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X-WR-CALDESC:Events for Operations Research Graduate Program
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SUMMARY:OR Seminar: Cranos Willilams
DESCRIPTION:Join us in welcoming Cranos Williams\, Goodnight Distinguished Professor of Agricultural Analytics at NC State\, as he discusses current operations research topics. Alums and friends of the program are always welcome. \nDID You Miss It?\nDon’t worry. You can Watch it Here!\nPasscode: 2JaK%+U3 \nTitle and Abstract\nHarnessing the Ag data revolution for modeling plant and agronomic systems across scale \nThe next revolution in precision agriculture solutions will require an improved understanding of the complex regulatory mechanisms that are instrumental in plant growth\, development\, and adaptation.  Key in these efforts is the ability to acquire and analyze data across biological scales (from molecular to phenotypic scales).  High-throughput data that have been collected across biological scales include molecular data such as gene expression profiles and confocal imaging to data capturing plant physiology such as hyperspectral imaging and remote sensing.  The diversity of these datasets (in combination with the complexity of plant systems) has created opportunities to develop novel computational intelligence and machine learning approaches that are capable of modeling plant systems within and across biological scales.  In this presentation\, we provide a brief overview of approaches for analyzing various types of high-throughput biological data.  These approaches address the many challenges associated with analyzing biological data\, including the need to mitigate high variation and/or uncertainty in data\, the need for novel segmentation and feature extraction\, and the integration of disparate datasets for making causal inferences across scale.  The application of these approaches has led to scientific contributions such as the modeling of key gene regulatory mechanisms involved in plant stress response\, the identification of emergent properties that link molecular activity to phenotypic outcomes\, and the development of automated high-throughput phenotyping approaches for early detection of plant diseases. The continued acquisition of high-throughput data across scale and the continued development of novel machine learning and modeling tools will provide opportunities to further push the boundaries of our understanding of plant systems and will be key to a better understanding of how plants respond to complex environments. \nBiographies\nCranos Williams is the Goodnight Distinguished Professor of Agricultural Analytics at North Carolina State University with primary and secondary appointments in the electrical and computer engineering and plant and microbial biology departments\, respectively.  Dr. Williams also serves as the Platform Director of the Data-Driven Plant Sciences research platform of the North Carolina Plant Sciences Initiative and is the head of the EnBiSys Research Laboratory.  He received his B.S. in electrical engineering from North Carolina A&T State University in 2001\, and his M.S. and Ph.D. in electrical engineering from North Carolina State University in 2002 and 2008\, respectively. Dr. Williams has developed a highly collaborative\, multidisciplinary research program focused on understanding biomolecular pathways associated with plant growth\, development\, and adaptation.  His research lab develops methodologies familiar to other areas of electrical and computer engineering (e.g.\, computational intelligence\, system identification\, nonlinear systems analysis and control\, and signal processing) to model and predict the impact that genetic and environmental perturbations have on overall plant response. The results from these works will have direct implications on key challenges associated with engineering plants for efficient biofuel production\, increased adaptability to changing environments\, and improved defense to biotic and abiotic stresses.
URL:https://or.ncsu.edu/event/or-seminar-cranos-williams-03-02-2026/
LOCATION:2341 Fitts-Woolard Hall\, 915 Partners Way Fitts-Woolard Hall\, Room 2341\, Raleigh\, NC\, 27606\, United States
CATEGORIES:OR Seminar Series
ATTACH;FMTTYPE=image/jpeg:https://or.ncsu.edu/wp-content/uploads/sites/354/2026/01/seminar-series-cranos-williams-03-02-2026-featured-image-01-2026-01.jpg
ORGANIZER;CN="OR Program":MAILTO:operationsresearch@ncsu.edu
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DTSTART;TZID=America/New_York:20260330T163000
DTEND;TZID=America/New_York:20260330T173000
DTSTAMP:20260505T203128
CREATED:20260116T135705Z
LAST-MODIFIED:20260330T154235Z
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SUMMARY:[CANCELED] OR Seminar: David Rasmussen
DESCRIPTION:NOTE: There has been a cancellation. Therefore\, no seminar will be held today.  \nJoin Zoom Meeting\nhttps://ncsu.zoom.us/j/98859624475?pwd=6ei7OBEGOYXJEoMyujk1tVj4OBsVem.1 \nMeeting ID: 988 5962 4475\nPasscode: 655269 \nTitle and Abstract\nDynamically optimizing pathogen genomic surveillance to inform the management of antimicrobial resistance \nGenomic surveillance of infectious pathogens provides many insights into their epidemiological and evolutionary dynamics not available from other sources of data. Yet it is generally unclear how to design sampling strategies that maximize the amount of epidemiological useful information when finite resources limit sampling to only a small fraction of all cases. By adopting a sequential decision making framework\, we recently showed how Markov decision processes (MDPs) can be applied to optimize genomic sampling. However\, this framework only considers the statistical information gained from genomic surveillance and not the value of this information to disease control efforts. While it is clear that control strategies require reliable up-to-date information\, it is less clear how to optimize surveillance to inform control strategies. Using antimicrobial resistance (AMR) management as a case study\, we consider how to jointly optimize both surveillance and control (i.e. treatment). This creates a challenging optimization problem because there is both a resource allocation trade-off and a trade-off between the short-term benefits of antimicrobial use to reduce near-term disease prevalence and the long-term costs of antimicrobial use due to resistance evolution. To find optimal strategies\, we take advantage of reinforcement learning methods based on Deep Q-Learning to predict the long-term expected rewards (future healthy patient days) of different surveillance and treatment strategies. Learning to predict these long-term rewards allows us to find surveillance strategies that can be leveraged into improved AMR management strategies through acquiring information about the evolutionary dynamics of AMR. \nBiography\nDavid Rasmussen is an Associate Professor in the Department of Entomology and Plant Pathology at NC State. He leads the Phylodynamics Research Group\, which develops new computational and statistical methods for genomic epidemiology\, population genomics and phylogenetics.
URL:https://or.ncsu.edu/event/or-seminar-david-rasmussen-03-30-2026/
LOCATION:2341 Fitts-Woolard Hall\, 915 Partners Way Fitts-Woolard Hall\, Room 2341\, Raleigh\, NC\, 27606\, United States
CATEGORIES:OR Seminar Series
ATTACH;FMTTYPE=image/jpeg:https://or.ncsu.edu/wp-content/uploads/sites/354/2026/01/seminar-series-david-rasmussen-03-30-2026-featured-image-01-2026-01.jpg
ORGANIZER;CN="OR Program":MAILTO:operationsresearch@ncsu.edu
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