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X-WR-CALDESC:Events for Information Systems Group
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DTSTART;TZID=America/Los_Angeles:20260116T130000
DTEND;TZID=America/Los_Angeles:20260116T140000
DTSTAMP:20260428T211057
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SUMMARY:Yiming Lin (UC Berkeley): AI-Powered Data Systems for Multimodal Analytics
DESCRIPTION:Time & Location:\n\n\nFriday Jan 16\, 2026\, 1:00 PM – 2:00 PM\nDonald Bren Hall 3011\, ICS\, UC Irvine \nLunch will be provided. \nTitle:\nAI-Powered Data Systems for Multimodal Analytics\n\nAbstract: \n\nWe live in a world overflowing with data\, and the emergence of AI\, such as Large Language Models (LLMs)\, is revolutionizing data analytics. However\, directly using AI to process massive and complex data is neither effective nor scalable. \nIn this talk\, I introduce my work on building database systems powered by AI to analyze and process multimodal data at scale\, focusing on tables and documents. On one hand\, when analyzing tables\, AI is often used to prepare data\, such as cleaning\, enriching\, or synthesizing data prior to query processing. This becomes prohibitively expensive when the data scale is large. To support scalable analysis over expensive data ingestion\, my work leverages the fact that not all data are needed to answer a query and explores a set of techniques to reduce AI operations unnecessary to analytics by optimizing the query engine in the database. On the other hand\, when analyzing documents\, current systems treat them as plain text and ignore underlying structures\, leading to limited accuracy and performance. In this regard\, we exhaustively identified three document structures that encompass most real-world documents we have encountered\, and we designed tools and systems to extract their structures and leverage them for accurate and efficient document analytics. Finally\, I’ll share my vision for building data systems for multimodal analytics\, including aspects of trustworthy systems\, interaction with hardware\, and co-optimization among different data modalities. \n\n\nBio: \n\n\n\n\nYiming Lin is a postdoctoral researcher at UC Berkeley\, and he received his Ph.D. from UC Irvine. His research interests span document analytics\, query processing and optimization\, and data cleaning\, with a current focus on building databases for multimodal analytics powered by AI. His work has had real-world impact: document analytics help public defenders\, journalists\, and the California police department process over 30\,000 pages\, while his efforts as part of TippersDB deliver high-quality IoT services to nursing homes\, industries\, and universities across five sites over six years. He has a number of publications and serves on the program committee of VLDB\, SIGMOD\, and ICDE. \n\n\n\n\nVolunteer: \nGuangxue Zhang
URL:https://isg.ics.uci.edu/event/yiming-lin-uc-berkeley-ai-powered-data-systems-for-multimodal-analytics/
LOCATION:DBH 3011
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DTSTART;TZID=America/Los_Angeles:20260123T130000
DTEND;TZID=America/Los_Angeles:20260123T140000
DTSTAMP:20260428T211057
CREATED:20251121T025541Z
LAST-MODIFIED:20251121T025541Z
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SUMMARY:Prof. Jianguo Wang (Purdue): Databases for AI: The Case for Vector Databases
DESCRIPTION:Title: Databases for AI: The Case for Vector Databases\n1/23/2026\, Friday\nDBH 3011\, UCI \n\nAbstract: Vector databases have recently emerged as a hot topic due to the widespread interest in LLMs\, where they provide relevant context that enables LLMs to generate more accurate responses. Current vector databases can be broadly categorized into two types: specialized and integrated. Specialized vector databases are explicitly designed for managing vector data\, while integrated vector databases support vector search within existing database systems (mostly relational databases). While specialized vector databases are interesting\, there is a significant customer base interested in integrated vector databases for various reasons\, such as reluctance to move data out\, the desire to link vector embeddings with their source data\, and the need for advanced vector search capabilities. However\, integrated vector databases face challenges in performance and interoperability. In this talk\, I will share our recent experience building integrated vector databases within two relational databases: SingleStore (VLDB’24) and PostgreSQL (CIDR’26). I will show how we address performance and interoperability challenges\, resulting in more powerful vector databases that support advanced RAGs. I will also present additional challenges in vector databases and our ongoing research to address them. Finally\, I will discuss the broader role of database systems in the era of LLMs and how to build future data infrastructure that extends beyond vector databases to better support AI.\n\nBio. Jianguo Wang is an Assistant Professor of Computer Science at Purdue University. He received his Ph.D. from the University of California\, San Diego. His research focuses on database systems for the Cloud and LLMs\, with a particular focus on Disaggregated Databases and Vector Databases. He has worked or interned at Zilliz\, Amazon AWS\, Microsoft Research\, Oracle\, and Samsung\, contributing to the development of various database systems. He regularly publishes and serves on program committees for premier database conferences\, including SIGMOD\, VLDB\, and ICDE. He also moderated the VLDB 2024 panel on vector databases and was invited to the Dagstuhl Seminar on vector databases. His research has impacted multiple industrial-strength database systems\, including Amazon Aurora\, Zilliz Milvus\, SingleStore\, and TigerGraph. His research has been recognized with multiple awards\, including the NSF CAREER Award\, the ACM SIGMOD Research Highlight Award\, the Google ML and Systems Junior Faculty Award\, and the IEEE TCDE Rising Star Award.
URL:https://isg.ics.uci.edu/event/prof-jianguo-wang-purdue-databases-for-ai-the-case-for-vector-databases/
LOCATION:DBH 3011
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260130T130000
DTEND;TZID=America/Los_Angeles:20260130T140000
DTSTAMP:20260428T211057
CREATED:20260127T031809Z
LAST-MODIFIED:20260324T060539Z
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SUMMARY:Andrew Chio: Building Resilient Systems for Critical Infrastructures: A Model-Driven and Data-Driven Approach
DESCRIPTION:Time & Location:\n\n\nFriday Jan 30\, 2026\, 1:00 PM – 2:00 PM\nDonald Bren Hall 3011\, ICS\, UC IrvineLunch will be provided. \n\n\nTitle:\nBuilding Resilient Systems for Critical Infrastructures: A Model-Driven and Data-Driven Approach\n\nAbstract:\nCritical infrastructures such as water\, power\, and buildings are large-scale distributed systems that serve as essential lifelines for communities worldwide. Today\, they face unprecedented resilience challenges affecting millions of people and cause billions in damage. In this talk\, I will present my research addressing fundamental computational challenges in these cyber-physical systems by combining model-driven approaches that encode physics and network constraints with data-driven techniques that learn from real-world patterns. I will demonstrate this across three critical infrastructure domains\, addressing key resilience challenges. First\, I present STEP\, a framework that solves the NP-hard sensor placement problem for detecting transient contamination events in stormwater networks. Second\, I introduce SEQUIN\, which leverages network science principles and physics-based optimization to identify sequential attack patterns. Third\, I showcase SmartSPEC\, an event-driven simulation framework that generates realistic synthetic human behavioral data by exploiting environmental semantics. Together\, these systems demonstrate how integrating models and data can address diverse resilience challenges for societal-scale systems.Bio:\nAndrew is a final PhD candidate in the Distributed Systems Middleware (DSM) Group at the University of California\, Irvine\, advised by Prof. Nalini Venkatasubramanian. He is also affiliated with the Los Alamos National Laboratory\, working with Dr. Russell Bent in the T-5 Theoretical Division Applied Mathematics and Plasma Physics Group. His research interests lie at the intersection of cyber-physical systems\, optimization\, middleware\, and artificial intelligence. His current work focuses on building systems that enhance the resilience of societal-scale cyber-physical systems such as electric power grids\, stormwater networks\, and smart buildings. His work has been published in top venues such as ACM/IEEE ICCPS\, IEEE PerCom\, ACM BuildSys\, and VLDB. He is the recipient of the NSF CPS Rising Stars Award in 2025\, the CPS-Week PhD Forum Best Poster Award in 2025\, the UC National Lab In-Residence Graduate Fellowship in 2022\, the ARCS Foundation Scholarship in 2022\, as well as the Best Paper Award in IEEE PerCom 2022. \n\n\n\n\n\n\n\n\n\n\n\n\n\nSponsors:
URL:https://isg.ics.uci.edu/event/andrew-chio-building-resilient-systems-for-critical-infrastructures-a-model-driven-and-data-driven-approach/
LOCATION:DBH 3011
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