Yanhai Xiong
I am currently an Assistant Professor in the Department of Data Science at William & Mary. Previously, I worked as an Assistant Professor in the Department of Computer Science and Engineering at the University of Louisville (2022 to 2023). I worked as a postdoc in the Department of Computer Science, Dartmouth College from 2018 to 2021, in collaboration with Professor V.S. Subrahmanian. I obtained my Ph.D. from Interdisciplinary Graduate School (IGS), Nanyang Technological University (NTU) in 2018, where I was advised by Professor Bo An and my research mainly focused on applying AI techniques to improve the efficiency of electric vehicle infrastructure. I got my B.S. in Engineering (major in Automation) from the University of Science and Technology of China (USTC) in 2013.
News
- August, 2023 - Joined the Department of Data Science at William & Mary.
- April, 2022 - Joined the Department of Computer Science at the University of Louisville.
- December, 2020 - Invited to serve as a SPC for the top AI conference IJCAI'21. [site]
- November, 2020 - Invited to serve as a judge for the ENVISION science competition held by the Women in STEM initiative. [link]
- November, 2020 - Our paper, "Generating Realistic Fake Equations in Order to Reduce Intellectual Property Theft" is accepted at IEEE Transactions on Dependable and Secure Computing (TDSC). [pdf]
- August, 2020 - Invited to serve as a reviewer for AAAI'2021.
Research
Selected Research Projects
As a promising solution of clean energy transportation, electric vehicles (EVs) have drawn interests from different communities. For successful introduction of EVs, the construction of charging facilities is of top-priority since it is essential to mitigate drivers' anxiety of running out electricity. We study the placement and management of EV charging stations with game-theoretical approaches, in which the mutual influence between EV infrastructure and the environment is delicately considered, especially with the consideration of the strategic charging behavior of participating human subjects, i.e., EV users.
Papers:
- EV Charging Station Placement, IJCAI'15 [pdf], Extended journal version, IEEE-TITS'17[pdf]
- EV Charging Station Management, AAMAS'16 [pdf]
- Complete version, PhD Thesis'18 [pdf]
- Charging Strategy Study and the Application, JAAMAS'20 [pdf]
Another work based on our champion agent which won 2017 Microsoft Malmo Collaborative AI Challenge is potential to be helpful in future research, expecially when auto-drive vehicles come to reality and. The Microsoft Malmo Collaborative AI Challenge (MCAC), which is designed to encourage research relating to various problems in Collaborative AI, builds on a Minecraft mini-game called “Pig Chase”. Pig Chase is played on a 9 × 9 grid where agents can either work together to catch the pig and achieve high scores, or give up cooperation and achieve low scores. After playing certain episodes (e.g., 100) of games, the agent who achieves the highest average scores wins the challenge.
The solutions underlying our agent HogRider are characterized by
- A novel agent type hypothesis approach for dealing uncertainties about the type of the other agent and the observation of the other agents' actions.
- A novel Q-learning framework to learn an optimal policy against each type of agent, which 1) employs state-action abstraction to reduce state-action space, 2) utilizes warm-start of Q-functions with pre-traning, and 3) exploits active-ε-greedy for active exploration of potentially more beneficial actions.
Paper and media coverage:
Android is the most widely used operating system. As a result, it is also favored by numerous attackers. Attackers develop Android malware to perform malicious behaviors on users' Android devices for stealing sensitive information or money. Part of my work is using machine learning and optimization to detect and analyze Android malware.
Papers:
- Detection via (Somewhat) Robust Irreversible Feature [pdf]
Papers on information security:
- Strategic vulnerability exploit and disclosure in cyber walfare, IEEE SJ'20[pdf]
According to Symantec, there average gap from the time a company is compromised by a zero-day attack to the time the vulnerability is discovered is 312 days. This leaves an adversary with a lot of time to exfiltrate corporate IP. Recent work has suggested automatically generating multiple fake versions of a document to impose costs on the attacker who needs to correctly identify the original document from a set of mostly fake documents.
Papers:
- Realistic Fake Equation Generation [pdf]
To make complex machine learning models intelligible for users, explainations are required. We buiilt a general and brief framework to explain why an arbitrary model makes its prediction on a particular case. The proposed approach, based on an optimization framework with tailored algorithm, has demonstrated inspiring performance on various tasks. Our paper is underreview.
Publication
Preprints/Under Review
- Yanhai Xiong, Dongkai Chen, and V.S. Subrahmanian. "Approximate Gradient-based Explanation of Blackbox Model Predictions with Anecdotes."
Publications
2021
- Qian Han, Cristian Molinaro, Antonio Picariello, Giancarlo Sperlì, V.S. Subrahmanian, Yanhai Xiong. "Generating Fake Documents using Probabilistic Logic Graphs." IEEE Transactions on Dependable and Secure Computing (IEEE TDSC) [pdf]
- Yanhai Xiong, Bo An and Sarit Kraus. "Electric Vehicle Charging Strategy Study and the Application on Charging Station Placement." Autonomous Agents and Multi-Agent Systems (JAAMAS) [pdf]
2020
- Yanhai Xiong, Giridhar Kaushik Ramachandran, Rajesh Ganesan, Sushil Jajodia, VS Subrahmanian. "Generating Realistic Fake Equations in Order to Reduce Intellectual Property Theft." IEEE Transactions on Dependable and Secure Computing (IEEE TDSC) [pdf]
- Qian Han, V. S. Subrahmanian and Yanhai Xiong. "Android Malware Detection via (Somewhat) Robust Irreversible Feature Transformations." IEEE Transactions on Information Forensics and Security (IEEE TIFS) [pdf]
- Haipeng Chen, Qian Han, Sushil Jajodia, Roy Lindelauf, V.S. Subrahmanian and Yanhai Xiong. "Disclose or Exploit? A Game Theoretic Approach Towards Strategic Decision Making in Cyber Warfare." IEEE Systems Journal (IEEE SYST J) [pdf]
- Haipeng Chen, Mohammad T. Hajiaghayi, Sarit Kraus, Anshul Sawant, Edoardo Serra, V.S. Subrahmanian and Yanhai Xiong. "PIE: A Data-Driven Payoff Inference Engine with Counter-Terrorism Applications." IEEE Transactions on Computational Social Systems (IEEE TCSS) [pdf]
2018
- Yanhai Xiong*, Haipeng Chen*, Mengchen Zhao, Bo An. “HogRider: champion agent of Microsoft Malmo collaborative AI challenge." Thirty-Second AAAI Conference on Artificial Intelligence (AAAI’18). *Equal contribution. (Full paper + Oral, acceptance rate 933/3800 = 24.6%) [pdf]
- Yanhai Xiong. "Electric vehicle charging station placement and management." Ph.D. Dissertation [pdf]
2017
- Yanhai Xiong, Jiarui Gan, Bo An, Chunyan Miao, and Ana LC Bazzan. “Optimal electric vehicle fast charging station placement based on game theoretical framework." IEEE Transactions on Intelligent Transportation Systems (IEEE TITS) [pdf]
2016
- Yanhai Xiong, Jiarui Gan, Bo An, Chunyan Miao, and Yeng Chai Soh. “Optimal pricing for efficient electric vehicle charging station management." Proceedings of the 2016 International Conference on Autonomous Agents & Multiagent Systems (AAMAS’16). (Full paper + Oral, acceptance rate 137/550 = 25%) [pdf]
2015
- Yanhai Xiong, Jiarui Gan, Bo An, Chunyan Miao, and Ana LC Bazzan. “Optimal Electric Vehicle Charging Station Placement." Twenty-Fourth International Joint Conference on Artificial Intelligence (IJCAI’15). (Full paper + Oral, acceptance rate 572/1996 = 28.6%) [pdf]
Awards & Certifications
- Winner of Microsoft Malmo Collaborative AI Challenge, 2017
- Recommended for Teaching, University Teaching Award, NTU, 2015
Professional Services
- Program Committee Member (PC Reviewer): AAAI 2019, 2021, 2022; AAMAS 2019; IJCAI 2019, 2020, 2021 (SPC)
- Journal Review: IEEE Intelligent Systems (since 2018), Journal of Ambient Intelligence & Humanized Computing (since 2018), IEEE Transactions on Vehicular Technology (since 2019), IEEE Transactions on Intelligent Transportation Systems (since 2020)
