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DTSTART;TZID=America/New_York:20240206T143000
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SUMMARY:NJIT Data Science Seminar Series
DESCRIPTION:Data Science Seminar Series in collaboration with the Department of Data Science \n\n\n\n“Structure-Enhanced Text Mining for Understanding and Augmenting Scientific Discovery” \n\n\n\nYu Zhang University of Illinois Urbana-Champaign \n\n\n\nLocation: Guttenberg Information Technologies Center (GITC) Building\, Room 4402 (4th floor lecture hall) (Coffee served at 2:15 PM)  \n\n\n\nZoom Meeting Link \n\n\n\nHosted by Shuai Zhang  \n\n\n\nLanguage models pre-trained on large-scale text corpora have achieved remarkable success in building text mining systems. Meanwhile\, text is usually accompanied by various types of structural signals\, such as document metadata\, concept ontologies\, and citation networks\, that can potentially benefit the understanding of text. To enhance the effectiveness of text mining methods\, my research focuses on teaching language models to exploit structural information for both fundamental tasks and advanced domain-specific applications\, with an emphasis on understanding and augmenting scientific discovery. In the first part of the talk\, I will present structure-aware classification algorithms that can predict relevant categories of a scientific paper from hundreds of thousands of candidate classes. These methods have been adapted into the Microsoft Academic Graph production pipeline. The second part of the talk will introduce seed-guided topic mining approaches that find category-indicative entities and structural signals. In the third part\, I will discuss how to leverage multi-task language model pre-training techniques to facilitate advanced applications in the scientific domain\, such as patient-to-article retrieval and paper-reviewer matching. Finally\, I will outline future research directions\, including structure-aware usage of large language models\, flexible translation between different types of scientific data\, and data mining for accelerating science and innovation.  \n\n\n\nYu Zhang is a Ph.D. candidate in the Department of Computer Science at the University of Illinois Urbana-Champaign\, advised by Prof. Jiawei Han. Prior to UIUC\, he received his B.Sc. degree in Computer Science from Peking University. Yu’s research focuses on structure-enhanced text mining and its applications in scientific literature understanding. His first-authored papers have been published in top-tier venues in the fields of data mining\, natural language processing\, and information retrieval. Yu has been awarded the UIUC Dissertation Completion Fellowship and the Yunni & Maxine Pao Memorial Fellowship.
URL:https://njbda.org/event/njit-data-science-seminar-series/
LOCATION:Guttenberg Information Technologies Center (GITC)\, 218 Central Ave\, Newark\, New Jersey\, 07102\, United States
CATEGORIES:lectures/talks
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DTSTART;TZID=America/New_York:20240212T110000
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CREATED:20240209T141031Z
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SUMMARY:Performance Engineering for Scalable AI
DESCRIPTION:Speaker: Xuhao Chen\, Research Scientist\, MIT CSAIL\n\n\n\nHosted by Stevens Institute of Technology \n\n\n\nLocation: Gateway North 303 (with virtual option)\n\n\n\nABSTRACT\n\n\n\nDue to massive size\, complex algorithms and irregular data structures\, AI applications are expensive and hard to scale\, which poses great challenges in computing system design. \n\n\n\nIn this talk\, I will discuss my approach called cross-stack performance engineering\, to address this challenge. I will describe software and hardware system design principles\, optimization techniques and automation methodologies\, across different layers of the system stack. I will show promising results to demonstrate that this cross-stack approach is effective to make AI scalable. \n\n\n\nBIOGRAPHY\n\n\n\nXuhao Chen is a Research Scientist at MIT CSAIL. Dr. Chen is broadly interested in parallel systems and architectures\, with a focus on AI and big-data applications. His recent work aims to make AI scalable by designing efficient algorithms\, software systems and hardware accelerators. His work has been published in OSDI\, ISCA\, MICRO\, VLDB\, ICS\, etc. \n\n\n\nZoom link: https://stevens.zoom.us/j/91365277622
URL:https://njbda.org/event/performance-engineering-for-scalable-ai/
LOCATION:Gateway Academic Center\, 6th Street Lot\, 601 Hudson St\, Hoboken\, New Jersey\, 07030\, United States
CATEGORIES:lectures/talks
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