جست‌وجوی استاد و پژوهشگر

• Table Representation Learning (TRL)
• Generative Models for Tabular Data
• Data Retrieval
• +۳ مورد دیگر

Madelon Hulsebos is a faculty member at Centrum Wiskunde & Informatica (CWI), leading the Table Representation Learning (TRL) Lab and part of the Database Architectures group. She is also an ELLIS faculty member with the Amsterdam unit. Her research focuses on TRL, generative models for tabular data, and end-to-end systems for data analysis. She holds a PhD from the University of Amsterdam (2019) and conducted postdoctoral research at UC Berkeley (2020–2024). Her work has been supported by grants like the NWO AiNed Fellowship and the Accenture-BIDS Fellowship. She organizes TRL workshops at NeurIPS, ACL, and SIGMOD, and her research spans over 1.7M tables from GitHub (GitTables), schema analysis (SchemaPile), and dataset search systems. Key achievements include founding the TRL workshop series and leading the $1.5M DataLibra project to democratize structured data insights. Education: PhD (2019) and postdoc at UC Berkeley. Grants: NWO AiNed ($1M+), NWO/AiNed DataLibra ($1.5M total). Affiliations: CWI, ELLIS Amsterdam, and collaborations with MIT, Sigma Computing, and industry sponsors. Research Interests: TRL, generative AI for tables, data retrieval systems, and scalable data science. Recent Work: SIGMOD 2024 presentations, TRL@NeurIPS 2024 workshop leadership, and the SchemaPile corpus. Community Roles: ELLIS society member, co-organizer of DEEM workshop, and TRL Discord/Bluesky communities.

Raul Castro Fernandezمشاهده پروفایل

استادیار

• Data Ecology
• Data Discovery
• Data Markets
• +۵ مورد دیگر

Raul Castro Fernandez is an Assistant Professor of Computer Science at the University of Chicago and co-founder and Chief Research Officer at invocate. He originated the concept of 'data ecology,' which frames his research on how data moves through and transforms technological, economic, and social systems—and how we can design interventions to make those ecosystems more valuable, equitable, and resilient. He co-leads the Data Ecology research initiative at the Data Science Institute and co-runs Chicago Data Night, a forum that brings together industry and academia in Chicago. Dr. Castro Fernandez's research centers on data ecology, data discovery, data markets, and data integration. His work develops both theoretical frameworks and practical systems that help organizations find, evaluate, and use data effectively. He approaches data as a socio-technical phenomenon, examining how data shapes our world and how we can shape it back through technical, economic, and social interventions. His research bridges computer science, economics, and social science to address fundamental challenges in data ecosystems. His recent publications reveal a strong focus on applying large language models to data management challenges, particularly for tabular data discovery and integration. He has developed innovative systems like Pneuma for LLM-based tabular data navigation, Solo for natural language data discovery, and Nexus for correlation discovery in spatio-temporal data. His work also addresses critical challenges in data valuation, data markets, and responsible data sharing, with applications across industry and research contexts. SIGMOD Test-of-Time Award (2023) NSF CAREER Award (2024) Sloan Research Fellowship (2025) Dr. Castro Fernandez actively mentors students across multiple levels, advising PhD students including Qiming Wang, Yue Gong, and Zhiru Zhu, as well as numerous master's and undergraduate students. His group has developed influential systems including Data Station (for trustworthy data sharing), Ver (for view discovery), Metam (for goal-oriented data discovery), and Solo (for natural language data discovery). These systems address fundamental challenges in data discovery, sharing, and integration, with applications across various domains. He leads the Data Ecology research group at the University of Chicago, which focuses on developing technical, economic, and social interventions to make data ecosystems more valuable, equitable, and resilient. His team works at the intersection of database systems, machine learning, and economics to build practical systems that address real-world data challenges faced by organizations and individuals, with a particular emphasis on the socio-technical aspects of data sharing and discovery.

Tomi Janhunenمشاهده پروفایل

استاد

• Knowledge Representation and Reasoning
• Answer Set Programming
• Satisfiability Checking
• +۶ مورد دیگر

Tomi Janhunen is a Professor in Computing Sciences at Tampere University, specializing in knowledge representation, automated reasoning, and logic programming. He previously served as Adjunct Professor at Aalto University (2019–2024) and maintains a Doctor of Science (Tech.) degree. His research spans answer set programming, satisfiability checking, optimization, and distributed computation. PhD, Aalto University (Doctor of Science (Tech.)) Adjunct Professor of Computer Science (Aalto University, 2019–2024) His research focuses on Answer Set Programming (modularity, verification, optimization), Satisfiability Modulo Theories , Nonmonotonic Logics , and Computational Complexity . He integrates logic programming into real-world applications like preventive maintenance scheduling and AI security systems. Recent work includes translating logic programs into integer programming, developing probabilistic reasoning systems (Plingo), and creating interpretable classifiers for tabular data. His publications emphasize stable model semantics , optimization techniques , and constraint networks . He supervises M.Sc., Lic.Sc., and Ph.D. theses and has completed pedagogical studies. Janhunen actively reviews for journals like Artificial Intelligence Journal and ACM Transactions on Computational Logic .