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DTSTART;TZID=America/Los_Angeles:20260417T130000
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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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DTSTART;TZID=America/Los_Angeles:20260424T130000
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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
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