Actively open to Machine Learning / Computer Vision roles
I build computer vision systems that graduate from research to production.
Doctoral scholar at the University at Albany turning papers and prototypes into reliable experiences. I design models, ship data pipelines, and partner with product teams to deliver measurable impact.
Recruiter snapshot
Detection, tracking, and data-centric training flows.
MLOps via Python, containerization, and experimentation discipline.
What I do
I combine research-grade modeling with engineering that stands up in production. Here is how I tend to add value to teams.
Applied research
I prototype quickly, benchmark honestly, and move the best ideas into polished pipelines with readable code and sane defaults.
Engineering for delivery
From dataset versioning to model packaging, I build the glue that lets research work live in CI/CD and observability loops.
Clear communication
I turn experiments into crisp docs, publishable visuals, and approachable demos that help stakeholders make decisions fast.
Featured work
Selected projects that show how I think about problem framing, model design, and the engineering needed to ship.
MNIST vision baseline
Designed a clean CNN baseline with disciplined experimentation for the classic MNIST benchmark, focusing on reproducibility and reporting.
Producer/Consumer systems demo
Explored OS-level synchronization with a semaphore-driven producer/consumer implementation, documenting patterns for thread-safe queues.
Authentication in MVC
Built a lightweight MVC login service that demonstrates separation of concerns, testable controllers, and simple credential flows.
Writing, reading, hobbies
A glimpse into how I think outside of code—research notes, books shaping my perspective, and hobbies that keep me observant.
Recent thoughts
Data-first vision models. Notes on cleaning, balancing, and validating small datasets before reaching for larger models.
Making experiments legible. How to write logs, tables, and visuals so collaborators can trust your results.
Bridging academia and product. Lessons from moving ideas from the lab to user-facing features.
Currently into
Deep Learning (Goodfellow), Designing Data-Intensive Applications (Kleppmann), Sapiens (Harari).
Photography walks, reading about design, and tinkering with new vision model papers.
Visitors around the globe
A live footprint of who’s stopping by and from which continent.
Dots scale as visits increase. Location is resolved on your device only.
Visits by continent
- North America—
- South America—
- Europe—
- Africa—
- Asia—
- Oceania—
Counts update instantly and stay anonymous—just a snapshot of global interest.
Let’s build something together
I thrive in teams that value rigorous experimentation and shipping. If you need an engineer who can translate research into dependable features, let’s talk.