Sarath Shekkizhar, Ph.D.

San Francisco Bay Area, California shekkizh.github.io shekkizh@usc.edu shekkizh shekkizh

Staff (Data) Scientist, Salesforce October 2024 - Present

Working on foundational and applied research on AI agents with focus on improving reasoning and safety.

Education

Ph.D. in Electrical and Computer Engineering Aug 2017 - May 2023

GPA: 3.93

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

M.S. in Computer Science Aug 2017 - May 2022

GPA: 4.0

University of Southern California, Los Angeles, CA

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

Publications

Reasoning in Large Language Models: A Geometric Perspective

R Cosentino, S Shekkizhar, arXiv Preprints, 2024

Characterizing Large Language Model Geometry Solves Toxicity Detection and Generation

R Balestriero, R Cosentino, S Shekkizhar, International Conference on Machine Learning (ICML), 2024

Towards a geometric understanding of Spatio Temporal Graph Convolution Networks

P. Das, S. Shekkizhar, A. Ortega, IEEE Open Journal of Signal Processing, 2024

Data Sampling using Locality Sensitive Hashing for Large Scale Graph Learning

S. Shekkizhar, N. Bulut, M. Farghal, S. Tavakkol, M. Bateni, A. Nandi, International Workshop on Mining and Learning with Graphs, Conference on Knowledge Discovery and Data Mining (KDD), 2023

A data-driven graph framework for geometric understanding of deep learning

S. Shekkizhar, A. Ortega, Graph Signal Processing Workshop 2023, 2023

Study of Manifold Geometry using Multiscale Non-Negative Kernel Graphs

C. Hurtado, S. Shekkizhar, J. Ruiz-Hidalgo, A. Ortega, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2022

The geometry of self-supervised learning models and its impact on Transfer learning

R. Cosentino, S. Shekkizhar, M. Soltanolkotabi, S. Avestimehr, A. Ortega, arXiv Preprints, 2022

NNK-Means: Data summarization using dictionary learning with non-negative kernel regression

S. Shekkizhar, A. Ortega, IEEE 30th European Signal Processing Conference (EUSIPCO), 2022

Channel redundancy and overlap in convolutional neural networks with Channel-wise NNK graphs

D. Bonnet, A. Ortega, J.Ruiz-Hidalgo, S.Shekkizhar, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2022

Channel-Wise Early Stopping without a ValidationSet via NNK Polytope Interpolation

D. Bonnet, A. Ortega, J.Ruiz-Hidalgo, S.Shekkizhar, Asia Pacific Signal and Information Processing Association (APSIPA), 2021

Model selection and explainability in neural networks using a polytope interpolation framework

S. Shekkizhar, A. Ortega, _Asilomar Conference on Signals, Systems, and Computers _, 2021

Revisiting local neighborhood methods in machine learning

S. Shekkizhar, A. Ortega, IEEE Data Science and Learning Workshop (DSLW), 2021

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, 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

Neighborhood and Graph Constructions using Non-Negative Kernel Regression

S. Shekkizhar, A. Ortega, arXiv, 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

Patents

Machine learning model compression, US18905761, Pending

R. Cosentino, D. Kalajdzievski, S. Shekkizhar, A. Earle, Filed: October 2024

Training a target activation sparsity in a neural network, US18802235, Pending

D. Kalajdzievski, R. Cosentino, S. Shekkizhar, A. Earle, Filed: August 2024

Domain aware large language model governance, US18745562, Pending

S. Shekkizhar, A. Earle, Filed: June 2024

Fine-tuning machine learning models while retraining accumulated knowledge, US18496698, Pending

R. Cosentino, S. Shekkizhar, A. Earle, D. Kalajdzievski, J. Weissenberger, I. Arel, Filed: October 2023

Data sampling using Locality Sensitive Hashing for large scale graph learning, US63517869, Pending

S. Shekkizhar, N. Bulut, M. Farghal, S. Tavakkol, M. Bateni, A. Nandi, Filed: August 2023

M. Plihal, E. Soltanmohammadi, S. Paramasivam, S. Ravu, A. Jain, S. Shekkizhar, P. Uppaluri, Filed: October 2017

Work Experience

Member of Technical Staff, Tenyx (Acquired by Salesforce) June 2023 - October 2024

Part of the founding team at Tenyx building Voice AI for customer support. Was primarily focused on research and creating intellectual property. Key accomplishments include research on continual learning [VB article], building TenyxChat series of models [VB article], and geometric characterization of LLMs [HN Discussion]. Was also involved in product development, particularly in endpointing, audio disambiguation, and agent governance.

Research Intern, Google, Sunnyvale, CA Sep 2022 - Dec 2022

Host: Mohamed Farghal, Animesh Nandi, Behavior Protections, Counter-Abuse Technology.
Worked on understanding the impact of input data used in training graph models and scalable sampling approaches to improve semi-supervised graph learning. Preliminary experiments with proposed graph learning showed 3x increased recall in abuse detection.

Software Engineer 2, 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: youtu.be/B7LyoWksHHE) Jan 2014 - Jun 2014

Researched and worked with animators and researchers on 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 (invizian.loni.usc.edu) Aug 2013 - Dec 2013

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

Co-Mentoring

Carlos Hurtado Comín, Universitat Politècnica de Catalunya (Visiting Researcher, USC) Mar 2022 - June 2023

Aryan Gulati, University of Southern California (CURVE program, USC) Aug 2021 - June 2023

David Bonet Solé, Universitat Politècnica de Catalunya (Visiting Researcher, USC) Dec 2020 - Aug 2021

Keisuke Nonaka, KDDI Research (Visiting Researcher, USC) Aug 2019 - July 2020

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, Spring 2013

Academic and Co Curricular Activities