cv

Basics

Name Md Rezwanul Haque
Label Grad Student
Email rezwan@uwaterloo.ca
Url https://rezwanh001.github.io/
Summary Looking for a career to demonstrate the best of my professional ability, research thinking, and strategies to improve my knowledge as well as to contribute to the best of my potential in my institution.

Work

  • 2024.04 - Present

    Ontario, Canada

    Researcher
    Centre for Pattern Analysis and Machine Intelligence (CPAMI) Lab
    • Artificial intelligence, Machine Learning, Deep Learning

Teaching

Education

  • 2024.05 - Present

    Ontario, Canada

    MASc
    University of Waterloo
    • Department of Electrical and Computer Engineering (ECE)
  • 2014.12 - 2019.02

    Khulna, Bangladesh

    BSc
    Khulna University of Engineering & Technology (KUET)
    • Department of Computer Science and Engineering (CSE)
  • 2013.01 - 2014.12

    Rajshahi, Bangladesh

    Higher Secondary Certificate (H.S.C)
    Rajshahi Govt. City College
    • Section: Science
  • 2007.01 - 2012.12

    Dinajpur, Bangladesh

    Secondary School Certificate (S.S.C)
    Balahar B.L. High School
    • Section: Science

Awards

  • 2015 – 2018
    Technical Scholarship
    KUET | Khulna University of Engineering & Technology
    This is awarded to the top students of the class every academic year.

Certificates

Machine Learning
Coursera | Stanford University
Deep Learning Specialization
Coursera | DeepLearning.AI
Deep Learning with Python and PyTorch
edX | IBM
Quantum Machine Learning
edX | University of Toronto
Pretraining LLMs
DeepLearning.AI
Multimodal RAG: Chat with Videos
DeepLearning.AI

Publications

  • 2023.08.19
    Badlad: A large multi-domain bengali document layout analysis dataset
    ICDAR 2023 | Springer
    This paper introduces BaDLAD, the first large multi-domain Bengali Document Layout Analysis (DLA) dataset, comprising 33,695 annotated document samples across six domains. The dataset, with 710K polygon annotations for text-boxes, paragraphs, images, and tables, enables deep learning-based Bengali document digitization and addresses the limitations of rule-based DLA systems.
  • 2022.09.01
    Breast cancer prediction: a comparative study using machine learning techniques
    Springer Nature Computer Science
    This paper compares five supervised machine learning techniques—SVM, K-nearest neighbors, random forests, ANN, and logistic regression—for breast cancer detection using the Wisconsin Breast Cancer dataset. The results show that artificial neural networks (ANNs) achieved the highest performance with 98.57% accuracy, 97.82% precision, and 0.9890 F1 score, outperforming other methods such as SVM.
  • 2021.01.25
    A novel technique for non-invasive measurement of human blood component levels from fingertip video using DNN based models
    IEEE Access
    This study proposes a non-invasive method to measure blood hemoglobin, glucose, and creatinine levels using fingertip PPG signals captured via smartphones. By extracting 46 features and applying a correlation-based feature selection method with genetic algorithms, the approach achieves high accuracy (R² = 0.922 for Hb, 0.902 for Gl, and 0.969 for Cr) in estimating blood component levels, offering potential for clinical use without blood sampling.

Skills

Artificial Intelligence
Machine Learning
Deep Learning
Natural Language Processing
Computer Vision

Languages

Bangla
Native speaker
English
Fluent

Interests

LLMs
Large Language Models
Transformer Models
Natural Language Understanding
GPT
BERT
Text Generation
MMML
Multimodal Machine Learning
Cross-modal Learning
Vision-Language Models
Multimodal Representation Learning
Text-to-Image Models
Audio-Visual Learning

References

Professor Fakhri Karray
Prof. Fakhri Karray is an Adjunct Professor Emeritus in the Department of Electrical and Computer Engineering. Prof. Karray held the Loblaws Research Chair in Artificial Intelligence and was the co-Director of the University of Waterloo Artificial Intelligence Institute.
Professor M.M.A. Hashem
Department of Computer Science and Engineering (CSE), Khulna University of Engineering & Technology (KUET).

Projects

  • 2017.10 - 2018.01
    Image Captioning Project
    In this project we define and train an image-to-caption model, that can produce descriptions for real world images!
    • Recurrent Neural Networks (RNN)
    • Convolutional Neural Networks (CNN)