Curriculum Vitae

Education

• Ph.D., Mathematics 2017
  University of Tulsa Tulsa, OK
• M.S., Mathematics 2013
  University of Tulsa Tulsa, OK
• B.S., Mathematics 2013
  University of Tulsa Tulsa, OK
• B.S., Geology 2013
  University of Tulsa Tulsa, OK

Professional Experience

• Postdoctoral fellow 2018-
  University of Pennsylvania Philadelphia, PA
• Graduate intern 2016-2017
  Laureate Institute for Brain Research Tulsa, OK
• Graduate research assistant 2015-2017
  University of Tulsa Tulsa, OK
• Undergraduate research assistant 2012-2013
  University of Tulsa Tulsa, OK

Select Software Packages

• npdr: Select features with nearest-neighbor concepts (2019)      
  a nearest-neighbor feature selection algorithm that fits a generalized linear model for projected distances of a given attribute over all pairs of instances in a neighborhood. In the NPDR model, the predictor is the attribute distance between neighbors projected onto the attribute dimension, and the outcome is the projected phenotype distance (for quantitative traits) or hit/miss (for case/control) between all pairs of nearest neighbor instances.
• pmlb: Penn Machine Learning Benchmarks (PMLB) (2020)      
  This repository contains the code and data for a large, curated set of benchmark datasets for evaluating and comparing supervised machine learning algorithms. These data sets cover a broad range of applications including binary/multi-class classification problems and regression problems.
• pmlb-r: an R interface to PMLB (2020)      
  pmlb is an R interface to the Penn Machine Learning Benchmarks data repository, a large collection of curated benchmark datasets for evaluating and comparing supervised machine learning algorithms.
• privateEC: Differential privacy for machine learning (2017)      
  a stochastic privacy-preserving machine learning algorithm that uses ReliefF for feature selection and random forest for privacy preserving classification.
• stir: ReliefF on steroid (2018)      
  Precursor of npdr
• treeheatr: Heatmap-integrated decision tree visualizations (2020)      
  treeheatr displays a more interpretable decision tree visualization by integrating a heatmap at its terminal nodes.

Courses and Workshops

• On visualization: Take a sad chart and make it better, R Ladies Philly, Dec 8, 2020      
  Every good chart started out as a bad one. In this workshop, we discuss 1) basic visualization principles, and 2) resources, tools, tips, and tricks that help improve our charts and refine our data stories. Some familiarity with R and ggplot will be useful but not required - novice R users are encouraged to attend.
• Fundamentals of AI guest lecturer, University of Pennsylvania, Mar 30, 2020      
  I give two 80-minute guest lectures on Information Theory and Stochastic Processes and their roles in artificial intelligence and biomedical applications.
• Machine learning workshop, R Ladies Philly, Dec 2, 2019      
  A brief introduction to machine learning and hands-on exercise of building models to predict the alcohol concentration of beers from a beer review dataset.
• Introduction to R seminar, University of Tulsa, Aug 25, 2017  
  An immersive, “bootcamp” styled seminar designed to rapidly build student proficiency in R programming. The seminar is conducted over a long weekend or similarly condensed timeframe in a computer laboratory setting.
• Precalculus instructor, University of Tulsa, Jan 11, 2016  
  I prepare 30+ students for the study of calculus.
• Calculus I & II teaching assistant, University of Tulsa, Jan 13, 2014  
  I lead Calculus I & II problem-solving sections and design and grade quizzes. I had 200+ students over the course of four semesters.
• Natural science tutors, University of Tulsa, Feb 7, 2011  
  I tutor students (including many student athletes) in math, physics and chemistry. I also worked in Math Lab - a free, walk-in tutor service for students at all levels.

Presentations

• treeheatr: an R package for interpretable decision tree visualizations. R/Medicine Virtual Conference, Aug 28, 2020      
• Analysis of ISCB honorees and keynotes reveals disparities. Conference on Intelligent Systems for Molecular Biology, Jun 30, 2020      
  https://youtu.be/x13G3gF5xAM
• Scaling tree-based automated machine learning to biomedical big data with a feature set selector. Genetic and evolutionary computation conference, Jun 10, 2020      
  https://youtu.be/h4onrAKizc0
• Multilocus risk scores: rethinking genetic risk scores to account for epistasis. International Joint Conference on Biomedical Engineering Systems and Technologies, Feb 25, 2020      
• Multilocus risk scores. Rocky Mountain Bioinformatics Conference, Dec 6, 2019      
• Detect network interactions and control for confounders and multiple testing. Rocky Mountain Bioinformatics Conference, Dec 6, 2019      
• Multilocus risk scores. Penn Genetics Retreat, Sep 4, 2019      
• TPOT: Overview and live demonstration. Clinical Research Informatics Core, University of Pennsylvania, Mar 13, 2019      
• STIR feature selection. Pacific Symposium on Biocomputing, Jan 5, 2019      
• Scalable automated machine learning. AI Therapeutics, Dec 21, 2018      
• Statistical Inference Relief (STIR) feature selection. Mid-Atlantic Bioinformatics Conference, Oct 29, 2018      
• Transcriptomics of Brain Age Gap Estimate. Society of Biological Psychiatry Meeting, May 11, 2018      
  Transcriptomics of Brain Age Gap Estimate (BrainAGE): association analysis of depressed and healthy individuals
• Deciphering interdependent immunologic biomarkers of lupus. DBEI and CCEB Research Day, University of Pennsylvania, Apr 10, 2018      
• Integrative network analysis for major depressive disorder. Organization for Human Brain Mapping Meeting, Jun 28, 2017      
  Integrative network analysis of resting-state fMRI and RNA-Seq data for major depressive disorder
• Optimized random forest classification accuracy. International Conference on Integral Methods in Science and Engineering, Jul 25, 2016      
  Optimized random forest classification accuracy by privacy-preserving simulated evaporative cooling feature selection
• Structural identifiability issues of epidemiological models. International Conference on Integral Methods in Science and Engineering Special Session, 2015      
• Simulation and identification of synchronization in resting-state fMRI networks. Research Colloquium, University of Tulsa, 2015      
• Structural and practical identifiability issues of epidemic models. American Institute of Mathematical Sciences Meeting, 2016      
• Spread of Avian influenza pandemic to USA via air travel. American Mathematics Society Sectional Meeting, 2013      
• Structural identifiability issues of epidemic models. Joint Mathematics Meetings, 2016      
• Mathematical analysis of the spread of HIV/AIDS using SEAIT model. Research Colloquium, University of Tulsa, 2013      
• Spread of Avian influenza pandemic to USA via air travel. Society for Industrial and Applied Mathematics Meeting, 2013      

Apprentices

• Hoyt Gong, undgraduate student (2020)  
• Jeremy Rubin, PhD student (2020)  
• Monica Ionescu, Master student (2019)

Summer

• Wara Laura, undgraduate student (2020)
• Beverly Chang, undgraduate student (2020)
• Praneel Chakraborty, undgraduate student (2020)
• Natasha Ray, highschool student (2020)
• Daniel Goldberg, undgraduate student (2020)

Publications

• Necibe Tuncer and Trang Le (2014) Effect of air travel on the spread of an avian influenza pandemic to the United States. doi:10.1016/j.ijcip.2014.02.001
• Trang T Le, Bryan A Dawkins and Brett A McKinney (2020) Nearest-neighbor Projected-Distance Regression (NPDR) for detecting network interactions with adjustments for multiple tests and confounding. doi:10.1093/bioinformatics/btaa024
• Trang T Le, W Kyle Simmons, Masaya Misaki, Jerzy Bodurka, Bill C White, Jonathan Savitz and Brett A McKinney (2017) Differential privacy-based evaporative cooling feature selection and classification with relief-F and random forests. doi:10.1093/bioinformatics/btx298
• Trang T Le, Weixuan Fu and Jason H Moore (2019) Scaling tree-based automated machine learning to biomedical big data with a feature set selector. doi:10.1093/bioinformatics/btz470
• Sahib S. Khalsa, ..., Trang T Le, ... and Nancy Zucker (2017) Interoception and Mental Health: A Roadmap. doi:10.1016/j.bpsc.2017.12.004
• Trang T. Le, Rayus Kuplicki, Brett A. McKinney, Hung-wen Yeh, Wesley K. Thompson, Martin P. Paulus and NA NA (2018, preprint) A nonlinear simulation framework supports adjusting for age when analyzing BrainAGE. doi:10.1101/377648
• Bryan A. Dawkins, Trang T. Le and Brett A. McKinney (2019, preprint) Theoretical properties of nearest-neighbor distance distributions and novel metrics for high dimensional bioinformatics data. doi:10.1101/857821
• Trang T Le, Weixuan Fu and Jason H Moore (2018, preprint) Scaling tree-based automated machine learning to biomedical big data with a dataset selector. doi:10.1101/502484
• Trang T Le, Ryan J Urbanowicz, Jason H Moore and Brett A McKinney (2018) STatistical Inference Relief (STIR) feature selection. doi:10.1093/bioinformatics/bty788
• Trang T Lê, Zach Osman, D K Watson, Martin Dunn and B A McKinney (2019) Generalization of the Fermi pseudopotential. doi:10.1088/1402-4896/ab0811
• Necibe Tuncer and Trang T. Le (2018) Structural and practical identifiability analysis of outbreak models. doi:10.1016/j.mbs.2018.02.004
• Trang T. Le, Rayus Kuplicki, Hung-Wen Yeh, Robin L. Aupperle, Sahib S. Khalsa, W. Kyle Simmons and Martin P. Paulus (2018) Effect of Ibuprofen on BrainAGE: A Randomized, Placebo-Controlled, Dose-Response Exploratory Study. doi:10.1016/j.bpsc.2018.05.002
• Trang T. Le, Bryan A. Dawkins and Brett A. McKinney (2019, preprint) Nearest-neighbor Projected-Distance Regression (NPDR) for detecting network interactions with adjustments for multiple tests and confounding. doi:10.1101/861492
• Trang T. Le, Daniel S. Himmelstein, Ariel A. Hippen Anderson, Matthew R. Gazzara and Casey S. Greene (2020, preprint) Analysis of ISCB honorees and keynotes reveals disparities. doi:10.1101/2020.04.14.927251