I work on AI projects where a working demo is only the first step. I care about baselines, failure cases, and honest evaluation—then I turn the useful part into something people can run.

Exploring graph learning · recommendation systems · model evaluation · practical AI tools
Open to algorithm, data science, and applied AI internships

Make it work: Trend2Video Pro turns a trend into a reviewable video package. Show the evidence: WorldCupROI turns mixed sports data into a decision prototype with baselines, uncertainty, and stated limits. Both follow the same loop: input → pipeline → inspectable output.

Trend or URL → scoring and strategy → script and storyboard → MP4 publish package

A local agent workflow that produces a video, subtitles, thumbnail, metadata, and quality report for manual review. The project includes a CLI, FastAPI endpoints, a Streamlit execution console, and SQLite trend memory.

Watch the demo · Inspect the pipeline · Run it locally

Evidence boundary: the no-key demo uses mock LLM responses; its benchmark does not establish real-world content performance.

Historical matches + source text + commercial proxies → baselines and intervals → scenario dashboard

A reproducible sponsorship analytics prototype that joins match context and attention signals, evaluates match and ROI-proxy baselines, and shows uncertainty and scenario comparisons in a dashboard.

Open the dashboard · Read the case study · Check the data card

Evidence boundary: sponsor spend, conversion, and ROI are constructed demo variables. Proxy-model scores do not validate real commercial return or campaign lift.