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Alex Gaudio

Explainable Machine Learning

for Medical Signal Analysis


Alex Gaudio researches explainable machine learning for the analysis of medical signals pertaining primarily to the heart, lungs, vascular system and eyes. Alex is currently a postdoctoral researcher at Johns Hopkins University. He has a PhD from Carnegie Mellon University (CMU), a PhD from the University of Porto, and an MS from CMU. He was honored as a Champion of Social Justice for his non-profit, NYC Makerspace. He worked as a data scientist and engineer in New York City for 7 years prior to the PhD. He received a B.A. in jazz performance and composition at Bard College, during which time he also worked as an Emergency Medical Technician in ambulances and in a hospital.

Advice to myself and to my friends and colleagues: 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. Setup a safety net, and then jump!

Multi-disciplinary Research Interests

  • Explainable Deep Machine Learning
  • Medical Image and Signal Analysis
  • Non-invasive medical devices, especially the stethoscope
  • Heart and lung auscultation, pulmonary hypertension, data efficient ML, compression, localization, privacy

My Five-Fifteen Year Plan to bridge University, Medical School and Industry:

  • Establish an applied research and teaching lab jointly in a university and medical school to study explainable machine learning pertaining to heart and lung medical signals, with particular emphasis on machine learning for non-invasive medical devices like the digital stethoscope.
  • Spin research ideas into companies. I don't want to run the companies, but I want to be part of some sort of academic incubator.
  • Give to my communities: Establish a non-profit to find and train individuals from underserved or disadvantaged backgrounds. I hope some of these individuals become employees and leaders of incubated companies we create. Advise and teach all people about learning machines, in the university, hospital, local community, and in industry.
  • Over the course of my career, I strive to bridge academia with medicine, industry, government, non-profits, and local community to drive political reform, shape the directions of future research, enhance incentives for academic research, and help shape the communities I am a part of or close to.
  • Let's collaborate. Contact me.


  • Johns Hopkins University

    Postdoctoral Researcher at the Computational Audio Perception Lab (Fall, 2023)
    Advisor: Prof. Mounya Elhilali

  • Carnegie Mellon University

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

  • University of Porto

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

  • Carnegie Mellon University

    M.S. in Electrical Computer Engineering (2023)

  • 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