AcaRevival Initiative

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

Read Manifesto →
MS

M. Sacco

Stanford University

No ratings yetBe the first to rate
Loading...

About M. Sacco at Stanford University (Stanford)

M. Sacco is an academic professional affiliated with Stanford University. Their primary research focus includes Computational Drug Discovery Methods, SARS-CoV-2 and COVID-19 Research, and Bacterial Genetics and Biotechnology. As a highly cited researcher, their work has accumulated over 1,302 citations, reflecting substantial influence across the academic community. Their H-index of 14 further reflects the breadth and sustained impact of their scholarly contributions.

Research Areas

Computational Drug Discovery MethodsSARS-CoV-2 and COVID-19 ResearchBacterial Genetics and BiotechnologyAntibiotic Resistance in BacteriaAntimicrobial Resistance in Staphylococcus

Academic Impact Matrix

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

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

Research Output

Total Citations1,302

Emerging researcher

Publications37

Selective publication record

h-index14

Developing track record

i10-index16

Early-stage portfolio

Lab Environment

No lab data yet for M. Sacco

+ 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.