Assistant Professor (Tenure-Track)School of Computing, the University of Georgia (UGA)
Fei Dou is an Assistant Professor in the School of Computing at the University of Georgia. She obtained her Ph.D. in 2023 from the Department of Computer Science and Engineering at the University of Connecticut, where she worked in the Laboratory of Machine Learning & Health Informatics under the supervision of Prof. Jinbo Bi. Her work contributed to fundamental Machine Learning (ML) research for the Internet of Things (IoT) and Cyber-Physical Systems (CPS), particularly in the areas of federated learning, reinforcement learning, and self-supervised learning.
Fei’s research focuses on advancing Machine Intelligence for Ubiquitous Computing in Distributed Systems, with an emphasis on scalable, responsible, and context-aware AI solutions.
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University of Connecticut (UConn)Department of Computer Science and Engineering
Ph.D.May 2023
Honors & Awards
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Women of Innovation(WOI) Academic Innovation and Leadership, Connecticut2023
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Provost Recognition of Teaching Excellence, University of Connecticut2017
News
Selected Publications (view all )
Self-Supervised Representation Learning and Temporal-Spectral Feature Fusion for Bed Occupancy Detection
Yingjian Song, Zaid Farooq Pitafi, Fei Dou, Jin Sun, Xiang Zhang, Bradley G Phillips, WenZhan Song
Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (UbiComp/IMWUT) 2024
This paper presents SeismoDot, a bed occupancy detection system that combines self-supervised learning and spectral-temporal feature fusion to improve generalization across diverse environments with limited data. Unlike traditional threshold-based methods, SeismoDot achieves high accuracy and F1 scores across 13 settings and remains effective even when trained on only 20% of the data, demonstrating strong adaptability and efficiency.
Self-Supervised Representation Learning and Temporal-Spectral Feature Fusion for Bed Occupancy Detection
Yingjian Song, Zaid Farooq Pitafi, Fei Dou, Jin Sun, Xiang Zhang, Bradley G Phillips, WenZhan Song
Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (UbiComp/IMWUT) 2024
This paper presents SeismoDot, a bed occupancy detection system that combines self-supervised learning and spectral-temporal feature fusion to improve generalization across diverse environments with limited data. Unlike traditional threshold-based methods, SeismoDot achieves high accuracy and F1 scores across 13 settings and remains effective even when trained on only 20% of the data, demonstrating strong adaptability and efficiency.
Mobilizing Personalized Federated Learning in Infrastructure-Less and Heterogeneous Environments via Random Walk Stochastic ADMM
Ziba Parsons, Fei Dou, Houyi Du, Zheng Song, Jin Lu
Advances in Neural Information Processing Systems 36 (NeurIPS) 2023
This paper proposes RWSADMM, a novel federated learning algorithm designed for infrastructure-less environments with isolated, heterogeneous nodes connected via wireless links. By leveraging server mobility and enforcing hard constraints among adjacent clients, RWSADMM enables efficient, personalized learning with provable convergence, reduced communication costs, and improved accuracy and scalability over baseline methods.
Mobilizing Personalized Federated Learning in Infrastructure-Less and Heterogeneous Environments via Random Walk Stochastic ADMM
Ziba Parsons, Fei Dou, Houyi Du, Zheng Song, Jin Lu
Advances in Neural Information Processing Systems 36 (NeurIPS) 2023
This paper proposes RWSADMM, a novel federated learning algorithm designed for infrastructure-less environments with isolated, heterogeneous nodes connected via wireless links. By leveraging server mobility and enforcing hard constraints among adjacent clients, RWSADMM enables efficient, personalized learning with provable convergence, reduced communication costs, and improved accuracy and scalability over baseline methods.
Polyhedron Attention Module: Learning Adaptive-order Interactions
Tan Zhu, Fei Dou, Xinyu Wang, Jin Lu, Jinbo Bi
Advances in Neural Information Processing Systems 36 (NeurIPS) 2023
This paper introduces the Polyhedron Attention Module (PAM), which forms interpretable, piecewise polynomial models by adaptively learning feature interactions within polyhedrons in the input space. PAM outperforms ReLU-based networks in expressive power and achieves superior classification results on large-scale click-through rate datasets while uncovering meaningful interactions in medical applications.
Polyhedron Attention Module: Learning Adaptive-order Interactions
Tan Zhu, Fei Dou, Xinyu Wang, Jin Lu, Jinbo Bi
Advances in Neural Information Processing Systems 36 (NeurIPS) 2023
This paper introduces the Polyhedron Attention Module (PAM), which forms interpretable, piecewise polynomial models by adaptively learning feature interactions within polyhedrons in the input space. PAM outperforms ReLU-based networks in expressive power and achieves superior classification results on large-scale click-through rate datasets while uncovering meaningful interactions in medical applications.
On-Device Indoor Positioning: A Federated Reinforcement Learning Approach With Heterogeneous Devices
Fei Dou, Jin Lu, Tan Zhu, Jinbo Bi
IEEE Internet of Things Journal (IOT-J) 2023
This paper proposes a personalized federated reinforcement learning (RL) framework for indoor localization that enables mobile devices to train locally while preserving data privacy and coping with limited connectivity and data heterogeneity. By combining personalized on-device RL with a global model trained via sparse updates, the approach achieves improved localization accuracy and robustness, and is further extended to support few-shot learning for rapid adaptation to new users with minimal data.
On-Device Indoor Positioning: A Federated Reinforcement Learning Approach With Heterogeneous Devices
Fei Dou, Jin Lu, Tan Zhu, Jinbo Bi
IEEE Internet of Things Journal (IOT-J) 2023
This paper proposes a personalized federated reinforcement learning (RL) framework for indoor localization that enables mobile devices to train locally while preserving data privacy and coping with limited connectivity and data heterogeneity. By combining personalized on-device RL with a global model trained via sparse updates, the approach achieves improved localization accuracy and robustness, and is further extended to support few-shot learning for rapid adaptation to new users with minimal data.
All publications
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