AcaRevival Initiative

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

Read Manifesto →
HC

Hian Leng Chan

Stanford University

No ratings yetBe the first to rate
Loading...

About Hian Leng Chan at Stanford University (Stanford)

Hian Leng Chan is an academic professional affiliated with Stanford University. Their primary research focus includes Fault Detection and Control Systems, Machine Fault Diagnosis Techniques, and Advanced machining processes and optimization. As an established researcher, their work has gained over 325 citations, reflecting growing recognition within the scientific community. Their H-index of 11 further reflects consistent scholarly impact.

Research Areas

Fault Detection and Control SystemsMachine Fault Diagnosis TechniquesAdvanced machining processes and optimizationStructural Health Monitoring TechniquesEnergy Efficiency and Management

Academic Impact Matrix

Research output metrics for Hian Leng Chan aggregated from public academic databases. Student lab experience data is pending.

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

Research Output

Total Citations325

Emerging researcher

Publications19

Selective publication record

h-index11

Developing track record

i10-index11

Early-stage portfolio

Lab Environment

No lab data yet for Hian Leng Chan

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

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.