Ryan T. White

Assistant Professor | Mathematical Sciences

Contact Information

Personal Overview

Research and teaching in the areas of deep learning, probability theory, statistics, stochastic analysis, and machine learning. Private-sector experience in algorithm development and data science.

Educational Background

Ph.D. Applied Mathematics - Florida Institute of Technology, 2015
Dissertation: Random Walks on Random Lattices and Their Applications to Strategic Network Defense

M.S. Applied Mathematics - Florida Institute of Technology, 2012

B.S. Mathematics - Chadron State College, 2008

Professional Experience

Florida Institute of Technology, 2013-present

Assistant Professor, 2019-present

  • Research in machine learning, deep learning, computer vision, probability theory, stochastic processes, random walks, reliability theory, queueing theory, and numerical algorithms in these areas

  • Participation in some applied projects computer vision in aerospace engineering, intrusion detection, statistical modeling in marine biology, and image segmentation for tracking glacier progression over time

  • Applied machine learning projects in international development and document classification

  • Recent teaching in probability, statistics, machine learning, and neural networks.

Instructor, 2015-2019

  • Teaching undergraduate math courses

  • Managed mathematics lab

  • Chosen by the Student Government Association as 2018 Professor of the Year in the College of Engineering and Science

Graduate Student Assistant, 2013-2015

  • Research in probability theory, stochastic analysis, random walks, and their application to strategic defense of large-scale communication networks

  • Teaching undergraduate recitation classes for Calculus 1-3 and Differential Equations/Linear Algebra, and general math tutoring
White Associates, R&D LLC
Principal - Data Science Practice, 2019-present
  • Machine learning project for data classification to organize, categorize, and analyze business documents, determine classification levels, etc.
Senior Consultant, 2008-2019
  • Several contracts with educational publishers to design student projects, assignments, solutions, and write a textbook (Practical Discrete Mathematics in progress).

  • Contract work for Neal Analytics and Microsoft, modeling groundwater flow with PDEs, wrote Microsoft cloud-ready Python simulations.

  • Contract work for an investment company, building efficient algorithms (order O(n2)) and methodology for real-time static portfolio replication with a transaction fee budget.

  • iOS (iPhone/iPad) app development for calculation of various rare (to iOS) statistical functions via efficient numerical techniques (smart interpolation, Fourier transforms, complex integration) and lightweight data visualization with an original graphing library.

Engage-AI, Senior Advisor on Data Sciences, Oct 2020 - Present

  • Applying artificial intelligence and machine learning to problems in sustainable global development

  • Pilot project with the Moroccan Haut Commissariat au Plan to leverage AI to build understanding of trends in labor in Morocco

Spencer Trask Collaborative Innovations, Intern with VenCorps: Community Powered Capital, Aug 2008 - Nov 2008

  • Developed scoring, ranking, vote-weighting algorithms and methodology, statistical analysis of social network patterns to create a dynamic vote-weighting system for crowdsourced venture capital decisions

Current Courses

Spring 2022 Courses
MTH 1020 Honors Calculus 2 (TR 2-3:15)
MTH 4224/CSE 4224 Intro to Machine Learning (MW3:30-4:450)
MTH 2332 Primer for Biomath
MTH 4990 Undergraduate Research
ORP 6095 Candidacy Prep OR
Fall 2021 Courses
MTH 2401 Probability and Statistics (TR 3:30-4:45)
MTH 4320/5320 Neural Networks (TR 2:00-3:15)
MTH 4990 Undergraduate Research
MTH 5050 Special Topics: Deep Learning for 3D Imagery
ORP 6005 Candidacy Preparation OR

Selected Publications

  • J. H. Dshalalow, V. M. Nguyen, R. Sinden, R. T. White. Determination of Mutation Rates with Two Mutation Types [in preparation]

  • B. Nagda and R. T. White. Hard pattern mining and ensemble learning for detecting DNA promoter sequences [in preparation]

  • R. T. White. Modeling the Reliability of Stochastic Networks with Empirically Distributed Failures [in preparation]

  • R. T. White. On the Exits of Multidimensional Renewal Processes from n-dimensional Hyper-rectangles [in preparation]

  • G. Neustel and R. T. White. Last exit times of random walks. [in preparation]

  • M. Attzs, T. Mahendrakar, N. Fischer, M. Roy, A. Tisaranni, A. Ekblad, R. T. White, M. Wilde, K. Smith, M. Moyou. A comparison study of YOLO implementations for satellite feature detection [in preparation]

  • M. Gilligan, K. Hunsucker, S. Rech, A. Sharma, R. Beltran, R. T. White, R. Weaver. Assessing the Biological Performance of Living Docks – A Citizen Science Initiative to Improve Coastal Water Quality Through Benthic Recruitment within the Indian River Lagoon, Florida [submitted]

  • R. T. White. On the Exiting Patterns of Sums of Independent Random Vectors with an Application to Stochastic Networks [submitted]

  • T. Mahendrakar, M. Wilde, R. T. White, et al. (2022). Performance Study of YOLO V5 and Faster RCNN for Autonomous Navigation around Non-Cooperative Targets. [accepted at IEEE AeroConf 2022]

  • J. H. Dshalalow and R. T. White (2021). Random Walk Analysis in a Reliability System under Constant Degradation and Random Shocks. Axioms 10(3), 199. https://doi.org/10.3390/axioms10030199

  • T. Mahendrakar, J. Cutler, N. Fischer, A. Rivkin, A. Ekblad, K. Watkins, R. T. White, M. Wilde, B. Kish, and I. Silver (2021). Use of Artificial Intelligence for Feature Recognition and Flightpath Planning Around Non-Cooperative Resident Space Objects. AIAA ASCEND 2021. https://arc.aiaa.org/doi/abs/10.2514/6.2021-4123 

  • T. Mahendrakar, R. T. White, M. Wilde, B. Kish, and I. Silver (2021). Real-time Satellite Component Recognition with YOLO-V5. 35th Annual Small Satellite Conferencehttps://digitalcommons.usu.edu/smallsat/2021/all2021/51/ 

  • J. H. Dshalalow and R. T. White (2021). Current Trends in Random Walks on Random Lattices. Mathematics 9(10): 1148. https://doi.org/10.3390/math9101148

  • J. H. Dshalalow, K. Nandyose, and R. T. White (2021). Time Sensitive Analysis Of Antagonistic Stochastic Processes With Applications To Finance And Queueing. Mathematics and Statistics, 9(4): 481-500. https://www.hrpub.org/journals/article_info.php?aid=11000 

  • R. T. White and J. H. Dshalalow (2020). Characterizations of Random Walks on Random Lattices and their Ramifications. Stochastic Analysis and Applications, 38: 307-342. https://doi.org/10.1080/07362994.2019.1694417

  • J. H. Dshalalow, A. Merie, and R. T. White (2020). Fluctuation Analysis in Parallel Queues with Hysteretic Control. Methodology and Computing in Applied Probability, 22: 295–327. https://doi.org/10.1007/s11009-019-09701-z

  • J. H. Dshalalow, K. Iwezulu, and R. T. White (2016). Discrete Operational Calculus in Delayed Stochastic Games. Neural, Parallel, and Scientific Computations, 24: 55-64. https://arxiv.org/abs/1901.07178 (arXiv preprint)

  • J. H. Dshalalow and R. T. White (2016). Time Sensitive Analysis of Independent and Stationary Increment Processes. Journal of Mathematical Analysis and Applications, 443 (2): 817-833 https://doi.org/10.1016/j.jmaa.2016.05.063

  • J. H. Dshalalow and R. T. White (2014). On Strategic Defense in Stochastic Networks. Stochastic Analysis and Applications, 32 (3): 365-396. https://doi.org/10.1080/07362994.2013.877351

  • J. H. Dshalalow and R. T. White (2013). On Reliability of Stochastic Networks. Neural, Parallel, and Scientific Computations, 21: 141-160. https://arxiv.org/abs/1901.07137 (arXiv preprint)

Recognition & Awards

External Grants Awarded

Data Engineering and Analysis of Sustainable Development and Labor Trends in Morocco. Engage-AI. (Role: PI; pending)

Object Tracking and Image Segmentation with Low Compute/Energy Budget. NVIDIA Applied Research Accelerator Program. (Role: PI; $7500; 2021-)

Semantic Segmentation of Glaciers from Satellite Imagery. European Space Agency. (Role: PI; $1424; 2021-23)

Machine Intelligence System for Autonomous Feature Recognition and Trajectory Planning Around Non-Cooperative Resident Space Objects. US Air Force Research Lab Space Vehicles Directorate. (Role: PI along with M. Wilde, B. Kish; $489500; 2021-22)

REU -- Statistical Models with Applications to Geoscience (SMG). National Science Foundation. (Role: faculty mentor; $309436; 2021-23)

Google Cloud Platform Educational Grant. Google. (Role: PI; $5050; 2020-201)


2018 Professor of the Year for the College of Science and Engineering by the Florida Tech Student Government Association


My background is in probability theory, analysis of stochastic processes, and the underlying mathematics: in short, the study of randomness.

Past work focuses on the reliability of stochastic networks, renewal theory, random walks, stochastic game theory, stochastic measure theory, and particularly the application of discrete and continuous operational calculus to stochastic processes. I continue some work in these areas and on some new ideas related to probability flux.

Most of my newer projects focus on machine learning, computer vision, deep learning, and statistical analysis focused on problems in aerospace engineering, climate science, sustainable global development, cybersecurity, and mathematical biology.

Research Interests & Projects

  • Machine Learning: deep learning, computer vision
    • On-orbit object detection of satellites with limited computational resources
    • Semantic segmentation of glaciers in satellite imagery
    • Tracking facial drooping of patients with neurological disorders
    • Spacecraft pose estimation with multiagent sensor fusion
    • Analysis of global development indicators and labor trends in Morocco

  • Probability and Stochastic Analysis: random walks, stochastic processes
    • Operational calculus analysis of stochastic models of networks, in queueing theory, games, and reliability
    • Probability flux, computational probability, and algorithms
    • Stochastic analysis of covert channel attacks, intrusion detection

  • Mathematical Biology:
    • Cell growth models and proliferation of mutations
    • Statistical analysis of benthic communities in the Indian River Lagoon
    • Detecting DNA promoter sequences

Research Students

PhD Students

Nehru Attzs -- real-time object detection under heavy computational constraints

Minh Nguyen -- deep representation learning for electronic health records

Mackenzie Meni -- feature recognition in medical imagery [not yet started]

William Harrington -- facial landmark tracking, feature extraction, explainable ML classification for diagnosing neurological disorders [currently completing MS thesis]

MS Thesis Students

William Harrington -- clinical facial landmark tracking, data collection with simple smartphone depth cameras, app development

Research Assistants

Andrew Ekblad -- satellite feature recognition

Nathan Fischer -- satellite feature recognition

Anthony Garcia -- satellite feature recognition

Mallory Roy -- satellite feature recognition

Ashley Tisaranni -- satellite feature recognition

Undergraduate Research Students

Derek Hanscom -- polarized waves in the magnetosphere project (2022)

Anthony Garcia and Emily Happy -- glacier segmentation from satellite imagery (2022)

Alex Bugielski and Nikhil Iyer -- spacecraft pose estimation (2022)

Candice Chambers -- Multi-Language Handwriting Recognition with Trees of CNN Ensembles (2021)

Graham Neutstel -- Last Exits of 2D Random Walks (2021)

Abulla Al-Nabit and Hanibal Alazar -- An AI-Enhanced Data Platform for Sustainable Global Development (2021)

Kelly van Woesik -- Tuning Tuna Models with Machine Learning (2021)


Academic Conference Talks

  • R. T. White. Exiting Patterns of Multivariate Renewal-Reward Processes. MAA-FTYCMA Joint Conferences. Feb 19-20, 2021. (Virtual format due to COVID-19)

  • R. T. White. On the Exiting Patterns of Multidimensional Random Walks. MCQMC 2020. Oxford, UK. Aug 9-14, 2020. (Virtual format due to COVID-19)

  • R. T. White. Fluctuation Analysis in Parallel Queues with Hysteretic Control. AMS Fall Southeastern Sectional Meeting. Gainesville, FL. Nov 2-3, 2019.

  • R. T. White. On Exits of Oscillating Random Walks Under Delayed Observation. AMS/MAA Joint Mathematical Meetings. San Diego, CA. Jan 10-13, 2018.

  • R. T. White. Time Sensitive Analysis of d-dimensional Independent and Stationary Increment Processes. AMS Fall Southeastern Sectional Meeting. Orlando, FL. Sept 23-24, 2017.

  • R. T. White. Time Sensitive Analysis of Multivariate Marked Random Walks. SIAM Conference on Computational Science and Engineering. Salt Lake City, UT. March 14-18, 2015.

  • R. T. White. Stochastic Analysis of Strategic Networks. 38th Annual SIAM Southeastern Atlantic Section Conference. Melbourne, FL. March 29-30, 2014.