Faizal Rahman
About Faizal Rahman
Faizal Rahman is a Summer Intern currently engaged in multiple roles, including AI/ML Coordinator at GDSC JNU and Placement Coordinator at JNU Placement Cell, both since 2023 in New Delhi, India. He has a background in Computer Engineering from Jawaharlal Nehru University and has developed algorithms for gene regulatory inference.
Current Role at GDSC JNU
Faizal Rahman serves as the AI/ML Coordinator at GDSC JNU since 2023. In this role, he is responsible for overseeing projects related to artificial intelligence and machine learning, contributing to the development of innovative solutions within the organization. His position involves coordinating efforts among team members and facilitating learning opportunities for participants.
Experience at JNU Placement Cell
Since 2023, Faizal Rahman has been working as a Placement Coordinator at the JNU Placement Cell. His responsibilities include assisting students in securing internships and job placements, providing guidance on career development, and collaborating with companies to facilitate recruitment processes.
Internship at Google Summer of Code
Faizal Rahman is currently a Summer Intern at Google Summer of Code, having started in 2024. His internship focuses on practical applications of programming and software development, contributing to open-source projects and gaining valuable experience in the tech industry.
Educational Background
Faizal Rahman is pursuing a BTech in Computer Engineering at Jawaharlal Nehru University, expected to graduate in 2025. He previously studied Engineering Science at Jamia Millia Islamia, where he earned a High School Diploma from 2018 to 2020. His foundational education was completed at Don Bosco Public School, where he achieved Junior High School from 2016 to 2018.
Technical Projects and Contributions
Faizal Rahman has engaged in various technical projects, focusing on gene regulatory networks. He employed benchmarks like BEELINE and algorithms such as GENIE3, GRNBoost2, and PIDC to evaluate the effectiveness of scGPT in capturing gene regulatory relationships. He developed a robust pipeline for assessing scGPT's performance in inferring gene regulatory networks from scRNA-seq data and designed a modular pipeline to integrate additional foundation models.