Stanford University

Stanford, CA

Hello! I'm

Varun Shenoy

I am a sophomore at Stanford University pursuing a career at the crossroads of mathematics, electrical engineering, and software. I'm interested in embedded systems, product design, philosophy, and technology applications in high-impact verticals (construction, space, climate change, healthcare, and security, to name a few). Long story short, I like to build things!

My current focus is split between automating circuit design with machine learning and democratizing mobile health. Reach out if you would like to grab a cup of coffee and chat!



Open Source Lead

Building a better platform for creating, managing, and scaling digital health apps at CardinalKit.

April 2020 - Present

Undergraduate Research Intern
Stanford HCI + Graphics

Developing machine learning methods to optimize fixed-function hardware. Significant area improvements over existing non-linear optimization techniques. Pending publication.

March 2020 - Present

Research Consultant
Stanford University | School of Medicine

Collaborating with Stanford Radiology to develop a web tool for automating radiology tasks utilizing deep learning and computer vision.

August 2018 - May 2020

Research Intern
Berkeley Artificial Intelligence Research

Applying computer vision to medical imaging. Designing deep neural networks for object detection in medical images and researching domain adaptation techniques for medical image synthesis.

Summer 2018

Data Scientist
Palo Alto Veterans Affairs Hospital

Currently collaborating with medical professionals to develop an automated method for assessing postoperative wounds using artificial intelligence.

June 2017 - June 2019

App Developer
Apple App Store

Building mobile applications for the App Store to solve a wide variety of problems. Currently have over 20,000 downloads for all my apps combined from users all over the world. Attended WWDC in 2016, 2017, and 2018 on scholarship.

August 2015 - Present


Automated Wound Assessment from a Smartphone

Theia is a deep learning based system for automated postoperative wound assessment with a convolutional neural network based backend and iOS frontend. It has won multiple awards at a variety of venues.

Embedded News Summarization using Machine Learning

Summit is an iOS app that summarizes the daily news. It helps busy people read the news in a matter of seconds. It has over 17,000 downloads and won me an Apple Scholarship to the Worldwide Developers Conference in 2016. It also won the 2017 Congressional App Challenge in the CA-17 (Silicon Valley) district.

Providing Intelligence to "Dumb" and Inexpensive Medical Monitors

Biosnap enables you to capture medical monitor data with a picture and store it automatically in your Health app swiftly without the usage of the mobile keyboard. With Biosnap, there's no reason to buy an expensive internet-connected medical monitor or maintain a log of measured biomarkers.

Precision Diagnosis of Bone Tumors on Radiography Using Bayesian Modeling of Radiological Observations Derived from Quantitative Image Analysis

During the month of August in 2018, I developed a web tool for bone x-ray annotation as well as the backend infrastructure for image masking and cropping. It will fit into the full application pipeline which includes a machine learning system to clasify bone features and tumors using the masked regions.

Varun Shenoy and other authors from Stanford University (2018)
Publication pending
A Novel Domain Adaptation Framework for Medical Image Segmentation
Brain Tumor Segmentation Challenge 2018

I spent the summer of 2018 at the UC Berkeley Artificial Intelligence Research (BAIR) Lab building 3D and 2D renditions of segmentation architectures for automated brain tumor semantic segmentation in MRI scans. I set up a 3D Unet for training with patch normalization and percentile thresholding. I developed a variety of different 2D segmentation algorithms to evaluate axial brain slices one at a time, including CycleGAN generated data. Finally, I implemented a masking strategy that leverages whole tumor segmentation maps to bolster dice scores on enhancing tumor and tumor core segmentations.

Amir Gholami, Shashank Subramanian, Varun Shenoy, Naveen Himthani, Xiangyu Yue, Sicheng Zhao, Peter Jin, George Biros, Kurt Keutzer (2018)
Accepted to Springer Lecture Notes in Computer Science (LNCS)
BraTS '18 Competition Paper
Deepwound: Automated Postoperative Wound Assessment and Surgical Site Surveillance through Convolutional Neural Networks

The incidence of postoperative wound infections after lower extremity bypass can be as high as 10%-20%. An automated method of diagnosing wound complications would serve to limit the expense of time and money from hospitals, doctors, insurers, and patients. The algorithmic classification of wound images, due to variability in the appearance of wound sites, is a challenge. Deep convolutional neural networks (CNNs), a subgroup of artificial neural networks that exhibit great promise in the analysis of of visual imagery, may be leveraged to categorize surgical site wounds. We present Deepwound, a multi-label CNN trained to classify wound images with image pixels and labels as the sole inputs.

Varun Shenoy, Elizabeth Foster, Lauren Aalami, Bakar Majeed, Oliver Aalami (2018)
Accepted to 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
Plenary Session Presentation at the 2018 Surgical Infection Society Annual Meeting (Top 10 Papers)
Patent pending under US 62/670,970
Utilizing Smartphone-Based Machine Learning in Medical Monitor Data Collection: Seven Segment Digit Recognition

Biometric measurements captured from medical devices, such as blood pressure gauges, glucose monitors, and weighing scales, are essential to tracking a patient's health. Trends in these measurements can accurately track diabetes, cardiovascular issues, and assist medication management for patients. Currently, patients record their results and date of measurement in a physical notebook. It may be weeks before a doctor sees a patient’s records and can assess the health of the patient. This research presents a mobile application that enables users to capture medical monitor data and send it to their doctor swiftly using HealthKit. A key contribution of this paper is a robust engine that can recognized digits from medical monitors with an accuracy of 98.2%.

Varun Shenoy, Oliver Aalami (2017)
Oral Presentation at the 2017 American Medical Informatics Association Annual Symposium


CS 106B Programming Abstractions in C++

CS 107E Computer Systems from the Ground Up

PHYSICS 61 Mechanics and Special Relativity

PHYSICS 63 Electricity, Magnetism, and Waves


MATH 51 Linear Algebra and Multivariable Calculus

MATH 52 Integral Calculus of Several Variables

MATH 53 Ordinary Differential Equations

MATH 115 Functions of a Real Variable


POLISCI 131L Modern Political Thought

ESF 15 College and the Good Life

MSE&E 178 + 472 The Spirit of Entrepreneurship

HUMBIO 51 Big Data for Biologists


WWDC Scholarship 2018

Apple Inc.

Recognized as a Young Innovator to Watch

Consumer Electronics Show 2018

$1,000 Academic Scholarship

WWDC Scholarship 2017

Apple Inc.

Overall Winner

2017 Congressional App Challenge

California's 17th District (Silicon Valley)

Global Oncology Grand Prize

health++ 2016

for Dermyx

WWDC Scholarship 2016

Apple Inc.


Google Science Fair 2019 Regional Finalist

Top 100 Submissions Worldwide

for Deepwound

2019 Cutler-Bell Prize in High School Computing


$10,000 Academic Scholarship

Lead Author of Publication

IEEE International Conference on Bioinformatics and Biomedicine 2018

for Deepwound

Top 10 Abstracts

Surgical Infection Society Annual Meeting 2018

for Deepwound

3rd Place

Northern California + Nevada JSHS 2018

$1,000 Academic Scholarship

Lead Author of Publication

American Medical Informatics Association Annual Symposium 2018

for Biosnap