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Achievements

Achievement: Pair Extraordinairex4Achievement: Pull Sharkx3Achievement: QuickdrawAchievement: YOLOAchievement: Starstruckx2

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alohays/README.md

Hi there, I'm Yunsung 👋

AI Research Engineer who bridges research and production

Head of Research, WoRV @ MaumAI · Adjunct Professor @ DGIST

I'm an AI research engineer with strong foundations in both research and practical engineering:

  • 🔬 Research background: 10+ publications in top-tier conferences (NeurIPS, CVPR, ICLR, AAAI) with 1.9K+ citations
  • 🤖 Embodied AI: Leading research on foundation models that unify language, vision, and action for robotics and autonomous driving
  • 💻 Engineering experience: Previously shipped commercial AI products at Wrtn reaching 5M+ MAUs
  • 🎓 Teaching: Undergraduate coursework in Physical AI at DGIST, School of Undergraduate Studies
  • 🍸 Fun fact: I'm a licensed home bartender in my free time — Korea's national Craftsman Bartender license!

What I'm passionate about

The work I find most interesting happens where a research result meets a running system. My focus right now:

  • 🦾 Vision-Language-Action models and robotics foundation models
  • 🌏 World models for robotics and autonomous driving
  • 🧠 Autonomous agents with memory and personalization
  • 🔄 Diffusion models and generative AI
  • 🚀 Scaling research prototypes to production-ready systems

Let's connect! ☕

I love meeting curious minds and sharing ideas. Whether you're a fellow researcher, student, entrepreneur, or just someone interested in AI - I'd be happy to chat!

📅 Book a virtual coffee chat with me - 30 minutes of free-form conversation about AI, research, career paths, or anything that sparks your interest.

🌐 More about me: alohays.github.io · CV

💼 We're hiring! The WoRV team is looking for people to work on Physical AI and robotics - full-time roles, internships, and 전문연구요원 (alternative military service) positions. → recruitment.worv.maum.ai

⭐ Check out some of my open-source work:

Robotics & Embodied AI - worv-ai

  • D2E - Scaling vision-action pretraining on desktop data for transfer to Embodied AI (ICLR 2026)
  • CostNav - A navigation benchmark for real-world economic-cost evaluation of Physical AI agents
  • vla-evaluation-harness - One framework to evaluate any VLA model on any robot simulation benchmark

Desktop agents - open-world-agents

  • open-world-agents - Everything you need to build a state-of-the-art foundation multimodal desktop agent, end-to-end
  • desktop-env - A real-time, high-frequency, real-world desktop environment for desktop-based ML development

Earlier work

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  1. allenai/vla-evaluation-harnessallenai/vla-evaluation-harnessPublic

    One framework to evaluate any VLA model on any robot simulation benchmark.

    Python 528 43

  2. awesome-visual-representation-learning-with-transformersawesome-visual-representation-learning-with-transformersPublic

    Awesome Transformers (self-attention) in Computer Vision

    271 36

  3. open-world-agents/open-world-agentsopen-world-agents/open-world-agentsPublic

    Everything you need to build state-of-the-art foundation multimodal desktop agent, end-to-end.

    Python 43 11

  4. worv-ai/CostNavworv-ai/CostNavPublic

    CostNav: A Navigation Benchmark for Real-World Economic-Cost Evaluation of Physical AI Agents

    Python 22 1

  5. paper2prpaper2prPublic

    AI/ML Paper → Presentation-ready Beamer + Quarto slides via Claude Code multi-agent workflow

    HTML 5 1

  6. dotfilesdotfilesPublic

    Standard-first, agent-friendly dotfiles for macOS/Linux/SSH: one-command install, clean replacement, token-efficient defaults, optional TUI polish.

    Shell 1