Washington DC-Baltimore Area
Working at the intersection of: - modeling and simulation - AI/autonomy - strategic planning and decision making as a quantitative analyst for solving vital qualitative policy questions. Key research interest: Implementation of algorithms for optimized theatre and campaign decision-making - specifically with wargaming. Transition of technology as quickly as possible through the "valley of death" and higher technology readiness levels (TRLs) for effective sponsor and stakeholder use. Open to pursuing a doctoral program with applied research within the realm of technology readiness level (TRL) 2/3 or higher. Quadrilingual in English, Cantonese, Mandarin, and Japanese with some French ability. Limited German and Spanish language skills, and able to read some Hangul (Korean). E(S/N)FP personality.
• Roboticist and operational-level modeller for a USMC-sponsored non-lethal autonomous robotics project. Focusing on simulation tools such as IsaacSim, Robot Operating System (ROS), and NVIDIA's Omniverse for agent to robot communication. This is in conjunction with departments and sectors across the lab (i.e. Research and Exploratory Development Department, Asymmetric Operations Sector, etc.) • Notetaker and qualitative analyst for a University-wide tabletop exercise, co-writing the analytical report with next steps and recommendations for University and Health System leadership. • Evaluated two suite tools for a net technical assessment (NTA)-focused project regarding quality of responses and comparing these without the user interfaces with ubiquitous LLM models such as GPT-4, GPT-5, and Opal. This is for OSD-level, and ideally service, combatant command, and fleet use as well. • Supported Generative Wargaming (GenWar) Tabletop Exercise (TTX) as a AI and digital wargaming developer and analyst. Integrated Generative AI tools such as large language models (LLMs), alongside other projects for sponsors, to aid with strategic-level, geopolitical decision making. Worked with operational codes, simulating leaders with agentic AI, and wrote scenarios and courses of action (COA) for fine-tuning data. • Engaged in LLM validation and evaluation, in addition to seeing its integration for strategic wargames as a backend Python developer, evaluator, and analyst for the Strategic Tabletop Exercise portion of the Generative AI Wargaming project. • Worked directly under Professional Principal Staff, an Assistant Director, and a former Undersecretary of Defense. Participated in a demonstration of the tool with three APL Senior Fellows (former DoD Under and Deputy Secretaries and a service-level Acting Secretary) and a former Department Head. • Analysing threat and target scenarios in AFSIM with capability questions and design of experiments. • Task lead for an Arctic project.
• Lead research projects and was engaged in providing technical advice and acquisition support for AI and autonomous systems at the command and service levels. Completed fellowship obligations in October 2024. • Served as a Division Head of Autonomy Research from July 2023 until February 2025. • Developed multi-agent proximal policy optimization (MAPPO) reinforcement-learning networks and integrated these algorithms into AFSIM software scenarios and vignettes for ship tracking of unmanned aerial vehicle (UAV) agents. • Wrote a division workforce development plan with the Director of Strategic Operations and the Data Fusion and AI Research Leads to bolster the AI, autonomy, data fusion skills within the warfare center. • Lead the NAVAIR Autonomy and Artificial Intelligence Community of Interest (COI), which hosts high-profile professors, researchers, and developers monthly to the DoD community with 300+ members and growing. • Wrote report on AI vendor demos for PEO Integrated Warfare Systems X. • Utilized supervised learning algorithms for anti-submarine warfare (ASW) to label and acoustic signatures for acoustic operators with eventual integration in the National Geospatial Intelligence’s (NGA) Map of the World tool. In addition, wrote Python scripts that generated and .kml files on ocean conditions around the globe. • At Chief Digital and AI Office’s (CDAO) Bits2Effects Hackathon developed a policy-iteration reinforcement learning algorithm that uses scenario data and assign them as state, action, transition, and result/policy values for multi-agent-based learning in theatre-level environments.
Fully-funded fellowship for graduate study at Columbia, followed by a commitment equal in length to the federal government. One of 400+ students selected nationwide for this competitive scholarship, with a selection rate of around 15%. Columbia Coursework taken: • MEBM E4439 - Modeling and System Identification of Dynamic Systems • MECE E4520 - Data Science for Mechanical Systems • IEOR E4004 - Optimization Models and Methods • MECE E4998 - MS Projects in Mechanical Engineering (Research) • ENGI E4000 - Professional Development and Leadership -- waived • STAT GU4001 - Introduction to Probability and Statistics • MECE E6616 - Robot Learning • MECE E6601 - Introduction to Control Theory • EAEE E6212 - Introduction to Carbon Management • IEME E4200 - Human-Centered Design Innovation • COMS W4995 - Mathematics of Machine Learning and Signal Recognition • INAF U6347 - US Role in World Affairs II (International Fellows Program) • HBSS 4116 - Health Education for Teachers Took part of this course via CVN: • IEOR E4106 - Stochastic Models
Systems Safety Branch