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Alex Gaudio

Explainable Machine Learning

for Medical Image Analysis

Bio

My research focuses on explainable machine learning for medical image analysis. I am a dual degree PhD candidate at Carnegie Mellon University and at the University of Porto. I also co-founded a non-profit, NYC Makerspace, to bring together diverse communities of people, give them advanced tools and education, and ignite individuals with the life-long passions to innovate, teach as a means of learning, and have fun by helping their friends grow. Previously, I worked as a data scientist and engineer in New York City for 7 years. I studied in jazz performance and composition at Bard College. I am fascinated by the ways people create and interpret their life paths.

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.

Research Interests

  • Explainable Deep and Machine Learning
  • Computer Vision
  • Signal Processing
  • Medical Image and Signal Analysis

Five Year Plan:

  • Develop next generation machine learning, vision, and signal processing algorithms, primarily for medical image and signal analysis.
  • Lead an R&D lab focused on explainable deep and machine learning for medical signal analysis
  • Incubate a community of uncommonly diverse individuals for real world application of our research, targeting sustainable growth in underserved communities.
  • Teach all people about learning machines, in the university, in the local community, local hospital and in industry.
  • Inspire amazing people like you to join me in accomplishing these goals.

Education

  • 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 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

Patent

Invited Talks

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.

Email: click@to.reveal.com