SYED SHAYAN AHMED

First part of your life you learn, Second part of your life you earn, third part of your life you return -
Denzel WashingtonAs a passionate and driven Mechanical Engineer (Class of 2025), I thrive on solving real-world problems through innovative design and technology. My academic journey at NED University (CGPA 3.766/4.000) culminated in advanced specialization in the core pillars of modern manufacturing including| AI & IoT / Mechatronics
| Computer Aided Engineering
| EHS and Project management
| Instrumentation and Control

My tenure as a University Research Technology Expert at the IMES Lab (Intelligent Mechanical Engineering Systems) at NED University was the developer for my Digital Engineering mindset.I learned to view raw data, machine learning outputs, and computational models not as abstract results, but as the fundamental input for mechanical systems.I shifted from waiting for instructions to taking intellectual ownership of complex problems. This included managing the data pipeline and proactively developing custom tools, such as Python-based software interfaces, to improve lab efficiency and knowledge transfer.My goal in every project became two-fold: achieving technical functionality and considering the pathway toward industrial scalability.For more details visit our Lab website: Intelligent Mechanical Engineering System

My tenure with the ASHRAE NED Student Branch (2022–2025) represents a continuous trajectory of leadership and institutional impact.
Starting as a Promotions Assistant, I quickly advanced to Technical Director (2023–2024), where I shifted the chapter’s focus from general events to advanced technical capacity building. In this role, I led the 'Learn & Compete' initiative, a technical training series designed to upskill undergraduates in engineering software, and managed large-scale flagship events such as 'NED Run' and 'STEMFEST,' managing cross-functional teams to ensure operational success. I had the privilege to connect with various international ASHRAE members in ASHRAE trends and CRC conferences
Recognizing my commitment to bridging the gap between academia and industry, I was appointed as Board Advisor (2024–2025). In this strategic role, I advised the executive board on operational sustainability, facilitated critical industrial networking visits to major manufacturing plants, and launched advanced technical courses, effectively creating a knowledge-transfer route for the next generation of engineering students.

I'm now currently working at Siemens Pakistan as a technical Associate. My practical industrial exposure has provided me with essential expertise in industrial quality assurance and process reliability. My primary function within the Quality Management department is resolving critical equipment failures and ensuring compliance for replacement parts.
This hands-on role in diagnosing failures, along with leading Quality Assurance for site replacement parts, guaranteeing adherence to manufacturing standards for sheet metal dimensioning, galvanizing, and paint. This has validated my capacity for technical precision and rigorous quality control required in advanced manufacturing environments.

FYP: Development of Drone for PV Cleaning Applications
The core platform is a custom-designed ZD 975mm Hexacopter, engineered for stability and load-bearing precision during cleaning operations. The system integrates an autonomous YOLOv11 Machine Vision model for real-time detection and precise localization of dusty PV panels. This deep learning model was rigorously trained on a specialized dataset containing over 12,000 clean and dusty PV panel images. Actuation is handled by an ESP8266-based cleaning mechanism, designed for targeted water/air dispensing. Flight control relies on a robust Pixhawk autopilot system, configured via Mission Planner for precise telemetry, calibration, and PID tuning. The successful prototype was validated by its 1st Place win at the 4th AI & IoT Exhibition. In addition to this FYP, I have successfully developed other specialized aerial platforms, including a Fire Detection UAV and several RC Competition Planes.

• watch on Youtube

QFIT: The automated Machine learning platform
The Qfit software is a self-initiated Digital Manufacturing solution designed to streamline data analysis workflows within the research lab. Recognizing the inefficiencies of traditional coding methods, I engineered a custom Graphical User Interface (GUI) built on Python, utilizing the powerful low-code capabilities of the PyCaret library. This application automates complex machine learning tasks, enabling researchers to perform multi-model regression, classification, and feature engineering through simple checkbox selections, acting as an efficient alternative to traditional Jupyter Notebook environments. The tool substantially improved the efficiency of model evaluation and reduced the time required for model selection, confirming my capacity to build practical, impactful digital tools that bridge the gap between mechanical data and predictive analytics.

• see the files on github

Aeromodelling based RC planes
The culmination of this effort was receiving the Best Report Award in Propellair 2024 and successfully mentoring the team that won the Propellair 2025 competition. The aircraft design leveraged a rigorous multi-stage process, beginning with conceptual design using Multi-Criteria Decision Making (MCDM) techniques for configuration selection (tapered mid-wing monoplane). The design was validated through comprehensive Computer-Aided Engineering (CAE), including Computational Fluid Dynamics (CFD) analysis on the NACA 2412 airfoil using Ansys Fluent, and Finite Element Analysis (FEA) to optimize the internal rib structure and landing gear. Fabrication was done via balsa sheet and laser cutting with monokote sheeting. Success continues into the present, as I currently lead the design and documentation efforts for the upcoming Propellair 2026 entry.

• Grab CAD