Seyyed Saleh Hosseini

Seyyed Saleh Hosseini

Machine Learning R&D
Canada

About Me

Proficient in modeling and optimizing modern generative ML architectures for CV and medical applications. Experienced in developing large NLP-based neural networks for sentiment analysis in healthcare services. Coding ski…

Experience

Machine Learning R & D

University Health Network (UHN), Toronto
Sep 2022 - Present · 4 years 1 month

<p>Designed and developed an advanced denoising diffusion probabilistic model (DDPM) with a UNet backbone including ResNet and Attention blocks to generate synthesized CTs from MRIs.<br>
Reduced discrepancy between real CT and sCT to less than 1% in dosage projections by RayStation, ensuring accuracy in cancer treatment.<br>
Developed and optimized an all-around GAN framework including Conditional GANs, CycleGANs, and StyleGANs with multiple serially-connected ResNets or UNets to generate high-resolution synthesized 512×512 CTs from 256×256 MRI patches.<br>
Achieved PSNR of 30dB and SSIM of 0.9.<br>
Partially automated sCT generation from MRI via U-Mamba auto-segmentation and shape matching (Hu Moments metrics).<br>
Streamlined decision-making on sCTs for specialists.<br>
Conducted preprocessing on DICOM images, including conversion into slices and application of image normalization techniques such as registration and min-max normalization.<br>
Performed post-translation reconversion to DICOM format for seamless integration into medical imaging systems.<br>
Distributed data-parallel (DDP) training on Azure to parallelize the training of advanced generative algorithms such as Cycle GAN, Conditional GANs, and DDPM.<br>
Accelerated the training process and allowed for quicker convergence and optimization of the models.<br>
Refined BERT for patient sentiment prediction through analysis of healthcare professionals’ notes to enhance the accuracy and depth of insights into patients’ satisfaction levels.<br>
Achieved an 85% F1 score by mitigating the data imbalance through rigorous precision and recall analysis.</p>

Machine Learning R&D

UHN
Feb 2022 - Present · 4 years 8 months

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AI Research Associate

McGill University & InterDigital, Inc., Montreal
Sep 2017 - Sep 2022 · 5 years

<p>Designed and developed advanced CNN-based methods for channel tracking in THz systems, leveraging VGGNets.<br>
Achieved 67% improvement in RMSE.<br>
Pioneered a low-complexity deep neural network (DNN) for wideband Direction of Arrival estimation.<br>
Dramatically reduced processing time.<br>
Achieved a 96% improvement in processing time compared to Root-MUSIC.<br>
Innovated Bert architectures with CNN and DNN placed at the top for anomaly detection in cell-free systems.<br>
Increased detection probability by 132.5% at low transmission powers.<br>
Improved processing time by 72%.</p>

Skills

Machine Learning Algorithms Machine Learning Research & Development Generative Adversarial Networks Feature Engineering Anomaly Detection Deep Learning Transformers Self-Attention Natural Language Processing Data Visualization Statistical Analysis Problem-Solving Critical Thinking Model Development Deployment Diffusion Models Python C++ SQL MATLAB DNNs CNNs ResNets GANs BERT DDPM UNets U-Mamba MLOps NLP Progressive Growing of GANs PyTorch LibTorch Keras TensorFlow Pandas Azure NumPy SciPy Scikit-Learn FastAPI OpenCV DDP Git Docker
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