About Me
I’m a computer engineer passionate about applying AI to solve real-world engineering challenges. My work spans deep learning, RISC-V system design, operating system development, and performance optimization of high-frequency trading systems. I’ve built projects ranging from geospatial image classification and Siamese neural networks to AI-powered QA systems, combining hands-on coding with model deployment. I enjoy tackling complex technical problems and exploring how AI can bridge low-level engineering with intelligent applications.
Outside of work, I climb and restore old technology, which keeps me persistent and creatively engaged. I expand on these hobbies in the Personal section below.
Projects
Siamese Neural Network for One-Shot Image Recognition
Description: Developed a Siamese Neural Network in a single Jupyter Notebook to perform one-shot image recognition, based on Koch et al. (2015). Engineered a deep learning model to generate discriminative embeddings for image similarity tasks, trained with contrastive loss on paired inputs, and evaluated using classification metrics and embedding visualizations. Demonstrated applications like identity verification and one-shot learning scenarios.
Technologies Used: Python, TensorFlow/Keras, NumPy, Matplotlib
Skills Gained:
- Deep learning
- Contrastive loss
- Embedding visualization
- One-shot learning applications
AI RAG Assistant Using LangChain
Date: September 2025
Description: PDF QA app built with IBM watsonx.ai, LangChain, Chroma, and Gradio. Users can upload PDFs and ask questions; the system retrieves relevant chunks and generates grounded answers.
Technologies Used: IBM watsonx.ai, LangChain, Chroma, Gradio
Skills Gained:
- Retrieval Augmented Generation (RAG)
- LangChain orchestration
- IBM watsonx LLM + embeddings integration
- Gradio app deployment
- Document chunking & vector databases
Experience
Software Engineering Intern — JamSystems
- Engineered and benchmarked low-latency C++ data structures for high-frequency trading systems, achieving 1.7×–3× speedups over standard library implementations.
- Optimized performance-critical code paths and prepared AI model retraining on the codebase to improve automated code understanding.
Tools: C++, AI/ML workflows
Software Validation Intern — NXP Semiconductors
- Conducted 100+ stability tests across multiple platforms for NFC hardware/software releases.
- Automated 30+ previously manual test cases using Python, JCShell, and robotic testing frameworks.
- Modified test cases to meet evolving customer requirements, improving testing efficiency and coverage.
Tools: Python, JCShell, automated testing frameworks
Skills / Tech Stack
Relevant Classes
- Applied Machine Learning: Practical ML techniques, including supervised/unsupervised learning, neural networks, and model deployment using Python, PyTorch, and TensorFlow.
- Applied Parallel Programming: Parallel computing concepts and implementation, covering multi-threading, GPU programming, and performance optimization.
- Data Structures: Core data structures and algorithms, emphasizing efficient data storage, retrieval, and manipulation.
- Computer Systems Engineering: Low-level systems design, including operating systems, RISC-V architecture, memory management, I/O devices, and system calls.
- Principles of Safe Autonomy: Design of reliable autonomous systems with focus on verification, reachability analysis, and safety-critical principles.
- Power Circuits & Electromechanics: Electric circuits, machines, and electromechanical systems, including power conversion, motors, and generators.
- Digital Systems Laboratory: Hands-on design and testing of digital circuits using FPGAs, Verilog, and embedded systems integration.
Resume / CV
Personal
Hobbies
In my free time, I love exploring and restoring old technology, like fixing Sony Walkmans or working on an IBM Selectric 3. I’m also passionate about film photography and vinyl records. When I’m not in the workshop, you’ll find me climbing, surfing, spearfishing, camping, or hiking.
