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X-WR-CALDESC:Events for Information Systems Group
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DTSTART:20260308T100000
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DTSTART;TZID=America/Los_Angeles:20260417T130000
DTEND;TZID=America/Los_Angeles:20260417T140000
DTSTAMP:20260722T020501
CREATED:20260414T044342Z
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SUMMARY:Sarah Asad: Teaching Data Science and AI/ML to Diverse Learners Using Apache Texera: An Experience Report
DESCRIPTION:We’ll have Sarah present her work for this week’s ISG seminar. \nTime & Location: \nFriday April 17\, 2026\, 1:00 PM – 2:00 PM\nDonald Bren Hall 3011\, ICS\, UC Irvine \nZoom: \nhttps://uci.zoom.us/j/95509222811?pwd=2V8Hnx71iP6dyfNsEPoo97NUfCFWTo.1\n\nLunch will be provided. \nTitle \nTeaching Data Science and AI/ML to Diverse Learners Using Apache Texera: An Experience Report \nAbstract \nThis 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. \nBio \nSarah 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.
URL:https://isg.ics.uci.edu/event/sarah-asad-teaching-data-science-and-ai-ml-to-diverse-learners-using-apache-texera-an-experience-report/
LOCATION:DBH 3011
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260424T130000
DTEND;TZID=America/Los_Angeles:20260424T140000
DTSTAMP:20260722T020501
CREATED:20260421T003909Z
LAST-MODIFIED:20260421T060244Z
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SUMMARY:Prof. Eduard Dragut (Temple University): Toward Scalable Knowledge Extraction with Weak Supervision and Large Language Models
DESCRIPTION:Friday April 24\, 2026\, 1:00 PM – 2:00 PM\nDonald Bren Hall 3011\, ICS\, UC Irvine \nZoom:\nhttps://uci.zoom.us/j/95509222811?pwd=2V8Hnx71iP6dyfNsEPoo97NUfCFWTo.1 \nLunch will be provided. \nTitle: Toward Scalable Knowledge Extraction with Weak Supervision and Large Language Models \nAbstract: 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. \nBio: 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.
URL:https://isg.ics.uci.edu/event/prof-eduard-dragut-temple-university-toward-scalable-knowledge-extraction-with-weak-supervision-and-large-language-models/
LOCATION:DBH 3011
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260501T130000
DTEND;TZID=America/Los_Angeles:20260501T140000
DTSTAMP:20260722T020501
CREATED:20260429T054607Z
LAST-MODIFIED:20260429T054607Z
UID:2426-1777640400-1777644000@isg.ics.uci.edu
SUMMARY:Yinan & Juncheng ICDE Practice Talk
DESCRIPTION:We’ll have Yinan & Juncheng present their ICDE works for this week’s ISG Seminar. This will be a shared session. \nTime & Location: \nFriday May 1\, 2026\, 1:00 PM – 2:00 PM\nDonald Bren Hall 3011\, ICS\, UC Irvine \nZoom: \nhttps://uci.zoom.us/j/95509222811?pwd=2V8Hnx71iP6dyfNsEPoo97NUfCFWTo.1\n\nLunch will be provided. \n———————————————————————————————————————— \nSpeaker \nYinan Zhou \nTitle \nSpendableStore: A UTXO-based Decentralized Data Store \nAbstract \nThe 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. \n———————————————————————————————————————— \nSpeaker \nJuncheng Fang \nTitle \nImmortalChopper: Real-Time and Resilient Distributed Transactions in the Edge-Cloud \nAbstract \nEmerging 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.\nIn 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. \nBio \nJuncheng 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. \n————————————————————————————————————————
URL:https://isg.ics.uci.edu/event/yinan-juncheng-icde-practice-talk/
LOCATION:DBH 3011
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260508T130000
DTEND;TZID=America/Los_Angeles:20260508T140000
DTSTAMP:20260722T020501
CREATED:20260505T192616Z
LAST-MODIFIED:20260505T192616Z
UID:2430-1778245200-1778248800@isg.ics.uci.edu
SUMMARY:Pratyoy (UCI): SmartRabbit An Interactive Query Processor
DESCRIPTION:For this week’s ISG seminar\, Pratyoy will do his SIGMOD practice talk.\n\n\nTime & Location:\n\n\nFriday May 8\, 2026\, 1:00 PM – 2:00 PM\nDonald Bren Hall 3011\, ICS\, UC Irvine\n\n\nTitle: SmartRabbit: An Interactive Query Processor.\n\nAbstract: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.\n\nBio: 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.\n\n\nZoom:\nhttps://uci.zoom.us/j/95509222811?pwd=2V8Hnx71iP6dyfNsEPoo97NUfCFWTo.1\n\nLunch will be provided.
URL:https://isg.ics.uci.edu/event/pratyoy-uci-smartrabbit-an-interactive-query-processor/
LOCATION:DBH 3011
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