Neal's Resume :)
Projects
- Robotic Arm 2D Simulation
- Developed a 2D robotic arm simulation environment with forward and inverse kinematics
- Implemented forward kinematics calculations for multi-joint robotic arm visualization
- Created inverse kinematics solver to determine joint angles for desired end-effector positions
- Built interactive visualization allowing real-time manipulation and position control
- Demonstrated practical applications of robotics fundamentals through interactive examples
- Developed a 2D robotic arm simulation environment with forward and inverse kinematics
- MPC Driver Assist for Autonomous Vehicles
- Developed Model Predictive Control systems for autonomous vehicle applications with real-time optimization and constraint satisfaction
- Lane Keeping System: Implemented bicycle model vehicle dynamics with kinematic constraints and steering/rate limits
- Designed quadratic cost function balancing lateral error, heading deviation, control effort, and smoothness
- Achieved sub-1ms solve times for 15-step prediction horizon using grid search optimization (41 steering options)
- Lane Changing System: Extended MPC framework for dynamic lane change maneuvers
- Implemented time-weighted cost function with adaptive heading control during lane transitions
- Added velocity tracking and quadratic barrier penalties for safe heading angle limits
- Used 501 steering options for higher precision control during complex maneuvers
- Obstacle Avoidance System: Developed collision avoidance capabilities for dynamic environments
- Integrated obstacle detection and avoidance constraints into MPC optimization framework
- Maintained real-time performance while ensuring safe trajectory generation around obstacles
- Demonstrated receding horizon with real-time trajectory prediction and constraint handling across all three systems
- Lane Keeping System: Implemented bicycle model vehicle dynamics with kinematic constraints and steering/rate limits
- Developed Model Predictive Control systems for autonomous vehicle applications with real-time optimization and constraint satisfaction
- Path Planning Algorithms: Theory & Implementation
- Implemented and analyzed multiple path planning algorithms (on paper)
- Learning-Based Methods
- Diffusion Policy
- Safety-Constrained Control (Pending)
- Control Barrier Function - Quadratic Programming (CBF-QP)
- Model Predictive Control
- Model Predictive Path Integral (MPPI)
- Trajectory Optimization
- Stochastic Trajectory Optimization for Motion Planning (STOMP), Covariant Hamiltonian Optimization for Motion Planning (CHOMP), Trajectory Optimization (TrajOpt)
- Sampling-Based Planning
- Rapidly-exploring Random Trees (RRT/ RRT*), Fast Marching Trees (FMT*), Neural RRT*
- Graph-Based Search
- Dijkstra, A*, Dynamic A* (D*)
- Learning-Based Methods
- Created detailed mathematical analyses and explanations for each algorithm, documenting theoretical foundations and practical implementations
- Implemented and analyzed multiple path planning algorithms (on paper)
- Control Algorithms: Theory & Implementation
- Implemented and analyzed fundamental control algorithms (on paper)
- Classical Control
- Proportional-Integral-Derivative (PID) Control
- State Estimation
- Kalman Filter
- Optimal Control (Pending)
- Linear Quadratic Regulator (LQR), Iterative Linear Quadratic Regulator (iLQR)
- Model Predictive Control (Pending)
- Model Predictive Control (MPC), Learning-based MPC
- Safety-Critical Control (Pending)
- Control Barrier Functions (CBF)
- Classical Control
- Developed mathematical foundations and practical implementations for each control method
- Implemented and analyzed fundamental control algorithms (on paper)
- Gunshot Classification Device
- Developed and trained classification CNN models using PyTorch – Tested LeNet5, ResNet50, and multiple custom models based on varying input shapes (1D vs 2D vector) and input styles (flattened raw audio vs Mel-Frequency Cepstral Coefficients); resulted in 99% accuracy in “ideal” test conditions, and ~20-25% accuracy in noise-heavy conditions (state-of-the-art in noise-heavy conditions is 14% accuracy)
- Created Python program to label audio files with the location of the signal and to chop the audio file with random quantity of noise leading and lagging the signal, improving model robustness during testing by preventing memorization of signal location in the input array
Skills
- Programming Languages: Python, Bash, C++, SQL, Julia
- Frameworks & Tools: Pytorch, Kubernetes, Tableau, Keyence, Minitab, Excel, AutoCAD, Revit
- Machine Learning: CNN models, LSTM models, audio classification, statistical analysis
- Controls & Motion Planning: PID control, Kalman filtering, A* pathfinding, RRT algorithms, STOMP optimization
- Manufacturing: Statistical Process Control (SPC), Gage R&R studies, OEE metrics, Computer Vision
- Life: Dog Petting
Education
- M.S. Computer Science & Engineering, University of Nevada, Reno, GPA 4.00 (Est. Aug 2026)
- B.S. Industrial and Systems Engineering, Rensselaer Polytechnic Institute, GPA 3.56 (Dec 2021)
Work Experience
TESLA INC.
Software Engineering Intern | Jan 2025 – May 2025
- Deployed a containerized, testable install script for Tesla PLM software using mdrip, ensuring repeatable installations on a new Linux system
- Developed a tool & design doc to diagnose TLS connection errors in distributed Java systems, slashing RCA time from hours/days to seconds
TESLA INC.
Data Analyst Intern | May 2024 – Aug 2024
- Implemented ETL to store Manufacturing Variance data across all of GFNV, coordinated with engineering and finance teams to validate estimated costs and realized waste; reduced analysis time by 99% for a given part and raised awareness of unseen factory waste
- Drove investigation into large scale waste using variance ETL and found inverter line process gap, resulted in 7-figure annualized savings
TESLA INC.
Associate Process Engineer | Dec 2022 – Dec 2023
- Built and deployed Extract, Transform, Load (ETL) processes using containerized Python and SQL applications – resulting MySQL tables permitted more advanced data analyses and creation of much faster Tableau dashboards
- Made improvements to and troubleshot production Computer Vision software, resulting in 4% yield increase / line on highest utilized station
- Established OLE, Utilization, and OEE metrics on production lines as well as respective targets for Yield, Availability, and Performance; bottleneck analysis pointed out potential to improve throughput capacity by 28% through targeted OEE improvements
- Performed Gage R&R Study, identifying high electrical tester variance as source of yield loss and driving investigation into equipment
- Led project to improve cooling system on manufacturing line, worked with facilities design, construction, manufacturing engineering, and design engineering to develop design, expected to improve line throughput capacity by 41%
- Established Statistical Process Control (SPC) standards with proper alarms, supporting line monitoring to process technicians for line capability
TESLA INC.
Associate Reliability Engineer | Sept 2021 – Dec 2022
- Utilized Python and SQL to track irregular asset failures and flag them, improving the Reliability team’s Preventative Maintenance efforts
- Developed and established asset tree data structure for facilities electrical equipment to optimize maintenance planning during downtime
- Troubleshot electrical and mechanical facilities equipment failures, implemented data acquisition methods, and spearheaded design improvements using P&ID or CAD markups; using collected data and statistical analysis to justify and illustrate effectiveness of design changes
- Led Root Cause Analysis meetings with various facilities teams to understand engineering / procedural cause issues and drive implementation of corrective actions through coordination with internal departments or third-party contractors, mitigating repeat failure modes
TESLA INC.
Reliability Engineering Intern | Jan 2021 – Sept 2021
- Implemented Python software to predict and flag potential stockouts based on usage data and recommend reorder points using Holt’s model
- Designed Python software to import unstructured data from contractors and validate the entries against the company database to catch data entry mistakes and change the format so the software team’s programs could seamlessly integrate the new data
- Revamped Gigafactory 1’s facility warehouse system by implementing primary keys, date stamps, request stages, etc. to provide structure for future data analysis and by establishing a priority system to improve important request flow; led to >50% improvement in priority request flow
BIOGEN INC.
Human Performance / EHS Data Scientist Co-op | Jul 2020 – Dec 2020
- Produced LSTM models in Amazon Sagemaker to enhance accuracy of previous SES models to forecast future company product demand
- Developed a Tableau heatmap for manufacturing processes with drill-down options to communicate deviation locations and improve production throughput, permitting faster resolution of production downtime while also improving stakeholder experience
- Digitalized information dashboards in Tableau and Power BI to update with live data and developed new databases to create product tracking across the production line
- Created forecasts using Python and SQL for future deviations and designed human performance metrics on root cause investigations to track resource effectiveness, driving process changes to improve efficiency