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UMass freshmen develop startup to combat media bias
The Massachusetts Daily Collegian
Apr 2025
I build AI systems, mostly applied ML and computer vision, and a few products around them. Sometimes I write code for research, sometimes for startups.
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River herring populations have been declining for decades, and monitoring juvenile emigration is a top federal research priority. The problem: researchers manually review thousands of underwater camera trap images, frame by frame, counting fish. It takes hundreds of hours per field season.
HerringNet replaces that entire workflow. It's a two-stage deep learning pipeline that uses a YOLOv12x model pretrained on 1.9M fish images to detect every fish in a frame, then corrects for double-counting across video using frame-residence-rate methodology. The system processes 2,320+ images at 1.4s each on a consumer CPU, with a 0.72 mean confidence score and zero false positives on debris, leaves, or rocks. No GPU required. Any lab can deploy it out of the box.
Presented at MassURC 2026. Methods paper in preparation targeting Limnology and Oceanography: Methods. Open source.
When a brand's ad shows up next to misinformation, hate speech, or low-quality content, it's a liability. The advertising industry calls this brand safety, and most companies still solve it with blocklists and manual review.
ContextGrade is the infrastructure layer that makes that automatic. We built a content analysis API that scans web pages and scores them for advertising risk across categories like misinformation, toxicity, and brand alignment. The platform started as a consumer browser extension for spotting media bias, then pivoted into B2B after we realized the real problem was on the enterprise side. Currently in talks with Omnicom, one of the five largest advertising holding companies globally, to deliver brand safety analysis at scale.
Started at 17 as a browser extension. Now an enterprise platform.
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