Yinxu Tang
PhD Candidate at Washington University in St. Louis, USA
Washington University in St. Louis
Biography
Yinxu Tang is a PhD Candidate in Computer Science in Yeoh’s Optimization and Decision Analytics (YODA) Lab at Washington University in St. Louis, MO, USA. She received M.Eng and B.Eng degrees from ShanghaiTech University, Shanghai, China. Her current research interests include: Explainable AI, Artificial Intelligence, Bandits and Reinforcement Learning.
Interests
- Explainable AI
- Artificial Intelligence
- Bandit and Reinforcement Learning
Education
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PhD in Computer Science, Present
Washington University in St. Louis
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M.Eng in Information and Communication Engineering, 2023
ShanghaiTech University
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B.Eng in Computer Science, 2020
ShanghaiTech University
Recent News
- 🎉 [Apr 2025] Successfully passed the qualification exam!
- 📝 [Dec 2024] One paper on learning user models in argumentation dialogues was accepted at AAAI 2025 and selected for an oral presentation.
Publications
(2025).
Does your AI agent get you? A personalizable framework for approximating human models from argumentation-based dialogue traces.
In Proceedings of AAAI.
(2024).
Toward Effective Retrieval Augmented Generative Services in 6G Networks.
In IEEE Network.
(2024).
Green Edge Intelligence Scheme for Mobile Keyboard Emoji Prediction.
In IEEE Transactions on Mobile Computing.
(2024).
Privacy-Preserving Edge Intelligence: A Perspective of Constrained Bandits.
In Proceedings of WCNC.
(2023).
Social-Aware Distributed Meta-Learning: A Perspective of Constrained Graphical Bandits.
In Proceedings of ICC.
(2023).
Social-Aware Edge Intelligence: A Constrained Graphical Bandit Approach.
In Proceedings of GLOBECOM.
(2022).
Learning-Aided Stable Matching for Switch-Controller Association in SDN Systems.
In Proceedings of ICC.
(2021).
Green Edge Intelligence Scheme for Mobile Keyboard Emoji Prediction.
In Proceedings of ICC.
(2021).
Joint Switch–Controller Association and Control Devolution for SDN Systems: An Integrated Online Perspective of Control and Learning.
In IEEE Transactions on Network and Service Management.
(2021).
History-Aware Online Cache Placement in Fog-Assisted IoT Systems: An Integration of Learning and Control.
In IEEE Internet of Things Journal.
(2020).
Proactive Cache Placement with Bandit Learning in Fog-Assisted IoT Systems.
In Proceedings of ICC.
(2020).
Joint Switch-Controller Association and Control Devolution for SDN Systems: An Integration of Online Control and Online Learning.
In Proceedings of IWQoS.
(2019).
Learning-Aided Online Task Offloading for UAVs-Aided IoT Systems.
In Proceedings of VTC.