Hooman Ramezani

I'm Hooman, I am a machine learning engineer currently researching health AI, specifically the application of vision-text transformers for lung cancer segmentation and treatment planning, that can train on small datasets. My research is in associated with morLab at the University of Toronto and UHN

I have completed my BASc in Systems Design Engineering at the University of Waterloo. I have completed six internships at companies such as AMD, DarwinAI (acquired by Apple) primarily focused on building custom deep learning pipelines for clients.

I'm expierienced training models and managing data-flow in cloud and distributed settings. I have deployed models in high-stakes settings such as domains of medical classification, drone defect detection, and robotic vision. I am passionate building AI that pushes boundaries and to create solutions that have a a positive impact to society.

In my free time you can find me playing guitar. Click here to see more.

Email  /  CV  /  LinkedIn  /  Github

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Research and Projects

I am interested in Health AI, Machine Learning, Computer Vision. My research expierience spans previous work with the UW VIP Lab, various internships, and my Masters at UofT.

Lung-DETR: Deformable Detection Transformer for Sparse Lung Nodule Anomaly Detection
Hooman Ramezani, Dionne Aleman, Daniel Letourneau, arXiv, 2024
arXiv

A novel architecture based to detect lung tumor, specifically designed to mitigate extreme class imbalance and find tumors among vastly health tissue.

Enhancing DL Interpretability: IBA for Transformer Attribution
Hooman Ramezani, University of Toronto , 2024  
Paper / Presentation

Information Bottleneck Attribution (IBA) leverages principles from information theory to identify critical information in neural networks for decision-making attribution. In this work IBA is successfully applied to CNN and Transformer models, enabling a detailed analysis of model decision-making.

Parkinsons Freezing of Gait Detection
Hooman Ramezani, Medical Time Series Deep Learning, 2023
Paper / Github

A deep learning network for time-series analysis designed to identify gait freezing in patients with Parkinson's disease, utilizing biometric signals for the prevention of falls.

Rat-Brain-Inspired Reinforcement Learning for Optimal Pathfinding in Mazes
Hooman Ramezani, Computational Neuroscience, 2023
Paper / Github

A deep reinforcement learning model inspired by the basal ganglia of mouse brains, designed to master maze navigation using Q-learning. It showcases the intricacies of decision-making and learning as the model identifies optimal paths through mazes.

Grasp-Proposition-Net: Robotic Vision For Grasping Everyday Objects
Hooman Ramezani, UW VIP Lab , 2022  
Github

Developed a 3D computer vision model with VIP-Lab and Festo for a robotic arm, designed to determine optimal grasp points using LiDAR camera data.

Drone-Aided Surface Defect Detection
Hooman Ramezani, Vison Model with Temporal Context, 2021
Github

A highly accurate embedded model for classifying surface defects via drones, utilizing a convolutional-RNN architecture and synthetic data generation. Model is optimized for on-device execution in real-world applications.