2018 - 2024

University of Alabama in Huntsville

Context-Aware Machine Learning for Low-Burden Brain-Computer Interfaces

2013 - 2015

University of Tennessee

Visual Analysis of Multivariate Transition Data

2009 - 2013

University of Tennessee

Mathematics Minor, Honors Concentration

Applying novel machine learning techniques in counter-UAS and operator assistive systems.

Tuning and implementation of data fusion, sensor processing, and navigation algorithms in swarm response and air defense software platforms.

Software for heart mapping devices used in treating chronic atrial fibrillation. Implementation, validation, and optimization of high-performance algorithms for realtime navigation within heart chambers during surgical procedures. MATLAB, C#, C++ with hardware acceleration in SYCL, OpenCL, Eigen, MKL.

Frontend design for controlling data acquisition during heart procedures. WPF.

Used modern 3D tools such as Unity to generate large-scale synthetic datasets to augment computer vision and machine learning applications. Integrated into pipeline for 3D visual reconstruction.

Used Google Cloud and other distributed platforms to execute customer-specific tasks to designing, training, adapting, and deploying automation models in industrial settings. Used standard industry toolsets including Juypter, Colab, Docker, Pandas, TensorFlow, Keras, PyTorch, FastAPI.

Led AI Innovation Cell to fund and organize new machine-learning projects. Spearheaded engagement with new technologies such as brain-computer interfaces, virtual reality interfaces, drone swarms, and battle management systems. Pitched government on new avenues for artificial intelligence integration into other sectors. Oversaw development of semi-autonomous drone software. Deployed to embedded platforms such as NVIDIA Jetson. Worked with sensor suites such as RGBD and LIDAR.


Led development of signals intelligence automation to predict specific terror attacks using modern tools such as deep anomaly detection. Analyzed its use in risk analysis, overfishing detection, and other real-world problems. Performed liaison duties with government for equipment, data storage, staffing.

Coordinate tech talks, submission archives, and industry situational awareness across community-wide community of subject matter experts in artificial intelligence and machine learning.

Led internal AI Innovation Cell.

Centralize efforts across multiple offices in multiple states to consolidate all AI-ML past performance into single, consistent portfolio indexed to performance date, keywords, and customer.

Worked across a 10+ company team to prototype web-based frontend as part of a technical scenario contract bid. Performed live usage of prototype during oral presentations.

Won two rounds of funding for continued development on SIGINT anomaly detection through a campaign of product demos and pitch meetings.

Prototyped swarm algorithm to orchestrate boat formations for Drive-the-Boats government competition.

Competed in pitch competition for neuroscience-focused medical training acceleration. Designed concept based on using social network analysis and machine learning to power smart-targeted transcranial direct current stimulation for motor skill learning such as applying sutures in the field. Won a round of funding and made it to 2020 semifinals in Radiance’s first foray into applied neuroscience sector.

Consulted on acquisition decisions, investment discussion, and company growth direction in AI-ML sector. Regularly managed, wrote, and submitted whitepapers, proposals, and RFI responses.

Produce slick sheets, kiosk displays, and other marketing materials for use at conferences and exhibition halls across the country. Led internal briefings, demonstrations, and training sessions to facilitate awareness of AI-ML throughout the company and evaluate its role in other R&D and integration activities. Pushed company into sectors including tactical edge computing, facial recognition, and applied machine vision.

Performed system architecting responsibilities for systems integration lab to facilitate swapping out hardware-in-the-loop, software-in-the-loop, and simulation-based components. Designed and built UI for analysts to author test scenarios in a drag-and-drop interface and monitor the state of the execution during testing. Coordinated with team of four using agile development process to develop auxiliary applications for recording, playing back, and displaying details of test. Wrote specification for configuration and data files. Performed lab testing of framework. Created Software Development Kit to assist other companies in developing adapters according to framework standards and provided regular support to integrating their applications within the lab.

Designed, implemented, and documented modular pipeline and API to execute processing stages in autoencoder-based anomaly detection system for intelligence applications. Served as development lead for technology pivot from previous pipeline. Worked with subject matter experts in intelligence domain to implement, test, and evaluate candidate modules. Produced data visualization to showcase results. Investigated and applied explainability algorithms to enrich output to analyst customers.

Designed and built UI for operator control of high-energy laser weapon system. Implemented networking layer backend to translate between weapon system communications and platform-agnostic command-and-control messages for frontend. Reverse-engineered existing networking API where documentation was lost or lacking from previous vendors. Built custom controls to add targeting assistance overlays onto streaming videos from on-board sensors. Added map views to show state of multiple weapons systems in integrated battlefield depiction. Interfaced with Xbox gamepad for ease of control and added user settings to customize controller properties. Created custom horizon overlay control to visualize targets and safe-to-shoot areas.

Assisted with projects involving gunfire detection, computer vision, target cueing. Oversaw design of new technologies from data acquisition, model design, and integration perspectives.

Performed customer-facing rapid prototyping of a UI and support algorithms to present integrated view of aircraft test range with auxiliary functionality such as automatic video routing, radar coordination, and no-go zones. Worked with teammate to rebuild prototype into full-scale application with associated simulation application for testing. Interfaced with range hardware devices to execute optimization algorithms and used terrain database to determine obstructions in video. Successfully passed all formal review processes including design reviews, factory testing, and site testing. Wrote user manual and performed on-site training. Received customer accolades stating that project was the “most joyful of experiences in developing and delivering an absolute new capability to the Range” in customer’s entire career.

Evaluated candidate algorithms for guided drone flight on mobile-GPU based quadcopter control boards and made recommendations to government customer.

Regularly mentored intern developers on how to work within design specifications to implement new features. Taught how to apply good coding standards and use version control within a team.

Provided stakeholder demonstrations, created training videos, and performed customer-interfacing duties alongside development work in most projects. Regularly gathered feedback from end-users and customers and responded with design adjustments to fit shifting customer requirements.

Maintained UNIX infrastructure including LDAP authentication and server upkeep. Developed internal modules for account maintenance. Made and ran network cables. Served as on-call for server room monitoring.

Supported walk-in users with account and lab resource issues. Handled support tickets. Performed on-site user assistance.

Developed software for user-directed analytics to visualize of human migration data and support rapid hypothesis generation of lung- and breast-cancer risk factor.

Supported social media mining and text analysis for research into public outreach by California-based providers of pediatric hospice care.

Explored using inter-keypoint relationships as feature descriptors for robotics-oriented depth-sensor object recognition. Implemented Histogram of Orientations to Nearest Keypoints descriptor.

Studied with the Lab for Brain-Like Computing and Machine Intelligence focused on EEG-based applications. Participated in executing data recording experiments. Assisted with English translation for in-draft textbooks and conference presentations.

2020 - xTechBOLT Phase I Winner

2019 - Associate Technical Fellow

2017 - Team Player Award (Company-Wide)

2017 - Technical Excellence Award (Integration & Prototyping)

2024 - UAH College of Science Certificate of Appreciation

2020 - IEEE IGARSS 3MT Finalist

2013 - EUReCA Award

2013 - Best Analytics Team

2011 - ACM Programming Team MVP

2012 - Edgar Wyman McCall Scholarship

2011 - Ready for the World Grant

2011 - Anthony L. Vest Engineering Scholarship

2008 - SkillsUSA State Gold Medal in 3D Visualization

2006 - Eagle Scout

C#

C++

C

Python

HTML/CSS/JavaScript

Java

Go

LaTeX

GLSL

XAML

.NET/WPF

Numpy/Scipy/Pandas

PyTorch/scikit-learn/TensorFlow/Keras

Bootstrap/Vue.js/d3.js

Processing/Arduino

CUDA

OpenCV

Unity

Blender

Illustrator/Inkscape

SketchUp

git/SVN/Jira

Machine Learning

Systems Automation

Desktop Applications/UX

UNIX Scripting

High-Performance Computing

Agile/Scrum/Waterfall

Brain-Computer Interface

Cognitive Computational Neuroscience

Bio-Inspired Computing

Data Visualization

Proposal Writing & Management

Public Speaking

Technical Writing

Business Development

Branding

T. W. d. Wit, V. Menon, T. Davis

SoutheastCon 2024, Atlanta, GA, USA, 2024, pp. 848-853

doi: 10.1109/SoutheastCon52093.2024.10500151.

T. W. d. Wit, V. Menon, N. Tenhundfeld, T. Davis

2023 IEEE International Conference on Tools with Artificial Intelligence (ICTAI) pp. 122-128

doi: 10.1109/ICTAI59109.2023.00025.

T. W. d. Wit and V. Menon

2019 IEEE International Conference on Big Data pp. 2948-2955

doi: 10.1109/BigData47090.2019.9006544.