WANG Jingxian - NUS Computing

COM2-03-32

651 65763

Jingxian WANG

NUS Presidential Young Professor

Ph.D., Carnegie Mellon University

I am an Assistant Professor at the CS Department of National University of Singapore. My research builds next-generation wireless systems and space networks. Before joining NUS, I was a researcher at Microsoft Research in Redmond where I led the Smart Surface (metasurface) for 6G and Space initiative. I received my Ph.D. from Carnegie Mellon University. Our lab pushes the frontiers of wirelessly networked devices—from wearables to space computers—advancing their networking, sensing, and computing capabilities. Our work has received research highlights three times at ACM SIGMOBILE and Communications of the ACM, best paper and demo at UbiComp, best paper at IPSN, and has also been selected for Microsoft's Accelerating Foundation Models program. I am a recipient of ACM SIGMOBILE Doctoral Dissertation Award, Microsoft Research Fellowship in North America, and inaugural Emerging Rockstar in IEEE Pervasive Computing.

RESEARCH AREAS

Systems & Networking

Media

Human-Computer Interaction
Ubiquitous Computing

Artificial Intelligence

RESEARCH INTERESTS

Wireless Systems, Sensing, Metasurfaces
Space Networks for AI, AI for Space Networks
Wireless Actuation
Multi-modal Sensors AI
Smart Materials

RESEARCH PROJECTS

AI for Space Networks; and Space Networks for AI

Jingxian Wang’s lab explores the intersection of AI with networks and systems in space. As mega-constellations (e.g., Starlink) scale rapidly, there’s surging demand for groundbreaking solutions that keep pace with exploding traffic and emerging services in low-Earth orbit. A recent example is SATE from the lab, published at ACM SIGCOMM 2025—an AI-accelerated traffic-engineering system that learns to produce near-optimal solutions in milliseconds for thousand-satellite constellations, delivering orders-of-magnitude speedups over classical solvers and higher throughput.

Artificial Intelligence, Systems & Networking

Multimodal AI × Sensors for Healthcare

Jingxian Wang’s lab develops multimodal AI with IoT sensors for healthcare applications. We explore non-invasive, everyday health diagnostics through intelligent sensing and AI. By integrating wireless, multimodal sensors with signal processing and machine learning, we build practical systems for early disease detection. Our approach emphasizes low-cost, continuous monitoring suitable for real-world deployment and daily use. This work is partially funded by Microsoft’s Accelerate Foundation Models Research Program.

Artificial Intelligence, Systems & Networking

RESEARCH GROUPS

Avant Lab

We push the frontiers of wirelessly networked AIoT devices—from wearables to space computers—advancing their networking, sensing, and computing capabilities.