I'm Derek Onken, a data scientist and machine learning researcher. I focus on data-intensive problems predominantly in the overlap of mathematics and computer science. Ultimately, I enjoy applying the theory to develop solutions for practical problems.
(Such approaches include the following buzzwords: deep learning, neural networks, big data, statistical analysis, GPUs, numerical optimization, continuous normalizing flows, optimal control)
Doctor of Philosophy - Emory University
Computer Science & Informatics track
Master of Science - Emory University
Bachelor of Science - University of Georgia
Majors: Mathematics and Computer Science
Minors: Physics and Classical Culture
- Machine Learning for Pharmaceutical Applications . (2021 - present)
Designing and implementing machine learning tools for manufacturing and clinical trials use cases.
PDE-based Machine Learning with Lars Ruthotto . (2018 - 2021)
Applying knowledge of optimizers and partial differential equation solvers to machine learning architectures (also known as neural differential equations). More specifically, we explore higher-order schemes, applications of optimal control, and the Discretize-Optimize approach. We also are interested in developing methods for lowering computational cost of continuous normalizing flows and tractably solving high-dimensional optimal control problems.
- Parkinson's Disease Telemonitoring and Voice Analysis via Mobile App. (2017)
Collecting and analyzing voice data, touch pressure, and rest tremor to detect Parkinson's disease symptoms via remote patient monitoring. Building machine learning classifier using motor and vocal features extracted from these patients.
- Using CDR data from H1N1 outbreak to predict future disease outbreaks (2016-2019)
Our dataset contains cellphone data from the H1N1 outbreak in 2009-2010 and linked health data. We develop a classifier to determine/predict sickness based on human behavior changes around Date of Diagnosis. Motivation: give health officials a more real-time and accurate estimation of sick individuals as an outbreak is occuring.
- Lunar study with Juan B. Gutierrez in UGA's Biomathematics Research Group. (2014-2016)
Analysis of 56 million natality records and sex ratio correlations.
- Comparison of Parallelization Methods with Juan B. Gutierrez in UGA's Biomathematics Research Group. (2014)
Exploration of MPI, CUDA, and OpenCL. Application of MPI to Geneset Enrichment Analysis (GSEA).
Publications/PreprintsD Onken, L Nurbekyan, X Li, S Wu Fung, S Osher, L Ruthotto. A Neural Network Approach for High-Dimensional Optimal Control Applied to Multi-Agent Path Finding. IEEE TCST 2022. paper code videos D Onken, L Nurbekyan, X Li, S Wu Fung, S Osher, L Ruthotto. A Neural Network Approach Applied to Multi-Agent Optimal Control. European Control Conference 2021. paper code videos D Onken, S Wu Fung, X Li, L Ruthotto. OT-Flow: Fast and Accurate Continuous Normalizing Flows via Optimal Transport. AAAI Conference on Artificial Intelligence 2021. paper code Y Vigfusson*, T Karlsson*, D Onken*, et al. Cellphone Traces Reveal Infection-Associated Behavioral Change. Proceedings of the National Academy of Science 2021. * denotes co-first author paper code D Onken and L Ruthotto. Discretize-Optimize vs. Optimize-Discretize for Time-Series Regression and Continuous Normalizing Flows. arXiv 2020. paper code videos
A Neural Network Approach for High-Dimensional Optimal Control @
Optimal Transport and Mean Field Games Seminar, University of South Carolina, Mar 2021, talk
Thanks to Wuchen Li for the invitation.
UCLA Applied Mathematics Seminar, Mar 2021, talk
Thanks to Levon Nurbekyan for the invitation.
Thanks to Peter E. Caines, Aditya Mahajan, Shuang Gao, Rinel Foguen Tchuendom, and Yaroslav Salii for the invitation.
A Neural Network Approach Applied to Multi-Agent Optimal Control @
European Control Conference, Jun 2021, talk
Thanks to the ECC organizers for the opportunity.
OT-Flow: Fast and Accurate Continuous Normalizing Flows via Optimal Transport @
Thanks to the AAAI organizers for the opportunity.
Georgia Scientific Computing Symposium, Feb 2021, poster
Thanks to the organizers at UGA for the opportunity.
SIAM Annual Meeting, Jul 2020, poster
Thanks to the SIAM AN20 organizers for the opportunity.
Discretize-Optimize Methods for Neural ODEs in Continuous Normalizing Flows @
SIAM Mathematics of Data Science, Jun 2020, slides
Thanks to Harbir Antil, Thomas Brown, and Ratna Khatri for the invitation.
Deep Learning in Medical Applications Workshop at Institute for Pure and Applied Mathematics (IPAM), Jan 2020, poster
Thanks to the IPAM organizers for the opportunity.
Emory Scientific Computing Seminar, Apr 2019, slides
Georgia Scientific Computing Symposium, Feb 2019, poster
Amazon Graduate Research Symposium, Oct 2017, poster
2021 - Present
Research Scientist for Eli Lilly
Graduate Data Science Intern for United Health Group
Graduate Data Science Intern for United Health Group
High Performance Computing Intern for Air Force Research Labs
TA for Dr. Li Xiong's Data Mining (CS378)
Lab Instructor for Dr. Lars Ruthotto's Numerical Analysis (MATH315)
TA for Dr. Michele Benzi's Numerical Analysis (MATH315)
TA for Dr. Davide Fossati's Intro to Java (CS170)