Search Results for author: Jianwu Wang

Found 24 papers, 8 papers with code

Cloud Optical Thickness Retrievals Using Angle Invariant Attention Based Deep Learning Models

no code implementations30 May 2025 Zahid Hassan Tushar, Adeleke Ademakinwa, Jianwu Wang, Zhibo Zhang, Sanjay Purushotham

Satellite radiance measurements enable global COT retrieval, but challenges like 3D cloud effects, viewing angles, and atmospheric interference must be addressed to ensure accurate estimation.

Retrieval

DeepTopoNet: A Framework for Subglacial Topography Estimation on the Greenland Ice Sheets

1 code implementation29 May 2025 Bayu Adhi Tama, Mansa Krishna, Homayra Alam, Mostafa Cham, Omar Faruque, Gong Cheng, Jianwu Wang, Mathieu Morlighem, Vandana Janeja

Understanding Greenland's subglacial topography is critical for projecting the future mass loss of the ice sheet and its contribution to global sea-level rise.

BACON: A fully explainable AI model with graded logic for decision making problems

no code implementations20 May 2025 Haishi Bai, Jozo Dujmovic, Jianwu Wang

As machine learning models and autonomous agents are increasingly deployed in high-stakes, real-world domains such as healthcare, security, finance, and robotics, the need for transparent and trustworthy explanations has become critical.

Decision Making

Enhancing Satellite Object Localization with Dilated Convolutions and Attention-aided Spatial Pooling

1 code implementation8 May 2025 Seraj Al Mahmud Mostafa, Chenxi Wang, Jia Yue, Yuta Hozumi, Jianwu Wang

Object localization in satellite imagery is particularly challenging due to the high variability of objects, low spatial resolution, and interference from noise and dominant features such as clouds and city lights.

feature selection Object +1

Joint Retrieval of Cloud properties using Attention-based Deep Learning Models

no code implementations4 Apr 2025 Zahid Hassan Tushar, Adeleke Ademakinwa, Jianwu Wang, Zhibo Zhang, Sanjay Purushotham

Accurate cloud property retrieval is vital for understanding cloud behavior and its impact on climate, including applications in weather forecasting, climate modeling, and estimating Earth's radiation balance.

Retrieval Weather Forecasting

Building Machine Learning Challenges for Anomaly Detection in Science

no code implementations3 Mar 2025 Elizabeth G. Campolongo, Yuan-Tang Chou, Ekaterina Govorkova, Wahid Bhimji, Wei-Lun Chao, Chris Harris, Shih-Chieh Hsu, Hilmar Lapp, Mark S. Neubauer, Josephine Namayanja, Aneesh Subramanian, Philip Harris, Advaith Anand, David E. Carlyn, Subhankar Ghosh, Christopher Lawrence, Eric Moreno, Ryan Raikman, Jiaman Wu, Ziheng Zhang, Bayu Adhi, Mohammad Ahmadi Gharehtoragh, Saúl Alonso Monsalve, Marta Babicz, Furqan Baig, Namrata Banerji, William Bardon, Tyler Barna, Tanya Berger-Wolf, Adji Bousso Dieng, Micah Brachman, Quentin Buat, David C. Y. Hui, Phuong Cao, Franco Cerino, Yi-Chun Chang, Shivaji Chaulagain, An-Kai Chen, Deming Chen, Eric Chen, Chia-Jui Chou, Zih-Chen Ciou, Miles Cochran-Branson, Artur Cordeiro Oudot Choi, Michael Coughlin, Matteo Cremonesi, Maria Dadarlat, Peter Darch, Malina Desai, Daniel Diaz, Steven Dillmann, Javier Duarte, Isla Duporge, Urbas Ekka, Saba Entezari Heravi, Hao Fang, Rian Flynn, Geoffrey Fox, Emily Freed, Hang Gao, Jing Gao, Julia Gonski, Matthew Graham, Abolfazl Hashemi, Scott Hauck, James Hazelden, Joshua Henry Peterson, Duc Hoang, Wei Hu, Mirco Huennefeld, David Hyde, Vandana Janeja, Nattapon Jaroenchai, Haoyi Jia, Yunfan Kang, Maksim Kholiavchenko, Elham E. Khoda, Sangin Kim, Aditya Kumar, Bo-Cheng Lai, Trung Le, Chi-Wei Lee, Janghyeon Lee, Shaocheng Lee, Suzan van der Lee, Charles Lewis, Haitong Li, Haoyang Li, Henry Liao, Mia Liu, Xiaolin Liu, Xiulong Liu, Vladimir Loncar, Fangzheng Lyu, Ilya Makarov, Abhishikth Mallampalli Chen-Yu Mao, Alexander Michels, Alexander Migala, Farouk Mokhtar, Mathieu Morlighem, Min Namgung, Andrzej Novak, Andrew Novick, Amy Orsborn, Anand Padmanabhan, Jia-Cheng Pan, Sneh Pandya, Zhiyuan Pei, Ana Peixoto, George Percivall, Alex Po Leung, Sanjay Purushotham, Zhiqiang Que, Melissa Quinnan, Arghya Ranjan, Dylan Rankin, Christina Reissel, Benedikt Riedel, Dan Rubenstein, Argyro Sasli, Eli Shlizerman, Arushi Singh, Kim Singh, Eric R. Sokol, Arturo Sorensen, Yu Su, Mitra Taheri, Vaibhav Thakkar, Ann Mariam Thomas, Eric Toberer, Chenghan Tsai, Rebecca Vandewalle, Arjun Verma, Ricco C. Venterea, He Wang, Jianwu Wang, Sam Wang, Shaowen Wang, Gordon Watts, Jason Weitz, Andrew Wildridge, Rebecca Williams, Scott Wolf, Yue Xu, Jianqi Yan, Jai Yu, Yulei Zhang, Haoran Zhao, Ying Zhao, Yibo Zhong

We present the different datasets along with a scheme to make machine learning challenges around the three datasets findable, accessible, interoperable, and reusable (FAIR).

Anomaly Detection scientific discovery

Hybrid Ensemble Deep Graph Temporal Clustering for Spatiotemporal Data

no code implementations19 Sep 2024 Francis Ndikum Nji, Omar Faruque, Mostafa Cham, Janeja Vandana, Jianwu Wang

Classifying subsets based on spatial and temporal features is crucial to the analysis of spatiotemporal data given the inherent spatial and temporal variability.

Clustering Diversity +1

YOLO based Ocean Eddy Localization with AWS SageMaker

no code implementations10 Apr 2024 Seraj Al Mahmud Mostafa, Jinbo Wang, Benjamin Holt, Jianwu Wang

Ocean eddies play a significant role both on the sea surface and beneath it, contributing to the sustainability of marine life dependent on oceanic behaviors.

Management

Causality for Earth Science -- A Review on Time-series and Spatiotemporal Causality Methods

no code implementations3 Apr 2024 Sahara Ali, Uzma Hasan, Xingyan Li, Omar Faruque, Akila Sampath, Yiyi Huang, Md Osman Gani, Jianwu Wang

This survey paper covers the breadth and depth of time-series and spatiotemporal causality methods, and their applications in Earth Science.

Causal Discovery Causal Inference +2

TS-CausalNN: Learning Temporal Causal Relations from Non-linear Non-stationary Time Series Data

no code implementations1 Apr 2024 Omar Faruque, Sahara Ali, Xue Zheng, Jianwu Wang

The growing availability and importance of time series data across various domains, including environmental science, epidemiology, and economics, has led to an increasing need for time-series causal discovery methods that can identify the intricate relationships in the non-stationary, non-linear, and often noisy real world data.

Causal Discovery Epidemiology +1

MT-HCCAR: Multi-Task Deep Learning with Hierarchical Classification and Attention-based Regression for Cloud Property Retrieval

1 code implementation29 Jan 2024 Xingyan Li, Andrew M. Sayer, Ian T. Carroll, Xin Huang, Jianwu Wang

In response, this paper introduces MT-HCCAR, an end-to-end deep learning model employing multi-task learning to simultaneously tackle cloud masking, cloud phase retrieval (classification tasks), and COT prediction (a regression task).

Classification Model Selection +3

Multi-graph Spatio-temporal Graph Convolutional Network for Traffic Flow Prediction

no code implementations10 Aug 2023 Weilong Ding, Tianpu Zhang, Jianwu Wang, Zhuofeng Zhao

In our method, data normalization strategy is used to deal with data imbalance, due to long-tail distribution of traffic flow at network-wide toll stations.

Prediction

MT-IceNet -- A Spatial and Multi-Temporal Deep Learning Model for Arctic Sea Ice Forecasting

1 code implementation8 Aug 2023 Sahara Ali, Jianwu Wang

Arctic amplification has altered the climate patterns both regionally and globally, resulting in more frequent and more intense extreme weather events in the past few decades.

Decoder

Deep Spatiotemporal Clustering: A Temporal Clustering Approach for Multi-dimensional Climate Data

1 code implementation27 Apr 2023 Omar Faruque, Francis Ndikum Nji, Mostafa Cham, Rohan Mandar Salvi, Xue Zheng, Jianwu Wang

Concentrating on joint deep representation learning of spatial and temporal features, we propose Deep Spatiotemporal Clustering (DSC), a novel algorithm for the temporal clustering of high-dimensional spatiotemporal data using an unsupervised deep learning method.

Clustering Representation Learning

Quantifying Causes of Arctic Amplification via Deep Learning based Time-series Causal Inference

no code implementations22 Feb 2023 Sahara Ali, Omar Faruque, Yiyi Huang, Md. Osman Gani, Aneesh Subramanian, Nicole-Jienne Shchlegel, Jianwu Wang

Through experiments on synthetic and observational data, we show how our research can substantially improve the ability to quantify leading causes of Arctic sea ice melt, further paving paths for causal inference in observational Earth science.

Causal Inference counterfactual +2

An Edge-Cloud Integrated Framework for Flexible and Dynamic Stream Analytics

no code implementations10 May 2022 Xin Wang, Azim Khan, Jianwu Wang, Aryya Gangopadhyay, Carl E. Busart, Jade Freeman

In this paper, we study how to best leverage edge and cloud resources to achieve better accuracy and latency for stream analytics using a type of RNN model called long short-term memory (LSTM).

Cloud Computing Edge-computing +3

Reproducible and Portable Big Data Analytics in the Cloud

1 code implementation17 Dec 2021 Xin Wang, Pei Guo, Xingyan Li, Aryya Gangopadhyay, Carl E. Busart, Jade Freeman, Jianwu Wang

To tackle these problems, we leverage serverless computing and containerization techniques for automated scalable execution and reproducibility, and utilize the adapter design pattern to enable application portability and reproducibility across different clouds.

Cloud Computing Descriptive

Sea Ice Forecasting using Attention-based Ensemble LSTM

1 code implementation27 Jul 2021 Sahara Ali, Yiyi Huang, Xin Huang, Jianwu Wang

Accurately forecasting Arctic sea ice from subseasonal to seasonal scales has been a major scientific effort with fundamental challenges at play.

Scalable and Hybrid Ensemble-Based Causality Discovery

no code implementations24 Dec 2020 Pei Guo, Achuna Ofonedu, Jianwu Wang

Causality discovery mines cause-effect relationships among different variables of a system and has been widely used in many disciplines including climatology and neuroscience.

Benchmarking Distributed Computing +2

A Deterministic Self-Organizing Map Approach and its Application on Satellite Data based Cloud Type Classification

no code implementations24 Aug 2018 Wenbin Zhang, Jianwu Wang, Daeho Jin, Lazaros Oreopoulos, Zhibo Zhang

A self-organizing map (SOM) is a type of competitive artificial neural network, which projects the high-dimensional input space of the training samples into a low-dimensional space with the topology relations preserved.

General Classification

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