Chun Kai Ling

Chun Kai Ling

Assistant Professor, Computer Science

National University of Singapore, School of Computing

Biography

I am an Assistant Professor in the Department of Computer Science in National University of Singapore (NUS). My research is on multiagent systems and computational game theory. Prior to joining NUS, I was at Columbia University working with Professors Christian Kroer and Garud Iyengar. I completed my PhD at Carnegie Mellon University under the supervision of Zico Kolter and Fei Fang, where I studied how to apply machine learning for large scale general-sum game solving, as well as inverse problems in game theory.

I am interested in (i) modeling games for real-world applications, such as those in cybersecurity, logistics, and select recreational games and (ii) studying and characterizing appropriate equilibrium concepts for such games and how to efficiently compute them. My current projects include attacker-defender structured games played on graphs, language agents for games of negotiation and bayesian persuasion, games with incomplete information, and sound opponent exploitation. If you are a prospective student interested in working in multiagent systems, feel free to send an email reaching out for a discussion!

Download my resumé.

Interests
  • Artificial Intelligence
  • Machine Learning
  • Game Theory
Education
  • PhD in Computer Science, 2017-2023

    Carnegie Mellon University

  • BEng in Computer Engineering, 2015

    National University of Singapore

Recent Publications

(2024). Contested Logistics: A Game Theoretic Approach. In GameSec 2024. Best Paper Award (out of 3).

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(2024). Online bipartite matching with imperfect advice. Accepted to ICML 2024 main track.

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(2024). Layered Graph Security Games. Accepted to IJCAI 2024 main track.

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(2024). Quantifying Interactions in Semi-supervised Multimodal Learning: Guarantees and Applications. Accepted to ICLR 2024 Main Track.

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(2023). Deep Copula-based Survival Analysis for Dependent Censoring with Identifiability Guarantees. Accepted to AAAI 2024 Main Track.

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(2023). Learning Coalition Structures with Games. Accepted to AAAI 2024 Main Track.

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(2023). Quantifying & Modeling Multimodal Interactions: An Information Decomposition Framework. In Neurips 2023 Main Track.

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(2023). Multi-defender Security Games with Schedules. In GameSec 2023. Best Paper Award.

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(2023). Abstracting Imperfect Information Away from Two-Player Zero-Sum Games. In ICML 2023 Main Track.

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(2022). Function Approximation for Solving Stackelberg Equilibrium in Large Perfect Information Games. In AAAI 2023 main track (oral).

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(2022). Safe Subgame Resolving for Extensive Form Correlated Equilibrium. In AAAI 2022 main track (oral).

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(2020). Deep Archimedean Copulas. Accepted to Neurips 2020 main track.

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(2020). Safe Search for Stackelberg Equilibria in Extensive-Form Games. Accepted to AAAI 2021 main track.

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(2020). Nonmyopic Gaussian Process Optimization with Macro-Actions. In AISTATS.

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(2019). Correlation in Extensive-Form Games: Saddle-Point Formulation and Benchmarks. In Neurips main track.

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(2019). Efficient Regret Minimization Algorithm for Extensive-Form Correlated Equilibrium. In Neurips main track (Oral).

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(2019). Large Scale Learning of Agent Rationality in Two-Player Zero-Sum Games. In AAAI 2019 main track.

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(2018). What game are we playing? End-to-end learning in normal and extensive form games. In IJCAI 2018 main track. Distinguished Paper Award..

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(2017). What game are we playing? Differentiably learning games from incomplete observations. In NIPS 2017 Deep RL Symposium.

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(2016). Gaussian Process Planning with Lipschitz Continuous Reward Functions. In AAAI.

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