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Alex Gaudio

Explainable Machine Learning

for Medical Signal Analysis


Alex Gaudio's research focuses on explainable machine learning for analysis of medical images and signals. His research focuses on explanations of bias and predictions in context of privacy, compression, and prediction. He is a dual degree PhD candidate at Carnegie Mellon University and at the University of Porto. He co-founded a non-profit, NYC Makerspace, to instill in regular people from diverse and disadvantaged backgrounds, that they can innovate with advanced technology and become community leaders. Previously, he worked as a data scientist and engineer in New York City for 7 years. He studied in jazz performance and composition at Bard College, during which he briefly worked as an Emergency Medical Technician in ambulances and in a hospital.

Advice to myself and to my friends and colleagues: The way people create and interpret their life paths is fascinating. Visionary people embrace challenge by inventing opportunities and pursuing ideas. Their success is achieved not by meeting requirements or emphasizing credentials, but by giving others the ability to appreciate, use and adapt the things they love most. Think big, then go a lot bigger, and find clever ways to make it happen. People will follow and support you when they can see, understand, and believe in what you are doing. Just do it!

Research Interests

  • Explainable Deep Machine Learning
  • Cardiology Research
  • Computer Vision
  • Signal Processing
  • Medical Image and Signal Analysis
  • Privacy and Data Access

This is my Five-Fifteen Year Plan:

  • Establish an applied research and teaching lab in a university medical hospital, focused on explainable machine learning for analysis of cardiology data.
  • Develop an incubator to spin-off our startup ideas and license the intellectual property from the research lab.
  • Establish a non-profit to find and train individuals from underserved or disadvantaged backgrounds to become employees and leaders of our incubated companies.
  • Advise and teach all people about learning machines, in the university, hospital, local community, and in industry.
  • By the way, I will completely revolutionize the digital stethoscope in medicine.
  • Do you want to collaborate with me? Contact me.


  • Carnegie Mellon University

    Ph.D. Candidate in Electrical Computer Engineering (expected 2023)
    Advisors: Prof. Asim Smailagic, Prof. Aurelio Campilho

  • University of Porto

    Ph.D. Candidate in Electrical Computer Engineering (expected 2023)
    (via CMU Portugal dual PhD degree program)

  • Carnegie Mellon University

    M.S. in Electrical Computer Engineering (expected 2021)

  • Bard College

    Bachelor of Arts in Music, 2010

Current Publications

Journal, peer reviewed

Conference, peer reviewed


Invited Talk


Contact Me

I am interested in collaborations of many kinds and with people of a wide variety of different experiences and skill-sets. I'm especially focused on academic collaboration. I am also usually looking for students to help with research projects. I am also interested in industry collaborations related to digital stethoscopes.

Email: click@to.reveal.com