AAAI2026

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Interview with Xinwei Song: strategic interactions in networked multi-agent systems

In this interview series, we’re meeting some of the AAAI/SIGAI Doctoral Consortium participants to find out more about their research. We hear from Xinwei Song about the two main research threads she’s worked on so far, plans to expand her investigations, and what inspired her to study AI. Could you start with a quick introduction […]

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Formal verification for safety evaluation of autonomous vehicles: an interview with Abdelrahman Sayed Sayed

In this interview series, we’re meeting some of the AAAI/SIGAI Doctoral Consortium participants to find out more about their research. We sat down with Abdelrahman Sayed Sayed to chat about his work on formal verification applied to autonomous vehicles. Could you tell us a bit about where you’re studying and the broad topic of your

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Scaling up multi-agent systems: an interview with Minghong Geng

In this interview series, we’re meeting some of the AAAI/SIGAI Doctoral Consortium participants to find out more about their research. Minghong Geng recently completed his PhD and is now working as a postdoctoral researcher at Singapore Management University. We sat down to discuss his research on multi-agent systems. Firstly, congratulations on completing your PhD! What

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#AAAI2026 invited talk: machine learning for particle physics

Simulated Large Hadron Collider CMS particle detector data depicting a Higgs boson produced by colliding protons decaying into hadron jets and electrons. Reproduced under a CC BY-SA 3.0 licence. Daniel Whiteson is a particle physicist, who uses machine learning and statistical tools to analyze high-energy particle collisions. He is also a dedicated science communicator, having

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Resource-constrained image generation and visual understanding: an interview with Aniket Roy

In the latest in our series of interviews meeting the AAAI/SIGAI Doctoral Consortium participants, we caught up with Aniket Roy to find out more about his research on generative models for computer vision tasks. Tell us a bit about your PhD – where did you study, and what was the topic of your research? I

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AI and Theory of Mind: an interview with Nitay Alon

In this interview series, we’re meeting some of the AAAI/SIGAI Doctoral Consortium participants to find out more about their research. We sat down with Nitay Alon whose research is at the intersection of cognitive science and AI. We talked about the fascinating topic of Theory of Mind, how this plays out in deceptive environments, multi-agent

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Studying the properties of large language models: an interview with Maxime Meyer

In this interview series, we’re meeting some of the AAAI/SIGAI Doctoral Consortium participants to find out more about their research. We sat down with Maxime Meyer to chat about his current research, future plans, and how he found the doctoral consortium experience. Could you start with an introduction to yourself, where you’re studying and the

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Studying multiplicity: an interview with Prakhar Ganesh

In this interview series, we’re meeting some of the AAAI/SIGAI Doctoral Consortium participants to find out more about their research. We sat down with Prakhar Ganesh to learn about his work on responsible AI, which is focussed on the concept of multiplicity. We found out more about some of the projects he’s been involved in,

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Reinforcement learning applied to autonomous vehicles: an interview with Oliver Chang

In this interview series, we’re meeting some of the AAAI/SIGAI Doctoral Consortium participants to find out more about their research. We caught up with Oliver Chang whose research interests span deep reinforcement learning, autonomous vehicles, and explainable AI. We found out more about some of the projects he’s worked on so far, what drew him

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Extending the reward structure in reinforcement learning: an interview with Tanmay Ambadkar

In this interview series, we’re meeting some of the AAAI/SIGAI Doctoral Consortium participants to find out more about their research. Tanmay Ambadkar is researching the reward structure in reinforcement learning, with the goal of providing generalizable solutions that can provide robust guarantees and are easily deployable. We caught up with Tanmay to find out more

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