AcaRevival Initiative

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

Read Manifesto →
LR

Lorenzo Rosasco

Massachusetts Institute of Technology

No ratings yetBe the first to rate
Loading...

About Lorenzo Rosasco at Massachusetts Institute of Technology (MIT)

Lorenzo Rosasco is an academic professional affiliated with Massachusetts Institute of Technology. Their primary research focus includes Sparse and Compressive Sensing Techniques, Stochastic Gradient Optimization Techniques, and Numerical methods in inverse problems. As a highly cited researcher, their work has accumulated over 8,017 citations, reflecting substantial influence across the academic community. Their H-index of 38 further reflects the breadth and sustained impact of their scholarly contributions.

Research Areas

Sparse and Compressive Sensing TechniquesStochastic Gradient Optimization TechniquesNumerical methods in inverse problemsNeural Networks and ApplicationsDomain Adaptation and Few-Shot Learning

Academic Impact Matrix

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

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

Research Output

Total Citations8,017

Above average

Publications348

Highly prolific researcher

h-index38

Established scholar

i10-index125

Broad impact

Lab Environment

No lab data yet for Lorenzo Rosasco

+ 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.