Information Systems Group

Yiming Lin (UC Berkeley): AI-Powered Data Systems for Multimodal Analytics
2026-01-16 13:00 - 14:00 (America/Los_Angeles) | DBH 3011
Time & Location:

Friday Jan 16, 2026, 1:00 PM – 2:00 PM
Donald Bren Hall 3011, ICS, UC Irvine
Lunch will be provided.
Title:
AI-Powered Data Systems for Multimodal Analytics

Abstract:

We 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.
In 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.

Bio:

Yiming 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.

Volunteer:
Guangxue Zhang

Prof. Jianguo Wang (Purdue): Databases for AI: The Case for Vector Databases
2026-01-23 13:00 - 14:00
Title: Databases for AI: The Case for Vector Databases
1/23/2026, Friday
DBH 3011, UCI

Abstract: 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.

Bio. 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.

Andrew Chio: Building Resilient Systems for Critical Infrastructures: A Model-Driven and Data-Driven Approach
2026-01-30 13:00 - 14:00
Time & Location:

Friday Jan 30, 2026, 1:00 PM – 2:00 PM
Donald Bren Hall 3011, ICS, UC IrvineLunch will be provided.

Title:
Building Resilient Systems for Critical Infrastructures: A Model-Driven and Data-Driven Approach

Abstract:
Critical 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:
Andrew 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.

Sponsors:

ISG seminar: "How grants and funding work in academia"
2026-02-06 13:00 - 14:00
[ISG] ISG seminar this Friday (Feb. 6): “How grants and funding work in academia”
In the ISG seminar this Friday (1 – 2 pm, DBH 3011), the ISG faculty will give a talk on “How grants and funding work in academia.” We will discuss how faculty write proposals, receive awards, and spend the funds to support students and research activities. We will also emphasize the importance of PhD students participating in proposal writing.

Santosh Hegde (Couchbase, Inc.): From Transactions to Intelligence: Evolution of Couchbase for AI
2026-02-13 13:00 - 14:00
The Department of Computer Science, Information Systems Group, UC Irvine
WELCOMES
Santosh Hegde
Couchbase, Inc.

From Transactions to Intelligence: Evolution of Couchbase for AI

02/13/2026, Friday, 1:00 – 2:00 pm
Place: DBH 3011

Abstract: When Couchbase started, it was built to solve a very specific problem, namely the handling of high-performance, low-latency transactional workloads at massive scale. Over time, however, customer needs have evolved from simply storing and retrieving data reliably at scale, to analyzing it in real time, and now to reasoning over it using AI. In this talk, we will explore the new capabilities in the Couchbase platform as it transforms from a pure NoSQL transactional database into a unified platform that supports transactions, analytics, and AI workloads all on the same data foundation.

Bio: Santosh Hegde is an engineering leader with over 18 years of experience in the design and development of large-scale distributed database systems. He currently serves as Vice President of Engineering at Couchbase, where his work focuses on the intersection of database systems, analytics, and artificial intelligence. Previously, he has held senior engineering leadership roles at IBM Software Labs and Visa Inc. His technical interests include query processing, database runtimes, columnar data systems, large-scale data ingestion, and database-as-a-service architectures. Santosh has contributed to the development of multiple enterprise data platforms, including Informix, DB2 (LUW) and Trino . More recently, his work has focused on the evolution of data platforms to support emerging AI-driven application patterns.

Ashwin Gerard Colaco (UCI): Bringing Simulators Inside the Database: A Vision for Interactive Scientific Exploration
2026-02-20 13:00 - 14:00
Time & Location:

Friday Feb 20, 2026, 1:00 PM – 2:00 PM
Donald Bren Hall 3011, ICS, UC Irvine
Lunch will be provided.

Title:
Bringing Simulators Inside the Database: A Vision for Interactive Scientific Exploration

Abstract:
Physics-based simulators are essential for scientific discovery and risk assessment, powering what-if analyses for events like wildfires and hurricanes. Yet today’s workflow is fundamentally disconnected: analysts manually run simulations, export results, and load them into a database before any analysis can begin. This linear pipeline is slow, brittle, and especially limiting when the analysis itself reveals the need for new or refined simulation data.

In this talk, I present a vision for GenIE, a new database paradigm that makes the database simulation-aware by seamlessly integrating multiple simulators as first-class components. Designed as an extension to PostgreSQL, GenIE dynamically invokes simulators based on the user’s query, avoids generating irrelevant data, reuses prior results, and supports iterative refinement at interactive speeds. I illustrate GenIE’s potential through two use cases: wildfire smoke dispersion analysis using WRF-SFIRE and HYSPLIT, and hurricane hazard assessment combining wind, surge, and flood models. Our preliminary results show how GenIE can transform these traditionally slow, static analyses into responsive explorations by intelligently managing the trade-off between simulation accuracy and runtime. I conclude by outlining the data engineering challenges and future research directions in realizing the full potential of simulation-aware databases for next-generation scientific data management.

Bio:
Ashwin Gerard Colaco is a PhD candidate in Computer Science at UC Irvine, advised by Sharad Mehrotra. His research focuses on the intersection of database systems and machine learning, with a focus on cost-efficient inference and simulation-driven data exploration. He’s supported by the Hasso Plattner Institute Fellowship for scalable databases for ML research.

Zexin Li (UCR): Unified Full-stack Co-design for On-device Machine Learning
2026-02-27 13:00 - 14:00
For this week’s ISG seminar, we’ll have an invited speaker: Zexin Li from University of California, Riverside to give us a talk.
Time & Location:
Friday Feb 27, 2026, 1:00 PM – 2:00 PM
Donald Bren Hall 3011, ICS, UC Irvine
(Zoom link will be shared by request)
Lunch will be provided.
Title:
Unified Full-stack Co-design for On-device Machine Learning
Abstract:
The integration of advanced artificial intelligence into Cyber-Physical Systems (CPS), such as multirotor UAVs and wheeled mobile robots, promises a future of edge intelligence. However, deploying complex machine learning models directly onto real-time embedded systems presents significant challenges, primarily due to strict timing constraints, limited memory, and dynamically changing environments. This talk presents a unified full-stack co-design approach to manage these complex, multidimensional trade-offs.
Bio:
Zexin Li is a Ph.D. student at the University of California, Riverside, advised by Cong Liu. His research interests lie in interdisciplinary fields of real-time embedded systems and on-device machine learning.
Volunteer:
Keming Li
Sponsors:

Zheng LUO (UCLA): Generating Join Trees for Yannakakis Algorithm
2026-03-06 13:00 - 14:00
ISG seminar on March 6, Friday, 1 – 2 pm, DBH 3011
Title: Generating Join Trees for Yannakakis Algorithm
Abstract:
Most research on query optimization has centered on binary join algorithms like hash join and sort-merge join. However, recent years have seen growing interest in theoretically optimal algorithms, notably Yannakakis algorithm. These algorithms require new optimization techniques, as they rely on join trees where each node represents a relation, very different from the operator trees for binary joins.
Our recent theoretical work proposes three approaches to constructing join trees for Alpha-acyclic queries:
(1) an algorithm to enumerate all join trees, which forms the basis of a cost-based optimizer;
(2) a 1-shot approach to construct a unique shallowest join tree for any Berge-acyclic query, thus enabling parallel execution of large join queries;
(3) a simple algorithm that converts any connected left-deep linear plan of a Gamma-acyclic query into a join tree, allowing reuse of existing optimizers developed for binary joins.
In this talk, we will also discuss how the theoretical results can turbocharge query processing in modern database systems.
Bio:
Zheng LUO is a Ph.D. student at the University of California, Los Angeles (UCLA), advised by Prof. Remy WANG.
His research interests are twofold, spanning from theory to systems.
(1) His current work centers on the theoretical aspects of query optimization in relational databases by examining the algorithms and data structures that improve the efficiency of query processing;
(2) He is also exploring ways to put theory into practice by implementing theoretical results and integrating them into systems.

Sarah Asad: Teaching Data Science and AI/ML to Diverse Learners Using Apache Texera: An Experience Report
2026-04-17 13:00 - 14:00
We’ll have Sarah present her work for this week’s ISG seminar.
Time & Location:
Friday April 17, 2026, 1:00 PM – 2:00 PM
Donald Bren Hall 3011, ICS, UC Irvine
Zoom:
https://uci.zoom.us/j/95509222811?pwd=[REDACTED]

Lunch will be provided.
Title
Teaching Data Science and AI/ML to Diverse Learners Using Apache Texera: An Experience Report
Abstract
This talk reports on our experiences teaching data science and AI/ML through a series of hands-on programs to learners ranging from high school to graduate students and non-STEM faculty. The programs are taught using Texera, an open-source system for collaborative data science and AI/ML using GUI-based workflows. A uniqueness of these programs is that they did not require participants to have prior coding skills. We describe the program-preparation process, curriculum structure, classroom experience, and feedback collected from participants. We summarize our insights regarding student engagement, effectiveness of interactive and collaborative learning environments, and practical considerations for designing accessible data science programs for learners with diverse backgrounds.
Bio
Sarah Asad is a second-year PhD student in the Computer Science Department at UC Irvine, with research interests in data systems, data science, and big data analysis. She is supervised by Prof. Chen Li.

Prof. Eduard Dragut (Temple University): Toward Scalable Knowledge Extraction with Weak Supervision and Large Language Models
2026-04-24 13:00 - 14:00
Friday April 24, 2026, 1:00 PM – 2:00 PM
Donald Bren Hall 3011, ICS, UC Irvine
Zoom:
https://uci.zoom.us/j/95509222811?pwd=[REDACTED]
Lunch will be provided.
Title: Toward Scalable Knowledge Extraction with Weak Supervision and Large Language Models
Abstract: Information extraction is a foundational capability for transforming unstructured text into structured knowledge, enabling downstream applications such as knowledge graph construction, semantic search, question answering, and scientific discovery. However, building high-quality extraction systems traditionally depends on large manually annotated datasets, which are costly to create and often impractical in specialized domains. In this talk, I will present recent advances toward scalable information extraction under limited supervision. I will discuss methods for improving the quality of weakly supervised training data through automatic label cleaning, show how richer benchmarks over full scientific documents expose new challenges for scientific information extraction beyond simplified abstract-level settings, and demonstrate how large language models can be leveraged in many-shot in-context learning regimes to perform competitive named entity recognition and generate high-quality annotations for low-resource domains. Together, these results suggest a promising path toward scalable knowledge extraction pipelines that reduce reliance on expensive manual annotation while improving the robustness and adaptability of systems used to build next-generation knowledge graphs and AI applications.
Bio: Eduard Dragut is a Professor in the Department of Computer and Information Sciences at Temple University. He is a senior member of the IEEE. He received his Ph.D. in Computer Science from the University of Illinois at Chicago. His research focuses on data management, information retrieval, and applied artificial intelligence, with an emphasis on building scalable systems for extracting and integrating knowledge from large and heterogeneous data sources. He also pursues interdisciplinary AI projects for social good, including work on assistive technologies such as augmentative and alternative communication (AAC) and AI-driven tools for knowledge discovery. He has published widely in leading venues in databases, natural language processing, and data mining.

Yinan & Juncheng ICDE Practice Talk
2026-05-01 13:00 - 14:00
We’ll have Yinan & Juncheng present their ICDE works for this week’s ISG Seminar. This will be a shared session.
Time & Location:
Friday May 1, 2026, 1:00 PM – 2:00 PM
Donald Bren Hall 3011, ICS, UC Irvine
Zoom:
https://uci.zoom.us/j/95509222811?pwd=[REDACTED]

Lunch will be provided.
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Speaker
Yinan Zhou
Title
SpendableStore: A UTXO-based Decentralized Data Store
Abstract
The literature on blockchain-based databases is divided into permissioned blockchains and permissionless account based blockchains. However, the former is not fully decentralized, and the latter suffers from challenges in scalability and practicality. We propose SpendableStore, a hybrid on/off-chain database that operates on top of permissionless UTXO-based blockchains as a novel approach to the problem of data decentralization. Our design integrates atomic data units into individual UTXOs to create a new blockchain concept called Spendable Data Objects that perform traditional CRUD operations. The integrity, immutability, and ownership of these Spendable Data Objects are safeguarded directly by the blockchain peer nodes, thus constraining the power of database administrators to achieve true data decentralization. We further support database transactions and propose an isolation mechanism called Future Now Snapshot Isolation to reason about transactional correctness in SpendableStore. We performed experiments on a major blockchain’s Mainnet and observed up to 16x better throughput compared to a state-of-the-art blockchain-based database.
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Speaker
Juncheng Fang
Title
ImmortalChopper: Real-Time and Resilient Distributed Transactions in the Edge-Cloud
Abstract
Emerging applications in the areas of real-time Internet of Things (IoT) and edge technologies require fast processing and response times. This motivates the utilization of edge nodes for storing and processing data close to the user. In settings with a vast number of edge nodes, the state of the data is distributed across a large number of edge nodes. This makes it expensive to perform distributed transactions as these transactions would span edge nodes that are connected via less reliable and relatively slow network infrastructure. It is prohibitive to use existing protocols like 2PC that require many rounds of communication across participants.
In this talk, we discuss ImmortalChopper, a distributed transaction processing protocol designed for the edge-cloud environment. The goal of ImmortalChopper is to provide One-Node Response (1n-Response), a guarantee of transaction commitment by contacting only one node without waiting for coordination with the other nodes. To achieve this, we build on and extend the literature of transaction chopping and lazy replication. However, combining transaction chopping and lazy replication without special care can lead to transactions operating on a stale state and potentially violating serializability. We present a new transaction chopping theory called ChopperGraph that integrates the notion of lazy replication and speculative execution. It ensures 1n-Response while preserving serializability.
Bio
Juncheng Fang is a 5th-year Ph.D. candidate in the Computer Science Department at UC Irvine, supervised by Prof. Faisal Nawab. His current research focuses on distributed transaction processing, specifically improving the concurrency by exploiting the semantics of transactions.
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Pratyoy (UCI): SmartRabbit An Interactive Query Processor
2026-05-08 13:00 - 14:00
For this week’s ISG seminar, Pratyoy will do his SIGMOD practice talk.

Time & Location:

Friday May 8, 2026, 1:00 PM – 2:00 PM
Donald Bren Hall 3011, ICS, UC Irvine

Title: SmartRabbit: An Interactive Query Processor.

Abstract:Traditional relational database systems optimize analytical queries to minimize their end-to-end latency. The resulting optimal plans are usually blocking, forcing users to wait until full query completion before seeing any results. This execution model precludes interactivity, i.e., users cannot observe partial results or gain early insights for long-running queries. Query optimizers rarely choose plans that promote interactivity, since such plans either incur prohibitively large latencies or involve operators for which interactive alternatives are unavailable. We introduce a novel interactive query processor SmartRabbit that promotes interactivity of answers while matching the end-to-end latency of blocking execution plans. We achieve this by first designing a plan optimized for interactivity for a given query, and then simultaneously executing this plan alongside a traditional blocking plan. The two executions are carefully synchronized to maintain the correct order of answers and prevent duplicates. We implement SmartRabbit in AsterixDB and show that SmartRabbit consistently delivers early and continuous results across various analytical queries, data scales, and parallel (multi-node, multi-partition) system instances, while matching the latencies of the standalone blocking executions.

Bio: Pratyoy is a 4th year PhD student under Professor Sharad Mehrotra. His research focuses on query optimization and query execution with specific interests in adaptive, interactive and progressive query optimization. Pratyoy had previously interned in the query optimization team of Amazon Redshift and was a Software Engineer at Microsoft before joining UC Irvine.

Zoom:
https://uci.zoom.us/j/95509222811?pwd=[REDACTED]

Lunch will be provided.
