Dr. Onim is a Post Doctoral Fellow at the Southern Methodist University, Dallas, Texas. He did his PhD in Computer Engineering in the Department of Electrical Engineering and Computer Science at the University of Tennessee, Knoxville, as a UT-ORII GATE Fellow. His research sits at the intersection of machine learning, quantum machine learning, and digital health, with a strong focus on applying hybrid quantum-classical models to real-world problems in healthcare, cyber-physical systems, and wearable sensing.
His work spans computer vision, deep learning, time-series modeling, and physiological signal analysis, with applications including stress and emotion detection, Alzheimer’s-related behavioral analytics, and anomaly detection. He also has experience building end-to-end research pipelines, from data acquisition and preprocessing to model development, evaluation, and deployment, often leveraging HPC environments and emerging quantum computing frameworks.
Before his PhD, he served as a Lecturer in the Department of Electrical, Electronic and Communication Engineering at the Military Institute of Science and Technology (MIST), Bangladesh. This role helped strengthen his teaching, mentoring, and curriculum development skills and reinforced his commitment to rigorous engineering education and student-centered learning.
He enjoys interdisciplinary collaboration and is always open to research partnerships, academic discussions, and industry-academia opportunities aligned with machine learning, quantum computing, and digital health. Feel free to reach out if you would like to connect or explore potential collaborations.
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Southern Methodist University
- Dallas, Texas, United States
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16:24
(UTC -04:00) - https://sites.google.com/view/md-saif-hassan
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dibaloke/skin-lesion-classification
dibaloke/skin-lesion-classification PublicClassification of skin lesion with Ensemble Architectures
Python 1
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Road_Traffic_Localization_in_ELL
Road_Traffic_Localization_in_ELL PublicImplementation of the paper "Road Traffic Localization in Extremely Low Light with Generative Adversarial Network"
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image-super-resolution
image-super-resolution PublicForked from idealo/image-super-resolution
🔎 Super-scale your images and run experiments with Residual Dense and Adversarial Networks.
Python 1
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