Sarath Shekkizhar

EEB 441, 3740 McClintock Ave., Los Angeles, CA 90089 shekkizh shekkizh


Ph.D. in Electrical and Computer Engineering, M.S. in Computer Science Aug 2017 - Present

GPA: 3.93

University of Southern California, Los Angeles, CA
Advisor: Antonio Ortega.

M.S. in Electrical Engineering (Computer Vision, Machine Learning) Aug 2012 - Dec 2013

GPA: 3.86

University of Southern California, Los Angeles, CA

B.Tech. in Electronics and Communication July 2008 - June 2012

GPA: 9.12

National Institute of Technology, Tiruchirappalli, India


Efficient graph construction for image representation [Best student paper]

S. Shekkizhar, A. Ortega, IEEE International Conference on Image Processing (ICIP), 2020

Graph-based Deep Learning Analysis and Instance Selection

K. Nonaka, S. Shekkizhar, A. Ortega, IEEE International Workshop on Multimedia Signal Processing (MMSP), 2020

DeepNNK: Explaining deep models and their generalization using polytope interpolation

S. Shekkizhar, A. Ortega, arXiv Preprints (under review), 2020

Graph Construction from Data by Non-Negative Kernel Regression

S. Shekkizhar, A. Ortega, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020

Graph construction from data using non negative kernel regression (NNK graphs) - Journal draft

S. Shekkizhar, A. Ortega, arXiv Preprints, 2019

M. Plihal, E. Soltanmohammadi, S. Paramasivam, S. Ravu, A. Jain, S. Shekkizhar, P. Uppaluri, US Patent Office, 2019

Detection and removal of Salt and Pepper noise in images by improved median filter

S. Deivalakshmi, S. Shekkizhar, P. Palanisamy, IEEE Recent Advances in Intelligent Computational Systems, 2011

Work Experience

Software Engineer, KLA Tencor, Milpitas, CA Mar 2014 - Oct 2016

Designed and developed tools to classify and visualize defect modulations for Process Window Qualification in wafer fabrication. Also, implemented and co-owned components for analysis and classification using decision trees and Random forests

Freelance Researcher, Toonchat (Demo: Jan 2014 - Jun 2014

Researched and worked with animators and researchers remotely on a real time face tracking under the advise of Dr. Eric Petajan for low bandwidth animations and anonymous video chat clients

Software Developer, Laboratory of Neurological Imaging, USC ( Aug 2013 - Dec 2013

Worked under the supervision of Dr. John Van Horn as part of the Information Visualization project which is a platform to interact and research on large amounts of brain data

Intern, Bharat Heavy Electrical Ltd, India June 2011 - July 2011

Designed an assembly level microcontroller program to measure the bend angles in pipes of different sizes.

Other Research Works

Manifold embedding using NNK Graphs Jan 2020 - May 2020

Revisited data embedding methods using graphs in terms of robustness and stability with respect to hyperparameters. NNK graphs are significantly sparser compared to other graph constructions, while being able to capture the structure of noisy manifolds.

Manifold Regularized Variational Autoencoder Aug 2019 - Dec 2019

Studied disentanglement in variational autoencoders with explicit regularization using graphs. This work was motivated from the perspective of locality often enforced in autoencoders using noisy sampling of embeddings.

Are combined Fuzzy Cognitve Maps (FCM) always better than individual maps? Aug 2018 - Dec 2018

Analysed the performance of decisions taken by individuals in a simple game against that of the additive. Combined FCM reduces the effect of error associated with each individual.

Impact of lp-norm choice on K-nearest neighbor graph construction Jan 2018 - May 2018

Explored the effectiveness of distance norms for K-nearest neighbor graph construction in high dimensional spaces using eigen analysis. Lower norms produce separate data clusters better than euclidean and higher norms.

Graph based Image Segmentation, Prof. Antonio Ortega Aug 2013 - Dec 2013

Performed experiments and analysis on graph based approach to Image Segmentation. The core idea was to leverage on methodologies for finding approximate Fiedler vector on a Graph Laplacian as an alternative to doing normalized cuts.

3D Face Recognition System, Dr. Jongmoo Choi, Prof. Gerard Medioni May 2013 - Aug 2013

Developed on the core recognition library and created an evaluation framework and data set for benchmarking. Made integration efforts on incorporating 3D modelling module for recognition.

Dynamic Face Warping, Prof. Antonio Ortega Jan 2013 - June 2013

Implemented a real time facial tracking and warping module in DaVinci DSP board. The project emphasized working under constrained resources and was targeted towards applications in mobile.

Optical Character Recognition, Prof. S. Deivalakshmi Jan 2012 - June 2012

A simple neural network based character recognition system for use in Title search and License Plate recognition was developed. The system was evaluated with different font sizes and types. Also analysis was done on the effect of various hyper parameters and the loss manifold.

Classification of Mammograms, Prof. S. Deivalakshmi Aug 2011 - Dec 2011

A method to differentiate and identify the nature of tumor in Mammograms based on discriminant analysis on extracted features was developed and evaluated on the MIAS database.

Side Projects

Deeplearning Projects using Tensorflow (

Highlights: DCGAN for generating flowers/ faces, Face completion using context, Deep dream, VGG visualization, Image Inversion, Neural Style Transfer

Neural Networks Experiments (

Experiments on Activation functions, Model Pruning (Optimal Brain Damage), Unsupervised Learning using AutoEncoders, VAEs, GANs

Fully Convolutional Networks for Semantic Segmentation (

Tensorflow implementation of FCNs for segmentation as in CVPR paper applied on MIT scene parsing challenge dataset

Energy Based Generative Adversarial Networks (

Model implementation of Junbo et. al’s paper of training GAN with energy based objective in tensorflow

Image Colorization (

Experiments on leveraging CNNs for colorizing grayscale images by statistical knowledge gained about objects and colors from a dataset.

Image Processing Projects (

Highlights: Eye Tracking, Facial Tracking and Localization, Seam carving, Image Stitching, Image calibration, Image filters, Object detection and processing for various use cases

Teaching Experience

Course Producer, CS 561 Foundations of Artificial Intelligence, Dr. Sheila Tejada Fall 2013

Course Grader, EE 483 Introduction to DSP, Prof. Edgar Satorius Summer 2013

Course Grader, EE 483 Introduction to DSP, Prof. Edgar Satorius Spring 2013

Academic and Co Curricular Activities