🤗 AI Researcher/Engineer in SF Bay Area, focusing on RL, World Models, Spatial Intelligence, with 5 years of experience in Agentic RL, Multimodal agent reasoning, scalable LLM/VLM/VLA systems. Master’s in CS from Georgia Tech and Mathematics from UChicago, part-time studying at Stanford GSB. Previously, a Quant Researcher (Stochastic Volatility, Machine Learning in Finance) in Chicago. A long-term thinker, resilient collaborator, and builder of high-impact AI systems.

X: https://x.com/junfanzhu98

Github (1.4k⭐️): https://github.com/junfanz1/

• 📊 QuantEval: A Benchmark for Financial Quantitative Tasks in Large Language Models [ACL 2026]
  🧪 Evaluation and domain knowledge are the core bottlenecks of Quant + AI. Without expert-level, strong verifiers for evaluation, models cannot reliably assess performance in multi-step strategy generation, risk control, or real-world trading effectiveness. QuantEval is proposed in this context, providing a reproducible benchmark framework that goes beyond static question answering and shifts toward evaluation grounded in realistic trading details. It represents an initial exploration of evaluating financial “World Models.” 🌍

• Finalist & Track Winner, 🏆 Y Combinator Hackathon 2025
  Inspired by Isaac Asimov’s Foundation, PsychoHistory is a probabilistic forecasting system that maps the branching futures of human events—combining history, data, and AI to model the flow of possibility. 🧠 Our approach blended SFT+RL, training the model not just what to predict but how to reason across alternative futures—like a psychohistorian trained on uncertainty itself.
• Meritorious Winner, Mathematical Contest in Modeling.
• 2nd Prize, Asia Supercomputer Challenge.
• Top 10 Algo Trader, Rotman International Trading Competition.
• Outstanding Thesis (1%).

My portfolio boasts pioneering projects in MoE & Attention for scalable LLM, reflective multi-agent orchestrations, and full-stack GenAI applications.

• 1. Awesome-AI-Engineer-Review
  In-depth review of industry trends in AI, LLMs, Machine Learning, Computer Science, and Quantitative Finance.
  • 2025 NVIDIA GTC Conference − Technical & Industrial Insight
  • 2025 Agentic AI Summit Berkeley − Technical & Industrial Insight
• 2. Agentic RL: GRPO Reinforcement Learning for Agentic Search in LLMs
  Search-R1 leverages Group Relative Policy Optimization (GRPO) to fine-tune LLMs at the token level, enabling stable reinforcement learning over multi-step search–reasoning trajectories. The model learns adaptive retrieval policies, deciding when to trigger searches and integrating results into its reasoning context for more precise answers.
• 3. MiniGPT-and-DeepSeek-MLA-Multi-Head-Latent-Attention
  Memory-efficient multi-head latent attention in PyTorch, that leverages low-rank approximation and decoupled rotary positional embeddings, to compress key–value representations, reducing inference memory while maintaining high performance in long-context language models.
• 4. DeepSeek-MoE-Mixture-of-Experts-in-PyTorch
  Implemented scalable 8-expert MoE model with top-k routing, expert load balancing, and capacity-aware gating; enabled parallel sparse activation and DeepSeek-R1-style distributed training scalability.
• 5. MCP-MultiServer-Interoperable-Agent2Agent-LangGraph-AI-System
  A decoupled real-time agent architecture connecting LangGraph agents to remote tools served by custom MCP servers via SSE and STDIO, enabling a scalable multi-agent system for LLM workflows. The design supports flexible multi-server connectivity and lays the groundwork for an Agent2Agent protocol, fostering seamless, cloud-deployable interoperability across diverse AI systems.
• 6. LangGraph-Reflection-Researcher
  Engineered LangGraph-based multi-agent system with self-reflection and retrieval-grounded alignment; integrated LangSmith trace for reasoning introspection, cutting hallucination 40% with iterative expert routing.
• 7. Cognito-LangGraph-RAG-Chatbot
  Advanced Retrieval Augmented Generation (RAG) chatbot that utilizes LangGraph to enhance answer accuracy and minimize hallucinations in LLM outputs.
• 8. Cursor-FullStack-AI-App
  Cursor Vibe Engineering: Full-stack micro SaaS AI application that processes GitHub URLs to generate insightful JSON reports powered by AI analytics.
• 9. Cryptocurrency-Blockchain-FullStack
  Comprehensive decentralized blockchain platform demonstrating practical applications of core blockchain concepts through a modular, full-stack approach.

Favorite project integrating Generative AI, Humanoid Robotics (RLHF), and Low-Altitude Economy.

I’m a traveler ✈️, violinist 🎻, licensed hunter with firearm 🔫 , certified boater ⛵, amateur bartender 🍷 and aspiring private pilot 🚁. I retired my passport stamping 🛂 across 6 continents 🗺️, 65 countries/regions 🌐, USA 50 states & 50+ national parks 🏞️.

I summited 🇹🇿 Kilimanjaro Uhuru Peak (5895m) at the Roof of Africa 🦒, trekked 🇳🇵 Annapurna Base Camp 🏔, hiked 🇬🇹 Volcán de Fuego 🌋, traversed a desert in 🇨🇳 Inner Mongolia 🏜, and completed 2 marathons (PB within 5h) 🏃.

My expeditions have taken me to beautiful adventurous journeys, such as 🇳🇴 Longyearbyen & Barentsburg (🥶 icebreaker ⛴) in Svalbard 🌌, 🇨🇱 Rapa Nui🗿, 🇨🇦 Iqaluit Nunavut 🐋, 🇦🇷 Ushuaia 🐧, 🇺🇸 Unalaska & Cold Bay in Aleutian Islands🗻 / Utqiaġvik & Prudhoe Bay Alaska ❄️, 🇨🇳 Tibet 🌄, 🇵🇫 Bora Bora 🪸, 🇺🇸 Molokaʻi 🏝️, 🇪🇨 middle of the Earth 🌎 and so on. These experiences have shaped my adaptability 👽, problem-solving skills ✍️, and global perspective 🌊.

📖 Motto: "Every man carries within him a world, composed of all that he has seen and loved, and it is to this world that he constantly returns, even when he seems to be journeying and living in another different world." — Chateaubriand, "Voyages en Italie" 🌅