Prashanth Harshangi

Co-Founder at Enkrypt AI
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Location
Bengaluru, Karnataka, India, IN

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Experience

    • United States
    • Technology, Information and Internet
    • 1 - 100 Employee
    • Co-Founder
      • Nov 2022 - Present

      EnkryptAI: Securing machine learning, one model at a time. EnkryptAI provides security layer for proprietary and valuable ML models using a mix of state of art encryption and techniques developed in-house. We have made technological advancements which bring the model latency to practically feasible (over 90% less then previous SOA model encryption methods). As a result, this enables hassle-free sharing of models without ever concerning of model misuse, model theft, reverse-engineering or adversarial attacks. We are looking to democratize ML models and at the same time, ensure it’s security and integrity towards intended use. Show less

    • United States
    • Software Development
    • 1 - 100 Employee
    • Consultant
      • Aug 2022 - Jan 2023

    • Applied Scientist
      • Apr 2020 - Aug 2022

      - Strategic data science team lead to solve critical business problems with data science- Designed and led model optimization and compression product in a mlops platform- Built and integrated simple and advanced model deployment strategies - Canary/Shadow/Batch/RealTime in a mlops platform- Designed and built model packager solution to make model deployment much simpler- Contributed to efforts to onboard models onto a mlops platform, foundations for a risk platform- Provide guidance to data scientists and recent graduates to develop critical thinking- Early days for Vianai to have titles Show less

    • China
    • Motor Vehicle Manufacturing
    • 100 - 200 Employee
    • Staff AI Scientist
      • Oct 2019 - Feb 2020

      Led algorithm development of multi-variate time series forecasting and rare event detection algorithms from heterogenous data sources (CAN, RosBags, Images) using classical ML and probabilistic neural networksImplemented production ready Spark pipeline components for data ingestion, preprocessing, model training, kpis and visualization (end-to-end ML lifecycle) for rare event detectionLed implementation of a black box optimization product utilizing open-source packages for black-box optimization using Bayesian Optimization Show less

    • Senior AI Scientist
      • Dec 2017 - Sep 2019

    • United States
    • Higher Education
    • 1 - 100 Employee
    • AI Fellow
      • Jul 2017 - Oct 2017

      Developed a flask based web app for a visual question answering (VQA) system Developed a flask based web app for a visual question answering (VQA) system

    • United States
    • Higher Education
    • 700 & Above Employee
    • Graduate Research Assistant
      • Aug 2012 - Aug 2017

      Designed novel adaptive control algorithms to synchronize uncertain and multi-agent heterogenous ODEs Proposed new analysis methods to prove stability of proposed adaptive control algorithms Designed novel adaptive control algorithms to synchronize uncertain and multi-agent heterogenous ODEs Proposed new analysis methods to prove stability of proposed adaptive control algorithms

    • United States
    • 1 - 100 Employee
    • PhD Research Intern - Predictive Analytics
      • May 2016 - Aug 2016

      Invented innovative method to overcome challenges of Bayesian Optimization in higher dimensions using deep learning; Resulted in 50% reduction in costly function evaluations Invented innovative method to overcome challenges of Bayesian Optimization in higher dimensions using deep learning; Resulted in 50% reduction in costly function evaluations

    • Research Assistant
      • Jul 2009 - Aug 2010

      Implemented a novel DNN based controller to adaptively control a time-varying nonlinear system Analyzed and mapped various preprocessing image filters in different noisy environments for face recognition using PCA, SVMs and DNNs Implemented a novel DNN based controller to adaptively control a time-varying nonlinear system Analyzed and mapped various preprocessing image filters in different noisy environments for face recognition using PCA, SVMs and DNNs

    • Semiconductor Manufacturing
    • 400 - 500 Employee
    • Project Intern
      • Jan 2009 - May 2009

Education

  • Yale University
    Doctor of Philosophy (PhD), Adaptive Systems
    2012 - 2017
  • University of Southern California
    Master of Science (M.S.), Electrical and Electronics Engineering
    2011 - 2012
  • P.E.S Institute of Technology
    Bachelor of Engineering (B.E.), Electrical, Electronics and Communications Engineering
    2005 - 2009

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