AcaRevival Initiative

Experienced academic misconduct or bullying? We're building a real weapon against it.

Read Manifesto →
JW

Jiangtao Wang

Massachusetts Institute of Technology

No ratings yetBe the first to rate
Loading...

About Jiangtao Wang at Massachusetts Institute of Technology (MIT)

Jiangtao Wang is an academic professional affiliated with Massachusetts Institute of Technology. Their primary research focus includes Carbon Nanotubes in Composites, Graphene research and applications, and Advancements in Battery Materials. As a highly cited researcher, their work has accumulated over 2,694 citations, reflecting substantial influence across the academic community. Their H-index of 22 further reflects the breadth and sustained impact of their scholarly contributions.

Research Areas

Carbon Nanotubes in CompositesGraphene research and applicationsAdvancements in Battery Materials2D Materials and ApplicationsMechanical and Optical Resonators

Academic Impact Matrix

Research output metrics for Jiangtao Wang aggregated from public academic databases. Student lab experience data is pending.

Academic data verified · April 2026 · Next sync: May 2026

Research Output

Total Citations2,694

Emerging researcher

Publications81

Selective publication record

h-index22

Developing track record

i10-index28

Early-stage portfolio

Lab Environment

No lab data yet for Jiangtao Wang

+ Contribute First Review
  • Supervisionawaiting data
  • Responsivenessawaiting data
  • Fundingawaiting data
  • Communicationawaiting data
  • Work-Life Balanceawaiting data

Top Publications

Reviews (0)

No reviews yet for this supervisor.

Be the first to share your experience!

Is your PI driving you crazy?

Featured Article

The Sunday Night Dread: Surviving a Micromanaging PhD Supervisor

Real advice from PhD students on recognizing and navigating difficult supervisor relationships

Your experience matters. After reading the guide, share your review to help other PhD students.

Frequently Asked Questions

Not sure how to interpret mixed signals? A structured decision guide can help you think through high-risk supervision choices more clearly. Download the free guide.