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Making AI systems more transparent and trustworthy: an interview with Ximing Wen

The latest interview in our series with the AAAI/SIGAI Doctoral Consortium participants features Ximing Wen who is researching transparent and trustworthy AI systems. We found out more about her work, her experience as a research intern, and what inspired her to study AI. Tell us a bit about your PhD – where are you studying, […]

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Panda and tiger reading

AIhub monthly digest: April 2026 – machine learning for particle physics, AI Index Report, and table tennis

Welcome to our monthly digest, where you can catch up with any AIhub stories you may have missed, peruse the latest news, recap recent events, and more. This month, we meet PhD students and early-career researchers, find out how machine learning is used for particle physics discoveries, cast an eye over the latest AI Index

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#AAAI2026 invited talk: Yolanda Gil on improving workflows with AI

Jamillah Knowles & Digit / Pink Office / Licenced by CC-BY 4.0 Yolanda Gil is a professor at the University of Southern California, where she also serves as Senior Director for major strategic AI and data science initiatives. From 2018 – 2020, she was president of AAAI. In her invited talk at AAAI 2026, she

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Interview with Deepika Vemuri: interpretability and concept-based learning

The latest interview in our series with the AAAI/SIGAI Doctoral Consortium participants features Deepika Vemuri who is working on interpretability and concept-based learning. We found out more about the two aspects of concept-based models that she’s been researching. Could you tell us a bit about your PhD – where are you studying, and what is

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Causal models for decision systems: an interview with Matteo Ceriscioli

How do you go about integrating causal knowledge into decision systems or agents? We sat down with Matteo Ceriscioli to find out about his research in this space. This interview is the latest in our series featuring the AAAI/SIGAI Doctoral Consortium participants. Could you start by telling us a bit about your PhD – where

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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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Interview with Sukanya Mandal: Synthesizing multi-modal knowledge graphs for smart city intelligence

In their paper LLMasMMKG: LLM Assisted Synthetic Multi-Modal Knowledge Graph Creation For Smart City Cognitive Digital Twins, which was published in the AAAI Fall Symposium series, Sukanya Mandal and Noel O’Connor introduced an approach that leverages large language models to automate the construction of synthetic multi-modal knowledge graphs specifically designed for a smart city cognitive

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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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