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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<title>SphereLab — Scalable Principles for Learning and Reasoning Lab</title>
<meta name="description" content="SphereLab is a research lab studying scalable principles for machine learning and reasoning at The Chinese University of Hong Kong." />
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<main class="layer">
<!-- ===== HERO ===== -->
<section class="hero" id="top">
<div class="col center">
<h1>
SphereLab.
</h1>
<p>
Scalable Principles for Learning and Reasoning Lab @ CUHK<br><br>
</p>
</div>
<div class="prose">
<p>
We believe the next leap in AI won't come from blindly stacking more parameters — it will come from finding the right principles. The simple ones. The ones that truly scale. So we hunt for them.<br>
</p>
<p>
We work on the principles that shape how machines learn — the structures, symmetries, and quiet assumptions that decide what a model can generalize to. We believe the right idea is often worth more than another order of magnitude of compute, and we work to turn the ones we find into functional algorithms and systems.<br>
</p><p>
<em>How we work.</em> Curiosity over trends. Depth over speed. Insight before application.<br>
</p>
<p>
<em>Director.</em> <a href="https://wyliu.com/">Weiyang Liu</a><br>
<em>Members.</em> <a href="TBA">He Guo</a>, <a href="https://huangyangyi.github.io/">Yangyi Huang</a>, <a href="https://www.linkedin.com/in/zeju-qiu-729b8018a/?originalSubdomain=de">Zeju Qiu</a>, <a href="https://scholar.google.com/citations?user=dX-aOIwAAAAJ&hl=en">Kexuan Shi</a>, <a href="https://siyuanwangw.github.io/">Siyuan Wang</a>, <a href="https://zhouliang-yu.github.io/">Zhouliang Yu</a>, <a href="https://haoquanzhang.github.io/">Haoquan Zhang</a><br>
<em>Affiliated members.</em> <a href="http://lijunc04.github.io/">Lijun Cai</a>, <a href="https://jc-chen1.github.io/">Jiacheng Chen</a>, <a href="https://www.linkedin.com/in/yamei-chen-ab924b14a/">Yamei Chen</a>, <a href="https://github.com/Vvauted">Kailebo Jiefulisi</a>, <a href="https://scholar.google.com/citations?user=tMihzcoAAAAJ&hl=zh-CN">Jiale Kang</a>, <a href="https://lhxcs.github.io/">Hanxuan Li</a>, <a href="https://yinleung.com/">Xianliang Li</a>, <a href="TBA">Luocheng Liang</a>, <a href="https://brandon-liu-jx.github.io/">Jinxiu Liu</a>, <a href="https://scholar.google.com/citations?user=CDyo3XgAAAAJ&hl=en">Siyuan Ma</a>, <a href="https://github.com/hetailang">Zihao Wang</a>, <a href="https://timx.me/">Tim Z. Xiao</a>, <a href="https://sihany077.github.io/">Sihan Yang</a>
<br>
</p>
</div>
</section>
<!-- ===== ABOUT =====
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<div class="col">
<div class="section-head center">
<div class="eyebrow">About</div>
<h2>We work on the principles behind learning, not only its results.</h2>
</div>
<div class="prose">
<p>
Modern machine learning is, in many ways, an empirical science. Models grow, benchmarks shift, and useful behaviors appear that no one fully understands. We are interested in the parts of this picture that admit explanation — the structural and geometric ideas that quietly do most of the work.
</p>
<p>
Our research spans parameter-efficient adaptation, formal mathematical reasoning, model merging, and the geometry of representations in large neural networks. We try to develop methods whose effectiveness can be reasoned about, and whose ideas transfer.
</p>
<p>
The lab is led by <a href="https://www.cse.cuhk.edu.hk/people/faculty/weiyang-liu/">Weiyang Liu</a>, an Assistant Professor in Computer Science and Engineering at CUHK. We collaborate broadly across institutions, and welcome students, interns, and visiting researchers who care about both theory and craft.
</p>
</div>
</div>
</section>-->
<footer>
SphereLab · Scalable Principles for Learning and Reasoning
<div class="links">
<a href="https://github.com/Sphere-AI-Lab">GitHub</a>
<a href="https://huggingface.co/SphereLab">Hugging Face</a>
<a href="https://www.cse.cuhk.edu.hk/">CUHK CSE</a>
</div>
</footer>
</main>
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const FADE_START = 0.0; // start fading immediately on scroll (in viewport heights)
const FADE_END = 0.1; // fully faded after ~30% of one viewport
const MIN_OPACITY = 0.45; // never fully invisible
let ticking = false;
function applyFade() {
const vh = window.innerHeight || 800;
// Normalize scroll progress to [0, 1] across the [FADE_START, FADE_END] range.
const raw = (window.scrollY / vh - FADE_START) / (FADE_END - FADE_START);
const t = Math.min(Math.max(raw, 0), 1);
const eased = t * t * (3 - 2 * t); // smoothstep on [0,1]
const op = 1 - eased * (1 - MIN_OPACITY);
wrap.style.opacity = op.toFixed(3);
ticking = false;
}
function onScroll() {
if (!ticking) {
window.requestAnimationFrame(applyFade);
ticking = true;
}
}
window.addEventListener('scroll', onScroll, { passive: true });
window.addEventListener('resize', onScroll, { passive: true });
applyFade();
})();
</script>
</body>
</html>