Keynotes
On Building Spatially-Aware Systems
Mohamed Mokbel (University of Minnesota, USA)
Abstract: The need to manage and analyze spatial data is hampered by the lack of specialized systems to support such data. System builders mostly build general-purpose systems that are generic enough to handle any kind of attributes. Whenever there is a pressing need for spatial data support, it is considered as an afterthought problem that can be addressed by adding new data types, extensions, or spatial cartridges to existing systems. This talk advocates for dealing with spatial data as first class citizens, and for always thinking spatially whenever it comes to system design. This is well justified by the proliferation of location-based applications that are mainly relying on spatial data. The talk will go through various system designs and show how they would be different if designed while thinking spatially. Examples of such systems include big data systems, data cleaning systems, and machine learning-based systems.
Bio: Mohamed Mokbel is a Distinguished McKnight University Professor at the University of Minnesota. Prior roles while on leave/sabbatical from UMN include Chief Scientist of Qatar Computing Research Institute, Founding Technical Director of GIS Technology Innovation Center in Saudi Arabia, and multiple times Visiting Researcher at Microsoft Research, USA. His research interests include database systems and spatial computing. Mohamed was recognized by the VLDB 10-Year Test-of-Time Award for his work on location privacy, the IEEE ICDE 10-Year Influential Paper Award for his work on big spatial data, the ACM SIGSPATIAL 10-Year Impact Award for his work on location-based social networks, and was short listed for another ACM SIGSPATIAL 10-Year Impact Award for his work on microblogs data management. Mohamed is the Editor-in-Chief for ACM Transactions on Spatial algorithms and Systems (ACM TSAS), and is/was on the Editorial Board of ACM Books, ACM TODS, and VLDB Journal. He has served as the elected Chair of ACM SIGSPATIAL, and PC Co-Chair for ACM SIGMOD and ACM SIGSPATIAL. Mohamed is an ACM Fellow and IEEE Fellow.
Model Lakes
RenÉe Miller (University of Waterloo, Canada)
Abstract: Data lakes emerged in the early 2010s as repositories to collect raw, unstructured data at scale — as organizations recognized the untapped value of this messy data. The study of data lakes has evolved and matured shaping how we store, manage, and extract insights from these massive heterogeneous information stores. Data Lake research is shaping data management outside and with the DBMS. We are now seeing the emergence of modellakes, repositories of large sets of pre-trained AI models. In this talk, I present a vision for the management of model lakes and consider how the application of principled data management can do for understanding and using AI models.
Bio: Renée J. Miller is a Professor and Canada Excellence Research Chair in Data Intelligence at the Cheriton School of Computer Science at the University of Waterloo. Renée is a Fellow of the Royal Society of Canada, Canada’s national academy of science, engineering, and the humanities. She received the U.S. Presidential Early Career Award for Scientists and Engineers (PECASE), the highest honor bestowed by the United States government on outstanding scientists and engineers beginning their careers. She also received an NSF CAREER Award, the Ontario Premier’s Research Excellence Award, and an IBM Faculty Award. She has been named the Bell Canada Chair of Information Systems and is a Fellow of the ACM and AAAS. Her work has focused on the long-standing open problem of data integration and has achieved the goal of building practical data integration systems. She and her colleagues received the ICDT Test-of-Time Award and the 2020 Alonzo Church Alonzo Church Award for Outstanding Contributions to Logic and Computation for their influential work establishing the foundations of data exchange. For her body of work, she has received the CS Canada Lifetime Achievement Award in Computer Science. Professor Miller served as president of the non-profit Very Large Data Base (VLDB) Foundation and an Editor-in-Chief of the VLDB Journal.
Scientific Relevance in a Spiral:
How to Remain Scientifically Relevant for 30 Years in Data and AI
Marcos André Gonçalves (Universidade Federal de Minas Gerais)
Honored Speaker
Abstract: How can one remain scientifically relevant in a field that reinvents itself every second? In this talk, we will dive into a 30-year career journey to reveal that true consistency in computing does not come from following the next technological wave, but from deepening the structural questions that outlast them. Moving beyond the idea of a linear or cyclical trajectory, I present the metaphor of the spiral: a model of evolution in which each new phase — from formal modeling and learning to rank to algorithmic responsibility and health — is not a rupture, but an expansion of impact and rigor. It is an invitation to look beyond the frameworks of the moment and understand science as a cumulative project, where technology changes, but the pursuit of foundations, ethics, and institutional depth remains vertical.
Bio: Full Professor at the Department of Computer Science, UFMG. He has received several awards and honors throughout his career, including multiple prizes in national thesis and dissertation competitions, most notably two CAPES Thesis Awards: 2024, 1st place, advisor; and 2020, Honorable Mention, co-advisor. He has also received several best paper awards. His work is in Computer Science, with an emphasis on Information Retrieval, Machine Learning, and Natural Language Processing. He has published more than 400 peer-reviewed papers in these research areas. His h-index is 61 on Google Scholar, with more than 15,000 citations, and he is listed in the Stanford Ranking as one of the world’s most influential scientists. He was General Chair of the ACM/IEEE Joint Conference on Digital Libraries in 2018 and is a Senior Program Committee Member of the leading conferences in his field, including ACM SIGIR, ACL, ACM RecSys, ACM CIKM, ACM WSDM, and ECIR. He is also Senior Editor of the Journal of the Brazilian Computer Society, responsible for the areas of Information Retrieval and Recommender Systems, and is a member of the editorial board of the Transactions of the Association for Computational Linguistics (TACL). He was an Affiliate Member of the Brazilian Academy of Sciences for a five-year term, is a CNPq Research Productivity Fellow, level 1-A, former full member of the Exact Sciences Chamber (CEX) of FAPEMIG, and Coordinator of the National Institute of Science and Technology (INCT) in Responsible Artificial Intelligence for Computational Linguistics and Information Processing and Dissemination — INCT-TILD-IAR.
