CV

Research Assistant

IoThink Lab, HSTU

Conducting research in image processing and machine learning. Aiding my supervisor in facilitating research work.

• MRI image denoising using wavelet transform

Lecturer

Govt. Shahid Akbar Ali Science and Technology College (SASTC)

Taught undergraduate courses in theory of computation, computer graphics, and machine learning.

• Designed and delivered courses on machine learning and image processing.

M.Sc.(Engineering) in Computer Science and Engineering

Hajee Mohammad Danesh Science and Technology University (HSTU)

Computer Science and Engineering

• GPA: 3.625/4.00 (up to first semester)

B.Sc.(Engineering) in Computer Science and Engineering

Hajee Mohammad Danesh Science and Technology University (HSTU)

Computer Science and Engineering

• GPA: 3.31/4.00

Womanium Quantum + AI 2024 Global Summer School Finalist

Womanium

Finalist and nominee for the Quantum Solution Launchpad Fellowship.

IBM Quantum Challenge 2024 (Rank: 11th Globally)

IBM

Successfully completed all six labs in the challenge.

6th Bangladesh Academy of Sciences (BAS) Divisional Science Olympiad

Bangladesh Academy of Sciences

Secured 3rd position in the division and was selected for the national level.

5th Physics Olympiad organized by Dinajpur Science Academy

Dinajpur Science Academy

Achieved 7th position in the Physics Olympiad.

Enhanced Brain Tumor Classification from MRI Images Using Deep Learning Model

2023 26th International Conference on Computer and Information Technology (ICCIT)

In the realm of image classification, traditional algorithms, encompassing both machine learning and deep learning, grapple with formidable challenges arising from uneven pixel ranges and dimensionality reduction. This results in a significant impediment to achieving accurate image categorization.Numerous examples of such traditional methods, including KNN, Random Forest, SVM, DNN, CNN etc, have encountered persistent issues such as inefficient performance of feature engineering, limited accuracy, etc. In response to these challenges, this paper introduces a novel image classification method that integrates pixel mapping, DWT, and CNN for improved efficiency and reliability. By resolving irregular pixel ranges through initial pixel mapping, our method establishes uniformity as a foundation for subsequent image analysis. Subsequently, DWT is employed to dissect and reduce image dimensionality, extracting essential features while lowering computational complexity. This two-step preprocessing approach forms a robust foundation for effective data classification. Within this framework, our proposed CNN architecture plays a pivotal role, utilizing both spectral and spatial information to address image categorization challenges. The network’s capacity to learn complex patterns enhances classification accuracy. In extensive evaluations, our methodology surpasses conventional classification techniques, yielding impressive results. With an Overall Accuracy (OA) of 96.9% and a Kappa statistic of 95.16%, our method showcases excellence and practical potential. These compelling achievements underscore the significance of our approach in tackling image classification challenges, paving the way for enhanced precision and efficiency across various domains.

MRI Image Denoising using Wavelet Transform

Applying wavelet transform techniques to reduce noise in MRI images and comparing results using metrics such as MSE, PSNR, and SSIM.

• Implemented various wavelet functions (db3, bior6.8, sym4)
• Hard and soft thresholding methods

QML for Conspicuity Detection in Production

Final project for the Womanium Quantum + AI 2024 program that earned me the QSL fellowship nomination. I implemented a Hybrid Quantum Convolutional Neural Network to detect anomalies in production.

• Implemented a 3x3 kernel using Quantum Circuit along with a classical CNN.
• Implemented various layers as ansatz(e) for a Quantum Regression Model and performed comparison.