Mohammad Shokoohi-Yekta

Redmond, Washington, United States Contact Info
11K followers 500+ connections

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About

Senior Director of AI in BioTech with 10+ years of experience at FAANG companies…

Activity

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Experience & Education

  • HeartBeam Inc. (NASDAQ: BEAT)

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Volunteer Experience

  • Vice President of Student Affairs

    Graduate Student Association, CSE Dept., UC Riverside

    - 7 months

    Politics

Publications

  • Clustering in the Face of Fast Changing Streams

    SIAM International Conference on Data Mining (SDM 2016)

    Clustering is arguably the most important primitive for data mining, finding use as a subroutine in many higher-order algorithms. In recent years, the community has redirected its attention from the batch case to the online case. This need to support online clustering is engendered by the proliferation of cheap ubiquitous sensors that continuously monitor various aspects of our world, from heartbeats as we exercise to the number of mosquitoes visiting a well in a village in Ethiopia. In this…

    Clustering is arguably the most important primitive for data mining, finding use as a subroutine in many higher-order algorithms. In recent years, the community has redirected its attention from the batch case to the online case. This need to support online clustering is engendered by the proliferation of cheap ubiquitous sensors that continuously monitor various aspects of our world, from heartbeats as we exercise to the number of mosquitoes visiting a well in a village in Ethiopia. In this work, we argue that current online clustering solutions offer a room for improvement. To some degree they all have at least one of the following shortcomings: they are parameter-laden, only defined for certain distance functions, sensitive to outliers, and/or they are approximate. This last point requires clarification; in some sense almost all clustering algorithms are approximate. For example, in general, k-means only approximately optimizes its objective function. However, streaming versions of the k-means algorithm are further approximating this approximation, potentially leading to very poor solutions. In this work, we introduce an algorithm that mitigates these flaws. It is parameter-lite, defined for any distance function, insensitive to outliers and produces the same output as the batch version of the algorithm. We demonstrate the utility and effectiveness of our ideas with case studies in entomology, cardiology and biological audio processing.

    See publication

  • Applications of Mining Massive Time Series Data

    Lambert

    My first book ;)

    Other authors
    • Eamonn Keogh
    See publication

  • Discovery of Meaningful Rules in Time Series

    SIGKDD

    Video Presentation: https://www.youtube.com/watch?v=iUTVR635SF8

    Other authors
    See publication

  • Generalizing Dynamic Time Warping to the MultiDimensional Case Requires an Adaptive Approach

    Data Mining and Knowledge Discovery

    Other authors
    • Eamonn Keogh

  • Towards Wind Farm Performance Optimization through Empirical Models

    IEEE Aerospace

    Other authors

  • UCR-USV: Software Tools for Analyzing Mice Vocalizations with Applications to Pre-Clinical Models of Human Disease

    Neural Information Processing Scaled for Bioacoustics

    Other authors

  • Parameter-Free Audio Motif Discovery in Large Data Archives

    International Conference of Data Mining

    Other authors

  • Linear-time noise mining in large volumes of heart beat time series to predict death following heart attacks

    presented as a poster at SDM

    Other authors
    • E. J. Keogh

  • 3D Sound Production in Virtual Reality Lab

    first Professional Conference on Flight Simulation, Ministry of Science, Research and Technology

  • On the Non-Trivial Generalization of Dynamic Time Warping to the Multi-Dimensional Case

    SDM 2015

    Other authors
    • jun wang
    • Eamonn Keogh
    See publication

Patents

  • System and Method of Controlling Devices Using Motion Gestures

    Issued US 62/738,339

Honors & Awards

  • IDEA Finalist at Apple

    -

  • Dean’s Distinguished Fellowship, 2010-2012

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  • Dissertation Year Program Fellowship, 2014

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  • Faculty offer from University of San Diego, 2014

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  • GAANN Fellowship, 2012 & 2013

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  • Lifeguard Certificate, Life Saving Federation, Tehran, 2005

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  • SIAM Scholarship, 2012

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  • The Best Teaching Assistant Award for the Year, UCR, 2012

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Languages

  • English

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  • Persian

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  • Arabic

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  • Chinese

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