Sourabh Tirodkar

Working at Cedar Sinai Medical Center
Studying at University of Southern Californi
Living in United States

About

I am a Graduate student at University of Southern California- Viterbi School of Engineering majoring in Electrical and Computer Engineering graduating in May 2021.

The ongoing Master's degree at USC and Bachelor's in India has prepared me well enough with the technical knowledge and the skill set to implement and excel in the industry.

Previous industry experience during the internship has been very beneficial in terms of team working, ability to lead, problem solving and high proficiency in communication.

Being identified as intuitive, creative, life time learner, responsible and honest individual. I am actively looking for Internships in Signal/ Image Processing, Computer Vision and Machine Learning domain.

Keywords: Electrical and Computer Engineer, Image and Video Processing, Computer Vision, Machine Learning, Signal Processing, MATLAB, Deep Learning.

Experience
Research Assistant- Cedar Sinai Medical Center (BioImage Informatics Lab) May 2020 - Present
• Implemented Computer Vision and Machine Learning algorithms to whole slide images, by first anonymizing the slides, and feature extraction, texture segmentation to measure P values for making cluster of Paneth cells for 5 stages cancer classification using K-means.

Communication Analyst Intern - AGC Networks Ltd. June 2018
• Analyzed, deployed and maintained critical services for 12+ clients improving efficiency of VoIP systems by 80%.
• Collaborated with Service Engineers to troubleshoot and document technical details of networking (TCP/IP) systems and applications aiming to support clients scale operations of communication services and reduced cost by 30%.

Aircraft Electrical Overhaul Division Intern - AIR INDIA Engineering Services. December 2017
• Maintained, tested daily operations of aircrafts to study Traffic Collision Avoidance and Instrument Landing system.
Projects
1. Generative Model for Text - Machine Learning, Natural Language Processing
• Built a generative machine learning model to mimic the writing of prominent author by making text corpus of 7 books, encoded to extended ASCII code and implemented LSTM model with choosing window size and hidden layers and predicting 1000 characters for a sample text with a loss of 1.658.

2. Image Classification using Successive Subspace Learning – Image Processing & Deep Learning
• Modelled the LeNet5 architecture with Convolutional Neural Network and Successive Subspace Learning (SSL) varying the hyperparameters, optimizers to CIFAR10 dataset for image classification and achieving 82.54% testing accuracy.
• Concepts used: Python, Tensorflow, Keras, Deep Learning, PixelHop, PixelHop++.

3. Hand Postures (Motion Capture) Classification - Mathematical Pattern Recognition.
• Performed exploratory analysis to extract features from raw data of hand postures dataset then performing LDA for classification using classifiers like SVM, RF, KNN, Naïve Bayes, Perceptron and optimized performance by 95%.
• Enhanced better understanding of results by iteratively revising and using cross validations to get desired outcome using Python’s Scikit Learn and fine-tuning classification methodologies resulting in 100% verified output.

4. Image Quality Enhancement, Defect Detection, Half Toning – Digital Image Processing
• Implemented Bilinear Transformations and MHC methods to reconstruct full color image and ameliorated quality by 60% using denoising techniques and deployed warping algorithms improving image visualization by 65%.
• Executed morphological operations to count stars, PCB holes and paths to facilitate defect detection by Connected Component Labelling (CCL). Extracted image features by half toning and increased image printing efficiency by 86%.
• Technologies implemented: MATLAB, Denoising, Edge Detection, Image warping, CCL, Half toning algorithms.