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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 […]
Read full articleThe 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, […]
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 […]
Learn the top Python frameworks for LLM apps covering fine-tuning, model loading, serving, RAG pipelines, multi-agent systems, and evaluation.
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 […]
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 […]
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 […]
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 […]
A new tool from Google Cloud aims to improve the accuracy of AI agents querying databases in multi-agent systems or applications. QueryData, which translates natural language into database queries with what the company claims is “near 100% accuracy,” is being pitched as an alternative to direct generation of queries by large language models (LLMs), which Google says can introduce inaccuracies due to their limited understanding of database schemas and their probabilistic reasoning. However, to create that necessary understanding, enterprise teams using QueryData first need to define what Google describes as “context” describing how data should be accessed and queried, which involves encoding details about database schemas, including descriptions of tables, relationships, and business meaning, along with deterministic instructions that guide how queries are generated or executed. Once the context and the guidelines are configured, teams can use the Context Engineering Assistant, a dedica
