Listed in reverse chronological order.

Real-Time Navigation for Autonomous Surface Vehicles In Ice-Covered Waters

ICRA 2023 Autonomous Ships Lattice Planning

Vessel transit in ice-covered waters poses unique challenges in safe and efficient motion planning. We exploit a lattice-based planner with a cost that captures the ship interaction with ice. The performance of our planner is evaluated across several levels of ice concentration both in simulated and in real-world experiments.

Efficient Ground Vehicle Path Following in Game AI

CoG 2023 Path Following Game AI

This short paper presents an efficient path following solution for ground vehicles tailored to game AI. Our solution pays particular attention to computing a target speed which uses quadratic Bezier curves to estimate the path curvature. The performance of the proposed path follower is evaluated through a variety of test scenarios in a first-person shooter game, demonstrating its effectiveness and robustness in handling different types of paths and vehicles.

A Fully Automated Method for Bladder Segmentation in PSMA PET/CT Scans

Image Segmentation Radiomics PyTorch

We developed a fully automated method using the 3D U-Net architecture for bladder segmentation in PSMA PET/CT scans. The proposed approach is a critical step towards automating prostate lesion detection and improving standardization of clinical reporting. This work was done in collaboration with the Quantitative Radiomolecular Imaging and Therapy (Qurit) lab which is led by UBC Professor Arman Rahmim. We presented our results at the EANM'21 conference.

Wildfire Smoke Detection Model

Computer Vision PyTorch

I participated in a 3-month machine learning research competition called ProjectX which focused on climate change related problems. As part of the UBC team, I led a group of 4 undergraduate students and together we proposed a smoke detection task on a network of firecameras spread across Western US. We demonstrated the feasibility of smoke detection in Pan-Tilt-Zoom camera data using a multi-label image classifier. We applied a gridded image approach where the model predicts the grid segments containing smoke.

Neural Network for Vehicle Dynamics

Model Predictive Control Autonomous Vehicles ROS C++

During summer 2020, I worked under UBC Professor Ian Mitchell as a funded undergraduate student. My work focused on a sampling-based Model Predictive Control (MPC) algorithm developed by Georgia Tech. This algorithm lacked an accompanying framework to generate a system dynamics model. As a result, I built a scalable pipeline to train a feedforward neural network for a prediction model of arbitrary vehicle dynamics. I tested and successfully validated this pipeline with recorded Gazebo simulation data.

Tagging Hadronic Jets in the ATLAS Particle Detector using Deep Learning

Particle Physics Multiclass Classification Keras

My undergraduate thesis was supervised by Alison Lister and consisted of detecting displaced hadronic jets at the ATLAS particle detector using deep learning. My work was part of a broader search for long-lived particles outside of the Standard Model of particle physics. I improved model performance by augmenting the existing deep-recurrent network and found that adding 1D convolutional layers prior to the LSTMs provided global feature extraction and dimensionality reduction.

Extending a Cloud Robotics Demo Application

Robotics SLAM ROS

For 16 months I was an intern at a research lab in Zurich, Switzerland. One of my core tasks involved extending the cloud robotics demo application with additional features. I offloaded the SLAM algorithm Google cartographer to the cloud which removed the dependence of powerful on-board hardware. Additionally, I implemented a text-to-speech functionality giving users remote access and control of the speech capabilities of the robots.

Configuring a Navigation System for a Dynamic Indoor Environment

Robotics Navigation LiDAR ROS

Another contribution I made during my time in Zurich was integrating vision sensors and configuring a robust navigation system on a robot platform designed for warehouse automation. Transitioning from the simulated model to the physical one, I encountered numerous technical obstacles — for instance, the physical robot frequently had trouble clearing expired obstacles from the navigation costmap due to noisy LiDAR data. To address this, I added temporal decay to costmap obstacles and added logic to remove LaserScan points with few neighbouring points.