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FOUNDER & CEO @ MAXORA AI ·PH.D. — NTU EE

Yun-Yen
Chuang

教 AI 自己學會怎麼探索

I build explorer–exploiter systems that learn how to explore — a second network that schedules noise, samples, and probes, so the generator can focus on what it does best. Diffusion, GANs, and meta reinforcement learning for natural language generation.

Meta-ExplorationText Diffusion Language GANsMeta RLNLG
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About

Yun-Yen Chuang (莊昀諺)
莊昀諺 · “Kloud” Yun-Yen Chuang Data Scientist · Founder & CEO @ Maxora AI · Ph.D. — NTU EE

I am the Founder & CEO of Maxora AI, where I turn research into product. My doctoral research at National Taiwan University, advised by Prof. Hung-yi Lee, works at the intersection of generative modeling and reinforcement learning for language.

My work asks a single question: instead of hand-crafting how a model adds noise, samples tokens, or explores — what if a second network learned that policy? The generator becomes the exploiter; a meta-trained scheduler or explorer does the searching.

▸ Research thread · one idea across the work

A recurring move runs through my papers: a second network that learns how to explore or schedule, so the main model can focus on generating or detecting. Meta-DiffuB and MetaEx-GAN make this explicit with a scheduler / explorer–exploiter pair; QMVDet uses a query-based-learning scheduler to decide when to guide a detector; and it traces back to RapGAN and SODM. Each method below is laid out as an instrument you can scroll through.

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What we're building

At Maxora AI I'm turning the meta-exploration idea into a long-running growth agent: an autonomous system that designs, ships, measures, and re-designs marketing — and never stops learning from what it launches.

Landing-page generationAd-creative generation Trend sensingMeta / Google performance feedback On-device audience tuningHarness-engineered agents
▸ RESEARCH NOTES Harness Engineering in production How I keep a long-running AI agent stable, observable, and self-improving — the engineering behind the loop. Read the notes → ▸ ENGINEERING NOTES Building a local Claude Code Self-hosting a small model, beating a tiny context window, and an async agent loop — how to build a privacy-first coding agent at Claude-Code level. Read the notes → ▸ ENGINEERING NOTES An image-gen system to rival Gemini Multi-reference identity-preserving compositing, instruction editing, and readable in-image text — Gemini-class image generation, self-hosted on consumer GPUs, from scratch. Read the notes →
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Research

Selected work on meta-exploration, text diffusion, and generative models. Each card opens a full, interactive walkthrough that steps through the method.

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Education

Ph.D., Electrical Engineering

National Taiwan University

SEP 2022 – 2025 · SPEECH LAB · ADVISOR — PROF. HUNG-YI LEE (李宏毅)

M.S., Engineering Science & Ocean Engineering

National Taiwan University

SEP 2015 – JUN 2017 · iCAN LAB · ADVISOR — PROF. RAY-I CHANG (張瑞益)

B.S., Computer Science

National Changhua University of Education

SEP 2011 – JUN 2015

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Get in touch

Open to research collaborations, talks, and conversations about meta-exploration, generative models, and applied AI.