Soban Saeed

Electrical Engineer | Embedded & AI-Driven Biomedical Systems

Electrical Engineer working on embedded and biomedical systems integrating embedded hardware, signal processing, and machine learning.

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Soban Saeed Portrait

About Me

Engineer driven by research, innovation, and real-world impact

I am a graduate Electrical Engineer (GPA: 3.83/4.00) with research experience spanning embedded systems, signal processing, and machine-learning-based analysis of real-world data. My work emphasizes system-level design, where sensing hardware, embedded computation, and data-driven models are developed and evaluated under practical constraints such as noise, latency, and limited resources.

Through academic projects and an international research internship, I have gained experience in EEG-based brain–computer interfaces, biomedical instrumentation and circuit design, signal acquisition and preprocessing pipelines, FPGA-based control systems, and AI-assisted inference models. I seek graduate research training (Master’s/PhD) in Electrical Engineering and Bioengineering, with a focus on embedded and intelligent biomedical systems.

Embedded Systems

Real-time hardware and system integration

Biomedical Signals

EEG, ECG, and physiological data analysis

Machine Learning

Data-driven methods under practical constraints

Education

Bachelor of Engineering (Electrical Engineering)

2021 – 2025

National University of Sciences & Technology (NUST), Islamabad, Pakistan

GPA: 3.83 / 4.00
Focus: Electronics, Embedded Systems, Digital Design, Control, and Algorithms, Artifical Intelligence

Experience

Research and industry exposure

BCI Program Intern

Jul 2025 – Sep 2025

University of Alberta, Canada

  • Synthesized literature on gait analysis and multimodal sensor fusion
  • Translated research-grade gait analysis methods into a MATLAB application
  • Automated IMU calibration and analysis pipelines, reducing manual workflows

Hardware Intern

Jul 2024 – Aug 2024

Teresol Pvt. Ltd., Islamabad, Pakistan

  • Developed and evaluated machine learning and deep learning models across numerical, categorical, signal, and image-based data
  • Implemented autoencoder-based models for biomedical signal generation and analysis
  • Applied computer vision techniques to real-world datasets using OpenCV and Python

Projects

Designing Intelligent Systems at the Intersection of Hardware, Signals, and AI

BCI Based Stress Management System

Designed an end-to-end brain–computer interface integrating custom EEG hardware, signal conditioning, and learning-based inference to study real-time stress detection under hardware and noise constraints.

Electronics Deep Learning Wavelet Transform
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Anomalous ECG Detection

Developed learning-based models to identify anomalous cardiac patterns from ECG time-series, emphasizing robustness to noise and inter-patient variability.

ECG Processing Autoencoders Representation Learning
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FPGA-Based Traffic Light Control System

Designed and implemented a real-time traffic light controller using FPGA-based digital logic, emphasizing deterministic timing, hardware-level control, and system verification.

Digital Logic Design FPGA Verification System Verilog
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Automatic Cruise Control System

Designed a real-time automatic cruise control system using feedback control, modeling vehicle dynamics to maintain stable speed under varying operating conditions.

Feedback Control Embedded Systems Navigation
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Counter-Driven Vowel Display

Designed and verified a flip-flop-based counter system with Karnaugh-map-optimized combinational logic for sequential vowel display, implemented in SystemVerilog and deployed on BCD-to-seven-segment hardware.

5-Step Counter System Verilog Sequential Logic
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Achievements & Recognition

Research selection, competitive programs, and technical distinction

Research Fellowships

  • Selected for the MITACS Globalink Research Internship (Canada), conducting international research in engineering and applied systems.

Research & Technical Competitions

  • Competitor in the MIT Data Science Hackathon (Winter 2025), developing and coding data-driven solutions under time-constrained evaluation.
  • Advanced to the penultimate stage of the FICS innovation challenge with a Brain–Computer Interface engineering solution.

Selected Coursework & Certifications

  • Data Science Bootcamp (Certification)
  • Introduction to Embedded Systems (Certification)
  • Digital Signal Processing
  • Digital Logic Design
  • Digital System Design
  • Electronic Circuit Design
  • Electronic Devices and Circuits

Manuscripts

Research Pre-prints demonstrating applied machine learning and system-level modeling

Research Pre-prints

  • ML-Driven Quantum Portfolio Optimization: Hybrid CNN–LSTM Architecture with Adaptive Zero-Noise Extrapolation on NISQ Devices
    Co-author
    Developed a hybrid deep learning framework for adaptive zero-noise extrapolation, enabling robust optimization under hardware noise on NISQ devices. The manuscript presents system-level modeling, training methodology, and evaluation using quantum optimization workflows.
    Research Square Preprint
  • Machine Learning Predictive Analytics for Social Media Enabled Women’s Economic Empowerment in Pakistan
    Co-author
    Applied supervised and unsupervised machine learning techniques to mixed socioeconomic datasets to identify predictive patterns linking digital engagement and economic outcomes.
    arXiv Preprint
  • Addressing Educational Inequities and Marginalization in Pakistan: A Comparative Analysis of Policy and Practice
    Co-author
    Employed principal component analysis and clustering methods to analyze structural inequities across marginalized communities using large-scale socioeconomic indicators.
    Research Square Preprint

Get In Touch

Let's collaborate on meaningful projects that create positive impact

ORCID

Soban Saeed