cv

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General Information

Full Name Joseph David Viviano
Date of Birth 24th Jan 1987
Languages English

Education

  • 2018 - 2020
    MSc Machine Learning, Computer Science
    Université de Montréal, Montréal, Canada
    • Internship at Imagia with co-supervision of research by Yoshua Bengio.
    • Methods for controlling and utilizing saliency maps in medical imaging.
    • Work led to the ICLR 2021 paper "Saliency is a Possible Red Herring When Diagnosing Poor Generalization".
    • Coursework on RL, representation learning, machine learning, and applications.
  • 2011 - 2013
    MSc Neuroscience, Biology
    York University, Toronto, Canada
    • High resolution fMRI for characterization of subcortical inhibitory pathway activity with supervision by Keith Schneider.
    • Work led to the Journal of Neuroscience 2012 paper "Interhemispheric interactions of the human thalamic reticular nucleus".
    • Coursework on the biological basis of behaviour, statistics, and basic neuroscience.
    • Teaching included introductory biology & statistics.
  • 2005 - 2009
    BSc, Psychology
    Queen's University, Kingston, Canada
    • Focus on Biological & Cognitive Psychology, and Philosophy.
    • Coursework project on attentional blindness.

Experience

  • 2023 - Now
    Machine Learning Scientist (unofficially an Engineer)
    Yoshua Bengio's Group, Mila - Quebec AI Institute, Montreal, Quebec, Canada
    • Development of open-source GFlowNet tools such as torchgfn.
    • ML for Science research with a focus on Biology.
    • Ongoing project with Amgen using GFlowNets for antibody loop design.
  • 2021 - Now
    Technical Mentor
    Creative Destruction Lab, Montreal, AI Stream.
    • Performed technical assessments of startups and provided feedback for a business and investor focused audience.
    • Technical consulting for startups in the NLP, CV, and ML for Bio spaces.
  • 2021 - 2022
    ML Research Scientist
    Deep Genomics, Toronto, Ontario, Canada
    • Self-supervised learning (autoregressive and masked-language modelling) for biological sequence representations.
    • Multi-modal methods facilitating biological sequence-to-sequence models to generalize to novel cell types.
    • Anomaly detection pipeline for our drug screening platform.
  • 2020-2021
    Applied Research Scientist
    Applied Machine Learning Research Team, Mila - Quebec AI Institute, Montreal, Quebec, Canada
    • Consult on, propose, and implement deep learning solutions for Mila's partners in optimal financial portfolio allocation and digital forgery detection.
  • 2020
    PhD to VC Fellow
    Fifty Years, California, United States of America
    • Fellowship where I sourced, performed due diligence on, and supported technical recruitment, for deep-tech pre-seed/seed stage companies.
  • 2020
    Research Intern
    Google, Pittsburgh, Pennsylvania, United States of America.
    • Built a research pipeline to test ideas on an internal click through rate dataset.
    • Uncertainty (epistemic) estimation methods for the search ads predicted click through rate team.
  • 2019
    Research Intern
    Imagia, Montreal, Quebec, Canada.
    • Developed methods for combining clinical notes with medical images to improve classification performance.
    • Developed methods for localizing disease without explicit labels of where the disease is located in an image.
  • 2014 - 2017
    Research Methods Specialist
    Aristotle Voineskos' Group, Center for Addiction and Mental Health, Toronto, Ontario, Canada.
    • Biomarkers for vulnerable schizophrenia and Alzheimer's patients.
    • Designed, built, and managed (team of 5) a data management platform, 22-node compute cluster, and QA tools used by team of 20.
    • Contributions to 2 successfully funded grants & 15 published papers.
  • 2013 - 2014
    Research Assistant
    Gary Turner's Group, York University, Toronto, Canada
    • Development of a custom data pipeline platform.
    • Mentored graduate students in neuroimaging analysis approaches, and contributed to 2 publications.