(* denotes equal contribution)
-
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]
-
Deep Learning and Foundation Models for Weather Prediction: A Survey
Jimeng Shi, Azam Shirali, Bowen Jin, Sizhe Zhou, Wei Hu, Rahuul Rangaraj, Shaowen Wang, Jiawei Han, Zhaonan Wang, Upmanu Lall, Yanzhao Wu, Leonardo Bobadilla, Giri Narasimhan
(Under review)
[PDF]
[Code]
(* denotes equal contribution)
-
CoDiCast: Conditional Diffusion Model for Weather Prediction with Uncertainty Quantification
Jimeng Shi, Bowen Jin, Jiawei Han, Giri Narasimhan
(Under review)
[PDF]
[Code]
-
ReFine: Boosting Time Series Prediction of 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]
-
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]
-
Physics-Informed Neural Network Approach for Solving the One-Dimensional Unsteady Shallow-Water Equations in Riverine Systems
Zeda Yin, Jimeng Shi, Linlong Bian, William Campbell, Sumit Zanje, Arturo Leon
Journal of Hydraulic Engineering
-
A Comprehensive Survey of Scoring Functions for Protein Docking Models
Azam Shirali, Vitalii Stebliankin, Jimeng Shi, Giri Narasimhan
BMC Bioinformatics
(* denotes equal contribution)
-
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 City, 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]
-
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 (Under review)
Part of work was presented at the I-GUIDE All-Hands Meeting, Chicago in 2022
[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]
-
Physic-Informed Neural Network Approach Coupled with Boundary Conditions for Solving 1D Steady Shallow Water Equations for Riverine System
Zeda Yin, Linlong Bian, Beichao Hu, Jimeng Shi, Arturo Leon
World Environmental and Water Resources Congress 2023 (Best paper award).
