{project.title}
{project.description}
import Image from "next/image"; import Link from "next/link"; import type { Metadata } from "next"; import graphAttentionImage from "@/public/images/graph-attention-topology/model-architecture.png"; const projects = [ { href: "/projects/graph-attention-topology", title: "Graph Attention Inference of Network Topology", eyebrow: "MECC 2024 / Multi-Agent Systems", description: "A graph-attention model that recovers hidden interaction graphs from trajectory data while learning to predict the next state.", status: "Published paper", year: "2024", image: graphAttentionImage, imageAlt: "Graph attention model architecture diagram", tags: ["Graph attention", "Topology inference", "Kuramoto oscillators"], }, ]; export const metadata: Metadata = { title: "Projects", description: "Selected projects on world models, ML hardware, GPUs, deep learning, and the occasional multi-agent systems paper.", openGraph: { title: "Projects", description: "Selected projects on world models, ML hardware, GPUs, deep learning, and the occasional multi-agent systems paper.", }, }; export default function ProjectsPage() { return (
Projects
Most of my current work sits around ML hardware, deep learning models, and world models for RL. This topology paper is the multi-agent exception.
{projects.length} active {projects.length === 1 ? "project" : "projects"}
{project.description}