Backend-heavy full stack engineer. Golang, distributed systems, and applied AI. Software Engineer 2 @ American Express · M.S. Applied Artificial Intelligence · San Francisco

Code has always been extremely impactful in my life. It started with a simple program that lit up a few LEDs on a breadboard wired to an Arduino, blinking in a sequence I determined. Trivial by any measure, but watching hardware obey something I imagined and typed into existence, I felt like a wizard. I never really stopped chasing that feeling.

That spark carried me from breadboards to production systems: leading backend teams at startups, shipping platforms serving tens of thousands of users, and now engineering event pipelines at American Express that move tens of millions of events a day. Along the way I picked up a master's in applied AI, because teaching machines to see reptiles, read X-rays, and hear composers felt like the natural next spell to learn.

As much as code already shapes the world, I think we're still at a remarkably young stage. Modern-day makers are like novelists, building brilliant worlds and telling gripping stories straight from imagination, except our stories compile and run. Everything I've built has reinforced the same lesson: with imagination, grit, and hard work, you can create almost anything.

And it's only accelerating. I imagine a future where computers are so deeply embedded in our environment and our perception that the people who understand and manipulate them won't just feel like wizards. They will be modern masters of the arcane. The next steps of human evolution are unfolding right in front of us, and I'm ecstatic to be an active participant.

Main quest: event ingestion & data persistence for Global Loyalty & Benefits at American Express. Golang, Kafka, and gRPC pushing tens of millions of events a day through a dozen pipelines, backed by Firestore and Elasticsearch behind a unified data access layer. I serve as a technical owner for the domain's most critical event processor and its resource APIs.

Side quests in applied AI:

• 🦎 Herpeton: computer vision for reptile conservation and ecological monitoring. An end-to-end deep learning pipeline trained on ~25k labeled BioTrove images spanning 189 species, benchmarking Vision Transformers, CNNs, and YOLOv10 against each other. The winning ViT model is deployed and identifying species live at herpeton.app.
• 📊 VenueSignal: my newest and most active project, an MIT-licensed data science build that is already picking up stars and outside forks. Started in 2026 and evolving fast.
• 🐠 IoT Smart Aquarium: hardware meets machine learning. Sensors stream live aquarium telemetry (temperature, water quality) into predictive models, with results visualized on a public dashboard. My favorite kind of project: physical inputs, digital insight.
• 🫁 AI Pneumonia Classifier: convolutional neural networks detecting pneumonia in chest X-rays, built with TensorFlow/Keras. Includes data augmentation experiments and rigorous evaluation across precision, recall, F1, and ROC-AUC, deployed as a live demo.
• 🎼 Composer Classifier: deep learning that predicts both genre and composer from raw MIDI. Piano-roll preprocessing feeding LSTM, BiLSTM with attention, and CNN architectures, with the best BiLSTM serving real-time predictions in a web demo.