Rafiqul Islam Rayan

B.Sc. in Computer Science and Engineering student at Bangladesh University of Engineering and Technology (BUET), Dhaka, Bangladesh.

Interested in building practical software systems, full‑stack web applications, and intelligent tools that solve real‑world problems.

Skills

Programming Languages

  • Python
  • Java
  • C/C++
  • JavaScript
  • SQL
  • Bash

Technologies & Frameworks

  • React.js
  • Flask
  • TensorFlow
  • Spring Boot
  • Node.js
  • OracleDB
  • MySQL
  • Redis

DevOps & Tools

  • Docker
  • Git
  • GitHub
  • Linux/Unix

Methodologies

  • RESTful APIs
  • Object-Oriented Programming (OOP)
  • Agile/Scrum

Education

2021 - Present

B.Sc. in Computer Science and Engineering

Bangladesh University of Engineering and Technology (BUET)

GPA: 3.56/4.00

Relevant coursework: Data Structures & Algorithms, Operating Systems, Database Systems, Software Engineering, Machine Learning.

2018 - 2020

Higher Secondary Certificate (HSC)

Notre Dame College, Dhaka, Bangladesh

Talentpool Scholarship recipient.

Projects

MediaSphere

Full-stack social platform that allows users to create communities, post content, participate in discussions, and receive content recommendations using AI-based features.

Full-stack React Node.js AI/ML

AuctionHub

Full-stack application for managing player auctions, including separate interfaces for administrators, teams, players, and bid managers, with support for real-time bidding and fund tracking.

Full-stack Real-time Auctions

3 Lazy Farmers

Automated plant care system that controls lighting, irrigation, fertilization, and pest management, with Bluetooth-based monitoring and user control.

IoT Embedded Automation

Wordle

Implementation of the Wordle word‑guessing game using OpenGL and the iGraphics library.

C/C++ OpenGL

Research

DeepRNA-Twist: language-model-guided RNA torsion angle prediction with attention-inception network

Briefings in Bioinformatics, Volume 26, Issue 3, May 2025

Introduced a deep learning framework to predict RNA torsion and pseudo-torsion angles directly from sequence. Utilizes RNA language model embeddings, dilated CNNs, and multi-head attention to capture local and global structural features, achieving state-of-the-art accuracy.

Deep Learning RNA Structure Transformers

View ResearchGate Profile

Contact

Feel free to reach out if you'd like to collaborate or just want to say hello!

You can reach me at rfqrayan@gmail.com.