Portfolio
Selected projects and explorations
Your Ride, Your Rules
Three language-model agents read the road, read the rider, and turn both into one driving decision. The same junction gets driven briskly or gently depending on who is in the car — and a rider can free a stuck vehicle with a sentence.
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What Is the Car Doing?
Seven public driving datasets, 190.8 hours from 26 countries, relabeled into one set of 16 lateral and 6 longitudinal actions. A video model trained on it reaches 82.7 and 83.5 mAP, and powers an agent that finds driving clips from a plain-English request.
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The Better Plan Was Already There
When a vision-language driving model plans badly, a good plan is often already among its own samples. We keep the model frozen, sample 16 trajectories, and train a small checker to pick one. Position error drops by about 15% across three models, with no retraining.
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Point First, Then Decide
Driving models describe a scene fluently and still cannot tell you which three things on the road are deciding what they do next. This project builds the supervision that makes them point first and plan second, and it makes eight different models better at both.
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SmallGoal
Bank and card transactions come in by CSV or automatic sync, get de-duplicated, categorized and paired up, and turn into a monthly report. The data stays on my laptop. Next: split each paycheck into savings, travel, growth and daily-spending accounts, and add an agent for financial analysis.
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What Kind of Driver Is an LLM?
Three AI models, four everyday traffic scenarios, three languages, 54,000 decisions. Each model turned out to have its own driving personality, and the language you ask in quietly changes it.
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Getting a Stuck Self-Driving Car Moving Again
Self-driving cars sometimes stop for a plastic bag and wait for a human to rescue them. We bolted a language model onto a standard AV stack so the car can reason its way out. Driving score 48.7 → 70.9, success rate 18% → 50%.
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