Mosarrat Rumman

About Me

I am Mosarrat Rumman

I am currently a Master's Student @ Ontario Tech University working with Vision Foundation models. I am highly interested in biological science, which is why I love to work with medical image analysis using machine learning models.

Beside my education, I have worked for almost 4 years in one of the largest banks in Bangladesh, as a Principal officer in the Application Architect and Development team, where I worked as a senior backend developer and database engineer of the Core Banking System. This job gave me the opportunity to build strong expertise in analyzing the huge database of the Core Banking System and writing complicated SQL queries for various reports and performance analyses. However, while pursuing my Master’s alongside this job, I realized my passion and thrill for research works. I was always full of ideas, but rarely had time to work on them while on this job. I love reading research papers and learning new technologies. Therefore, despite having smooth career growth (2 promotions in 3 years), I left the job to pursue my dream of being a researcher.

6+

Years Experience

5

Published Papers

15+

Projects Done

9+

Awards and scholarships

Research & Publications

A Contrastive Learning Approach to Bug Severity Classification with Large Language Model Embeddings

Automatically classifying bug severity helps reduce manual effort and improve response times in software maintenance. This study leverages Large Language Models (LLMs), specifically CodeBERT, to generate contextual embeddings of bug reports for automated severity classification. To enhance the quality of embeddings, we integrate Contrastive Learning, which structures the embedding space by bringing similar bug reports closer and pushing dissimilar reports apart.

Link to paper
Enhancing Parkinson’s Disease Diagnosis through Synthetic Image Augmentation and Deep Learning Model Evaluation

Augmenting images in the Parkinson's Disease dataset with synthetic images results into significant improvement of the performance of all models. We performed experiments with with ViT achieving the highest test accuracy of 98%. The proposed Inception-VGG16 model performed second best, achieving a test accuracy of 95%. These results suggest that synthetic augmentation can enhance the performance of pre-trained models in detecting Parkinson's disease, presenting a promising approach for enhancing automatic diagnostic tools.

Link to paper
Best Paper Award Winner - Parts of Speech Tagging in Bangla Sentences using Supervised Learning: A Performance Comparison between Viterbi and Bidirectional-LSTM Models

Parts of speech (POS) tagging is done on Bangla - which is a low resource language. Two POS taggers were built: 1. Using a Hidden Markov Model. 2. Using deep learning model - biLSTM. The performance of both the models were analyzed on mutliple variables. It can be inferred from the results that increasing the size of dataset has greater positive impact on the performace of Bi-LSTM model than on the HMM model.The paper is presented in 2021 IEEE International Women in Engineering (WIE) Conference on Electrical and Computer Engineering (WIECON-ECE).

Link to paper
Early detection of Parkinson's disease using image processing and artificial neural network

In this paper, SPECT images of early diagnosed patients and healthy controls are collected from PPMI database. Instead of using a computational heavy CNN, a simple approach is used where only the region of Interest, i.e,. the dopeminergic region is considered and a single perceptron is trained with the are of ROI. The model achieved an accuracy of 94%.The paper was presented in 2018 Joint 7th International Conference on Informatics, Electronics & Vision (ICIEV) and 2018 2nd International Conference on Imaging, Vision & Pattern Recognition (icIVPR) and is publised in IEEE Xplore. The paper has 21 citations till now.

Link to paper

Skills

Python
  • Proficient in Tensorflow, Pytorch
  • Building REST API using Flask
  • Data cleansing and preprocessing using Pandas, OpenRefine
  • Data visualization with Matplotlib
Other Programming languages
  • Java
  • IBM RPG
Database
  • Highly skilled in SQL
  • Moderately skilled in MongoDB
Language Proficiency
  • IELTS 7.5 Breakdown - S: 8, R: 8, L: 7.5, W: 7

Education

MSc in Computer Science

Ontario Tech University

CGPA: 4.3 / 4.3

Projects:
  • Uncertainty-Aware Fusion of Foundation and Task-Specific Models for Cardiac MRI Segmentation
  • A Contrastive Learning Approach to Bug Severity Classification with Large Language Model Embeddings
  • Enhancing Parkinson’s Disease Diagnosis through Synthetic Image Augmentation and Deep Learning Model Evaluation
BSc in Computer Science & Engineering

Brac University

CGPA: 3.51 / 4

Projects:
  • IoT based weather station (Arduino, sensors and google firebase)
  • Object detection for blind people (Raspberry pi, sensors, Google Vision API)
  • Hospital Management System Web Application (PHP, MySQL, HTML, CSS, Bootstrap)

Contact

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