Kyle W Singleton

Kyle W Singleton

Curriculum Vitae

PROFESSIONAL EXPERIENCE

Senior Data Science Analyst: Apr 2022 - Present

Mayo Clinic, Phoenix, AZ

Precision Neurotherapeutics Program

Mathematical Neuro-Oncology Lab

  • Collaborated on development of deep learning networks for automated magnetic resonance image (MRI) identification and automated tumor segmentation.
  • Applied predictive radiomics models of T-cell abundance to 200+ multi-modality MRI cases.
  • Implemented end-to-end predictive mathematical pipeline and API.
  • Executed routine SQL queries and ETL tasks on clinical and imaging data to support research projects in the lab.
  • Maintained and updated Django research website and MySQL database housing data for 2000+ patients, 200,000+ MRIs, and associated clinical and treatment records.
  • Transitioned EC2 and RDS resources to current AWS standards.

Senior Research Fellow: 2019 - 2022

Mayo Clinic, Phoenix, AZ

Precision Neurotherapeutics Lab

Mathematical Neuro-Oncology Lab

  • Spearheaded development of machine learning algorithms for image pre-processing, normalization, and registration pipelines in Python.
  • Processed MRIs and stereotactic surgical coordinates from 70+ image-guided biopsy clinical trial patients with the previous algorithms.
  • Coordinated lab efforts to train artificial intelligence algorithms on the Mayo Clinic Cloud and Google Platform.
  • Maintained multiple on-premises GPU computer systems for deep learning research.
  • Contributed informatics and imaging knowledge to 20+ grant submissions (U01, U54, P01), including a funded $12,000,000 U54 center grant.

Faculty Associate: 2018 - 2022

Arizona State University, Tempe, AZ

College of Health Solutions

  • Taught online special topics courses for master’s students.
  • Provided feedback and instruction to students during seven-week courses.
  • Designed new lecture videos and course assignments to update outdated course information.
  • Transitioned course lectures and materials from Blackboard to Canvas.

Research Fellow: 2016 - 2019

Mayo Clinic, Phoenix, AZ

Precision Neurotherapeutics Lab

  • Analyzed 4 patient cohorts with varied drug treatment regimens using a novel clinical response metric, Days Gained.
  • Generated 3D models of brain MRI and simulated tumor models for videos and 3D prints used to educate patients about their disease.
  • Enhanced image registration tools in Django and Python framework resulting in 2500 newly applied registrations.
  • Supported multiple research projects through SQL querying and ETL data wrangling techniques.
  • Conceptualized and initiated development of an image pre-processing and normalization pipeline.

Graduate Student Researcher: 2012 - 2016

UCLA Medical Imaging Informatics, Los Angeles, CA

  • Supervised team of three graduate students, reviewing clinical and imaging data from medical records.
  • Created retrospective dataset from 500+ UCLA brain cancer cases.
  • Employed lung and brain cancer data from public data repositories (National Lung Screening Trial and The Cancer Genome Atlas).
  • Constructed and evaluated disease models of brain cancer survival (Glioblastoma Multiforme).
  • Implemented and evaluated a novel simulation approach for interpreting the transportability (external validity) of predictive models in independent patient cohorts.

National Library of Medicine Fellow: 2008 - 2012

UCLA NLM Medical Imaging Informatics Training Program, Los Angeles, CA

  • Completed coursework in medical informatics, statistics, and machine learning.
  • Programmed, deployed, and supported tablet-based surveying systems used to collect medical data from 20,000+ patients in Los Angeles clinics.

EDUCATION

University of California, Los Angeles - Los Angeles, CA

Doctor of Philosophy in Biomedical Engineering - 2016

Specialization in Medical Imaging Informatics

University of Virginia - Charlottesville, VA

Bachelor of Science in Biomedical Engineering, Minor in Computer Science - 2006

DISSERTATION

Investigating Predictive Disease Model Transportability through Cohort Simulation and Causal Analysis

Advisors: Alex A.T. Bui and William Hsu

PUBLICATIONS

Google Scholar

TEACHING

INSTRUCTOR

Arizona State University

BMI 598 Topic: Biomedical Device Design - Spring 2022

BMI 598 Topic: Biomedical Device Design - Spring 2021

BMI 598 Topic: Biomedical Device Design - Spring 2020

BMI 598 Topic: Biomedical Device Design - Spring 2019

BMI 598 Topic: Imaging in Diagnostics - Spring 2018

GUEST LECTURER

Arizona State University

BMI 598 – Topic: Imaging in Diagnostics - Spring 2019

University of California, Los Angeles

BME 220 Introduction to Medical Informatics - Fall 2012

BME 223A Programming Laboratory for Medical Informatics I - Fall 2013

TEACHING ASSISTANT/READER

University of California Los Angeles

BME 223A Programming Laboratory for Medical Informatics I - Fall 2013

FELLOWSHIPS AND AWARDS

First Place Student Paper - Knowledge Discovery and Data Mining Working Group (KDDM-WG): 2014

Finalist - AMIA 2014 Student Paper Competition: 2014

UCLA Graduate Student Researcher: 2012 - Present

NLM Biomedical Informatics Training Fellowship: 2008 - 2012

NIH Postbaccalaureate Intramural Research Training Award: 2006 - 2008