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Ryan T. White

Assistant Professor | College of Engineering and Science - Mathematics and Systems Engineering

Contact Information

Personal Overview

I regularly teach undergraduate and graduate courses in deep learning, machine learning, and probability, as well as honors calculus courses.

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

I lead the NEural TransmissionS (NETS) Lab. We focus on developing deep learning models, traditional machine learning, and statistical analysis for applications in aerospace, life sciences, and glaciology.

Outside academia, I have industrial experience in algorithm development, data science, and educational writing as well as current projects with non-profit Engage-AI using machine learning to extract insights for global development data.

Educational Background

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

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 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.

  • Several contracts with educational publishers to design student projects, assignments, solutions, and write a textbook (Practical Discrete Mathematics)
Senior Consultant, 2008-2019
  • 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 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

MTH 2401 Probability/Statistics
MTH 4224 Intro to Machine Learning
 
Past Courses
 
Fall 2023 Courses
 
MTH 2401 Probability/Statistics
MTH 4320/5320 Deep Learning
ORP 5090 Special Topics in ORP: Explainable AI
ORP 5090 Special Topics in ORP: Information Theory
 
Summer 2023 Courses
 
MTH 2401 Probability/Statistics
 
Spring 2023 Courses
 
MTH 1020 Honors Calculus 2
MTH 4224 Intro to Machine Learning
 
Fall 2022 Courses
 
 
MTH 4320/5320 Neural Networks
 
Summer 2022 Courses
 
MTH 2401 Probability and Statistics
MTH 5050 Special Topics: Context-based Object Recognition
 
Spring 2022 Courses
 
MTH 1020 Honors Calculus 2
MTH 4224/CSE 4224 Intro to Machine Learning
MTH 2332 Primer for Biomath


Fall 2021 Courses
 
MTH 2401 Probability/Statisics
MTH 4320/5320 Neural Networks
 

Selected Publications

See my Google Scholar profile for an up-to-date list of publications.

Publications as of Jan 1, 2024:

  • B. M. NagdaV. M. Nguyen, and R. T. White. promSEMBLE: Hard Pattern Mining and Ensemble Learning for Detecting DNA Promoter Sequences. IEEE/ACM Transactions on Computational Biology and Bioinformatics. https://doi.org/10.1109/TCBB.2023.3339597
  • A. Tikayat Ray, O. J. Fischer, D. N. Mavris, Ryan T. White, and B. F. Cole. aeroBERT-NER: Named-Entity Recognition for Aerospace Requirements Engineering using BERT. AIAA SCITECH Forum 2023https://doi.org/10.2514/6.2023-2583
  • A. Tikayat Ray, B. F. Cole, O. J. Pinon-Fischer, R. T. White, D. N. Mavris. aeroBERT-Classifier: Classification of Aerospace Requirements Using BERT. Aerospace 10, 279. https://doi.org/10.3390/aerospace10030279
  • A. DespeignesA. SharmaR. BeltranS. RechM. Gilligan, K. Hunsucker, R. T. White, N. N. Kachouie. Impact of Benthic Organisms to Mitigate Water Pollution in the Indian River Lagoon. Water, Air, & Soil Pollution. 234, 546 (2023). https://doi.org/10.1007/s11270-023-06528-w
  • A. Tikayat Ray, B. F. Cole, O. J. Pinon Fischer, A. P. Bhat, R. T. White, and D. N. Mavris. Agile Methodology for the Standardization of Engineering Requirements using Large Language Models. Systems. 2023; 11(7):352. https://doi.org/10.3390/systems11070352
  • A. Tikayat Ray, A. P. Bhat, R. T. White, V. M. Nguyen, O. J. Pinon Fischer, D. N. Mavris. Examining the Potential of Generative Language Models for Aviation Safety Analysis: Insights from ASRS Case Study. Aerospace 10(9), 770. https://doi.org/10.3390/aerospace10090770
  • T. Mahendrakar, M. N. Attzs, J. M. Duarte, A. L. Tisaranni, R. T. White, and M. Wilde. Impact of Intra-class Variance on YOLOv5 Model Performance for Autonomous Navigation around Non-Cooperative Targets. AIAA SCITECH Forum 2023https://doi.org/10.2514/6.2023-2374
  • T. Mahendrakar, S. Holmberg, A. Ekblad, E. Conti, R. T. White, M. Wilde, and I. Silver. Autonomous Rendezvous with Non-cooperative Target Objects with Swarm Chasers and Observers. AAS/AIAA Spaceflight Mechanics Conference 2023. https://arxiv.org/abs/2301.09059
  • B. Caruso, T. Mahendrakar, V. M. Nguyen, R. T. White, Todd Steffen. 3D Reconstruction of Non-cooperative Resident Space Objects using Instant NGP-accelerated NeRF and D-NeRF. AAS/AIAA Spaceflight Mechanics Conference 2023. https://arxiv.org/abs/2301.09060
  • A. Ekblad, T. Mahendrakar, R. T. White, M. Wilde, I. Silver, B. Wheeler. Resource-constrained FPGA Design for Satellite Component Feature Extraction. To appear at IEEE Aerospace Conference 2023. https://ieeexplore.ieee.org/abstract/document/10115681
  • T. Mahendrakar, R. T. White, M. Wilde, and M. Tiwari. SpaceYOLO: A Human-Inspired Model for Real-time, On-board Spacecraft Feature Detection. IEEE Aerospace Conference 2023. https://ieeexplore.ieee.org/abstract/document/10115705
  • M. N. Attzs, T. Mahendrakar, M. Meni, R. T. White, and I. Silver. A Comparison of Tracking-By-Detection Algorithms for Real-time Satellite Component Tracking. 37th Annual Small Satellite Conferencehttps://digitalcommons.usu.edu/cgi/viewcontent.cgi?article=5751&context=smallsat
  • J. H. Dshalalow and R. T. White (2022). First Passage Analysis in a Queue with State Dependent Vacations. Axioms 11 (11), 582. https://doi.org/10.3390/axioms11110582 
  • J. H. Dshalalow and R. T. White (2022). Fluctuation Analysis of a Soft-Extreme Shock Reliability Model. Mathematics 10 (18), 3312. https://doi.org/10.3390/math10183312 
  • J. H. Dshalalow, V. M. Nguyen, R. T. White, R. R. Sinden (2022). Determination of Mutation Rates with Two Symmetric and Asymmetric Mutation Types. Symmetry 14 (8), 1701. https://doi.org/10.3390/sym14081701 
  • M. Gilligan, K. Hunsucker, S. Rech, A. Sharma, R. Beltran, R. T. White, R. Weaver (2022). 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. Journal of Marine Science and Engineering 10 (6), 823. https://doi.org/10.3390/jmse10060823 
  • R. T. White (2022). On the Exiting Patterns of Multivariate Renewal-Reward Processes with an Application to Stochastic Networks. Symmetry 14 (6), 1167. https://doi.org/10.3390/sym14061167 
  • 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. IEEE Aerospace Conference 2022https:/doi.org/10.1109/AERO53065.2022.9843537
  • 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 A. Tikayat Ray (2021). Practical Discrete Mathematics. Packt Publishers.
  • 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 (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 (preprint)

 

Recognition & Awards

External Grants Awarded

Information Theoretic Learning and Explainable Convolutional Neural Networks. U.S. Army Corps of Engineers. (Role: PI; $49,735, 2023-24)

Data Engineering and Analysis of Sustainable Development and Labor Trends in Morocco. Engage-AI. (Role: PI; $1200; 2022)

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; $489500; 2021-22)

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

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

Awards

2022-23: Honored as top performer in teaching in the Department of Mathematics and Systems Engineering

2019 Professor of the Year for the College of Science and Engineering by the Florida Tech Student Government Association

Research

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

Research Interests & Projects

I lead the NEural TransmissionS (NETS) Lab. We focus on developing deep learning models, traditional machine learning, and statistical analysis.

In addition to some smaller-scale projects and collaborations, our ongoing projects include:

  • Object detection for satellite feature recognition and real-time flightpath planning -- collaboration with Dr. Markus Wilde and the ORION Research Lab.

    • Includes developing a novel object tracking algorithm for edge hardware with my PhD student Monty (Nehru) Attzs

    • Includes work funded by the U.S. Air Force Research Laboratory

  • Synthetic generation of electronic health record data using generative adversarial networks (GANs) with my PhD student Minh Nguyen.

  • Development of explainable computer vision models with my PhD student Mackenzie Meni
  • Computer vision-based 6-DOF pose estimation for rigid bodies--that is, estimating the 3D position and orientation with a team of undergraduate students.

    • Includes 3D modeling for synthetic data generation

  • Glacier segmentation in multispectral satellite imagery with neural networks with undergraduate students Emily HappyAnthony Garcia, and Neel Shettigar.

    • Includes work for funded by the National Science Foundation

  • Development of an app for clinical facial landmark tracking for diagnosing neurological disorders with my MS student William Harrington.

  • Probability and Stochastic Analysis: random walks, stochastic processes

    • Operational calculus analysis of stochastic models of networks, in queueing, games, and reliability

    • Probability flux, density tracking, and related algorithms

  • Mathematics of Life Sciences:

    • 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

Monty (Nehru) Attzs -- real-time object detection under heavy computational constraints

Minh Nguyen -- deep representation learning for electronic health records

Mackenzie Meni -- explainable AI for computer vision

Undergraduate Research Students

Emma Sandidge -- 3D modeling of spacecraft on orbit

Past Students

MS Thesis Students

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

Research Assistants

William Stern -- ML in global development data

Andrew Ekblad -- satellite feature recognition

Steven Holmberg, Sam Lovelace, Josseanne Duarte, Dylan Barnes -- space robotics

Undergraduate Research Students

Joey Weatherwax, Giulio Martini -- computer vision-based 3D pose estimation 

Anthony Garcia and Emily Happy -- Semantic Segmentation of Glaciers in Multispectral Satellite Imagery with Deep Neural Networks (2022)

Alex Bugielski and Nikhil Iyer -- Spacecraft Pose Estimation with Deep Learning (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 Talks

Slides are available for many of these talks, and more at my website at publications and presentations.

  • M. Meni and R. T. White. Information-Informed Neural Networks: Probabilistically Interpretable Neural Decision-making. Frontiers in Geosciences Seminar. Los Alamos National Laboratory, Los Alamos, NM, Aug 2023.

  • 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.
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