Can Jin

CBIM, Busch Campus can.jin@rutgers.edu

I’m a Computer Science Ph.D. student, starting in Fall 2024 at Rutgers University, New Brunswick, under the guidance of Professor Dimitris N. Metaxas. I hold both a Bachelor’s and a Master’s degree in Mathematics from the University of Science and Technology of China. Prior to my Ph.D., I spent two years as a Machine Learning Engineer at Meituan Dianping Corporation. My research interests include LLM reasoning/generalization/reliability, Efficient AI, and 3D/image/video/multimodal generation.

I am excited to announce that I will be joining Adobe Research as a Research Scientist Intern in Summer 2025.

I’m also open to collaborating on related projects. Please contact me via email if you share similar interests.

Research

🧠 Large Language Models

Reasoning | Generalization | Reliability
Developing advanced techniques to improve LLM capabilities, including:

Training innovations: Supervised fine-tuning (SFT), reinforcement learning with human/AI feedback (RLHF/RLAIF), Direct Preference Optimization (DPO)
Post-training refinement: Self-critique mechanisms, iterative self-refinement
Reliability: Robustness, Harmlessness, Helpfulness

Recent progress: NeurIPS 2024, submitted to ICML 2025

⚡ Efficient AI

Computational Efficiency | Effectiveness
Exploring methods to improve the efficiency of ML models, including:

Model compression via distillation/pruning
Prompt (engineering) for task adaptation

Recent progress: AAAI 2025, ICLR 2025

🎨 Multimodal Generation

3D | Video | Image | Multimodal generation
Exploring methods to generate 3D, video, image, and multimodal content using generative models. This is an ongoing research direction.

Academic Services

Teaching

24Fall: CS210: Data Management for Data Science
25Spring: CS534: Computer Vision

Peer Review

Reviewer: CVPR 2025, ICML 2024 Workshop, Alexandria Engineering Journal, Information Fusion, Pattern Recognition, Signal Processing

selected publications

NeurIPS

Learning from Teaching Regularization: Generalizable Correlations Should be Easy to Imitate

Can Jin^*, Tong Che^*, Hongwu Peng^†, Yiyuan Li^†, Dimitris Metaxas^‡, and Marco Pavone^‡

In Advances in Neural Information Processing Systems, 2024

Bib PDF Code Poster Website

@inproceedings{jin2024lot,
  author = {Jin, Can and Che, Tong and Peng, Hongwu and Li, Yiyuan and Metaxas, Dimitris and Pavone, Marco},
  booktitle = {Advances in Neural Information Processing Systems},
  editor = {Globerson, A. and Mackey, L. and Belgrave, D. and Fan, A. and Paquet, U. and Tomczak, J. and Zhang, C.},
  pages = {966--994},
  publisher = {Curran Associates, Inc.},
  title = {Learning from Teaching Regularization: Generalizable Correlations Should be Easy to Imitate},
  url = {https://proceedings.neurips.cc/paper_files/paper/2024/file/01ce1ae7f94d139e4917f9e4425a4f38-Paper-Conference.pdf},
  volume = {37},
  year = {2024},
}

ICLR

LoR-VP: Low-Rank Visual Prompting for Efficient Vision Model Adaptation

Can Jin , Ying Li , Mingyu Zhao, Shiyu Zhao, Zhenting Wang, Xiaoxiao He, Ligong Han, Tong Che, and Dimitris N. Metaxas

In The Thirteenth International Conference on Learning Representations, 2025

Bib PDF Code

@inproceedings{jin2025lorvp,
  title = {LoR-{VP}: Low-Rank Visual Prompting for Efficient Vision Model Adaptation},
  author = {Jin, Can and Li, Ying and Zhao, Mingyu and Zhao, Shiyu and Wang, Zhenting and He, Xiaoxiao and Han, Ligong and Che, Tong and Metaxas, Dimitris N.},
  booktitle = {The Thirteenth International Conference on Learning Representations},
  year = {2025},
  url = {https://openreview.net/forum?id=5btFIv2PNb},
}

AAAI

Visual Prompting Upgrades Neural Network Sparsification: A Data-Model Perspective

Can Jin^*, Tianjin Huang^*, Yihua Zhang, Mykola Pechenizkiy, Sijia Liu , Shiwei Liu, and Tianlong Chen

In The 39th Annual AAAI Conference on Artificial Intelligence, 2024

Bib PDF Code

@inproceedings{jin2024visual,
  title = {Visual Prompting Upgrades Neural Network Sparsification: A Data-Model Perspective},
  author = {Jin, Can and Huang, Tianjin and Zhang, Yihua and Pechenizkiy, Mykola and Liu, Sijia and Liu, Shiwei and Chen, Tianlong},
  booktitle = {The 39th Annual AAAI Conference on Artificial Intelligence},
  year = {2024},
  url = {https://arxiv.org/pdf/2312.01397},
}

WWW

APEER: Automatic Prompt Engineering Enhances Large Language Model Reranking

Can Jin^*, Hongwu Peng^*, Shiyu Zhao, Zhenting Wang, Wujiang Xu, Ligong Han , Jiahui Zhao, Kai Zhong, Sanguthevar Rajasekaran, and Dimitris N Metaxas

In Companion Proceedings of the ACM Web Conference 2025, Sydney, NSW, Australia, 2025

DOI Bib PDF Code

@inproceedings{jin2025apeer,
  title = {APEER: Automatic Prompt Engineering Enhances Large Language Model Reranking},
  author = {Jin, Can and Peng, Hongwu and Zhao, Shiyu and Wang, Zhenting and Xu, Wujiang and Han, Ligong and Zhao, Jiahui and Zhong, Kai and Rajasekaran, Sanguthevar and Metaxas, Dimitris N},
  booktitle = {Companion Proceedings of the ACM Web Conference 2025},
  location = {Sydney, NSW, Australia},
  series = {WWW '25},
  year = {2025},
  doi = {10.1145/3701716.3717574},
  isbn = {979-8-4007-1331-6/2025/04},
  url = {https://arxiv.org/abs/2406.14449},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  keywords = {Prompt engineering, Information Retrieval, Large Language Model, ReRanking},
}

WWW

RankFlow: A Multi-Role Collaborative Reranking Workflow Utilizing Large Language Models

Can Jin^*, Hongwu Peng^* , Anxiang Zhang , Nuo Chen , Jiahui Zhao, Xi Xie , Kuangzheng Li, Shuya Feng, Kai Zhong, Caiwen Ding, and Dimitris N Metaxas

In Companion Proceedings of the ACM Web Conference 2025, Sydney, NSW, Australia, 2025

DOI Bib PDF Code

@inproceedings{jin2025rankflow,
  title = {RankFlow: A Multi-Role Collaborative Reranking Workflow Utilizing Large Language Models},
  author = {Jin, Can and Peng, Hongwu and Zhang, Anxiang and Chen, Nuo and Zhao, Jiahui and Xie, Xi and Li, Kuangzheng and Feng, Shuya and Zhong, Kai and Ding, Caiwen and Metaxas, Dimitris N},
  booktitle = {Companion Proceedings of the ACM Web Conference 2025},
  location = {Sydney, NSW, Australia},
  series = {WWW '25},
  year = {2025},
  doi = {10.1145/3701716.3717575},
  isbn = {979-8-4007-1331-6/2025/04},
  url = {https://arxiv.org/abs/2502.00709},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  keywords = {Information Retrieval, Large Language Model, ReRanking},
}