Aidan Whitedeer
CS Undergraduate at Stanford and Researcher at Stanford Virtual Human Interaction Lab. Previously at Microsoft, building at MTS Live, incoming at Uber AI. Research interests widely include Reinforcement Learning, World Models, and Virtual Spaces.
Research
Deep Reinforcement Learning with Roblox Agents for Obbies (Obstacle Courses)
Department of Computer Science · Stanford University · 2026
A complete RL pipeline (Behavioral Cloning → DAgger → PPO) built inside Roblox Studio for training AI agents to navigate obstacle courses. We instrument a custom environment with Luau, bridge game physics to a PyTorch policy loop, and evaluate generalization across differing obby layouts.
Sentiment-Augmented Cross-Sectional Stock Selection
Department of Computer Science · Stanford University · 2026
We investigate whether sequentially filtering cross-sectional equity signals (first by traditional factors, then by FinBERT sentiment) outperforms joint modeling, and identify the conditions under which sentiment adds conditional predictive value within a factor-screened universe.
Experience
Featured Projects
An AI-powered DJ system with dual-deck audio engine, BPM detection, automated transitions, 3-band EQ, and bass/voice/melody isolation. Built with Next.js 16, React 19, TypeScript, and Grok/X API integration. Features a real-time 3D visualizer with Three.js/React Three Fiber, voice control with speech recognition and TTS, and AI copilot music automation.
An AI-powered SDR system with automated lead scoring and pipeline management. Built automated lead scoring, email generation, and pipeline management with REST APIs, multi-model evaluation, error handling with retries, and production-ready architecture.
An AI-powered Web3 payment system built with TypeScript/Next.js, Coinbase CDP wallets, Claude agents, and Vapi voice calls. Supports autonomous transaction flows, HTTP 402 payments, cost-based tool chaining, phone approvals, and gas-abstracted on-chain payments using the Coinbase + Anthropic SDK.
Creating framework for tribes to finetune mBERT models for endangered Native American Languages. Working with Raices Collaboration Project, mentored by Ken Attocknie. Received Dreamstarter Grant for $20,000 towards the project.