Developer

01. About Me

Hi, I’m Amir — a Software Engineer Intern at NASA Ames and UC Merced B.S. Computer Science & Engineering graduate (GPA 3.83). Incoming M.S. in Cybersecurity at Georgia Tech (August 2026). I build AI-driven systems spanning computer vision, oculomotor analytics, and HPC — from accessibility tools to lunar-lander analog research. Let’s connect!

Skills

Experience

Education

Programming

Python C++ Java JavaScript/TypeScript SQL Ruby Go MATLAB Kotlin

AI & Data Tools

TensorFlow Keras PyTorch Scikit-learn Pandas NumPy Matplotlib MongoDB Tableau

Systems & Platforms

React Node.js Flask FastAPI Git Perforce Linux CUDA OpenCV MediaPipe

Specialties

Data Analysis Model Evaluation Software Engineering Agentic AI Human–Computer Interaction
  • May 2026 – Present
    NASA Ames Research Center — Software Engineer Intern
    Engineered PsychoPy eye-tracker calibration tooling, Python pipelines for clock sync and drift correction, and LLM-assisted oculomotor / mental workload analysis for a lunar-lander analog study.
  • August 2025 – Present
    PADSYS — Undergraduate Research Intern
    Investigating I/O bottlenecks in HPC systems by profiling AI workloads; deploying and benchmarking CV models (YOLOv12, mmdetection) in Dockerized Ubuntu environments.
  • Incoming: August 2026
    Georgia Tech — M.S. in Cybersecurity (Information Security) — Online
  • Graduated: May 2026
    UC Merced — B.S. in Computer Science & Engineering
    GPA: 3.83
    Relevant Coursework: Data Structures & Algorithms, Linear Algebra, Probability & Statistics, Machine Learning, Discrete Math, Boolean Algebra

02. Featured Projects

Innovative solutions that make a difference

NASA Ames

Gaze Telemetry & Oculomotor Analysis

Software Engineer Intern • May 2026 – Present

Engineered a cross-platform PsychoPy frontend for 9-point eye-tracker calibrations with VSYNC-aligned timestamps for precise gaze telemetry. Built a Python pipeline (NumPy, Pandas) to synchronize hardware clocks and correct tracking drift via 2D affine transforms, then computed kinematic features and integrated LLMs to analyze oculomotor behavior and mental workload for a lunar-lander analog study.

Python PsychoPy NumPy Pandas LLMs
View Project
Winner • SF Hacks 2025

AIVue: Control with Vision

Team Lead & Developer • March 2025 – Present

Engineered a hands-free computer control system for people with disabilities using eye-tracking, blink detection, and speech commands. Implemented computer vision with Python, OpenCV, MediaPipe, and Kalman filters; integrated speech-to-text AI and MongoDB Atlas. Improved gaze accuracy by 32% and reduced latency by 18% through adaptive AI filtering.

Python OpenCV MediaPipe Kalman Filters MongoDB Atlas
View Project
Winner • HackDavis 2025

EthicScope

Founding Developer • April 2025 – May 2026

Designed a mobile and web app to analyze product barcodes and assess ethical & environmental impact using ML-based data aggregation. Built backend with Flask, MongoDB, and Cerebras; developed visual data dashboards using React + Tailwind. Helped over 1,000 simulated test users make informed, transparent purchasing decisions.

Flask MongoDB Cerebras React Tailwind CSS
View Project
AI4ALL Research

AI Tumor Detection ML Model

Undergraduate Machine Learning Researcher • January 2024 – October 2025

Developed a VGG-16 CNN for brain MRI tumor classification, achieving 94% accuracy on validation data. Collaborated with interdisciplinary researchers to improve model interpretability and bias detection.

Python VGG-16 TensorFlow Keras Scikit-learn
Learn More

03. Experience

May 2026 – Present

Software Engineer Intern

NASA Ames Research Center — Mountain View, CA

  • Engineered a cross-platform PsychoPy frontend for 9-point eye-tracker calibrations, logging VSYNC-aligned timestamps for precise gaze telemetry
  • Developed a Python pipeline (NumPy, Pandas) to synchronize hardware clocks and correct physical tracking drift using 2D affine transformations
  • Computed kinematic features and integrated LLMs to analyze oculomotor behavior and mental workload for a lunar-lander analog tracking study
August 2025 – Present

Undergraduate Research Intern

PADSYS — Merced, CA

  • Investigating I/O bottlenecks in High-Performance Computing (HPC) systems by profiling AI workloads, focusing on processes like checkpointing and data loading
  • Deploying and benchmarking computer vision models (YOLOv12, mmdetection library) in containerized Ubuntu environments using Docker to analyze I/O performance

04. Contact Me

East Palo Alto, CA

amirkhabaza@gmail.com

(650) 353-8009

Download CV

Copyright © Amir. Made with by Mohammed Amir Khabaza