I am a fifth-year PhD student in Computer Science at UT Arlington. My research interests are in
Machine Learning, Deep Learning, Computer Vision, and Medical Image Analysis.
I am doing my PhD under the supervision of Dr. Junzhou Huang.
My research projects include Cell/Nuclei Segmentation, Detection, Classification, etc.
I completed my Bachelor in Computer Science & Engineering from Bangladesh University of Engineering & Technology (BUET) in 2014.
After that, I worked as a Software Engineer at Therap Services
for two years.
Python, C, C++, Java, MATLAB, Scala, Linux Shell script, Prolog, Assembly 8086
ML tools :
PyTorch, TensorFlow, Keras, MatConvNet, TensorBoard
Oracle, MySQL, PL/SQL
Frameworks & API :
Spring, Servlet, Hibernate, Zend, JUnit, jQuery, Bootstrap, OpenGL
Git, AWS, Gradle, Oracle SQL Developer, LaTeX, Packet Tracer
As a Graduate Teaching Assistant (GTA), my responsibilities include
conducting classes, exam proctoring, grading exams and assignments,
and helping students with their assignments and codes. So far, I have
served as a GTA for these courses: Algorithms & Data Structures,
Data Analysis & Modeling Techniques, Distributed Systems, Programming Languages,
Object-Oriented Programming with C++, Introduction to Programming, and Introduction
to Programming for Engineers.
As a Software Engineer, I was involved in developing new features
and enhancing functionality of existing modules, proper integration of
third-party software, solving critical issues, fixing bugs, code refactoring,
code reviewing, and doing research on user authentication and access control.
While working there, the language and tools that I used include Java, Oracle,
Sub-type Cell Segmentation:
Simultaneous cell/nuclei segmentation and classification framework implemented in Python and PyTorch. Instance segmentation, fine-grained classification and self-supervised learning techniques were applied.
Domain Adaptation for Cell Segmentation:
A semantic segmentation model implemented in Python, PyTorch and Keras. Adversarial learning was applied due to the lack of annotations.
Face Recognition with Deep Convolutional Neural Network:
An implementation of Deep CNN model in which FaceNet was used as feature extractor. The model was written in Python and TensorFlow.
Cell Detection and Classification with Convolutional Neural Network:
Simultaneous cell detection and classification framework implemented in MATLAB and MatConvNet.
Deep Neural Network for Image Classification:
A deep L-layer image classifier implemented in Python. NumPy, Matplotlib, PIL and SciPy packages were used.
Traffic Crash and Driver Distractions Classification:
A crash-severity predictive model using Logistic Regression and Decision Tree algorithm. The model can also classify if a driver is distracted or not. The model was implemented in Python using Pandas and NumPy packages.
Face Recognition with Kernel Support Vector Machine:
This framework was written in MATLAB. Principal Component Analysis and Linear Discriminant Analysis were applied for dimensionality reduction. Polynomial kernel with degree 2 was chosen as the kernel. Linear SVM and K-NN algorithm were also implemented for performance comparisons.
Altered Fingerprint Matching:
An altered/obfuscated fingerprint matching framework using Support Vector Machine with radial basis kernel, and K-nearest neighbor algorithm. Model was implemented with MATLAB and libSVM library. NBIS was used for extracting features from fingerprint images.
Text article Classification with Bayesian Learning:
An implementation of Naive Bayes classifier for text documents classification. The model was written in Java.
An N-node Distributed System:
Implementation of Berkeley’s algorithm for logical clock synchronization and Vector Clock algorithm for totally ordered multicasting. Project was built in Java.
Bd-Bay System Design:
System design for an online payment-on-delivery system using Use-Case diagram, Class diagram, Collaboration diagram, Sequence diagram and State-chart diagram.
Adversarial Domain Adaptation for Cell Segmentation
Medical Imaging with Deep Learning (MIDL), 2020
Graph Attention Multi-instance Learning for Accurate Colorectal Cancer Staging
Medical Image Computing & Computer Assisted Intervention (MICCAI), 2020
Problem Solver at Leetcode and UVa Online Judge
Participant, Kaggle Data Science Bowl 2017 (Ranking 344)
Volunteer & Code Reviewer, Java Fest 2014, Bangladesh
Contestant, Weekly Programming Contest, BUET