Dhanashri Bhole

Intro

I am Dhanashri Bhole, alumini of VNIT Nagpur from computer science master program, currently working as machine learning engineer at Sasken Technologies. I am a part of technology and solutions team. I have worked on PoCs for Tier 1 automotive,retailer,insurance companies. My objective is to work as professional,progressing with varied experiences which will help me to broaden my knowledge through consistent hard work and commitment to contribute effectively to the organization. Apart from my work I like to watch movies, listening to songs and hang out.

Work

@Sasken Technologies

I was a part of building PoC for Tier 1 Silicon company MediaTek where we developed camera based fruit recognition and detection for smart refrigerator. AI enabled refrigerator was presented at CES 2020, capable of analyzing the products being stored and intuitively answer questions provided by user. It is seamlessly integrated with Alexa providing live updates of content in fridge. I have also built PoC for insurance based company to classify voice based sentiments using machine learning. Exploratory data analysis has been done and emotions has been classified into positive and negative bucket using convolutional neural network.

@Persistent Systems

I worked on designing web application for multinational healthcare company to facilitate web data creation for scientists in Roche at user and admin level with technologies MVC, JavaScript with C#. I have aslo worked on Persistent System's internal project to design web application for HR professionals in Persistent Systems Ltd to facilitate on boarding process for new employees in an innovative way.

Academic Research Project

Skeleton based Recognition of Abnormal Human Activity in Bank ATM : Objective was to recognize abnormal human activity in ATM using skeleton data provided by Kinect sensor. It is achieved with two classification methodologies: Support Vector Machine and Dynamic Time Warping. Proposed solution achieved an accuracy of 95.24% using DTW (Dynamic Time Warping), 97.43% and 97.56% using SVM classification method when linear and polynomial kernels used respectively.

Skills

Programming Languages: Python, C, Java

Machine Learning Frameworks : Tensorflow, Keras, Opencv- DNN.

Tools : Python Jupyter Notebook, Git

IDE: Pycharm, Eclipse, Visual Studio, Android Studio

OS : Linux, Windows

Database : SQL, HBase.

Accomplishments

• LinkedIn
• GitHub

Elements

i = 0;

while (!deck.isInOrder()) {
    print 'Iteration ' + i;
    deck.shuffle();
    i++;
}

print 'It took ' + i + ' iterations to sort the deck.';