Georgia Institute of Technology, Atlanta, GA

Formal Methods & Autonomous Control of Transportation Systems Lab, Georgia Institute of Technology

Postdoctoral Fellow, January 2026 – Present

Applied efficient reachability techniques to biomedical problem setting, developing intelligent algorithms for safe control of automated medication delivery equipment for patients in critical care scenarios.

Graduate Research Assistant, August 2019 – December 2025

Developed a generalized method leveraging mixed monotonicity and Gaussian Process modeling that incorporates unknown time-dependent and state-dependent disturbances and efficiently calculates reachable set over-approximations of dynamical systems that hold with high probability.
Applied this efficient reachable set over-approximation method to the safe aviation autonomy problem setting, resulting in a model predictive controller for general systems (e.g. autonomous vehicles, quadrotors, etc.) that is safe with high probability as well as runtime assurance mechanisms that guarantee adherence to a reference trajectory within a desired safety threshold.
Developed an algorithm to facilitate cooperation in multi-agent systems with heterogeneous capabilities by assigning agents to resolve obstacles that prevent other agents from achieving objectives.

Intelligent Vision and Automation Lab, Georgia Institute of Technology

Undergraduate Research Assistant, June 2016 – October 2020

Performed evaluation experiments on different pose estimation strategies and their efficacy in improving pick-and-place performance of a robotic manipulator.
Created Python scripts to render a series of images that had to simulate realistic behavior in both a robotic manipulator and a baby, to be used as a training dataset for vision algorithms, resulting in the ability to create images at a rate of about 5000 per thirty minutes.
Simulated a robotic manipulator using ROS and Gazebo which included collision detection to generate training data, enabling the team’s control concept to be proven within simulation before testing on a physical hardware robotic manipulator.
Trained and applied Machine Learning algorithms, including Randomized Decision Forests, to the created dataset to test accuracy as a potential robot arm pose estimator.

Sandia National Laboratories

Intern, May 2023 – Present

Integrated simulation tools for use on quadruped walking robots to develop robust automated and tele-operated package transportation and delivery methods.
Interfaced with quadrupedal walking robot platform hardware to ensure operability and compatibility.
Developed simulations using Robot Operating System (ROS) and MoveIt Motion Planning tools for robotic manipulation tasks, including scenarios wherein multiple robotic arms must cooperate to achieve an objective.

Qualcomm

AR/XR Development Intern, May 2020 – August 2020

Augmented a stereo vision augmented reality training dataset for classifying and detecting the pose of a user’s hands with synthetically generated data.
Leveraged a Generative Adversarial Network (GAN) to modify the synthetic data such that it closely resembles real hands, allowing for the automation of the data annotation process while avoiding bias.
Implemented image preprocessing, Intersection Over Union Loss, Scale-Invariant Feature Transform (SIFT) and Fast Library for Approximate Nearest Neighbors (FLANN) matching to reduce pose and stereo matching errors introduced by the GAN.

Airbus Intelligence (formerly Airbus Aerial)

Machine Learning Intern, October 2019 – May 2020

Assisted in the evaluation and implementation of object detection and classification machine learning models for use on aerial imagery, enabling it to locate and predict levels of damage on structures.
Developed a pipeline for performing object detection inference on large-scale imagery through WMS and WMTS server requests, allowing the web portal to quickly provide damage prediction data over large user-specified areas.
Implemented unit tests for all machine learning tools.

Johns Hopkins University Applied Physics Laboratory

Intern, May 2019 – August 2019

Developed a Python API that implemented SpaCy, Stanford CoreNLP, and Apache OpenNLP Named Entity Recognition tools, allowing the lab to leverage these existing tools in their document classification and Named Entity Recognition Active Learning pipelines.
Designed and ran experiments to test different ranking functions to be used in Active Learning, resulting in a comprehensive analysis of the efficacy of each function.

NASA Jet Propulsion Laboratory

Intern – Mission Planning & Sequencing, June 2017 – August 2017

Integrated Google Test framework into existing application code base, resulting in the creation of 30+ new unit tests as well as more efficient unit test creation.
Restructured the method in which libraries were handled within the application code base, allowing the software to use libraries stored on the user’s machine, rather than having to download them separately.
Modified build process to use C++11 compiler and to have end user specify whether to include Google Test capabilities, resulting in software that works with the latest libraries and allows for customization.

The Aerospace Corporation

Ground Systems Prototyping Intern, June 2015 – August 2015

Researched various methods to control a quadcopter using a laptop through WiFi, allowing for more accurate and robust control.
Created a script to allow communication between cloud services provided by Amazon Web Services and a local machine, which enabled the ability to utilize cloud computing for autonomous control.
Developed pipeline to translate results from cloud image processing into quadcopter control commands, resulting in a demonstrable application of cloud computing, big data, and autonomous control.
Performed bug testing on a web application which included attempting SQL injections and locating general access privilege errors, resulting in a more robust final product.

Innovative Architects

Intern, June 2014 – July 2014

Assisted with various projects including improving/updating social media pages, coding various sections of websites to client specifications, and creating presentations to be used in company-sponsored workshops.

Siemens

Intern, July 2013 – August 2013

Researched additive manufacturing industry to determine if Siemens had the proper product fit, the result of which was presented to headquarters in Germany.

Programming

JAX, Python, MATLAB, Git, Java, C++, C, Google Test, Bash Scripting

Software

ROS, ROS 2, MoveIt, Gazebo, TensorFlow, SpaCy, CoreNLP, OpenNLP, Blender, AWS, Android

Hardware

mbed, Raspberry Pi, Kinect, Basic Combinatorial Logic, Integrated Circuits

Curriculum Vitae