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Alex Gaudio

Explainable Machine Learning

for Medical Signal Analysis


Alex Gaudio researches explainable machine learning and signal processing and the analysis of heart and lung sounds. Alex is a postdoctoral fellow at Johns Hopkins University. In 2023, he received a Ph.D. from Carnegie Mellon University (CMU), a Ph.D. from the University of Porto, and an M.S. from CMU. He was honored as a Champion of Social Justice for his non-profit, NYC Makerspace in 2018, and he worked as a data scientist and engineer in New York City for seven years (2011-2018). 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
  • Heart and lung auscultation, pulmonary hypertension, data efficient ML, compression, localization, privacy
  • Non-invasive medical devices, especially the stethoscope

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

  • Develop next-generation non-invasive devices and AI technologies for heart and lung sound auscultation, with emphasis on prediction problems, haemodynamic analysis, and visualization.
  • Build real world applications that bundle the research with people, funding, IP and validation studies. I create the sufficient conditions to deploy my research.
  • Teaching and community service: I live to help others discover the thrill of playing with ideas and concepts, especially when these concepts feel impossible to learn or pursue. 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 (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