Shafayet Rajit bio photo

Shafayet Rajit

e-Mail LinkedIn Github   Scholar   ORCID   Researchgate

I graduated in Computer Science and Engineering from North South University, Bangladesh, and currently work as a Coding Artificial Intelligence Trainer at Invisible Technologies Inc. In my role, I work on improving how Large Language Models (LLM) understand and generate code-based instructions. This includes evaluating their performance on diverse tasks, identifying strengths and weaknesses, and contributing to benchmarks focused on accuracy, coherence, and execution.


My research interests lie at the intersection of AI and healthcare, focusing on innovative solutions to improve patient outcomes. Currently, I’m working on 2D image-based drug discovery method for Dengue treatment, using visual representations of molecular structures to accelerate the drug discovery process. Under the guidance of Dr. Tanzilur Rahman, this research aims to contribute to the global fight against Dengue.


In parallel, I am working under the supervision of Dr. Mohammad Rashedur Rahman on developing efficient techniques tailored for resource-constrained devices. This project focuses on optimizing advanced computational methods to ensure reliable performance within limited hardware environments, making them more accessible for practical, real-world use.

Experience

Invisible Technologies Inc., USA

Coding Artificial Intelligence Trainer —Apr 2025 - Present

Advanced Artificial Intelligence Trainer —Feb 2025 - Mar 2025

Intermediate Artificial Intelligence Trainer —Apr 2024 - Jan 2025


North South University, Bangladesh

Research Assistant (under Dr. Mohammad Rashedur Rahman) —Apr 2025 - Present

Research Assistant (under Dr. Shahnewaz Siddique) —Dec 2023 - Sept 2024

Education

North South University, Bangladesh

Department of Electrical and Computer Engineering

BS, Computer Science and Engineering

2019 - 2023

Publications

  • M. Y. Hossain, M. M. H. Rakib, S. Rajit, I. R. Nijhum, R. M. Rahman, Adaptive and automatic aerial image restoration pipeline leveraging pre-trained image restorer with lightweight Fully Convolutional Network, Expert Systems with Applications, 2025 [Paper]

  • S. Rajit, Z. F. Ananna, M. M. Ehsan, N. N. Punom and S. Siddique, Multi-Class Brain Tumor Classification of MRI Image Using Federated Learning with Blockchain, IEEE Region 10 Symposium (TENSYMP), 2024 [Paper]

  • S. Rajit, M. A. A. Sayed, Federated Learning Based Histopathological Image Classification for Oral Squamous Cell Carcinoma, 8th IEEE-EMBS Conference on Biomedical Engineering and Sciences (IECBES), 2024 [Paper]