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General Information

Full Name Randolph Linderman

Education

  • 2020--
    PhD
    Duke University
    • Advisor: Yiran Chen, Ph.D.
    • Thesis: Exploiting interclass relationships for improved deep neural network generalization in open-world environments
    • Courses
      • Deep learning approaches in machine learning
      • Probabilistic machine learning
      • Software engineering: Object oriented design w/ Java
      • Programming in C++/C
      • Intro to computer architecture
  • 2017
    Masters of Engineering
    Cornell University
    • Advisor: Jonathan Butcher, Ph.D.
    • Thesis: Computational fluid dynamics analysis of the hemodynamic environment in the developing cardiac outflow tract
    • Courses
      • Computer analysis of biomedical imaging
      • Soft tissue biomechanics
      • Quasilinear viscoelasticity and biphasic theory
      • Advanced bio-fluid mechanics
      • Principles of tissue engineering
      • Innovation and design of biomedical technologies
  • 2016
    Bachelors of Science
    Cornell University

Experience

  • May - Aug 2024
    R&D Intern on the Computer Vision Team
    Kitware Inc. Carrboro, NC
    • Advisors: Brian Clipp, Ph.D. and Daniel Davila
  • 2018 - 2020
    Associate Computer Engineer, DR-01
    Air Force Research Laboratory, Information Directorate, Rome, NY
    • Advisors: Qing Wu, Ph.D. and Morgan Bishop
  • 2014 - 2017
    Graduate and Undergraduate Researcher
    Cardiovascular Developmental Bioengineering Lab, Cornell University
    • Advisor: Jonathan Butcher, Ph.D.
    • Researched mechanotransductory relationship between hemodynamic forces and pharyngeal arch artery (PAA) remodeling during critical developmental stages of avian outflow tract development. Performed computational fluid dynamics simulations on reconstructed micro-CT geometries to determine the mechanical forces acting on vessel walls. Correlated hemodynamic forces to geometric changes in PAAs. Reconstructed 3D geometries on day 3 to day 7 chick embryos with Mimics, 3-matic, and Geomagic software packages. Presented findings at laboratory meetings every semester.
  • 2016
    Summer Internship
    Air Force Research Laboratory, Information Directorate, Rome, NY
    • Advisors: Qing Wu, Ph.D.
  • 2014
    Summer Internship
    Air Force Research Laboratory, Information Directorate, Rome, NY
    • Advisors: Qing Wu, Ph.D. and Morgan Bishop
  • 2013
    Summer Internship
    Air Force Research Laboratory, Information Directorate, Rome, NY
    • Advisors: Qing Wu, Ph.D. and Morgan Bishop

Honors and Awards

  • 2022
    • Honorable mention for impact and relevance to DoD research objectives at the NDSEG National Fellows Conference
  • 2020
    • National Defense Science and Engineering Graduate (NDSEG) Fellowship

Invited Talks

  • Jan. 18, 2023
    Machine Learning Tech Talk
    MADLab Center of Excellence Air Force Research Lab

Professional Service

  • Journal and Conference Reviewing
    • International Conference on Machine Learning (ICML)

Teaching Experience

  • Spring 2022
    Software Engineering: Object Oriented Design w/ Java
    Duke University, Durham, NC
    • Professor: Andrew Hilton, Ph.D.
    • Guided 2 groups' team projects. Groups tasked with creating a strategy game following agile processes with multiple delivery dates throughout the semester.
  • Fall 2021
    Programming, Data structures, and Algorithms in C++
    Duke University, Durham, NC
    • Professors: Andrew Hilton, Ph.D. & Genevieve Lipp, Ph.D.
    • Taught recitation sessions to reinforce concepts
    • Hosted office hours to aid in the students' understanding
  • Spring 2017
    Electrical and Chemical Physiology
    Cornell University, Ithaca, NY
    • Professors: Peter Doerschuk, Ph.D. & Shivaun Archer, Ph.D.
    • Prepared, organized, and supervised 3-hour laboratory exercises
    • Hosted office hours to aid in the students' understanding of the laboratory
  • Sp & Fall 2013
    Computer Programming -- MATLAB
    Cornell University, Ithaca, NY
    • Professor: C. Lindsay Anderson, Ph.D.
    • Prepared, organized, and supervised 3-hour laboratory exercises
    • Hosted office hours to aid in the students' understanding of the laboratory