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X-WR-CALNAME:EECS Faculty Candidate Seminar: Benjamin Riggan
X-WR-TIMEZONE:Eastern Time (US & Canada)
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DTSTAMP:20260711T182310Z
UID:tag:localist.com\,2008:EventInstance_52089834239481
DTSTART:20260216T160000Z
DTEND:20260216T170000Z
DESCRIPTION:Redefining AI for Agriculture and Forestry Systems through Doma
 in-Adaptive\, Physics-Aware Co-Design\n \n\nAbstract\n\nMany challenges wi
 th artificial intelligence and machine learning (AI/ML) for Agriculture an
 d Forestry arise from the physical and environmental conditions in which t
 hese systems operate. Extreme temperature\, humidity\, wind\, atmospheric 
 effects\, and geolocation introduce substantial variability and uncertaint
 y that degrade data quality and limit the reliability of data-driven\, phy
 sics-agnostic AI approaches. While canonical AI methods often assume relat
 ively constrained data conditions\, real-world agricultural and forestry e
 nvironments demand resilient systems capable of operating across large spe
 ctral\, temporal\, and spatial variations. \n\nThis talk introduces a new 
 paradigm for domain-adaptive AI that explicitly integrates AI/ML with opti
 cal physics. By co-designing learning algorithms and sensing modalities\, 
 this framework models the physical processes underlying image and signal f
 ormation to improve robustness\, generalization\, and trustworthiness. I w
 ill highlight recent advances at the intersection of computer vision\, sig
 nal and image processing\, biometrics\, and optical physics that address c
 hallenges such as spectral variability\, limited labeled data\, and spatia
 lly and temporally varying atmospheric turbulence. \n\nThese methods are m
 otivated and validated through applications in precision agriculture and p
 recision livestock systems. I will conclude by outlining emerging research
  directions and a long-term vision for building sustainable\, interdiscipl
 inary research programs in AI for Agriculture and Forestry\, where bridgin
 g AI/ML and optical physics to enable reliable decision-making in complex\
 , real-world environments directly impact national food security and ecosy
 stem resilience.\n\n \n\nBiography\n\nBenjamin Riggan\, Associate Professo
 r in the Department of Electrical and Computer Engineering at the Universi
 ty of Nebraska–Lincoln (UNL)\, has research that is focused on domain ad
 aptation\, optical physics-informed sensing\, image and signal processing\
 , and biometrics\, with applications in challenging real-world environment
 s where data quality and variability impede traditional AI systems.\n\nRig
 gan received his BS in computer engineering\, MS in electrical engineering
 \, and PhD in electrical engineering from North Carolina State University 
 in 2009\, 2011\, and 2014\, respectively. Prior to joining UNL in 2019\, h
 e was a Research Scientist and Postdoctoral Fellow at the US Army Research
  Laboratory’s Image Processing and Networked Sensing branches\, where he
  conducted research on long-range and cross-spectrum recognition.\n\nHis w
 ork has been supported by agencies including USDA\, IARPA\, Army Research 
 Laboratory\, National Strategic Research Institute\, and he currently serv
 es as a Senior Editor for the IEEE Transactions on Aerospace and Electroni
 c Systems. He has published extensively in top peer-reviewed venues\, rece
 ived multiple Best Paper Awards\, and contributes leadership to the broade
 r scientific community through conference organization and editorial servi
 ce.
GEO:35.958178;-83.924516
LOCATION:Min H. Kao Electrical Engineering and Computer Science\, 435
SUMMARY:EECS Faculty Candidate Seminar: Benjamin Riggan
URL;VALUE=URI:https://calendar.utk.edu/event/eecs-faculty-candidate-seminar
 -benjamin-riggan
CATEGORIES:Lectures & Presentations
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