AI Planning for Data Exploration
Sihem Amer-Yahia, Centre national de la recherche scientifique (CNRS)

Abstract: Data Exploration is an incremental process that helps users express what they want through a conversation with the data. Reinforcement Learning (RL) is one of the most notable approaches to automate data exploration and several solutions have been proposed. With the advent of Large Language Models and their ability to reason sequentially, it has become legitimate to ask the question: would LLMs and, more generally AI planning, outperform a customized RL policy in data exploration? More specifically, would LLMs help circumvent retraining for new tasks and striking a balance between specificity and generality? This talk will attempt to answer this question by reviewing RL training and policy reusability for data exploration. Biography: Sihem Amer-Yahia is a Silver Medal CNRS Research Director and Deputy Director of the Lab of Informatics of Grenoble. She works on exploratory data analysis and algorithmic upskilling. Prior to that she was Principal Scientist at QCRI, Senior Scientist at Yahoo! Research and Member of Technical Staff at at&t Labs. Sihem served as PC chair for SIGMOD 2023 and as the coordinator of the Diversity, Equity and Inclusion initiative for the database community. In 2024, she received the 2024 IEEE TCDE Impact Award, the SIGMOD Contributions Award, and the VLDB Women in Database Award.

When Data & AI Converge for Good, Societal Impact Accelerates
Nitesh Chawla, University of Notre Dame

Abstract: As data and AI increasingly converge to drive societal impact, their true potential emerges at the intersection of innovation and translational research. In this keynote, I will present our group’s work spanning the journey from data to algorithms to real-world translation — advancing methods for learning on graphs, addressing imbalanced data, and developing large language models, all with an eye toward meaningful impact across domains such as healthcare, sciences, and peace processes. I will also examine the “last-mile” challenge: bridging the gap between algorithmic advances and practical deployment, including issues of data quality, bias mitigation, governance, and equitable access, and discuss how closing this gap can accelerate inclusive and responsible progress. Biography: Nitesh Chawla is the Frank M. Freimann Professor of Computer Science and Engineering and the Founding Director of the Lucy Family Institute for Data and Society at the University of Notre Dame. His research is focused on artificial intelligence, data science, and network science, and is motivated by the question of how technology can advance the common good through convergence. He is a Fellow of: the Institute of Electrical and Electronics Engineers (IEEE); the Association of Computing Machinery (ACM); the American Association for the Advancement of Science (AAAS); and a Fellow of the Association for the Advancement of Artificial Intelligence (AAAI). He is the recipient of multiple awards, including the National Academy of Engineers New Faculty Fellowship, IEEE CIS Outstanding Early Career Award, Rodney F. Ganey Community Impact Award, IBM Big Data & Analytics Faculty Award, IBM Watson Faculty Award, and the 1st Source Bank Technology Commercialization Award. He is co-founder of Aunalytics, a data science software and cloud computing company.

Urban Computing: Enabling Spatio-temporal Intelligences in Cities
Yu Zheng, JD.COM

Abstract: Urban computing aims to tackle the challenges that cities face in the physical world, where tasks and data are naturally endowed with spatial and temporal properties. Affected by many complex factors, urban spaces are massive, dynamic, high-dimensional and nonlinear, and thus are difficult to model. Urban computing creates a data-centric computing framework, which connects urban sensing, urban data management, urban data analytics and providing services into a recurrent process to unlock the power of urban big data (particularly spatial and spatio-temporal data), for an unobtrusive and continuous improvement of people’s lives, city operation systems, and the environment. This talk will present unique properties of spatio-temporal data and the framework that can enable spatio-temporal intelligence. In each layer of urban computing, we will discuss its key research challenges, such as capturing spatio-temporal properties in AI models and cross-domain multimodal data fusion in the physical world, and introduce fundamental methodologies to tackle these challenges. Real-world deployments of urban computing will be also presented at the end of this talk. Biography: Dr. Yu Zheng is the Vice President and Chief Data Scientist of JD.COM, and the president of JD Intelligent Cities Research. Before Joining JD.COM, he was a senior research manager at Microsoft Research. He is also a chair professor at Shanghai Jiao Tong University and an adjunct professor at Hong Kong University of Science and Technology. Zheng had published over 200 quality papers at prestigious conferences and journals and received over 6,4000 citations (H-index 114). He founded the research field of urban computing, which had been widely followed by world-class scientists. His monograph published by MIT Press becomes the first text book of this field. He was the Editor-in-Chief of ACM Transactions on Intelligent Systems and Technology (2015-2021) and had served as the program co-chair of ICDE 2014 and CIKM 2017. He was a keynote speaker of AAAI 2019, KDD 2019 Plenary Keynote Panel and IJCAI 2019 Industrial Days. He received SIGKDD Test-of-Time Award twice (in 2023 and 2024) and SIGSPATIAL 10-Year-Impact Award four times (in 2019, 2020, 2022, and 2024). He was named one of the Top Innovators under 35 by MIT Technology Review (TR35), an ACM Distinguished Scientist (2016) and an IEEE Fellow (2020), for his contributions to spatio-temporal data mining and urban computing. After joining JD.COM, he has served over 70 cities with his technology, generating a revenue over 1 billion USD.