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Muhammad Abubakar Farooq

NUST · 2022
Email
abfarooq936@gmail.com
Phone
923304575453
LinkedIn
https://www.linkedin.com/in/muhammad-abu-bakar-farooq-a434bb262
GitHub

Academic

Program
BE Electrical
CGPA
2.78
Year
2022
Education
SEECS
Address
12-A Islam Street , Old muslim town , Lahore , Pakistan
DOB

Career

Current role
Target role
Skills
machine learning, statistical modeling, algorithm design, applied AI, system-level projects, data preparation, modeling, evaluation, deployment, temperature sensing, microcontroller-based PID control, thermal regulation systems, relays, MOSFET drivers, signal conditioning circuits, user-interface modules, PCB-level testing, diagnostics, embedded closed-loop systems, computer vision, deep learning, U-Net image segmentation, Dice Score, mAP metrics, NVIDIA Jetson Nano, edge computing

Verbatim text

The exact text the LLM saw on the page (or the booklet text from the old import). This is what powers semantic search.
Muhammad Abubakar Farooq
Cell: 923304575453 |  Email: abfarooq936@gmail.com
LinkedIn: https://www.linkedin.com/in/muhammad-abu-bakar-farooq-a434bb262
Address: 12-A Islam Street , Old muslim town , Lahore , Pakistan
PROFESSIONAL PROFILE
Neutral academic record graduate with a foundation in machine learning, statistical modeling, a n d algorithm design,
complemented by hands-on experience in applied AI and system-level projects. Proficient in modern machine learning frameworks,
with practical exposure to data preparation, modeling, evaluation, and deployment. 
EDUCATION
BE Electrical
SEECS , Islamabad , 2.78 (2022)
INTERNSHIP EXPERIENCE
PCSIR Laboratories Complex
26-Jun-2023 - 29-Jul-2022
Completed a 4-week hands-on internship in the design and optimization of precision laboratory equipment, including laboratory
ovens and thermostatic dry baths. Gained practical experience with temperature sensing, microcontroller-based PID control, and
thermal regulation systems. Worked with relays, MOSFET drivers, signal conditioning circuits, and user-interface modules.
Performed PCB-level testing and diagnostics, demonstrating strong understanding of embedded closed-loop systems.
FINAL YEAR PROJECT
Olive Yield Prediction using Computer Vision and Edge computing
An AI-based system for automated olive yield estimation using computer vision and deep learning. The project employs a lightweight
U-Net image segmentation model trained on a custom, manually annotated dataset to accurately detect olives under natural orchard
conditions. Model performance was evaluated using Dice Score and mAP metrics, demonstrating improved robustness over
traditional object detection approaches in complex scenes. The optimized model was deployed on an NVIDIA Jetson Nano for real-
time inference from a live camera feed, enabling low-latency, edge-based agricultural monitoring.
TECHNICAL EXPERTISE

AI enrichment

Muhammad Abubakar Farooq is an Electrical Engineering graduate with a focus on machine learning, computer vision, and embedded systems. He has practical experience in deploying AI models on edge devices and working with microcontroller-based hardware control systems.
Skills (AI)
["Machine Learning", "Computer Vision", "Deep Learning", "Python", "PyTorch", "TensorFlow", "U-Net", "Edge Computing", "NVIDIA Jetson", "Embedded Systems", "Microcontrollers", "PID Control", "PCB Testing", "Data Preprocessing", "Model Deployment"]
Status: ai_done
Provenance
Source file: SEECS - Electrical Engineering-2026.pdf
From job #259 page 193
Created: 1778168427