long-Sequence Recommendation
  SIGIR 2025 (Oral) | Code
  Fei Li, Qingyun Gao, Yizhou Dang, Enneng Yang, Guibing Guo, Jianzhe Zhao and Xingwei Wang.

• Data Augmentation as Free Lunch: Exploring the Test-Time Augmentation for Sequential Recommendation
  SIGIR 2025(Oral) | Code
  Yizhou Dang, Yuting Liu, Enneng Yang, Minhan Huang, Guibing Guo, Jianzhe Zhao and Xingwei Wang.

• Distributionally Robust Graph Out-of-Distribution Recommendation via Diffusion Model
  WWW 2025 | Code
  Chu Zhao, Enneng Yang, Yuliang Liang, Jianzhe Zhao, Guibing Guo, Xingwei Wang.

• Graph Representation Learning via Causal Diffusion for Out-of-Distribution Recommendation
  WWW 2025 (Oral) | Code
  Chu Zhao, Enneng Yang, Yuliang Liang, Pengxiang Lan, Yuting Liu, Jianzhe Zhao, Guibing Guo, Xingwei Wang.

• Efficient Prompt Tuning by Multi-Space Projection and Prompt Fusion
  AAAI 2025 (Oral) | Appendix
  Pengxiang Lan, Enneng Yang, Yuting Liu, Guibing Guo, Linying Jiang, Jianzhe Zhao, Xingwei Wang.

• Augmenting Sequential Recommendation with Balanced Relevance and Diversity
  AAAI 2025 (Oral) | Appendix Code
  Yizhou Dang, Jiahui Zhang, Yuting Liu, Enneng Yang, Yuliang Liang, Guibing Guo, Jianzhe Zhao, Xingwei Wang.

• Representation Surgery for Multi-Task Model Merging
  ICML 2024 | Code
  Enneng Yang, Li Shen, Zhenyi Wang, Guibing Guo, Xiaojun Chen, Xingwei Wang, Dacheng Tao.

• AdaMerging: Adaptive Model Merging for Multi-Task Learning
  ICLR 2024 | Code
  Enneng Yang, Zhenyi Wang, Li Shen, Shiwei Liu, Guibing Guo, Xingwei Wang, Dacheng Tao.

• An Efficient Dataset Condensation Plugin and Its Application to Continual Learning
  NeurIPS 2023 | Code
  Enneng Yang, Li Shen, Zhenyi Wang, Tongliang Liu, Guibing Guo.

• Data Augmented Flatness-aware Gradient Projection for Continual Learning
  ICCV 2023 | Appendix Code
  Enneng Yang, Li Shen, Zhenyi Wang, Shiwei Liu, Guibing Guo, Xingwei Wang.

• AdaTask: A Task-aware Adaptive Learning Rate Approach to Multi-task Learning
  AAAI 2023 (Oral) | Appendix Code
  Enneng Yang, Junwei Pan, Ximei Wang, Haibin Yu, Li Shen, Xihua Chen, Lei Xiao, Jie Jiang, Guibing Guo.

• Uniform Sequence Better: Time Interval Aware Data Augmentation for Sequential Recommendation
  AAAI 2023 (Oral) | Code
  Yizhou Dang, Enneng Yang, Guibing Guo, Linying Jiang, Xingwei Wang, Xiaoxiao Xu, Qinghui Sun, Hong Liu.

Journal Papers

• Exploring and Tailoring the Test-Time Augmentation for Sequential Recommendation
  TPAMI 2026 | Code
  Yizhou Dang, Enneng Yang, Yuting Liu, Jianzhe Zhao, Xingwei Wang, and Guibing Guo.

• Efficient and Effective Weight-Ensembling Mixture of Experts for Multi-Task Model Merging
  TPAMI 2025 | Code
  Li Shen, Anke Tang, Enneng Yang$^{\dagger}$ ($^{\dagger}$ indicates corresponding authors), Guibing Guo, Yong Luo$^{\dagger}$, Lefei Zhang, Xiaochun Cao, Bo Du$^{\dagger}$, and Dacheng Tao.

• Preference Logical Reasoning with Preference Operators for Explainable Recommendations
  TOIS 2025 | Code
  Fei Li, Enneng Yang, Guibing Guo, Linying Jiang, Jianzhe Zhao, and Xingwei Wang.

• Symmetric Graph Contrastive Learning against Noisy Views for Recommendation
  TOIS 2025 | Code
  Chu Zhao, Enneng Yang, Yuliang Liang, Jianzhe Zhao, Guibing Guo, and Xingwei Wang.

• Revisiting Flatness-aware Optimization in Continual Learning with Orthogonal Gradient Projection
  TPAMI 2025 | Code
  Enneng Yang, Li Shen, Zhenyi Wang, Shiwei Liu, Guibing Guo, Xingwei Wang, and Dacheng Tao.

• Continual Learning From a Stream of APIs
  TPAMI 2024
  Enneng Yang, Zhenyi Wang, Li Shen, Nan Yin, Tongliang Liu, Guibing Guo, Xingwei Wang, Dacheng Tao.

• Efficient and Adaptive Recommendation Unlearning: A Guided Filtering Framework to Erase Outdated Preferences
  TOIS 2024 | Code
  Yizhou Dang, Yuting Liu, Enneng Yang, Guibing Guo, Linying Jiang, Jianzhe Zhao, Xingwei Wang.

• TiCoSeRec: Augmenting Data to Uniform Sequences by Time Intervals for Effective Recommendation
  TKDE 2023 | Code
  Yizhou Dang, Enneng Yang, Guibing Guo, Linying Jiang, Xingwei Wang, Xiaoxiao Xu, Qinghui Sun, Hong Liu.

📖 Educations

• 2024.03 - 2025.03: Visiting Ph.D. Student at Nanyang Technological University, Singapore.
• 2021.09 - 2025.06: Ph.D. Student at Northeastern University, China.

💻 Internships

• 2023.05 - 2023.09: Intern at Digital China Group Co., Ltd.
• 2022.01 - 2022.06: Research Intern at Tencent Inc, mentored by Junwei Pan.
• 2020.05 - 2021.02: Research Intern at Tencent Inc, mentored by Junwei Pan and Dr. Xiaoqing Cao.

🏆 Honors and Awards

• 2025.01: Youth Talents Support Project - Doctoral Student Special Program (First Session)
• 2024.10: National Scholarship (Top 1%)
• 2023.10: National Scholarship (Top 1%)
• 2020.05: Tencent Rhino-Bird Elite Talent Training Program (51 People Worldwide)
• 2019.10: National Scholarship (Top 1%)
• 2017.10: National Scholarship (Top 1%)

🔖 Services

• Conference Reviewers: ICML 2026, ACL 2026, CVPR 2026, ICLR 2026, AAAI 2026, NeurIPS 2025, ICCV 2025, ICML 2025, WWW 2025, ICLR 2025, AAAI 2025, NeurIPS 2024, ICML 2024, KDD 2024, AAAI 2024, WWW 2023, WSDM 2023
• Journal Reviewers: TKDE 2026, EAAI 2025, ML 2025, TMLR 2025, TSC 2024, TBD 2024, TCSVT 2024, NCAA 2024, TORS 2022