About Me

Introduction

Hi there! I am a postdoctoral researcher at the University of Illinois Urbana-Champaign (UIUC), working with Prof. Shaowen Wang (advisor) and Prof. Jiawei Han. I obtained my PhD in Computer Science from Florida International University (FIU), advised by Prof. Giri Narasimhan. My research focus on developing and applying AI tools to address real-world problems in Natural Science and Earth Science, while FIU has featured my work multiple times [link1, link2, link3]. South Florida Business Journal featured my AI for flood prediction and control project. This work has also attracted attenion from Florida House Natural Resources & Disasters Subcommittee, and my PhD advisor, Dr. Giri was invited to share expert opinions on AI for flood management. Also, a shout-out to NSF I-GUIDE, a research center, for consistently supporting my work.

[Call for collaboration] I am working on a project on document compression (e.g., DeepSeek-OCR) and agents with reinforcement learning. Feel free to reach out if you are interested in it.

Research Interests

• AI for Science and Engineering
• Environment: flood prediction (WaLeF, FloodGTN, ReFine), flood mitigation (FIDLAr), physics-informed neural networks (PINNs), explainable AI with information bottleneck (TimeX++).
• Climate: diffusion model for weather prediction (CoDiCast), foundation models for weather and climate (Survey)
• Biology: vision transformer for protein binding (PIsToN), evaluating protein docking models (Survey)
• Data Mining to Boost LLMs
• Retrieval and Structing: Structured RAG (HyperCube-RAG, MultiCube-RAG)
• In-domain Foundation Models: Benchmark (Water-Bench), Retrieval-Augmention Forecast (Water-RAF)
• Agentic AI & RL
• Tool Calling: coming soon
• Multi-Agent with RL: coming soon

Selected Publications [Full List]

• Hypercube-RAG: Hypercube Retrieval-Augmented Generation for Scientific Question-Answering
  Jimeng Shi, Sizhe Zhou, Bowen Jin, Wei Hu, Runchu Tian, Shaowen Wang, Giri Narasimhan, Jiawei Han
  (Under review)
  [PDF] [Code]
  
  
• FIDLAR: Forecast-Informed Deep Learning Approaches for Flood Mitigation
  Jimeng Shi, Zeda Yin, Arturo Leon, Jayantha Obeysekera, Giri Narasimhan
  The 39th Annual AAAI Conference on Artificial Intelligence (AAAI 2025) (Oral)
  [PDF] [Code] [Media 1] [Media 2]
  
  
• Deep Learning Models for Water Level Prediction in South Florida
  Jimeng Shi^*, Zeda Yin^*, Rukmangadh Myana, Arturo Leon, Jayantha Obeysekera, Giri Narasimhan
  Journal of water resources planning and management, 151(10)
  [PDF] [Code] [Media: South Florida Business Journal]
  
  
• CoDiCast: Conditional Diffusion Model for Weather Prediction with Uncertainty Quantification
  Jimeng Shi, Bowen Jin, Jiawei Han, Sundararaman Gopalakrishnan, Giri Narasimhan
  The 34th International Joint Conference on Artificial Intelligence (IJCAI 2025) (Oral)
  [PDF] [Code] [Media]
  
  
• ReFine: Boosting Time Series Prediction Under Extreme Events by Reweighting and Fine-tuning
  Jimeng Shi, Azam Shirali, Giri Narasimhan.
  2024 IEEE International Conference on Big Data (IEEE BigData 2024) (Oral)
  [PDF] [Code]
  
  
• The Power of Explainability in Forecast-Informed Deep Learning Models for Flood Mitigation
  Jimeng Shi, Vitalii Stebliankin, Giri Narasimhan
  NeurIPS 2023 workshop on Climate Change AI (Poster)
  [PDF] [Code]
  
  
• Graph Transformer Network for Flood Forecasting with Heterogeneous Covariates
  Jimeng Shi, Vitalii Stebliankin, Zhaonan Wang, Shaowen Wang, Giri Narasimhan
  NSF I-GUIDE Forum in New York, 2023 (Oral)
  [PDF] [Code]
  
  
• Explainable Parallel RCNN with Novel Feature Representation for Time Series Forecasting
  Jimeng Shi, Rukmangadh Myana, Vitalii Stebliankin, Azam Shirali, Giri Narasimhan
  ECML-PKDD 2023 workshop on Advanced Analytics and Learning on Temporal Data (Oral)
  [PDF] [Code]
  
  
• TimeX++: Learning Time-Series Explanations with Information Bottleneck
  Zichuan Liu, Tianchun Wang, Jimeng Shi, Xu Zheng, Zhuomin Chen, Lei Song, Wenqian Dong, Jayantha Obeysekera, Farhad Shirani, Dongsheng Luo
  The 41st International Conference on Machine Learning (ICML 2024) (Poster)
  [PDF] [Code]
  
  
• Evaluating protein binding interfaces with transformer networks
  Vitalii Stebliankin, Azam Shirali, Prabin Baral, Jimeng Shi, Prem Chapagain, Kalai Mathee, Giri Narasimhan
  Nature Machine Intelligence (IF=25.89)
  [PDF] [Codes] [Media]
  

Education

• Ph.D. Computer Science - Florida International University (2021 - 2025).
• M.S. Computer Science - Florida International University (2019 - 2021).
• B.S. Vehicle Engineering - Tianjin University of Science & Technology (2014 - 2018).

Experience

• University of Illinois Urbana-Champaign - Postdoctoral Researcher
• Working on AI for geospatial reasoning with Prof. Shaowen Wang (advisor) and Prof. Jiawei Han. (2025.08 - present)
• Florida International University - Research Assistant
• During Ph.D., Working with Prof. Giri Narasimhan on the development and application of Machine Learning algorithms. (2021.08 - 2025.07)
• During Master's Degree, I had been working with Prof. Cheng-Xian Lin on numeric analysis and simulation on solar enginerring using Matlab. (2019.01 - 2020.06)
• Columbia University - Research Fellow
• Worked at NSF LEAP on generative AI for climate emulation while mentoring two REU students. (2024.05 - 2024.08)
• Lid Vizion - Machine Learning Engineer (Internship)
• Worked at Lid Vizion Company at Miami, Florida, USA. I was in charge of applying Yolo-v4 model to detect objects (paper or cardboard) on the river. (2020.09 - 2020.12)

Professional Services

• Committee Member
• International Conference on Deep Learning Technologies (ICDLT) (2023, 2024)
• World Academy of Science, Engineering and Technology (WASET) (2022, 2023, 2024)
• Paper Reviewer
• AAAI Conference on Artificial Intelligence (AAAI), 2026
• International Conference on Learning Representations (ICLR), 2025, 2026
• International Joint Conference on Artificial Intelligence (IJCAI), 2025
• SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2024
• NeurIPS Workshop: Tackling Climate Change with Machine Learning, 2024
• ICLR Workshop: Tackling Climate Change with Machine Learning, 2024, 2025
• International Conference on Information and Knowledge Management (CIKM), 2023
• European Conference on Artificial Intelligence (ECAI), 2025
• European Conference on Machine Learning (ECML), 2023, 2024, 2025
• 6th International Conference on Computer Science and Application Engineering (CSAE) 2022
• IEEE Transactions on Big Data, 2025
• Journal of Intelligent & Fuzzy Systems, 2021, 2022
• Volunteer
• Helped to organize the International Collegiate Programming Contest (ICPC) Southeast USA Regional Contest at Florida International University. (2022, 2023, 2024)
• Helped to organize the High School Programming Contest at Florida International University. (2025)