Muneeb Ur Rehman

Academic

Career

Verbatim text

Muneeb Ur Rehman
Cell: 923349682146 |  Email: muneebtahir08@gmail.com
LinkedIn: https://www.linkedin.com/in/muneeb-ur-rehman-5a509831a/
Address: HOUSE # 32 BLOCK H GOVERNMENT EMPLOYEES HOUSINGSCHEME NEAR IUB BAGHDAD CAMPUS BAHAWALPUR , Bahawalpur ,
Pakistan
PROFESSIONAL PROFILE
I am a final-year Electrical Engineering undergraduate at the National University of Sciences and Technology (NUST), Islamabad,
with a strong interest in building and understanding systems end-to-end. My academic and project experience has given me
exposure to embedded systems, digital design, and intelligent hardware, software integration, while my broader interests have
pushed me to think beyond isolated technical problems and consider how engineering solutions operate in real-world, constrained
environments.
What differentiates me is my inclination toward leadership, structured problem-solving, and adaptability. I enjoy learning new
technical domains quickly, understanding how different components of a system interact, and working with people to turn ideas into
practical outcomes. Rather than focusing narrowly on one tool or role, I aim to develop as a versatile engineer who can grow across
technical, research, and cross-functional responsibilities.
Alongside my technical foundation, I have been actively involved in community-oriented initiatives and leadership roles, which has
shaped how I approach engineering problems, with responsibility, awareness of societal impact, and long-term thinking. I am
particularly interested in sustainability-aware engineering and the evolving intersection of technology, energy systems, and
responsible innovation, and I continue to explore how engineers can contribute meaningfully in these areas.
I am currently seeking graduate-level opportunities where I can apply my engineering fundamentals, learn from experienced teams,
and grow into roles that require both technical depth and leadership potential.
EDUCATION
Bachelors in Electrical Engineering
SEECS , islamabad (2026)
INTERNSHIP EXPERIENCE
Islamia University of Bahawalpur
10-Jun-2025 - 01-Sep-2025
Research Internship - RISC‑V Based Low‑Power AI & Neuromorphic Systems ●Conducted an in‑depth literature review of RISC‑V-
based edge AI and neuromorphic accelerator architectures for real‑time visual detection applications. ●Analyzed and compared
low‑power AI inference techniques, focusing on efficiency, latency, and hardware-software co‑design trade‑offs. ●Studied
neuromorphic computing concepts (event‑driven processing, spiking models) and their relevance to energy‑efficient vision systems.
●Investigated existing AI accelerator and edge‑vision research papers to identify reproducible methodologies and research gaps.
●Assisted in reproducing and validating results from selected peer‑reviewed studies to establish a strong experimental baseline.
●Explored RISC‑V ISA and system‑level integration for embedded AI workloads, emphasizing open‑source ecosystems.
●Documented findings in a structured technical report and research manuscript, adhering to academic and IEEE‑style standards.
●Demonstrated strong research planning, independent learning, and technical documentation skills throughout the internship.
FINAL YEAR PROJECT
RISC-V-Controlled SNN Processor for Real-Time Sensor Data Analysis on Low-Power FPGAs
In this project, we are designing and implementing a low-power neuromorphic accelerator for Spiking Neural Networks (SNNs)
targeted at real-time sensor data processing on embedded platforms. Conventional deep learning models are often unsuitable for
wearable and near-sensor systems due to their high computational and energy requirements. SNNs provide a more energy-efficient
alternative by processing information through sparse, event-driven spikes, making them well-suited for time-varying sensor signals.

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