About
I'm a machine learning engineer working on computer vision, multimodal models and large language models. I build systems that understand images and video, and language models that answer from evidence rather than from memory.
I care most about reliability: whether a model still works outside its benchmark, and whether a person can check what it says. That shapes how I build models and how I evaluate them.
I'm open to research collaborations in these areas. If you're working on something related, I'd be glad to hear about it.
Research interests
- Computer vision and video understanding
- Vision-language and multimodal models
- Large language models and grounded generation
- Reliable, verifiable AI
Across all of these, I'm drawn to one question: do models still behave reliably once they leave the benchmark?
News
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2026
Paper
Submitted Joint Prediction of UHPC Properties Using a Feature-Tokenizer Transformer to Applied AI Letters. It's under review.
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Jan 2025
Career
Joined Accelx Inc. as a Machine Learning Engineer.
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Jan 2021
Thesis
Graduated from AUST with a thesis on arrhythmia classification using 2-D CNNs.
Selected projects
All projects →Overcooked AAR
I had one model write an after-action review of real human–human Overcooked play twice, from the event log and from the gameplay video, and checked every claim it made against the log.
Sentinel
Tracking-based video anomaly detection. YOLOv8 + BoT-SORT tracking feeds a rule engine for falls, loitering, abandoned objects, crowding, and wrong-way motion.
DocuRoute
Routes financial questions to hybrid sparse–dense retrieval or sandboxed text-to-SQL over SEC 10-K filings, with cited answers and a custom evaluation harness.
DepthCraft
Lifts Depth Anything V2 metric depth into a 3D point cloud and measures real distances with uncertainty, from a single photo.
Let's collaborate
I'm open to research collaborations, especially on vision, video, or language models. A short email about what you're working on is the best way to start.