Dr. Kerby

 
Dr. Leslie Kerby
CEADS Director
Associate Professor, Computer Science
Affiliate Faculty, Nuclear Engineering
Idaho State University
CV

 
 

Highlights of Dr. Kerby and the CEADS research group accomplishments over the past 7 years at ISU:

  • Almost $1.5 million in external funding from a dozen+ contracts with Idaho National Laboratory, Oak Ridge National Laboratory, and Los Alamos National Laboratory
  • Trained more than a dozen students, including graduating a handful of PhD and MS students
  • Over 30 peer-reviewed publications
  • Dr. Kerby has developed and taught Advanced AI Methods, Applied Neural Networks, Data Science and Applied Machine Learning, Scientific Computing, and Modern Nuclear Computing; taught Data Structures and Algorithms, Introduction to Python, Introduction to Informatics and Analytics, and Monte Carlo Methods and Applications
  • Dr. Kerby was nominated to be American Nuclear Society Mathematics and Computation division chair-elect
  • Dr. Kerby, invited member of panel, “Current Issues in Computational Methods - Roundtable for Mathematics and Computation Division,” American Nuclear Society Winter Meeting, December 1, 2021, Washington, D.C., USA
  • Dr. Kerby, gave one-day “Introduction to Data Science” workshop in CAES May 2019 with INL collaborator Josh Peterson; developed two-day “Introduction to Data Science and Machine Learning” workshop given in August 2019
  • Dr. Kerby, invited speaker, “CEM and LAQGSM Event Generators: Modern Software Development”, at the Cross Sections for Cosmic Rays @ CERN (XSCRC2019), November 2019 in CERN, Geneva, Switzerland
  • Dr. Kerby, invited workshop, “Introduction to Data Science with Python”, at the American Nuclear Society Student Conference, April 2019 in Richmond, VA
  • Dr. Kerby, invited member of panel, “Thermal Hydraulics Applications of Machine Learning and Data Science”, at the American Nuclear Society Winter Meeting, November 2018 in Orlando, FL
  • Dr. Kerby, first Place in the IEEE Big Data and IEEE Brain Hackathon, July 2018 in Tokyo, Japan

Publications
Expanded Analysis of Machine Learning Models for Nuclear Transient Identification Using TPOT

Evaluation of Tree-based Regression over Multiple Linear Regression for Non-normally Distributed Data in Battery Performance

Volumetric Spherical Polynomials

and more

Student Theses
Pedro Mena, PhD, 2022
Auto Machine Learning Applications for Nuclear Reactors: Transient Identification, Model Redundancy and Security

Brycen Wendt, PhD, 2018
Functional Expansions Methods: Optimizations, Characterizations,and Multiphysics Practices

Shovan Chowdhury, MS, 2022
Artificial Intelligence Based Li-ion Battery Diagnostic Platform

Chase Juneau, MS, 2019
The Development of the Generalized Spallation Model

Pedro Mena, MS, 2019
Reactor Transient Classification Using Machine Learning

Classes Taught
Advanced AI Methods
Applied Neural Networks
Data Science and Applied Machine Learning
Scientific Computing
Data Structures and Algorithms
Introduction to Programming with Python
Introduction to Informatics and Analytics
Monte Carlo Methods and Applications
Modern Nuclear Computing
and more

Other
Co-Lead, CAES Computing, Data, and Visualization
Lead, ISU Data Science Alliance
Graduate Coordinator, Department of Computer Science
Reviewer, Nuclear Technology
Reviewer, JOM: Journal of The Minerals, Metals Materials Society (TMS)
Reviewer, IEEE Transactions on Nuclear Science
Reviewer, ANS M&C
Reviewer, Nuclear Science and Engineering