Bushra Naeem

Academic

Career

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Bushra Naeem
Cell: 923110673190 |  Email: bushranaeem2005@gmail.com
Address: Cantt Superstore, Cantt Chowk, Abu Dhabi Road,Rahim Yar Khan , Rahim yar khan , Pakistan
PROFESSIONAL PROFILE
A highly motivated and driven final-year Electrical Engineering student at NUST with a strong academic foundation and a passion for
innovation at the intersection of hardware and intelligent systems. Possesses hands-on research experience in control systems,
complemented by practical knowledge in coding, Artificial Intelligence (AI), and Machine Learning (ML). Eager to apply a robust
theoretical understanding and technical skills to develop advanced, data-driven solutions in robotics, automation, or intelligent
systems engineering. A dedicated learner seeking to contribute to and grow within a forward-thinking engineering team.
EDUCATION
Electrical Engineering
NUST H-12 SEECS (School of Electrical Engineering and Computer Science) , Islamabad , 2.4 (2026)
INTERNSHIP EXPERIENCE
Undergraduate Researcher — Graduate Research Complex, SEECS – NUST
20-Sep-2024 - 20-Sep-2025
This research investigates optimized nonlinear control for modeling brain tumor dynamics in response to treatment. We propose a
robust control framework based on Sliding Mode Control (SMC), with parameters optimized using a hybrid metaheuristic approach
combining the Red Fox Optimization (RFO) algorithm and Parallel Particle Swarm Optimization (PPSO) to enhance convergence and
therapeutic precision. To facilitate real-time computational feasibility, the optimized controllers are encapsulated within efficient
Artificial Neural Network (ANN) and Bidirectional LSTM (BiLSTM) surrogate models. Extensive simulations in MATLAB/Simulink
validate the system's performance in trajectory tracking (tumor suppression) and disturbance rejection (modeling biological noise),
with quantitative evaluation using IAE, ISE, and ITAE metrics. Results demonstrate that the proposed method effectively minimizes
control chattering and improves tracking accuracy, offering a novel computational strategy for optimizing nonlinear therapeutic
protocols in neuro-oncology.
FINAL YEAR PROJECT
PAYTKN - A Decentralized Ecosystem for Global Payments & Merchant Infrastructure
The PAYTKN ecosystem is a decentralized infrastructure designed to solve the high costs and fragmentation of global payments by
connecting users, merchants, and subscriptions within a single stable economy. It enables users to make fast, low-fee transactions
while earning rewards, and allows merchants to accept crypto seamlessly in a unified token, avoid conversion fees, and earn through
staking. The system is powered by its native $PAYTKN token, which is supported by a sustainable token economy featuring
buybacks, a tax-to-treasury mechanism, and balanced minting logic to ensure long-term price stability and liquidity. By integrating
cross-chain tools like LI.FI and a self-sustaining treasury that funds staking rewards and ecosystem growth, PAYTKN bridges DeFi
with real-world usability, creating a scalable, incentive-driven payment standard for global adoption.
TECHNICAL EXPERTISE
Programming and Tools
Languages: Python, C/C++, MATLAB/Simulink, Embedded C AI/ML Simulation & Modeling: MATLAB/Simulink, Machinations,
Version Control & Collaboration: Git, GitHub, LaTeX Tools & Framwork: LI.FI, Hardhat/Foundry, ethers.js/web3.js,
MetaMask/WalletConnect

AI enrichment