Master's Student @ UPenn

Hello, I'm

David
Young.

Software Engineer & CS Researcher
Computer Vision • Deep Learning • AI Systems

Building intelligent systems at the intersection of mathematical rigor and software architecture. Advancing from applied mathematics into scalable AI and computer vision engineering.

View Projects View Research Resume GitHub
David Young
Open to Opportunities

01. About Me

Mission

"My background is applied math, and I've been moving into the software side of robotics: perception, kinematics, planning. The gap between clean theory and code that has to run on real hardware is where most of the interesting problems live, and I'm still learning how to close it well."

Coming from a background in Applied Mathematics, I initially treated coding strictly as a research tool. However, I quickly discovered that I was just as invested in the engineering behind the algorithms as I was in the theoretical results.

That realization drove my pivot into Computer Science. I wanted to move beyond writing code that works for a single experiment to building robust, scalable systems that handle real-world complexity.

Now, as a graduate student at UPenn, I combine analytical problem-solving with modern software engineering, focusing on computer vision, deep learning, and how algorithmic efficiency drives intelligent technology.

Core Focus Areas

Computer Vision & Deep Learning

Object detection, image classification, and neural network architectures for visual understanding.

Algorithmic Design

Optimizing computational efficiency using mathematical frameworks and data structures.

Software Architecture

Designing scalable, maintainable systems with strong object-oriented foundations.

02. Education

UPenn

University of Pennsylvania

Master of Computer & Information Technology (MCIT)

Spring 2026 - Present

Expected May 2027 | GPA 4.00 | Full-Time

Relevant Coursework: Data Structures & Software Design, Computer Systems Programming, Artificial Intelligence, Deep Neural Networks

Queens College

CUNY Queens College

B.A. in Applied Mathematics

2022 - 2025

03. Research

Computational Biophysics

Collective Behavior of Daphnia

QC Physics Dept. • Supervised by Dr. Oleg Kogan

Apr 2024 - Sep 2025

Investigated whether collective interactions among Daphnia magna induce systematic deviations from random turning behavior. Developed a computational pipeline to track, model, and statistically analyze rotational dynamics across hundreds of trials.

MATLAB Python TRex Tracking Statistical Modeling

Epigenetics & Behavioral Biology

Epigenetic Regulation in Daphnia

QC Biology Dept. • Supervised by Dr. Sebastian Alvarado

Apr 2024 - Sep 2025

Analyzed how environmental stimuli trigger epigenetic modifications driving phenotypic plasticity and behavioral adaptation. Validated computational models of mutation response pathways using ChIP-seq and ATAC-seq datasets.

Python ChIP-seq ATAC-seq Epigenomics

04. Technical Projects

CHIRON In Development

Cybernetic Hardware Interface for Robotic Operations & Networking

A geometry-driven robot motor cortex running in MuJoCo at ~500 Hz. Built around damped least-squares inverse kinematics (1000 iter, 5mm tolerance) and a self-measuring gripper model that reads finger offset, pad height, and actuator range directly from the robot definition, eliminating hardcoded constants across arm configurations.

Handles pick-and-place end-to-end with gripper-envelope collision checks, corridor clearance along planned paths, adaptive carrying-height search, grasp verification with auto-retry, and automatic decomposition of stacked scenes into executable sub-tasks.

Python MuJoCo ROS 2 Inverse Kinematics
Built with Swan Yi Htet
Deep Dive GitHub
BROTEUS In Development

Behavior Recognition, Object Tracking & Environmental Understanding System

An open-vocabulary perception pipeline combining YOLO-World detection (87% confidence, 21 FPS on CPU), IoU-based multi-object tracking, and monocular depth estimation via MiDaS, feeding a four-criteria grasp affordance heatmap.

Dual-hand gesture and animation recognition uses MediaPipe's 42 keypoints, 35-dimensional feature vectors with palm-orientation encoding for rotation invariance, and Dynamic Time Warping for speed-invariant temporal matching, with per-hand persistence to disk.

Python YOLO-World MiDaS MediaPipe PyTorch
Built with Swan Yi Htet
Deep Dive GitHub
ATHENA Planetary Rover Simulator
ATHENA Live

Autonomous Terrain & Hazard Exploration Navigation Agent

A browser-based 3D rover autonomy simulator with four interchangeable pathfinding algorithms (A*, Dijkstra, RRT, D* Lite) behind a unified stepper interface. Features real-time search visualization, cost heat mapping, and multi-waypoint mission chaining.

Built on a deterministic chunk-based terrain generator using seeded 3-layer fractal Brownian motion noise and spatial cell hashing, with parameterized planetary profiles for Mars, Venus, Europa, and Titan driving geometry, lighting, fog, and surface coloring from configuration.

React Three.js A* Dijkstra RRT D* Lite Procedural Gen
Built with Swan Yi Htet
Launch Demo Deep Dive GitHub

05. Achievements

Presentations

Evolution Conference 2025

Athens, GA • June 2025

"Collective Behavior of Daphnia"

CIRE Research Conference

New York, NY • Aug 2024

"Collective Behavior of Daphnia"

Honors & Grants

$2,000

CUNY Immersive Research Experience (CIRE)

Queens College • Fall 2024 - Spring 2025

Selected for a year-long funded research program designed to deepen undergraduate participation in authentic STEM research. Recognized for advanced scholarly work in computational biophysics and preparation for graduate-level study.

$2,000

Summer Research Program

Queens College • Aug 2024

Awarded for outstanding contribution to the Undergraduate Research Program. Recognized for commitment to scholarly inquiry and excellence in biophysical computation.

06. Technical Stack

Languages

Python C++ Java JavaScript MATLAB SQL

ML & Data

PyTorch NumPy Pandas OpenCV Matplotlib Seaborn

Tools

Git Linux React Three.js Mathematica
System.out.println("Hello World");

Ready to Collaborate?

Actively seeking opportunities in software engineering, computer vision, and AI research. Whether you have a role to discuss or simply want to connect, please reach out.

Email

[email protected]

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