Shezan Rohinton Mirzan

MS in Computer Science · University of Massachusetts Amherst · Samsung Research America

Currently pursuing my Master's in Computer Science at University of Massachusetts Amherst. Previously employed at Samsung Research Bangalore as a Software Engineer to work with a research lab that involves in techniques like ML and Reinforcement Learning to enhance cognitive intelligence of devices. Experienced in engineering 'light-weight' Machine Learning Applications to run on embedded devices. Recently, also worked on implementing end-to-end Scala Applications to be deployed on large servers for data mining.

Open for Software Development Roles

Education

University of Massachusetts Amherst

Master of Science
Computer Science - Concentration in Data Science

GPA: 4.0

August 2019 - May 2021*

Indian Institute of Technology, Guwahati

Bachelor of Technology
Major : Electronics in Communication Engineering
Minor : Computer Science and Engineering

GPA: 9.03

August 2013 - May 2017

Experience

Machine Learning Engineer

Samsung Research America
Websocket-based client-server application for Anomaly detection on sensory data
  • Delivering an end-to-end client-server websocket-based application which can run anomaly detection algorithms for sensory data collected by wearable sensors to alert user.
  • Fetching of data using websocket application, real-time streaming to the server to run the model and reporting to the user in web-application format.
May 2020 - Present

Graduate Student Research

Microsoft
Interpretable Tree models for Anomaly Reasoning
  • Collaborated with Microsoft’s AI Development Acceleration Program for a comparative study between the open source SHAP solution for model-agnostic interpretations and Griffon’s tree-based reasoning solution created by Microsoft
  • If possible, extend SHAP to be used to interpret Griffon's reasoning by replacing Tree-based interpreter package native to Griffon to improve performance.
January 2020 - Present

Senior Software Engineer

Samsung Research Institute Bangalore
Pattern Analysis for Device Usage Statistics
  • Mined frequent device usage patterns using Apache Spark Framework for users from the SmartThings data on AWS EMR instances. Sharded Data on MongoDB to enable efficient GDPR implementation.
  • Implemented end-to-end Scala application running on Spark framework by using association rule mining. Project commercialized in 2019 with the release of Samsung Galaxy Note 10.
Lightweight User Presence Detection backend on powered Embedded devices
  • Designed Neural Network based Voice Activity Detection in C++ on TizenOS to detect human presence at Home for Smart Speakers. Used MRCG features and Tensorflow Lite to optimize time and memory. Conferred with performance award for reducing inference time by 5-folds.
Behavioral AI framework to enable user personalization in Social Robots
  • Designed and Implemented Behavioral Intelligence framework in C++/Python by jointly employing Neural Network alongside Q- Learning for implementation of User Personalization among robots to achieve 3X faster convergence with twice the accuracy against standard Reinforcement Learning Techniques.
July 2017 - August 2019

Skills

Programming Languages & Tools
  • C/C++
  • Python
  • Java/Javascript
  • Scala
  • R
  • MATLAB
Data & Machine Learning Tools
  • Apache Spark
  • PySpark
  • Hadoop
  • Numpy
  • Tensorflow
  • AWS
  • SkLearn
  • Keras
Miscellaneous
  • Agile
  • Git
  • LaTeX
  • MySQL

Projects

OSMI Mental Health Data Exploratory Data Analysis
  • Exploratory Data Analysis on the OSMI Mental Health Data to understand how mental health is viewed in the Tech industry, the spread of mental disorders among the employees and to gauge the system present to tackle these conditions.
  • D3 based visualizations based on the survey results of 3 different years implemented to gain a multi-faceted view of our data. Site hosted at shezanmirzan.github.io/DataVis-Mental-Health
Deep Multiple Instance Leaning based Video Classification
  • Developed Anomaly detection algorithm for classifying real - Surveillance videos that spanned across different scenes.
  • Converted the classification problem to a regression task by extracting C3D features and feeding it to deep Multiple Instance Learning based architecture to get higher scores on video segments that contained anomaly.
  • Tried different model architectures and feature extraction and compared ROC curves to decide on the best model.
Tracking of Multiple Skin-Colored Objects Under Occlusion
  • Developed Real-time tracking of skin coloured objects framework in MATLAB using object hypothesis tracking. removal and synthesis.
  • Found applications in the field of hand-gesture recognition to detect gestures involving occlusion

Patents & Publications

A Control System for a Health Monitoring System
  • Inventors : Shezan R. Mirzan, Jay Sharma
  • Pubslished with Indian Patent Office #20184103833