Wei Jin’s Homepage

Hi there! I am an Assistant Professor in the Deparment of Computer Science at Emory University, with a secondary appointment in the Department of Biostatistics and Bioinformatics. I obtained my Ph.D. from Michigan State University in 2023 under the supervision of Prof. Jiliang Tang. Prior to that, I completed my B.E. degree at Zhejiang University in 2019.

I have notable accomplishments such as NIH R01 Award, INNS Doctoral Dissertation Runner-up Award, KAUST Rising Stars in AI, AAAI New Faculty Highlights, Snap Research Fellowship, Most Influential Papers in KDD and WWW by PaperDigest, and top finishes in three NeurIPS competitions. In addition, I regularly serve as organizers, (senior) PC members, and reviewers for multiple international conferences and journals in machine learning and data science such as ICLR, KDD, ICML, NeurIPS, AISTATS, AAAI, IJCAI, WWW, WSDM, CIKM, TPAMI, TKDE, TKDD and TNNLS.

[Recruiting Ph.D. students (Fall’26) and interns] I am actively seeking highly-motivated students for Ph.D. or Research Intern positions. If you are interested, please send me your CV, transcripts, and brief descriptions about why you want to work with me (with the subject line starting with [PhD Application] or [Intern Application]). Due to the substantial volume of emails I receive daily, I would like to apologize in advance as I may not be able to respond to each of them. I appreciate your understanding.

Research Interests:

  • Graph Neural Networks, Graph Machine Learning
  • Time-Series Analysis (e.g., Forecasting and Classification)
  • AI for Epidemiology, Science and Healthcare
  • Machine Learning Robustness and Interpretability
  • Large Language Models (LLMs) (If you have experience in LLMs and are interested in our group, please do reach out to us!)

Email: wei.jin At emory.edu. Find me on Github, Twitter and Linkedin.

Open-Source Projects

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GitHub starsGitHub forks
High-Dimensional Time Series Forecasting
ArXiv 2025
[Paper] [Code]

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GitHub starsGitHub forks
EpiLearn: A Python Library for Machine Learning in Epidemic Modeling
In KDD epiDAMIK 2024
[Paper] [Code] [reading list]
We sincerely welcome your feedback on our package. If you encounter any issues while using it, our team is committed to promptly resolving them.

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GitHub starsGitHub forks
DeepRobust: A Platform for Adversarial Attacks and Defenses
In AAAI 2021
[Website] [Paper] [Code]

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GitHub starsGitHub forks
DANCE: A Deep Learning Library and Benchmark for Single-Cell Analysis
In Genome Biology
[Website] [Code] [paper] [reading list]

News

  • [10/2025] Excited to receive an NIH R01 Award as contact PI to support our research on Epidemic Modeling!
  • [09/2025] Thanks to Theta Labs for providing computing credits to our lab!
  • [09/2025] Our benchmark on Graph Condensation accepted by NeurIPS’25! We benchmarked existing graph condensation methods on their privacy preserving ability and robustness.
  • [08/2025] Recent preprints: LLMs for Symbolic Reasoning, RAG for LLMs, and High-Dimensional Time Series
  • [07/2025] Excited to receive an NSF Medium Award as a lead PI for our research on Graph Machine Learning!
  • [06/2025] Excited to receive an NIH R01 Award ($3.5M) as an MPI for our research on AI+Epidemiology!
  • [06/2025] Excited to receive an NSF SaTC Award as a Co-PI for our research on Robust and Privacy-Preserving EHR systems!
  • [05/2025] Invited talk at French Consortium of Infectious Disease Modellers
  • [05/2025] One paper on GraphODE for Epidemic Modeling has been accepted to ICML’25!
  • [05/2025] Check out our KDD’25 survey on Graph + Differential Equations (ODE/PDE/SDE)
  • [04/2025] Excited to receive Emory URC Award!
  • [03/2025] Invited talk “Epidemic Time Series Forecasting in the Era of Machine Learning” at University of Notre Dame
  • [03/2025] Excited to share that our workshop on LLMs for Scientific and Societal Applications has been accepted by KDD’25!
  • [03/2025] Excited to release TimeDistill, the first framework for cross-architecture knowledge distillation in time series forecasting! We effectively transferred knowledge from teacher models (e.g., Transformers, CNNs) to MLP, achieving better forecasting performance and higher effiency
  • [02/2025] Check out our new preprint on Scalable Graph Condensation with Evolving Capabilities! We provide a scalable framework for compressing graph data into a signicantly smaller version (up to 1,000 times faster than previous methods)
  • [02/2025] Check out our recent preprint on Pre-Training on Epidemic Time Series! We demonstrated that pre-training on diverse epidemic data can effectively improve forecasting performance for new diseases!
  • [02/2025] Invited to serve as the co-chair for CIKM 2025 AnalytiCup!
  • [01/2025] Excited to share that Data Packing for LLMs and LLM for Decision-Making are accepted by NAACL’25!
  • [01/2025] Excited to share that our paper on Graph Data Valuation is accpeted by ICLR’25!
  • [12/2024] Excited to receive MP3 INITIATIVE SEED GRANTS to support our research in AI for Epidemiology!
  • [12/2024] Excited to share that Emory Artificial Intelligence and Data Association (EAIDA) has been officially recognized as a student organization by Emory University! I am serving as the faculty advisor; please check out our website if you are interested in joining us!
  • [12/2024] Our tutorial on Time Series Analysis is accepted by WWW’25!
More news

Personal

I enjoy many different kinds of sports including running, basketball, ping-pong and tennis. During my undergrad, I got several champions in 400m, 400m hurdles and 4*400m relay races at the university sports meet.

Now my goal is to bulk up and hopefully get a certificate of personal trainer. Let’s see what will happen 5 4 3 2 years later :)