AcaRevival Initiative

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

Read Manifesto →
KC

Kimberly Villalobos Carballo

Massachusetts Institute of Technology

No ratings yetBe the first to rate
Loading...

About Kimberly Villalobos Carballo at Massachusetts Institute of Technology (MIT)

Kimberly Villalobos Carballo is a researcher based at Massachusetts Institute of Technology. They specialize in Machine Learning in Healthcare, Artificial Intelligence in Healthcare and Education, and COVID-19 diagnosis using AI, with ongoing contributions to these areas. Their research has drawn over 420 citations, marking them as an increasingly recognized voice in their field. A solid H-index of 7 speaks to the quality and reach of their work.

Research Areas

Machine Learning in HealthcareArtificial Intelligence in Healthcare and EducationCOVID-19 diagnosis using AICOVID-19 Clinical Research StudiesAdversarial Robustness in Machine Learning

Academic Impact Matrix

Research output metrics for Kimberly Villalobos Carballo aggregated from public academic databases. Student lab experience data is pending.

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

Research Output

Total Citations420

Emerging researcher

Publications14

Selective publication record

h-index7

Developing track record

i10-index6

Early-stage portfolio

Lab Environment

No lab data yet for Kimberly Villalobos Carballo

+ 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.