Muhammad Sameer Amjad

PROFESSIONAL PROFILE Final-year Computer Science undergraduate at NUST (CGPA 3.56) with strong experience in full-stack web development, artificial intelligence, and research-driven problem solving. Hands-on experience building and deploying scalable production systems using modern JavaScript frameworks, alongside applied research in deep learning, large language models, and Reconfigurable Intelligent Surfaces for next-generation wireless communication. Published researcher with contributions in AI for business, finance, and environmental monitoring, and a CFA Level I candidate with growing expertise at the intersection of technology and finance. Adept at translating complex technical ideas into impactful, real-world solutions. EDUCATION Matriculation Kims High School, NST Sargodha , Sargodha , 1058 (2020) Intermediate Punjab Group of Colleges, Sargodha , Sargodha , 1010 (2022) Bachelors of Science in Computer Science School of Electrical Engineering and Computer Science (SEECS), NUST , Islamabad , 3.56 (2022) INTERNSHIP EXPERIENCE Machine Vision and Intelligent Systems Lab, SEECS 01-Jun-2024 - 01-Mar-2025 Contributed to deep learning-based projects, applying advanced techniques to solve real-world problems such as Glacier Monitoring. Also authored a systematic research paper analyzing recent applications of Machine Learning and Deep Learning (DL) in Business and Finance Research, Innovation & Commercialisation, NUST 01-May-2024 - 30-Aug-2024 Developed and deployed the NUST Digital Research Repository using the MERN stack, successfully launching a funded institutional project now live and used by over 1500+ faculty members. I designed the frontend interface and built APIs, improving user experience by 30% based on feedback, while enabling seamless access to over 10,000+ research publications. Codenext Solutions SMC Limited, Islamabad 01-Nov-2024 - 01-Jan-2026 Developed and maintained production-level, fully developed systems using a wide range of modern technologies. Contributed to long-serving systems, ensuring high performance, scalability, and reliability. FINAL YEAR PROJECT Realtime RIS Optimization for 6G networks using Multi Agent Framework Current methods for controlling Reconfigurable Intelligent Surfaces (RIS) in wireless networks typically rely on static, model-based optimization techniques—such as alternating optimization, successive convex approximation (SCA), semidefinite relaxation (SDR), or MMSE beamforming—tailored to fixed or semi-static environments. While these approaches work well under controlled assumptions, they struggle to cope with real-world dynamics like user mobility, fast-fading channels, or environmental interference. Existing literature lacks systems that can autonomously monitor changing conditions and adapt RIS configurations in real time. This gap impedes the scalability and resilience of RIS deployments in next-generation 6G infrastructure.

Final-year Computer Science undergraduate at NUST (CGPA 3.56) with strong experience in full-stack web development, artificial intelligence, and research-driven problem solving. Hands-on experience building and deploying scalable production systems using modern JavaScript frameworks, alongside applied research in deep learning, large language models, and Reconfigurable Intelligent Surfaces for next-generation wireless communication. Published researcher with contributions in AI for business, finance, and environmental monitoring, and a CFA Level I candidate with growing expertise at the intersection of technology and finance. Adept at translating complex technical ideas into impactful, real-world solutions.