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Python Assistant

Clemson, SC
April 26, 2020

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Vihang • Clemson, Khare South Carolina • 917-***-**** • Education

Master of Science, Computer Engineering August 2020 Clemson University, Clemson, South Carolina GPA : 3.55/4.00 Bachelor of Engineering, Electronics and Telecommunication June 2017 University of Pune, Pune, India

Work Experience

Graduate Research Assistant Clemson University Summer 2019 – Spring 2020 Master’s Thesis

Thesis Topic: A camera based semi-automated method to learn appearance variability in machine parts for inspection

• Developed a system which learned the appearance variability of machine parts

• Using the learned variability, inspected 10 different parts of washing machines, on an assembly line, manufactured at the Samsung Electronics Home Appliances (SEHA) plant in Newberry, South Carolina.

• Implemented image triggering, image clustering and template matching

• Utilized various image processing techniques to solve problems like poor lighting, trigger failures, occlusions

• Reduced memory usage of the clustering algorithm by 97%, C++ Graduate Teaching Assistant Clemson University Fall 2017 - Spring 2019

• Conducted Basic Electrical Engineering Laboratories for undergraduate students at Clemson University Business Analyst Intern Lincare Holdings Summer 2018

• Queried sales data from the IBM DB2 using SQL and created a dashboard using Tableau Skills

• C, C++, Python, MATLAB

• OpenCV, Keras

Project Experience and Coursework

Computer Vision (C) Summer 2019 - Fall 2019

• Implemented template matching and image clustering to learn appearance variability in appearance of machine parts

• Built a character recognizer using Sobel edge detection filter Deep Learning Specialization (Python) Coursera Spring 2019

• Gained an overview of the different deep learning models and the current research in the field of deep learning

• Implemented RNNs and CNNs on test data provided by Coursera using Keras or TensorFlow General Purpose Computing on GPUs (C) Fall 2018

• Tested CPU vs GPU performance using CUDA and OpenCL on matrix multiplication

• Improved performance using different techniques like parallel reduction and atomics Tracking Systems (MATLAB) Fall 2018

• Implemented Kalman Filter, Extended Kalman filter and Particle Filter using MATLAB on test data Data structures (Python) Fall 2018

• Implemented linked lists, queue, stack, hash maps, trees and graphs in python

• Designed a Fibonacci heap class and implemented different priority queue operations

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