I am a first year graduate Biomedical Engineering student at the Johns Hopkins University. I currently work on developing tools to detect overfitting of Neural Network generated Ultrasound Images at the Photo acoustic and Ultrasonic Systems Engineering Lab under Prof Muyinatu Bell. During my undergraduate degree at the Indian Institute of Information Technology, Design and Manufacturing, Kancheepuram, I was advised by Prof Sudhir Varadarajan and Prof Karthic Narayanan.

I am currently exploring problems in Biomedical Device Design for Prosthesis, Imaging and Point-of-Care Diagnosis. I work on utilising data analysis, machine learning and deep learning tools for biomedical signals and images along with understanding how these can be implemented in products.

When I am not busy coding or hatching plans for what my next startup would look like you can find me writing here, reading, doodling and dreaming about my pet cat.

Experience

Independent Research, Photo acoustic and Ultrasonic Systems Engineering Lab (October 2021 - Present)
Developing tools to detect overfitting of Conditional Generative Adversarial Network generated Ultrasound Images.

Research Asistant, RadScholars Inc. (January 2021 - Present)
Developing tools to evaluate SICUS(Small Intestine Contrast Ultrasound) for Crohn's Disease.

Project Associate, Indian Institute of Technology, Delhi (September 2020 - March 2020)
Designed a simulator to understand and visualise the behavior of an Electromygraphic Signal based Upper limb prosthesis while performing certain standardised tasks.

Project Intern, MaDeIT. Innovation Foundation (January 2020 - June 2020)
Designed and developed the statistical inferencing and the predictive model to monitor athlete performance.

Teaching Assistant, IIITDM, Kancheepuram (Spring 2020)
INT303-P Product Design Practice: Dr Sudhir Varadarajan

Artificial Intelligence Intern, Scermlind Healthcare Innovations (October 2019 - December 2019)
Developed algorithms for a wearable device to predict and track sleep, stress and recovery of athletes.

Systems Engineering Intern, Startoon Labs (May 2019 - October 2019)
Worked on Electromyographic (EMG) Signal Processing and Analytics and the Range of Motion detection for their wearable physiotherapy toolkit, Pheezee.

Research

Physiological Modelling of Athlete Behaviour to Improve Performance (Ongoing)
MaDeIT Innovation Foundation
The aim of the project is to understand curated data of various athletes trained under a controlled environment. This study mainly focuses on providing insights using the above data. The variables used for this study to understand this behaviour are Heart Rate (HR) in BPM, Volume of Oxygen / carbon dioxide (V’O2 / V’CO2) in L / min and energy expenditure (EE) which provide insights about training and recovery to the athletes to improve their performance.

Reliability of Smart Wearable Device PHEEZEE Versus Other Traditional Devices in a Podiatric Setting: A Comparative Study [Publication]
Haaris Mohsin Moosa, Mythreyi Kondapi, Prabha Sahiti Mandaleeka, Susurla V S Suresh
Abstract in proceedings of the IFASCON 2019, 32nd Annual Conference of the Indian Foot and Ankle Society.

The aim of the project was to improve the accuracy of the IMU algorithms to detect the Range of Motion of a particular joint. The algorithms were developed to improve the range of motion detection for the foot and ankle joint and the accuracy was compared with the current gold standard, the Digital Goniometer.