Muhammad Abubakar Farooq
NUST
· 2022
Email
abfarooq936@gmail.com
Phone
923304575453
GitHub
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Academic
Program
BE Electrical
CGPA
2.78
Year
2022
Education
SEECS
Address
12-A Islam Street , Old muslim town , Lahore , Pakistan
DOB
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Career
Current role
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Target role
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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
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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.pdfFrom job #259 page 193
Created: 1778168427