Head of AI | Engineering the Future of Drug Discovery
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Machine Learning in Med-tech AI. Skills: Bioinformatics & Deep Learning. Data Science: Predictive Analytics, Data integration, & Data Visualization. Machine Learning: TensorFlow, Keras, Theano. Databases: MongoDB Cloud Technologies: AWS Programming Languages: Python, Java. Version Control: Git
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.
Cultural Fit Analysis
The candidate's career trajectory shows a strong focus on AI in healthcare and genomics, which aligns well with specialized, impact-driven organizations. Their experience in both startup (Frélii) and more established (GATC Health) environments suggests adaptability. The personal projects demonstrate initiative and a passion for applying ML to significant societal challenges. However, the breadth of experience outside of healthcare AI is limited, which might impact fit in roles requiring broader domain expertise or diverse project types.
Soft Skills & Operational Fit
The candidate's progression from Founding ML Engineer to Head of AI at GATC Health, coupled with product lead responsibilities, indicates strong leadership, project management, and strategic thinking abilities. Their involvement in sponsored challenges and research assistant roles suggests a proactive and problem-solving mindset. However, without psychometric test results, a detailed assessment of stress handling, work attitude, and team collaboration is not possible.