AcaRevival Initiative

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

Read Manifesto →
AS

Arman Siahvashi

Massachusetts Institute of Technology

No ratings yetBe the first to rate
Loading...

About Arman Siahvashi at Massachusetts Institute of Technology (MIT)

Arman Siahvashi is an academic professional affiliated with Massachusetts Institute of Technology. Their primary research focus includes Spacecraft and Cryogenic Technologies, Phase Equilibria and Thermodynamics, and Catalytic Processes in Materials Science. As a highly cited researcher, their work has accumulated over 1,099 citations, reflecting substantial influence across the academic community. Their H-index of 15 further reflects the breadth and sustained impact of their scholarly contributions.

Research Areas

Spacecraft and Cryogenic TechnologiesPhase Equilibria and ThermodynamicsCatalytic Processes in Materials ScienceCatalysts for Methane ReformingCatalysis and Oxidation Reactions

Academic Impact Matrix

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

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

Research Output

Total Citations1,099

Emerging researcher

Publications37

Selective publication record

h-index15

Developing track record

i10-index21

Early-stage portfolio

Lab Environment

No lab data yet for Arman Siahvashi

+ 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.