Hooman Ramezani

I'm Hooman, a machine learning engineer researching fine-tuning medical LLM and ViT models on multimodal data. My research focus' on improving clinical workflows by processing CT slices and unstructured clinical text to generate segmentation masks and treatment recommendations. My research is in collaboration 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 skilled in ML infrastructure and platform engineering, including large-scale LLM training on GPU clusters, model compression (quantization, pruning, knowledge distillation), low-latency inference deployment with Triton and TensorRT, and building robust end-to-end MLOps and data pipelines.

In my free time you can find me playing guitar. See more here.

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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.

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

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.

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.

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.

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.

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.