Research Philosophy
I am drawn to engineering problems in which physical insight, mathematical rigor, and thoughtful design must work together. Across high-power optical systems, bioreactors, autonomous robots, reactor physics, and orbital mechanics, my underlying interest is the same: to understand governing physics deeply enough to design, build, analyze, and control systems that behave reliably in complex environments.
My work spans physical-system design and scientific computation—from developing mechanical and optical assemblies to constructing first-principles models and purpose-built learning algorithms. I use machine learning as a mathematical engineering tool, designing architectures, objectives, and loss functions around a problem's governing physics to support analysis, optimization, inference, and control. Computation is not a substitute for physical understanding; it is one means of extending what we can understand and create.
I am most motivated by problems that have not yet been neatly packaged. I enjoy identifying the underlying technical question, determining what must be understood, and creating the mathematical, computational, or physical tools required to pursue it. My aim is not merely to analyze existing systems, but to originate useful methods and working systems that others can test, apply, and extend.
In the long term, I hope to bring this combination of physical design, computational physics, and intelligent control to autonomous marine technology. I am particularly interested in systems capable of responsibly exploring, monitoring, and interacting with difficult environments—engineering tools that make complex phenomena more knowable and ambitious physical systems more achievable.
Research
Active Projects
Engineering, to me, is the pursuit of making complex systems more identifiable, more predictable, more controllable, and, ultimately, more knowable.
Completed Work
Additional Research
Heat Transfer PINN
Physics-Informed Neural Network (PINN) derivation and application for transient heat conduction using sparse temperature measurements. This self-directed project documents my derivation of the neural-network and physics-constrained training framework, then applies it to thermal-field reconstruction governed by the transient heat equation and boundary conditions. It serves as a technical precursor artifact to my later work in AI-assisted laser optimization. See the attached document for derivations, implementation details, and results.
Beam Quality Derivation Software
Developed image-processing and computational software capable of estimating laser beam quality metrics from limited beam image measurements. A GUI was created for the program in MATLAB.
Nonlinear Market Dynamics, Forecasting & Volatility Modeling
Developed proprietary mathematical and machine-learning frameworks for analyzing and forecasting Exchange-Traded Fund (ETF) and equity behavior, including stock market responses to disturbances. The primary framework incorporates dynamic-systems modeling, statistical learning, optimization, and other nonlinear methods with modeling procedures designed and validated across multiple market regimes and regime transitions.
Separately developed volatility models for comparative analysis against option-implied volatility. This work is methodologically distinct from the market-dynamics framework. Additional methods, model structures, validation procedures, and implementation details remain proprietary.
I welcome academic and mathematical discussion with researchers and practitioners in quantitative finance (especially options), applied mathematics, nonlinear dynamics, and machine learning.
Initiatives
Upcoming Research
Marine Environmental Sampling Robot
Leading an interdisciplinary undergraduate engineering team developing an autonomous marine sampling platform for environmental monitoring supporting offshore resource operations and regulatory compliance.
Now Open to Additional Team Members
Public-Facing Market Modeling and Contract Optimization Platform
Developing a public-facing research platform for visualizing nonlinear financial market dynamics, options mechanics, and AI-assisted decision support using both established financial theory and proprietary mathematical models.
Laboratory
UF SYBORGS Lab
Mechanical & Aerospace Engineering · Dry Laboratory
University of Florida SYBORGS Laboratory
Synthetic Biological Optimization, Regulation, or Generation Systems Laboratory. Contributing to the design, fabrication, integration, and testing of robotic systems across terrestrial, biological, and space-inspired environments.
Role: Mechanical & Systems Engineering Undergraduate Researcher
Explore the LaboratoryTechnical Capabilities
Selected Methods & Systems
Physical Design & Experimental Engineering
- —Mechanical & Optomechanical Design
- —CAD, Tolerance Analysis & Design for Manufacturability
- —Precision Machining (CNC & Manual), Fabrication & Assembly
- —Structural, Stress & Vibration Analysis
- —Experimental Apparatus & Test-Fixture Design
- —Instrumentation, Testing & Validation
- —PCB Design & Embedded Electronics Integration
Optical, Dynamic & Control Systems
- —Laser & Optical Systems
- —Laser Beam Characterization & Stabilization
- —System Dynamics & Random Vibrations
- —Control Theory & System Identification
- —Signal Processing
- —Sensors, Actuators & Electromechanical Integration
- —Intelligent Control Architectures
Scientific Computation & Machine Learning
- —First-Principles Modeling & Computational Physics
- —Scientific Machine Learning & Physics-Informed Methods
- —Neural-Network Architecture & Loss-Function Design
- —Numerical Methods & Partial Differential Equations
- —Computational Multiphysics
- —Optimization & Inverse Problems
- —Scientific Software, GUIs & Engineering Visualization
Software & Engineering Tools
- —MATLAB & App Designer
- —Python
- —JavaScript, React & Three.js
- —SolidWorks & Fusion 360
- —KiCad, PCB Layout & Custom Footprints
- —Arduino & ESP32
- —CAM Programming & CNC Workflows
- —LabVIEW
- —LaTeX
About
The Researcher
Bridging artificial intelligence, computational physics, and engineering design.
I am a Mechanical and Aerospace Engineering student at the University of Florida (UF), completing dual B.S. degrees with a Minor in Mathematics. I work on both sides of the boundary between equations and hardware: deriving physical models, developing scientific software, designing mechanical and optical systems, fabricating components, and integrating sensing and control.
My work includes government-funded laser research at Aqwest (www.aqwest.com), where I have independently developed new computational approaches for advanced optical systems; robotic and bioreactor development in the UF SYBORGS Laboratory, involving physical design, fabrication, integration, and testing; and self-directed projects in gravitational-wave physics, orbital mechanics, reactor simulation, and scientific machine learning. Across these settings, I aim to create more than analyses or demonstrations, but rather, to create reusable methods, working systems, and clear technical foundations that make further work possible.
Several of my projects began not with prior expertise, but with a question I could not leave alone. Pursuing them required me to teach myself JavaScript and Three.js for an orbital and gravitational-wave simulator; neural-network formulation and custom objective design for laser optimization; coupled neutronics and thermal modeling for reactor simulation; and PCB design and embedded electronics for robotic systems. I tend to follow a problem until I have acquired the mathematics, software, and physical understanding needed to make something of it. Unfamiliarity has rarely felt like a boundary; it is usually where the interesting work begins.
I am preparing for graduate research in intelligent physical systems, where I hope to bring this breadth to problems requiring both theoretical depth and physical realization.
Research Interests
- — Scientific Machine Learning & Physics-Informed Methods
- — Computational Multiphysics & Numerical Methods
- — Nonlinear Dynamics, System Identification & Control
- — Inverse Problems & Engineering Design Optimization
- — Optical & Optomechanical Systems
- — Autonomous Robotics, Sensor Fusion & Navigation
- — Marine Technology for Deep Sea Mining & Environmental Monitoring
Research Trajectory
Autonomous Systems for Difficult Environments
My long-term research direction is autonomous robotics for difficult and poorly observed environments, particularly marine systems supporting seabed-resource exploration, environmental monitoring, and regulatory compliance. I hope to combine physical-system design, intelligent control, scientific machine learning, and multimodal sensing to create systems that remain capable under uncertainty, sparse measurements, limited communication, and severe environmental constraints.
My background in laser and optical systems is part of this trajectory rather than separate from it. Optical sensing, imaging, ranging, and communications—used where environmental conditions permit and integrated with acoustic, inertial, and other sensing modalities—can provide important capabilities for difficult-environment robotics. Graduate study would allow me to deepen this foundation and apply it toward autonomous systems that can perceive, reason, and operate reliably where direct human access is limited.
Why I Build
Personal
I came to engineering through philosophy and mathematics. Philosophy taught me to remain dissatisfied with shallow explanations; mathematics gave me a language for making ideas precise; engineering supplied the decisive test—the idea must survive contact with the physical world. That progression still shapes how I approach research.
I am motivated by the pursuit of excellence—not as prestige or perfectionism, but as disciplined workmanship. It means understanding the assumptions beneath a model, attending to consequential details, designing and fabricating with care, validating claims honestly, and continuing to refine a system after the excitement of the first idea has passed. In difficult engineering, these habits are not ornamental; they are often the difference between an impressive concept and a reliable result.
I am also comfortable departing from convention when the evidence warrants it. I question inherited assumptions, test alternatives, and accept the possibility of being wrong. In engineering, however, originality must earn its keep: an unconventional idea must still survive mathematics, experiment, fabrication, and use. That independence is personal as well as technical; I prefer to choose deliberately how I work and live rather than accept convention as sufficient justification.
Outside research, I am a student pilot, an entrepreneur, a hiker and backpacker, and a persistent reader of philosophy and history. Aviation and time spent outdoors continually renew my appreciation for precision, judgement, and humility in environments that cannot be negotiated with.
Currently Reading
- Hegel—Phenomenology of Spirit
- Leibniz—The Monadology
- Yasser Mohamed Hamada—Nonlinear fractional diffusion model for space-time neutron dynamics
Currently Learning
- —Marine international regulations and mining compliance
- —French (B1) — Intermediate
- —Advanced proof-based mathematics
- —Scientific machine learning for multi-physics systems
Beyond Research
Student pilot — where precision is not optional
Resume
Curriculum Vitae
Education
Expected May 2027
Gainesville, FL
B.S. Mechanical Engineering
University of Florida
Senior standing. Current GPA: 3.92/4.00. Coursework emphasizes manufacturing, controller design, numerical analysis, and physics-based engineering.
Expected May 2027
Gainesville, FL
B.S. Aerospace Engineering
University of Florida
Senior standing. Coursework emphasizes precision design, laboratory testing, applied mathematics, and advanced physics-based engineering.
Expected May 2027
Gainesville, FL
Minor in Mathematics
University of Florida
Focus on partial differential equations and heavily proof-based coursework including Linear Algebra and Abstract Algebra.
Experience
Summer 2024 — Present
Aerospace Engineer
Aqwest LLC
Conduct multidisciplinary research and development spanning high-power laser systems, scientific machine learning, optical stabilization, dynamics and control, experimental hardware, engineering software, precision manufacturing, and government-funded technology development.
— Independently designed and developed deep-learning systems for optimizing high-power laser performance in support of U.S. Navy anti-submarine warfare and undersea imaging research.
— Developed a customer-facing MATLAB platform integrating multiple neural networks, data-management tools, and user-selected design and performance requirements to evaluate and optimize multiple laser architectures and support engineering trade studies.
— Co-led a technical customer briefing for the U.S. Navy, presenting the optimization platform, analytical capabilities, research progress, and proposed follow-on development. Additional project scope and funding are currently under consideration.
— Co-authored "Solid-State Laser Design Process Empowered by Machine Learning and Deep Neural Networks" and presented the work as a featured presenter at SPIE Photonics West 2026.
— Designed a custom three-axis vibration-test platform for an active laser-stabilization programme, including PSD-based vibration requirements, structural and vibration analysis, shaker and mount design, piezoelectric-actuator selection, and fabrication planning.
— Developed and began implementing the mathematical, signal-processing, control, and machine-learning architecture for real-time beam-pointing stabilization, including disturbance estimation, actuator modeling, adaptive system-model updating, mode-matching methods, and coordinated feedforward and feedback control.
— Developed MATLAB software for estimating laser beam-quality metrics from limited beam-image measurements, including image processing, fitted-beam geometry, numerical analysis, and a dedicated graphical interface.
— Delivered internal technical lectures and research briefings covering machine learning, scientific machine learning, mathematical foundations, project progress, and proposed research directions.
— Authored and edited seven-figure photonics and optical-engineering proposals, technical reports, and customer deliverables for organisations including the Department of Defense, Department of Energy, U.S. Navy, U.S. Air Force, and NASA.
— Manufactured precision optical-quality components for research prototypes and deliverable systems using CNC and manual machining processes.
— Assisted with the assembly, integration, and troubleshooting of laser water-cooling systems intended for clean-room operation.
— Supported government-funded research through technical reporting, project accounting, customer communication, design reviews, conferences, and internal programme-management activities.
Fall 2024 — Present
Mechanical & Systems Engineering Undergraduate Researcher
University of Florida SYBORGS Laboratory
— Designed mechanical subsystems and six-axis robotic-arm concepts for HyBRIDS, an autonomous planetary-construction platform using biologically produced structural materials.
— Designed a controlled pressure vessel and supporting electrical systems for LEAP, a pressure-sealed biological payload intended for suborbital flight environments.
— Designed and fabricated precision bioreactor and robotic components using CNC mills, manual mills, engine lathes, and CAD/CAM workflows in SolidWorks and Fusion 360.
— Programmed and integrated Arduino- and Python-based sensing, actuation, and control systems for DECODER, a terrestrial microgravity-analogue bioreactor.
— Designed custom PCBs in KiCad for sensor integration, actuator control, and embedded-system operation.
— Supported multidisciplinary assembly, testing, troubleshooting, and integration across mechanical, electrical, biological, and software subsystems.
— Mentored an undergraduate assistant in CNC machining, CAM programming, safe fabrication practices, and biotechnology project fundamentals.
Lab website: syborgs.mae.ufl.edu
Publications & Achievements
Jan 2026
Conference Presentation and Paper — SPIE Photonics West 2026
SPIE
Presented research on deep-learning in laser design optimization at SPIE Photonics West, the premier international optics and photonics conference. Paper: Solid-state laser design process empowered by machine learning and deep neural networks.
2022 — 2025
Dean's List
University of Florida
Awarded each semester for maintaining a GPA above 3.2.
SolidWorks Certified Associate
ACE CNC Certification
COMSOL Preliminary Training
FAA Certified Student Pilot
Link to Full PDF Resume
Relevant Academic Projects
Coursework & Technical Studies
Strain-Gauge Cantilever Scale: Physics-Based Estimation vs Experimental Calibration
Mechanics of Materials Laboratory: Built and instrumented a 6061-T6 aluminum cantilever scale using a bonded strain gauge, quarter-bridge Wheatstone circuit, signal amplification, a 14-bit data-acquisition device, and a custom LabVIEW interface. I developed two independent methods for inferring applied load: a first-principles model derived from beam-bending mechanics and strain-gauge response, and an empirical model obtained through four-point calibration. I compared their accuracy, repeatability, and propagated uncertainty under repeated placement and progressive unloading. The calibrated method closely matched reference measurements, while the mechanics-based method remained repeatable but exhibited a systematic sensitivity discrepancy.
Cold-War Orbital Forensics: From State Vector to Ground Track
Two-Part Individual Astrodynamics Project: Developed a modular MATLAB toolchain for reconstructing and propagating an unknown spacecraft orbit from a single Earth-centered inertial position and velocity state. In Part I, I derived the true-anomaly time-of-flight integral, implemented Gauss–Legendre quadrature, recovered orbital properties and node-crossing times, and evaluated numerical convergence. Part II extended the analysis through Newton-based anomaly propagation, position and velocity reconstruction, ECI-to-ECEF transformation, geocentric longitude and latitude calculation, three-dimensional orbit visualization, and Earth-relative ground-track analysis.
Steady 2-D Heat Conduction with Finite Differences and Successive Over-Relaxation
Independent Numerical Methods Coursework: Developed a MATLAB solver for the steady two-dimensional heat equation in a plate with prescribed boundary conditions. I discretized Laplace’s equation using finite differences, implemented an iterative relaxation solver, and evaluated temperature distributions, wall heat fluxes, and convergence behavior. A relaxation-parameter sweep identified λ=1.91 as the most efficient tested setting, converging in 258 iterations.
Mechanical Characterization through Tensile & Compression Testing
Mechanics of Materials Laboratory: Characterized the tensile response of an unknown metal, biaxial carbon-fiber composite, and nylon, together with the compressive response of plaster of Paris, using an Instron 5967 Universal Testing Machine. I converted load, extensometer, and crosshead-displacement measurements into engineering stress–strain curves, correcting non-extensometer measurements for machine compliance. From these results, I evaluated elastic modulus, yield behavior, ultimate and fracture strengths, toughness, elongation, specific properties, failure modes, and measurement uncertainty. The unknown metal’s measured mechanical behavior was most consistent with 2014-T3 aluminum.
Contact
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Whether you're a fellow researcher, potential advisor, or industry professional — I welcome correspondence on AI, computational physics, engineering design, and research opportunities.