Building Wearable Assistive Systems
PhD student in Biomedical Engineering at the University of Toronto, working at the intersection of wearable systems, signal processing, embedded computing, and rehabilitation technology.
About
I'm a PhD student in Biomedical Engineering at the University of Toronto, working in the Dr. Kei Masani Lab on wearable assistive systems and rehabilitation technology.
My Master's, under Dr. Steve Mann, focused on signal processing, embedded computing, and hardware acceleration for biomedical sensing, including GPU-accelerated adaptive time–frequency methods and embedded EEG data acquisition systems aimed at making rehabilitation technology more accessible. My current PhD research builds on that systems perspective, applying sensing and computational methods to wearable technologies for mobility and rehabilitation.
I'm particularly interested in the space between algorithms and physical systems: developing signal-processing methods that are fast and practical enough to run on real sensing hardware, in service of assistive and rehabilitation technology. More broadly, my work sits at the intersection of signal processing, embedded sensing, and wearable assistive systems.
Research
Adaptive Chirplet Transform
My Master's research focused on developing and accelerating adaptive chirplet-based methods for analyzing non-stationary signals, including EEG, EMG, and radar signals whose frequency content changes over time. I investigated how to make these methods substantially more practical, including GPU acceleration, residual-based decomposition, and improvements to numerical stability and multi-channel scalability.
This work has been applied to biomedical signals including EEG, with applications spanning sleep-state and seizure-state analysis.
See the chirplet transform in actionPublications
My research has resulted in publications spanning adaptive time-frequency signal processing, biomedical sensing, embedded systems, and rehabilitation technology.
- Compact Order-Invariant Adaptive Chirplet Features for Patient-Specific EEG Seizure Prediction
- Toward a Stable and Deployable Adaptive Chirplet Transform: Residual Projection, Hybrid GPU Acceleration, and Multi-Channel Scalability
- Portable EEG-Based Data Acquisition and Multi-Sensor Integration Using the Muse S Headband
Projects
Alongside my research, I build software and hardware systems to explore ideas in sensing, computation, and human movement.
Drone Development
Custom drone with a 3D-printed frame and embedded flight controller. Currently going through a redesign.
Awards
Recognition received for research contributions and technical work.
Contact
Reach out about research, collaborations, or anything below.