Vaishnavi Narayan

Brigham Young University

Master's degree, Computer Science

January 1, 2016 – January 1, 2018

Visvesvaraya Technological University

Bachelor’s Degree, Computer Engineering

January 1, 2011 – January 1, 2015

GATC Health

Head of AI

October 1, 2025 – Present

GATC Health

Principal AI Engineer & Product Lead

December 1, 2023 – October 1, 2025

GATC Health

Sr. Machine learning Engineer & Product Manager

January 1, 2021 – December 1, 2023

Frélii

Sr. Machine Learning Engineer

March 1, 2019 – December 1, 2020

Frélii

Founding Machine Learning Engineer

March 1, 2018 – February 1, 2019

Brigham Young University

Graduate Research Assistant

August 1, 2016 – April 1, 2018

Provo, Utah, United States

IBM

Technical Analyst

January 1, 2016 – January 1, 2016

Bengaluru, Karnataka, India

Toxic Comment Classification Challenge - Sponsored by Jigsaw, A Google Company

January 1, 2018 – April 1, 2018

Discussing things you care about can be difficult. The threat of abuse and harassment online means that many people stop expressing themselves and give up on seeking different opinions. Platforms struggle to effectively facilitate conversations, leading many communities to limit or completely shut down user comments. The Conversation AI team, a research initiative founded by Jigsaw and Google (both a part of Alphabet) are working on tools to help improve online conversation. One area of focus is the study of negative online behaviors, like toxic comments (i.e. comments that are rude, disrespectful or otherwise likely to make someone leave a discussion). So far they’ve built a range of publicly available models served through the Perspective API, including toxicity. But the current models still make errors, and they don’t allow users to select which types of toxicity they’re interested in finding (e.g. some platforms may be fine with profanity, but not with other types of toxic content). In this project, we're building a multi-headed model that’s capable of detecting different types of toxicity like threats, obscenity, insults, and identity-based hate better than Perspective’s current models. We will be using a dataset of comments from Wikipedia’s talk page edits. Technologies Used: MongoDB, NLTK, NLP, Keras, Glove, NumPy, SciKit-Tensor

Kaggle Data Science Bowl 2017 - Lung Cancer Detection; Sponsored by - Booz | Allen | Hamilton

January 1, 2017 – March 1, 2017

Lung cancer is the most common type of cancer worldwide, affecting nearly 225,000 people each year in the United States alone. Low-dose computed tomography (CT) is a breakthrough technology for early detection, with the potential to reduce lung cancer deaths by 20 percent. But, the technology must overcome a relatively high false positive rate. Using anonymized high-resolution lung scans in one of the largest data sets to be made publicly available, provided by the National Cancer Institute (NCI), participants created algorithms that can improve lung cancer screening technology. We created algorithms that could accurately determine when lesions in the lungs are cancerous and dramatically decrease the false positive rate of current low-dose CT technology. We were able to achieve an accuracy of 88%. Dataset Used: LUNA16 Technologies used: TensorFlow, Keras(backend), NumPy, SciKit-image, SciKit-learn, MatPlotLib, PyDicom, SimpleITK

Optimization of routing in Distributed Sensor Networks using heuristic techniques

January 1, 2015 – April 1, 2015

Distributed Sensor Network consists set of distributed nodes having the capability of sensing, computation, and wireless communications. Power management, various routing, and data dissemination protocols have been specifically designed for DSN, where energy consumption is an essential design issue for routing. Optimization of routing method is an essential for routing of DSN because of long communication distances between distributed sensor nodes and the sink node in a network can greatly drain the energy of sensors and decrease the lifetime of the network. In this project, simulation is carried out for optimization of routing in DSNs using NS-2, the network simulation software. The objective is to maximize the network lifetime and improve the energy efficiency using a heuristic technique. A proposed Genetic Algorithm based routing protocol is used for solving an optimization through the evolution of genes parameters, which are coded by strings of characters or numbers and genetic operations (selection, crossover, and mutation) are iterated. Finally, the performance parameters for the proposed scheme are evaluated and are shown in terms of energy and routing efficiency, time computation and network lifetime.