Welcome!

Hi, I am a fourth-year Ph.D. student in computer science and engineering student at University of North Texas. My research interest areas are computer vision, Deep learning, and medical image analysis. My main goal for my doctoral research is to develop efficient techniques for computer-aided diagnosis(CAD). I am currently working on a domain adaptation challenge for colonoscopy images.

I have much experience with teaching. I was an instructor for the Fundamental of Database course and worked as a teaching assistant for the application of cryptography, programming, and problem-solving with C++, parallel and distributed database systems, and multimedia computing.
My supervisor is Dr. JungHwan Oh.
Other committee members are: Dr. Mark Albert, Dr. Serder Bozdag, Dr. Heng Fan.

About Me

I currently work at Multimedia Information Group lab. After completing my bachelor's degree (in electronics and telecommunication engineering)from Rajshahi University of Engineering and Technology, I joined this lab.

I published my first research paper in 2016. It was a project mainly focused on vehicle tracking with Arduino. I did a lot of electronics and IoT(Internet of Things) related projects back then. Studying engineering in my undergrad was the best decision of my life as it enabled me to explore different pathways of technology. My knack for problem-solving and love for mathematics also make me a suitable candidate to earn the degree.

In my final year of undergrad, I tried to solve some biomedical coil(transcranial magnetic coil) design problems for brain stimulation(to aid neurological disorders). I published two research papers on that topic. After completing my undergraduate study, I decided to create more impact with cancer diagnosis-related problems. I believe if a precise diagnosis can be performed, then it will be always easier to prevent cancer-related health risks.
After an extensive search, I found a complete match with my supervisor's work. Our lab has 'focused research' on computer-aided diagnosis for gastrointestinal diseases. In colonoscopy or endoscopy images, because of the images' nature (texture of colon, specular reflection, blurry frames), it is always hard to get useful information from those frames. There are also data scarcity, image annotation, and domain adaptation issues for this type of medical image dataset. Solving those problems will create a big impact on medical image analysis. I mainly focus on solving those medical image-related problems with deep learning and standard image processing techniques.

News

• Nov 2023: One paper got accepted at the SC ‘23 Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis.
• Aug 2023: appointed as TA for CSCE5350(Fundamental of Database)
• Feb 2023: Presented full paper at Biosignal 2023