Max Kaznady

Max Kaznady

Cambridge, Massachusetts, United States
3K followers 500+ connections

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About

Specialties: Computer Vision, Machine Learning, Data Science, Applied Mathematics…

Activity

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Experience

  • Microsoft Graphic

    Microsoft

    Redmond, Washington, United States

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    Cambridge, MA

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    Cambridge, MA

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    Toronto, Canada Area

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    Toronto, Canada Area

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    Toronto, Canada Area

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    Toronto, Canada Area

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    Toronto, Canada Area

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    Toronto, Canada Area

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    Toronto, Canada Area

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    Toronto, Canada Area

Education

  • University of Toronto Graphic

    University of Toronto

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    Activities and Societies: Teaching Assistant

    optimization methods, Gaussian copula factor model, C++, Boost, GSL, OpenMP
    - thesis: learning of Gaussian copula models for Collaterized Debt Obligation pricing
    - A+ course average (includes kernel methods and SVMs) & NSERC CGS-M scholarship

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    Activities and Societies: Varsity Tennis Team, Salsa, Researcher

    - quantum information publication in a top physics journal (Physical Review A)
    - technical paper on American option pricing with numerical PDE methods
    - 90% course average in last 20 courses (includes two graduate machine learning courses)

    Coursework includes, but is not limited to:
    regression analysis, mathematical statistics, probability theory, real analysis, time series analysis, spectral time series analysis, statistical computation, machine learning, linear algebra…

    - quantum information publication in a top physics journal (Physical Review A)
    - technical paper on American option pricing with numerical PDE methods
    - 90% course average in last 20 courses (includes two graduate machine learning courses)

    Coursework includes, but is not limited to:
    regression analysis, mathematical statistics, probability theory, real analysis, time series analysis, spectral time series analysis, statistical computation, machine learning, linear algebra, differential equations (PDEs and ODEs), numerical analysis, complexity and computability, introductory economics, etc.

Licenses & Certifications

Volunteer Experience

  • Massachusetts Institute of Technology Graphic

    Mentor

    Massachusetts Institute of Technology

    - 5 months

    Education

    MIT Post-Doctoral Association Mentor @CSAIL

  • Boston University Graphic

    BU Spark innovation program advisor

    Boston University

    - 9 months

    Education

  • University of Toronto Graphic

    Mentor - School of Graduate Studies, Departments of Physics & Statistics

    University of Toronto

    - 1 year 9 months

    Science and Technology

    Mentoring graduate and undergraduate students in the physical sciences regarding career and job opportunities outside of academia.

Publications

  • DeepSeismic: a Deep Learning Library for Seismic Interpretation

    European Association of Geoscientists & Engineers

    We introduce DeepSeismic, an open source Github repository (https://github.com/microsoft/seismic-deeplearning) that provides implementation of deep learning algorithms for seismic facies interpretation. The repository provides composable machine learning pipelines, that enables a data scientists and geophysicists to use state-of-the-art segmentation algorithms for seismic interpretation (e.g. UNet: Ronneberger et al. (2015) , SEResNet: Hu et al. (2018) , HRNet: Sun et al. (2019) ). We provide…

    We introduce DeepSeismic, an open source Github repository (https://github.com/microsoft/seismic-deeplearning) that provides implementation of deep learning algorithms for seismic facies interpretation. The repository provides composable machine learning pipelines, that enables a data scientists and geophysicists to use state-of-the-art segmentation algorithms for seismic interpretation (e.g. UNet: Ronneberger et al. (2015) , SEResNet: Hu et al. (2018) , HRNet: Sun et al. (2019) ). We provide scripts to reproduce benchmark results from running these algorithms using various public seismic datasets (Dutch F3, and Penobscot). Finally,the repository provides documentation, and quick start Jupyter notebook and Python scripts to enable the community to get started with seismic interpretation projects quickly. We believe the results in this paper provide a strong baseline on which others can build upon. To the best of our knowledge,these provide state-of-the-art result on Dutch F3 data set. We have released the code and the models in an open-source GitHub repository with permissive MIT license.

    See publication

  • Numerical strategies for quantum tomography: Alternatives to full optimization. [Quantum State Estimation]

    Physical Review A (PRA)

    We examine a variety of strategies for numerical quantum-state estimation from data of the sort commonly measured in experiments involving quantum-state tomography. We find that, in some important circumstances, an elaborate and time-consuming numerical optimization to obtain the optimum density matrix corresponding to a given data set is not necessary and that cruder, faster numerical techniques may well be sufficient; in other words, “the best” is the enemy of “good enough.”

    Other authors
    See publication

Patents

Languages

  • English

    Native or bilingual proficiency

  • Russian

    Native or bilingual proficiency

  • Ukrainian

    Native or bilingual proficiency

  • German

    Elementary proficiency

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