PhD student in the Vision and Image Processing Lab at the University of Waterloo, specializing in computer vision, explainable AI, and machine learning. Extensive internship experience in computer vision applications across various industries.
PhD student in the Vision and Image Processing Lab at the University of Waterloo, specializing in computer vision, explainable AI, and machine learning. Extensive internship experience in computer vision applications across various industries.
Incorporated synthetic data for training lane detection models.
Developed data pipeline and experimented with synthetic data incorporation.
Resolved critical failure scenarios blocking PathNetV4 release.
Developed tools to better visualize and identify challenging scenarios.
Time series light signal detection for autonomous vehicles.
Detection of blinking light signals.
Defined labelling guidelines and potential model architectures.
Trained proof of concept classification network using public data.
Traffic data analysis with computer vision.
Led project to introduce active learning techniques to data ingest pipeline.
Optimized selection of images for labeling from large unlabelled pool.
42% improvement in mean average precision between models.
Developed and analyzed CNN models for detecting threats in security x-ray scans.
Developed fine grain rotational data augmentation method.
Significantly improved model performance in underrepresented classes.
Used LSTM to detect anomalies in time series vibration data and predict machine failure in industrial robots.
Robotic navigation using reinforcement learning and IR sensors.
95% success rate in simulation and 85% success rate on physical robot.
PhD
MASc
BASc
Text to speech model for fiction novels trained using automatically labeled audiobook dataset.
Finalist at the 2022 IEEE International Conference on Automation Science and Engineering.
A paper on superquadric-based object pose estimation for robotic grasping presented at the CVPR 2023 WICV workshop.
A study on deep learning explainability presented at the CVPR 2023 XAI4CV workshop.
Research on a large-scale benchmark dataset for robotic grasping presented at the 2022 IEEE International Conference on Automation Science and Engineering.
Development of an enhanced deep neural network for COVID-19 detection from ultrasound imaging, presented at the CVPR 2022 WICV workshop.