Bio
I am an AI researcher and engineer, specializing in machine learning and
deep learning, with over five years of experience in both academia and industry. 🔬
My goal is to build and democratize AI systems that learn with less data, compute and better understand the world around us.
I am working to identify and overcome challenges in deploying AI tools, driven by their real-world potential,
from autonomous systems, to assistive technologies, to healthcare. 🤖
I have created AI tools addressing complex real-world challenges, such as robustness, efficiency, data imbalance and learning from limited supervision.
I have several lead-author publications at top venues like WACV, BMVC, ICIP and IEEE TMI, and received awards like the
MITACS Accelerate Fellowship.
I've also built practical and production-grade ML solutions, from framing to deployment on cloud and edge. I helped
companies improve the accuracy and efficiency of systems, increase user engagement, develop skills, cut costs, save time and resources. 🏭
Besides, I contribute to open-source software in top deep learning libraries like TensorFlow, Kornia & YOLOv6,
have been runners up in ML competitions like ImageCLEF Medical (2nd) and AICITY at CVPR (3rd),
and mentor AI practitioners.
I constantly learn to stay current in the field, and share them through articles and
videos to help others grow. 🧑🏫
I try to practice Slow Science.
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Quantifying imbalanced classification methods for leukemia detection
Deponker SarkerDepto, Md. Mashfiq Rizvee, Aimon Rahman, Hasib Zunair, M. Sohel Rahman, M.R.C. Mahdy
Computers in Biology and Medicine, 2022
Paper /
Code
We benchmark several methods designed to tackle class imbalance and find that loss-based methods
outperform GAN-based and input-based methods for leukemia detection.
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Machine Learning Competitions
Product Counting and Recognition for Retail Checkout, AI City Challenge, CVPR Workshop, 2022 (3rd Place)
Paper / Code / Leaderboard
Tuberculosis Type Classification from 3D CT Scans, ImageCLEF, 2021 (2nd Place)
Paper / Code / Leaderboard
Nuclei Segmentation and Classification from Whole Slide Images, MoNuSAC, 2020 (11th Place)
Paper / Code / Leaderboard
Tuberculosis Prediction, ImageCLEF, 2019 (5th Place)
Paper / Code / Leaderboard
Bengali Handwritten Digit Recognition, Bengali.AI, 2019 (6th Place)
Paper / Code / Leaderboard
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Datasets
These include datasets I've created.
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Synthetic Dataset of COVID-19 Chest X-rays
Hasib Zunair and A. Ben Hamza
ICML Workshop on Computational Biology, 2021
Dataset Link
Collection of 21,295 synthetic COVID-19 chest X-ray images.
The primary use of this dataset is to be used as additional data for training machine learning models.
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Other Projects
These include coursework, side projects and unpublished research work.
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Tutorials & Invited Talks
Machine Learning Research Paper Writing Tutorial, 2024
Leveraging Vector Databases with Embeddings for Fast Image Search and Retrieval, 2024
Building and Applying Generative Models using PyTorch, Ericsson Canada, 2024
Build and Deploy Custom Docker Images for Object Recognition, Towards AI, 2023
Deep Learning in Computer Vision with PyTorch, 2023
Intro to Deep Learning with NumPy, NSU, 2022
Building ML models with TensorFlow, Ericsson Canada, 2021
How to get started with building Computer Vision systems, NSU, 2021
3D image classification from CT scans, Keras, TensorFlow, 2020
Programming with Python, NSU, 2019
Intro to Deep Learning for Image Classification using Python, NSU, 2019
Basics of Image Processing and Computer Vision, NSU, 2018
Intro to Robotics (ROBO101), a semester-long series of workshops, NSU, 2018
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Template stolen from Jon Barron! Thanks for dropping by.
Last updated February 2025.
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