Educational Background

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

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

Tenured Associate Professor, Aug 2025 -

• Research in machine learning, deep learning, computer vision, stochastic analysis, random walks.
  
  
• Projects on inspection, rendezvous proximity operations, navigation, and docking with autonomous spacecraft -- low-power computing, 3D reconstruction, GNC, flight tests
  
  
• Research in optimal information flow, bias, and interpretability in neural networks, explainable & human-inspired AI
  
  
• Recent teaching in machine learning, deep learning, and AI

Assistant Professor, 2019 - Aug 2025

• Research in machine learning, deep learning, computer vision, probability theory, stochastic processes, random walks, and queueing theory.
  
  
• Computer vision for inspection, rendezvous proximity operations, navigation, and docking with autonomous spacecraft
  
  
• Information-theoretic analysis of neural networks, methods for increased explainability, efficiency, and performance.
• Applied machine learning projects in international development
  
  
• Collaborative applied projects: physics-informed ML for fast fluid dynamics, ML for estimating airport flight conditions, NLP for requirements engineering, statistical modeling in the sciences
• 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

• Developed a digital twin of the orbital environment for simulation of chaser spacecraft orbits around satellites on orbit, generating synthetic camera feeds from the chaser.
  
  
• Studied inspection orbits providing optimal data acquisition for training high-fidelity 3D models using only passive imagery.
  
  
• Optimized 3D reconstruction algorithms for low-powered Jetson computers and demonstrated performance.

• 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)

• 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

Selected Publications

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

Publications as of Jan 1, 2024 (student authors in bold):

• B. M. Nagda, V. 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 2023. https://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. Despeignes, A. Sharma, R. Beltran, S. Rech, M. 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 2023. https://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 Conference. https://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 2022. https:/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 Conference. https://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 Support

Impact of Quantum Processes in Reactive Oxygen Species Molecular and Cellular Redox Biology. National Science Foundation. (Role: Co-PI; $787,016, 2025- )

ASCEND: Leveraging HPC and AI for Enhanced Cybersecurity, Resilience and Innovation in Aerospace and Defense Industry. National Institute for Standards and Technology. (Role: Co-PI; $2,323,000, 2025- )

Mutual Information-based Analysis of Latent Neural Representations for Interpretable Computer Vision. U.S. Army Engineer Research and Development Center. (Role: PI; $66,590, 2024- )

Optimal Data Acquisition for Satellites Inspection and Capability Identification. Air Force Research Lab. (Role: Summer Faculty Fellow; $24,560, 2025)

Passive Reconnaissance of Satellites for AI-enabled 3D Modeling and Capability Identification. Air Force Research Lab. (Role: Summer Faculty Fellow; $24,560, 2024)

Information Theoretic Learning and Explainable Convolutional Neural Networks. U.S. Army Engineer Research and Development Center. (Role: PI; $49,735, 2023-24)

Data Engineering and Analysis of Sustainable Development and Labor Trends in Morocco. Engage-AI. (Role: PI; $1,200; 2022)

Artificial Intelligence for Feature Recognition and Trajectory Planning Around Non-Cooperative Satellites in orbit. (Role: PI; $65,000; 2022-23)

Object Tracking and Image Segmentation with Low Compute/Energy Budget. NVIDIA Applied Research Accelerator Program. (Role: PI; $7,500; 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; $489,500; 2021-22)

REU - Statistical Models with Applications to Geoscience (SMAG). National Science Foundation. (Role: faculty mentor; $309,436; 2021-23)

Google Cloud Platform Educational Grant. Google. (Role: PI; $5,050; 2020-201)

Awards

2023-24: Honored as top performer in Research -- Department of Mathematics and Systems Engineering

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

2019 Professor of the Year for the College of Engineering and Science -- 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:

• Real-time, on-orbit satellite inspection, capability  and real-time flightpath planning.

  • Collaboration with former PhD students Dr. Trupti Mahendrakar, Dr. Mackenzie Meni, Dr. Minh Nguyen.

  • Novel object tracking algorithm development for edge hardware with my PhD student Monty (Nehru) Attzs

  • Digital twin development, designing orbits for optimal data acquisition, and automated 3D modeling on edge hardware with PhD student Arianna Issitt.
    
    
  • Fabrication of satellite mockup and articulating arm for real-world lab testing of vision algorithms; automated characterization, guidance, navigation algorithm development with aerospace, computer engineering, and math senior design students.

  • Funded four times by the Space Vehicles Directorate of the U.S. Air Force Research Laboratory

• Development of explainable computer vision models using information theoretic model design, optimal data processing, and representation learning.
  
  
  • Collaboration with former PhD student Dr. Mackenzie Meni.
  • Development of information theoretically diverse ensemble models with student Blake Gisclair.
    
    
  • Analysis of mutual information patterns in neural latent spaces for explainability with PhD student Arianna Issitt.
    
    
  • Twice funded by the US Army Engineer Research and Development Center.
• Hyperspectral computer vision on magnetic resonance microscopy to identify and elicit cell behavior with quantum processes.
  
  
  • Collaboration with Dr. Robert Usselman bridges the gap between atomic, molecular, and cellular levels to address cell aging, cancer detection, and automated spectroscopy.
    
    
  • AI/ML model development for extremely high-dimensional microscopy feeds with PhD students Blake Gisclair and Lennon Shikhman
  • Funded by the National Science Foundation.
    
    
• Physics-informed machine learning for estimating blood flow dynamics.
  
  
  • Collaborative work with Dr. Keshav Chivukula and former PhD student Dr. Ruth White.
    
    
  • Rapid estimation velocity fields for blow flow in the heart and vasculature with PhD student Marcello Mattei.
    
    
  • Previously funded by the American Heart Association.
    
    
• Climate and weather data-driven estimation and forcasting of meteorological conditions.
  
  
  • Collaborative work with Professor Michael Splitt, Dr. Steven Lazarus, and PhD student Marcus Cote
    
    
  • Predicting aviation flight rules based on arrays of meteorological sensors with PhD student Emily Happy
    
    
  • Funded by the Federal Aviation Administration.

• Probability and Stochastic Analysis: random walks, stochastic processes

  • Data-driven learning of Stochastic Differential Equation solutions with PhD student Lennon Shikhman.

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

  • Probability flux, density tracking, and related algorithms

Research Students

PhD Students

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

Arianna Issitt -- spacecraft inspection, optimal data acquisition, 3D modeling, digital twin development, low-SWaP computing

Emily Happy -- automated aviation flight rules prediction using meteorological sensor arrays

Marcello Mattei -- physics-guided neural networks for hemodynamic velocity and pressure field estimation

Lennon Shikhman -- stochastic "physics-informed" neural networks for stochastic differential equations

MS Students

Steven Holmberg -- artificial potential field-based guidance of spacecraft for rendezvous proximity operations.

Lamine Deen

Undergraduate Research Students

Blake Gisclair -- information theoretic learning and explainable AI; hyperspectral computer vision for magnetic resonance microscopy

Ava Nieburg + aerospace team 1 -- automated flightpath planning, guidance and navigation with cooperative spacecraft

Tyler Davis + aerospace team 2 -- building real-world experimental on-orbit simulator to emulate in-space conditions

Blake Gisclair, Sloan Hatter, Victor Ferraguz -- 3D modeling of spacecraft with active sensing

Amy Alvarez, Yash Jani -- 3D reconstruction of spacecraft with passive sensing

Kari Curtin

Past Students

PhD Students

Dr. Mackenzie Meni -- information theoretic learning for explainable AI, representation learning, and model design (2024)

Dr. Trupti Mahendrakar -- on-board satellite inspection, guidance/navigation for rendezvous proximity operations and docking (2024)

Dr. Minh Nguyen -- generative AI and natural language processing (2024)

MS Thesis Students

William Harrington -- clinical facial landmark tracking, data collection with simple smartphone depth cameras, app development (2023)

Research Assistants

Alex Merino

Emma Sandidge -- 3D modeling of spacecraft on orbit

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

Pedro Moura -- information theoretic analysis of neural latents of large language models (2024)

Joey Weatherwax, Giulio Martini -- computer vision-based 3D pose estimation (2023)

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.