Stanford University

Stanford, CA

Hello. I'm

Varun Shenoy

I am a freshman at Stanford University pursuing a career at the crossroads of computer science, electronics engineering, and healthcare. I strive to build compelling products, design clean user experiences, and learn about new technologies. My current research focus is applying cutting-edge machine learning technologies to the frontlines of healthcare.



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 (paid position).

August 2018 - Present

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

Eagle Scout
Troop 407, Cupertino, CA

Eagle Scout with Silver Palm from Troop 407 from Cupertino with over 40 merit badges, from Fishing to Electronics. Inducted into the Order of the Arrow, the Boy Scout honor society.

August 2011 - June 2019

Online Editor
The Prospector

Redesigned the online side of Cupertino High School's student-run publication at

August 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

Curriculum Developer
TechLab Education

Developed comprehensive iOS curriculum for TechLab Education covering everything from basic Swift programming to web queries and Firebase. Taught iOS students a variety of concepts, from maps to database management.

Summer 2016


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.

Skin Lesion Diagnosis via Deep Learning

Dermyx uses machine learning for bring rapid melanoma diagnosis on a desktop and mobile app. Dermyx was the only high school based team, competing against university medical and CS students, at Stanford health++ 2016. Dermyx won the Global Oncology Grand Prize.

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.

Fundraising for Clubs Made Simple

Agora is a mobile application that enables clubs on campus to raise money by holding digital yard sales. It was built for the Future Business Leaders of America (FBLA) 2017 Mobile Application Development competition, winning 1st place in California and 2nd place at Nationals.

Pocket Chemistry Assistant

Nucleon is the go to chemistry app for high school and college students taking any chemistry course, including AP® Chemistry. It provides a gateway to a wide variety of resources, including a exhaustive periodic table, molar mass calculator, ion chart, molecular composition calculator, and a personal MSDS database builder, all wrapped within a beautifully designed and immersive user interface.

Feynman Library Manager
Simplifying the Library Managment Process

Feynman Library Manager is the first simple to use, no hassle, library managment software. Librarians can easily track books, patrons, and fines in an immersive user interface. It was built for the Future Business Leaders of America (FBLA) 2018 Coding & Programming competition, winning 1st place in California.

Connecting Refugees with Shelters Worldwide

In November 2015, multiple terrorist attacks resulted in the deaths of over 120 innocent French citizens. Society’s response was the “PorteOuverte” initiative, an attempt to shelter victims of the attacks. The government has acknowledged that this response was ineffective due to discord between safe-house “hosts” and authorities. Our overall aim is to provide those in need with temporary shelter and supplies, and expedite the rescue of refugees all over the globe.

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
A Machine Learning Model for Essay Grading via Random Forest Ensembles and Lexical Feature Extraction through Natural Language Processing

Written assignments provide a greater degree of assessment on the student’s depth of the subject matter and promote deep cognitive analysis. Often, the same essay prompts are recycled year after year. The cultivation of manual data from essays on the same prompt year after year can help teachers create a rapid, autonomous method to grade essays through the fields of statistics and machine learning. This research proposes a novel automated method to grade essays for syntactical correctness through training a machine learning model. The final model was able to achieve a quadratic weighted kappa score of 0.96 across all essay sets, a near perfect agreement with the human rater. Therefore, this research represents a near human-level accuracy in essay scoring.

Varun Shenoy (2016)


WWDC Scholarship 2018

Apple Inc.

1st Place

2018 California FBLA State Leadership Conference

Coding & Programming

Recognized as a Young Innovator to Watch

Consumer Electronics Show 2018

$1,000 Academic Scholarship

2nd Place

FBLA Nationals 2017 (Anaheim, CA)

Mobile Application Development

WWDC Scholarship 2017

Apple Inc.

1st Place

2017 California FBLA State Leadership Conference

Mobile Application Development

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.

1st Place

2016 California FBLA State Leadership Conference

Computer Game & Simulation Programming

Most Appealing App Design

2016 Congressional App Challenge

California's 17th District (Silicon Valley)


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

2nd Place in Posters

National JSHS 2018

Computer Science + Math

Top 10 Abstracts

Surgical Infection Society Annual Meeting 2018

for Deepwound

3rd ACM Student Prize

Synopsys Science Fair 2018

for Deepwound

1st Prize, Morgan Lewis

Synopsys Science Fair 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