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UID:0-6357@eng.ufl.edu
DTSTART;TZID=America/New_York:20240119T120000
DTEND;TZID=America/New_York:20240119T130000
DTSTAMP:20251201T210800Z
URL:https://www.eng.ufl.edu/news-events/events/cise-colloquium-speaker-tal
 k-series-dr-vipin-kumar/
SUMMARY:CISE Colloquium Speaker Talk Series: Dr. Vipin Kumar
DESCRIPTION:Zoom Link: https://ufl.zoom.us/my/anandrajan\nBio: Vipin Kumar 
 is a Regents Professor and holds William Norris Chair in the Department of
  Computer Science and Engineering at the University of Minnesota. His rese
 arch spans data mining\, high-performance computing\, and their applicatio
 ns in Climate/Ecosystems and health care. He also served as the Director o
 f Army High Performance Computing Research Center (AHPCRC) from 1998 to 20
 05. He has authored over 400 research articles\, and co-edited or coauthor
 ed 11 books including two widely used textbooks ``Introduction to Parallel
  Computing"\, "Introduction to Data Mining"\, and a recently edited collec
 tion\, “Knowledge Guided Machine Learning”. Kumar's current major rese
 arch focus is on knowledge-guided machine learning and its applications to
  understanding the impact of human-induced changes on the Earth and its en
 vironment. Kumar’s research on this topic is funded by NSF’s AI Instit
 itues\, BIGDATA\, INFEWS\, STC\, GCR\, and HDR programs\, as well as ARPA-
 E\, DARPA\, and USGS. He has recently finished serving as the Lead PI of a
  5-year\, $10 Million project\,"Understanding Climate Change - A Data Driv
 en Approach"\, funded by the NSF's Expeditions in Computing program. Kumar
  is a Fellow of the AAAI\, ACM\, IEEE\, AAAS\, and SIAM. Kumar's foundatio
 nal research in data mining and high-performance computing has been honore
 d by the ACM SIGKDD 2012 Innovation Award\, which is the highest award for
  technical excellence in the field of Knowledge Discovery and Data Mining 
 (KDD)\, the 2016 IEEE Computer Society Sidney Fernbach Award\, one of IEEE
  Computer Society's highest awards in high performance computing\, and Tes
 t-of-time award from 2021 Supercomputing conference (SC21).\nTitle: Knowle
 dge-Guided Machine Learning: A New Framework for Accelerating Scientific D
 iscoveryand Addressing Global Environmental Challenges\nAbstract: Process-
 based models of dynamical systems are often used to study engineering and 
 environmental systems. Despite their extensive use\, these models have sev
 eral well-known limitations due to incomplete or inaccurate representation
 s of the physical processes being modeled. There is a tremendous opportuni
 ty to systematically advance modeling in these domains by using state of t
 he art machine learning (ML) methods that have already revolutionized comp
 uter vision and language translation. However\, capturing this opportunity
  is contingent on a paradigm shift in data-intensive scientific discovery 
 since the “black box” use of ML often leads to serious false discoveri
 es in scientific applications. Because the hypothesis space of scientific 
 applications is often complex and exponentially large\, an uninformed data
 -driven search can easily select a highly complex model that is neither ge
 neralizable nor physically interpretable\, resulting in the discovery of s
 purious relationships\, predictors\, and patterns. This problem becomes wo
 rse when there is a scarcity of labeled samples\, which is quite common in
  science and engineering domains. This talk makes the case that in real-wo
 rld systems that are governed by physical processes\, there is an opportun
 ity to take advantage of fundamental physical principles to inform the sea
 rch of a physically meaningful and accurate ML model. While this talk will
  illustrate the potential of the knowledge-guided machine learning (KGML) 
 paradigm in the context of environmental problems (e.g.\, Ecology\, Hydrol
 ogy\, Agronomy)\, the paradigm has the potential to greatly advance the pa
 ce of discovery in a diverse set of discipline where mechanistic models ar
 e used\, e.g.\, weather forecasting\, and pandemic management.
CATEGORIES:Seminars
LOCATION:Nvidia Auditorium 1000\, 1889 Museum Road\, Gainesville\, Florida\
 , 32611\, United States
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=1889 Museum Road\, Gainesvi
 lle\, Florida\, 32611\, United States;X-APPLE-RADIUS=100;X-TITLE=Nvidia Au
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