中文

Xiaowen (David) Zhu

I hold a B.S. in Data Science (AI track) from NYU Shanghai and am currently studying Business Analytics at Columbia.

I care a lot about AI products and about this field itself, and I want to work as an AI agent builder or AI product manager. More than executing against a spec someone else wrote, I want to define the product: taking a real business need from zero to one and actually solving it.

The core of the job, as I see it, is connecting business and engineering. Business teams often can't describe their own need or pain precisely; they can only point in a rough direction. Which steps suit a model, where the model's ceiling is, what output counts as good enough, and how to get the thing shipped and running — all of that falls to product to break apart and push through. That's where I think I add the most value, and it's where most of my last two years has gone: pulling a vague pain point apart layer by layer until it converges into a path a model can execute reliably, then using an evaluation system to check that the path was the right one.

AI is still moving fast — plenty of what was consensus six months ago no longer holds, and nobody knows what comes next. I'm a fairly geeky person, and I'd like to keep exploring in this direction.

Nanfu Battery · Feb 2026 – Jun 2026

AIGC Marketing Image Factory

Cutting the time it takes to produce a campaign image, so business teams can ship placement-ready assets consistently.

  • ~70% of image production handled by the agent
  • 5 QC dimensions scored automatically
  • 2 brand rule sets and channel rubrics
  • LangChain
  • Multi-agent
  • Multimodal QC
  • Prompt engineering
  • Docker / Tencent Cloud
Read case study →

Embodied Agent Research Project · May 2025 – Dec 2025

Does an Avatar Improve Perceived Empathy in AI Chatbots?

A controlled A/B test of how an avatar actually affects users' perceived empathy, giving conversational-AI teams evidence for the investment decision.

  • A/B test avatar vs. text conditions
  • 186 real human–AI conversations
  • CHI 2026 accepted to BiAlign Workshop
  • A/B testing
  • Soul Machines digital human
  • React
  • FastAPI / MongoDB
  • LLM-as-a-judge
  • Transformer fine-tuning
Read case study →

AI product

  • Problem decomposition
  • Capability boundaries
  • Human-AI division of labor
  • Evaluation design
  • PRD writing
  • Cross-team delivery

LLM & agents

  • LangChain
  • LangGraph
  • LangSmith
  • Multi-agent
  • RAG
  • ReAct
  • MCP
  • LLM-as-a-judge
  • Prompt engineering

Engineering, data & ML

  • Python
  • SQL
  • Bash
  • PyTorch
  • Hugging Face Transformers
  • scikit-learn
  • MongoDB
  • R
  • Stata
  • Git / GitHub

I'd love to meet people who share the same enthusiasm — to talk about turning model capability into products that actually run inside a business, and about whatever is worth digging into along the way.