export default function JiaweiAcademicHomepage() { const news = [ { date: “2026-05”, text: “Joining HKUST(GZ) as a tenure-track Assistant Professor in Intelligent Transportation.”, }, { date: “2026-01”, text: “Two papers accepted to ICLR 2026: ELLMob and TrajFlow.”, }, { date: “2024-12”, text: “Our paper on LLM agents for personal mobility generation was presented at NeurIPS 2024.”, }, ];
const pubGroups = [ { title: “Mobility Generation and Cognition with Foundation Models”, subtitle: “Large language models for human mobility generation, event-aware behavior modeling, and urban cognition.”, papers: [ { title: “Large language models as urban residents: An LLM agent framework for personal mobility generation”, venue: “NeurIPS 2024”, authors: “Jiawei Wang, et al.”, desc: “Behaviorally grounded LLM agents for simulating personal mobility in cities.”, }, { title: “ELLMob: Event-Driven Human Mobility Generation with Self-Aligned LLM Framework”, venue: “ICLR 2026”, authors: “Jiawei Wang, et al.”, desc: “An event-aware self-aligned LLM framework for human mobility generation under dynamic urban contexts.”, }, ], }, { title: “Generative Modeling for Traffic Simulation”, subtitle: “Generative AI methods for traffic simulation, trajectory synthesis, and transportation system modeling.”, papers: [ { title: “TrajFlow: Nation-wide Pseudo GPS Trajectory Generation with Flow Matching Models”, venue: “ICLR 2026”, authors: “Jiawei Wang, et al.”, desc: “A flow matching framework for large-scale pseudo GPS trajectory generation at national scale.”, }, { title: “Traffic speed prediction for urban transportation network: A path-based deep learning approach”, venue: “Transportation Research Part C 2019”, authors: “Jiawei Wang, et al.”, desc: “A path-based deep learning approach for network-level urban traffic state modeling.”, }, { title: “Understanding the daily operations of electric taxis: From macro-patterns to micro-behaviors”, venue: “Transportation Research Part D 2024”, authors: “Jiawei Wang, et al.”, desc: “A data-driven analysis of electric taxi operations from citywide patterns to fine-grained behavioral dynamics.”, }, ], }, { title: “Reinforcement Learning for Public Transportation Control”, subtitle: “Learning-based control and optimization for bus operations, transit reliability, and coordinated mobility systems.”, papers: [ { title: “Dynamic holding control to avoid bus bunching: A multi-agent deep reinforcement learning framework”, venue: “Transportation Research Part C 2020”, authors: “Jiawei Wang, et al.”, desc: “A multi-agent deep RL framework for adaptive bus holding control.”, }, { title: “Reducing bus bunching with asynchronous multi-agent reinforcement learning”, venue: “IJCAI 2021”, authors: “Jiawei Wang, et al.”, desc: “Asynchronous multi-agent reinforcement learning for robust bus bunching mitigation.”, }, { title: “Robust dynamic bus control: A distributional multi-agent reinforcement learning approach”, venue: “IEEE TITS 2022”, authors: “Jiawei Wang, et al.”, desc: “Distributional multi-agent RL for robust transit control under uncertainty.”, }, { title: “Multi-objective multi-agent deep reinforcement learning to reduce bus bunching for multi-line services with a shared corridor”, venue: “Transportation Research Part C 2023”, authors: “Jiawei Wang, et al.”, desc: “Multi-objective RL for coordinated control in multi-line bus systems.”, }, ], }, ];
const experience = [ { role: “Incoming Assistant Professor”, org: “HKUST (Guangzhou)”, time: “May 2026 – Future”, desc: “Building a research program on Generative Intelligent Transportation.”, }, { role: “Postdoctoral Researcher”, org: “Graduate School of Interdisciplinary Information Studies, The University of Tokyo”, time: “2023 – Present”, desc: “Research on AI for transportation, mobility generation, and traffic simulation.”, }, { role: “Visiting Scholar”, org: “Center for Spatial Information Science, The University of Tokyo”, time: “2023 – Present”, desc: “Research on urban mobility intelligence and spatial data-driven transport modeling.”, }, { role: “Ph.D. in Civil Engineering”, org: “McGill University”, time: “2019 – 2023”, desc: “Transportation systems, reinforcement learning, and mobility operations.”, }, { role: “M.Eng. in Traffic Information Engineering and Control”, org: “Sun Yat-sen University”, time: “2016 – 2019”, desc: “Transportation data analysis and intelligent transportation systems.”, }, { role: “B.Eng. in Transportation Engineering”, org: “Sun Yat-sen University”, time: “2012 – 2016”, desc: “Foundations in transportation engineering and systems analysis.”, }, ];
return ( <div className="min-h-screen bg-white text-neutral-900"> <header className="sticky top-0 z-20 border-b border-neutral-200 bg-white/90 backdrop-blur"> <div className="mx-auto flex max-w-7xl items-center justify-between px-6 py-4 lg:px-8"> Jiawei Wang <nav className="hidden gap-6 text-sm text-neutral-600 md:flex"> About News Research Experience </nav> </div> </header>
<main id="top" className="mx-auto grid max-w-7xl grid-cols-1 gap-12 px-6 py-10 lg:grid-cols-[300px_minmax(0,1fr)] lg:px-8">
<aside className="lg:sticky lg:top-24 lg:self-start">
<div className="overflow-hidden rounded-3xl border border-neutral-200 bg-white shadow-sm">
<img
src="/个人图片.png"
alt="Jiawei Wang"
className="aspect-[4/5] w-full object-cover object-center"
/>
<div className="space-y-5 p-6">
<div>
<h1 className="text-3xl font-bold tracking-tight text-neutral-950">Jiawei Wang</h1>
<p className="mt-2 text-base text-neutral-700">
Incoming Assistant Professor in Intelligent Transportation
</p>
<p className="mt-1 text-sm text-neutral-500">HKUST (Guangzhou)</p>
</div>
<div className="space-y-2 text-sm leading-6 text-neutral-600">
<p>Postdoctoral Researcher, The University of Tokyo</p>
<p>Visiting Scholar, CSIS, The University of Tokyo</p>
<p>AI for Transportation · Generative Intelligent Transportation</p>
</div>
<div className="rounded-2xl bg-neutral-50 p-4 text-sm leading-6 text-neutral-700">
<p className="font-medium text-neutral-950">Research keywords</p>
<p className="mt-2">
LLMs for Mobility · Flow Matching · Generative Traffic Simulation · Reinforcement Learning · Transit Control
</p>
</div>
<div className="flex flex-wrap gap-2 text-sm">
<a
className="rounded-full border border-neutral-300 px-3 py-1.5 transition hover:bg-neutral-100"
href="https://scholar.google.com/citations?hl=zh-CN&user=Y1gU9wYAAAAJ"
>
Google Scholar
</a>
<a
className="rounded-full border border-neutral-300 px-3 py-1.5 transition hover:bg-neutral-100"
href="https://github.com/Wangjw6"
>
GitHub
</a>
<a
className="rounded-full border border-neutral-300 px-3 py-1.5 transition hover:bg-neutral-100"
href="https://www.linkedin.com/in/its-jiawei-wang"
>
LinkedIn
</a>
</div>
</div>
</div>
</aside>
<section className="space-y-12">
<section id="about" className="rounded-3xl border border-neutral-200 bg-white p-8 shadow-sm">
<h2 className="text-2xl font-semibold tracking-tight text-neutral-950">About</h2>
<div className="mt-5 space-y-4 text-[15px] leading-7 text-neutral-700">
<p>
Jiawei Wang is an incoming tenure-track Assistant Professor in Intelligent Transportation at the Systems Hub,
HKUST (Guangzhou), where he will join in May 2026. He is currently a Postdoctoral Researcher at the Graduate
School of Interdisciplinary Information Studies, The University of Tokyo, and a Visiting Scholar at the Center
for Spatial Information Science.
</p>
<p>
He received his Ph.D. in Civil Engineering from McGill University. Prior to that, he obtained his B.Eng. in
Transportation Engineering and M.Eng. in Traffic Information Engineering and Control from Sun Yat-sen University.
</p>
<p>
His research lies at the intersection of artificial intelligence and transportation systems, with a particular
interest in building an integrated scientific route from <span className="font-medium text-neutral-950">traffic generation</span>
, to <span className="font-medium text-neutral-950">traffic cognition</span>, and further to
<span className="font-medium text-neutral-950"> traffic control</span>. He develops data-driven and behaviorally grounded
methods for mobility generation, generative traffic simulation, and public transportation optimization.
</p>
</div>
</section>
<section id="news" className="rounded-3xl border border-neutral-200 bg-white p-8 shadow-sm">
<div className="flex items-center justify-between gap-4">
<h2 className="text-2xl font-semibold tracking-tight text-neutral-950">News</h2>
<span className="text-sm text-neutral-500">Selected updates</span>
</div>
<div className="mt-6 space-y-4">
{news.map((item) => (
<div key={item.date + item.text} className="flex gap-4 rounded-2xl border border-neutral-100 p-4">
<div className="min-w-[84px] text-sm font-medium text-neutral-500">[{item.date}]</div>
<p className="text-[15px] leading-7 text-neutral-700">{item.text}</p>
</div>
))}
</div>
</section>
<section id="research" className="rounded-3xl border border-neutral-200 bg-white p-8 shadow-sm">
<div className="flex items-center justify-between gap-4">
<h2 className="text-2xl font-semibold tracking-tight text-neutral-950">Research</h2>
<a
href="https://scholar.google.com/citations?hl=zh-CN&user=Y1gU9wYAAAAJ&view_op=list_works&sortby=pubdate"
className="text-sm text-neutral-500 transition hover:text-neutral-950"
>
Full publication list
</a>
</div>
<div className="mt-8 space-y-8">
{pubGroups.map((group) => (
<section key={group.title} className="rounded-2xl border border-neutral-100 p-6">
<h3 className="text-xl font-semibold tracking-tight text-neutral-950">{group.title}</h3>
<p className="mt-2 text-[15px] leading-7 text-neutral-600">{group.subtitle}</p>
<div className="mt-5 space-y-4">
{group.papers.map((paper) => (
<article key={paper.title} className="rounded-2xl bg-neutral-50 p-5">
<h4 className="text-lg font-medium leading-7 text-neutral-950">{paper.title}</h4>
<p className="mt-1 text-sm font-medium text-neutral-500">{paper.venue}</p>
<p className="mt-2 text-sm text-neutral-600">{paper.authors}</p>
<p className="mt-3 text-[15px] leading-7 text-neutral-700">{paper.desc}</p>
<div className="mt-4 flex flex-wrap gap-3 text-sm text-neutral-600">
<a href="#" className="underline underline-offset-4">PDF</a>
<a href="#" className="underline underline-offset-4">Code</a>
<a href="#" className="underline underline-offset-4">BibTeX</a>
</div>
</article>
))}
</div>
</section>
))}
</div>
</section>
<section id="experience" className="rounded-3xl border border-neutral-200 bg-white p-8 shadow-sm">
<h2 className="text-2xl font-semibold tracking-tight text-neutral-950">Experience</h2>
<div className="mt-6 space-y-5">
{experience.map((item) => (
<div key={item.role + item.org} className="rounded-2xl border border-neutral-100 p-5">
<div className="flex flex-col gap-1 md:flex-row md:items-center md:justify-between">
<h3 className="text-lg font-semibold text-neutral-950">{item.role}</h3>
<p className="text-sm text-neutral-500">{item.time}</p>
</div>
<p className="mt-1 text-sm font-medium text-neutral-600">{item.org}</p>
<p className="mt-3 text-[15px] leading-7 text-neutral-700">{item.desc}</p>
</div>
))}
</div>
</section>
</section>
</main>
</div> ); }