Bioengineering. Wearables. Female Health.
Broad experience spanning wet-lab and computational biology, data analysis, machine learning, and software development, culminating in designing wearable-based studies to gain biological insights for performance optimization.
2014 - 2019
Lead researcher on gene expression analysis in lymphatic vessels of cancer patients through a multi-institutional collaboration between Texas A&M, Imperial College London and Imperial College Medicine. Gained extensive wet-lab experience, collaborative and clinical research insights.
Developed immune cell response models in lymph nodes using Java and MATLAB. Gained expertise in high-performance computing and advanced statistical analysis techniques.
Co-authored a comprehensive review paper on modelling aspects of the lymphatic system including the use of microfluidic devices and compuational models. I published original research on a) the expression of miRNA and the inflammatory state of lymphatic vessels compared to patient prognosis and b) an Agent Based Model of a lymph node incorporating lymph node swelling and chemotaxic influence on immune cell movement and proliferative response.
2019 - 2021 | Internship → Full-time
Developed vGym - a virtual rehabilitation platform combining IMU-based gait analysis with musculoskeletal modeling to identify underused muscles and prescribe targeted exercises for post-surgical patients.
Adapted and compiled C++ OpenSim plugins for ground reaction force estimation, improving muscle activation predictions. Built company website during rebranding, leveraging web development skills.
In a lean startup environment (<30 employees), contributed across biomechanical modeling, data analysis, software development, social media, and outreach, especially during 2020 restructuring.
2021 - Present
Lead researcher on female athlete health study investigating menstrual cycle impacts on performance and injury risk. Designed comprehensive data collection protocols combining wearable technology with physiological monitoring for 200+ females. Injury data for 60 males was also collected enabling comparison.
Lead researcher on training load and injury prediction using the same particpants with retrospective high resolution training data over 2.5 years.
Joint project using a dataset provided by WHOOP assessing correlations between behaviour, biometrics and hormone-driven events such as the menstrual cycle and the menopausal transition.
Developed web application integrating with Garmin Connect API for automated training data collection. Created interactive data labeling tools and survey platforms for longitudinal study management. The core framework of this tool has also been developed for use in other studies i collaborate in.
Active collaborator in Wu Tsai Human Performance Alliance projects, including testosterone therapy monitoring, return-to-sport after stress fracture protocol, T3 levels and ultramarathon training. Organized 2023 Stanford conference trainee meeting.