Analysis Version

Summary

A research-focused engineer specializing in Computer Vision, Signal Processing, and Deep Learning with a strong academic portfolio. Demonstrates high proficiency in implementing complex mathematical algorithms for Radar and HDR imaging using Python, while also possessing capability in C# and Unity for VR applications. Their work prioritizes rapid prototyping and theoretical depth, though production engineering practices like testing and dependency management are areas for growth.

Tech Stack

PrimaryPython9·ProficientPyTorch4·FamiliarC#1Unity1Jupyter Notebook1HTML1

Repositories

uDoppler-Classification

Classification of Human Movement using mmWave FMCW Radar Micro-Doppler Signature

“Their most popular repo (107 stars) demonstrating complex signal processing (PCA, ICA) and radar data classification.”

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MetaHDR

Model-Agnostic Meta-Learning for HDR Image Reconstruction. By learning the common structure between all LDR-to-HDR conversion tasks, our model is able to adapt it's predictions given extra exposures of a scene. This novel approach reframes LDR-to-HDR conversion as a meta-learning problem.

“High-quality research implementation of Meta-Learning with clean modularity and strong configuration management.”

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Borealis

Object Classification via Range Doppler Plots on PMCW Radar Data

“Showcases object classification capabilities on radar data, reinforcing their specialization in sensor fusion/CV.”

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blackjack

This project applies online Q-Learning to the game of Blackjack.

“A clean, modular implementation of Reinforcement Learning (Q-Learning) that demonstrates solid Python architecture.”

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ee267_viper

Virtual Reality environment for testing out video-sourced human pose estimations as animation assets for Unity games.

“Demonstrates versatility by applying engineering skills to VR and Unity, stepping outside pure data science.”

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Score History

Persona

Academic Prototyping9/10

Code is optimized for generating research results and paper artifacts rather than long-term maintenance.

Modularity7/10

Good separation of concerns in projects like MetaHDR and Blackjack, though code duplication exists in Borealis.

Production Readiness3/10

Frequent use of hardcoded absolute paths, debug artifacts (pdb), and lack of dependency files limits portability.

Skills

Python8/10

Primary language used across most repositories for complex tasks like meta-learning, DSP, and reinforcement learning.

Computer Vision & Signal Processing8/10

Demonstrated deep expertise in mmWave radar processing (uDoppler, Borealis) and HDR image reconstruction (MetaHDR).

Machine Learning (PyTorch)7/10

Implements advanced concepts like Model-Agnostic Meta-Learning and custom loss functions, though code often lacks scalability.

C# & Unity6/10

Capable of building functional VR environments (ee267_viper), though relies on legacy assets and hardware-coupled logic.

Software Testing2/10

Unit tests are consistently absent across all major projects, posing high regression risks.

Documentation7/10

Projects feature excellent high-level READMEs with theoretical context and visualizations, though setup instructions can be brittle.

Growth

1.Implement automated testing: Add unit tests (e.g., using pytest) for core logic, especially for mathematical functions and data loaders, to prevent regressions.

2.Remove hardcoded dependencies: Replace absolute file paths and system-specific configurations with environment variables or relative paths to improve portability.

3.Standardize dependency management: Include `requirements.txt` or `poetry.lock` files in repositories to facilitate easier onboarding and reproducibility.

4.Refactor for reusability: Apply DRY principles to remove code duplication (as seen in Borealis) and package core logic to be importable.

5.Clean up debug artifacts: Remove `pdb` traces and commented-out code from main branches to ensure a professional, production-ready codebase.

edwin-pan