ML Engineer & AI Researcher
Expert in Edge AI, Computer Vision, and sustainable AI systems.
Expert in Edge AI, Computer Vision, and sustainable AI systems.
A passionate researcher and engineer dedicated to advancing AI technology for sustainable solutions.
Machine Learning Engineer with 10+ years of experience in AI research, deep learning, and real-time deployment across academia, industry, and research. Specializing in sustainable AI systems, Edge AI, and Computer Vision.
I have optimized models that improved efficiency by 30% and reduced inference time by 20%. Proven expertise in algorithm optimization, cloud-based model deployment, and hardware acceleration.
Central South University, China
2020 – Present
International Islamic University, Pakistan
2015 – 2017 (Gold Medalist)
Bond and Built Pvt Ltd
July 2024 – March 2025
DLISION, Pakistan
May 2021 – Sept 2022
Published 10 research papers with 30 total citations.
Computers & Electrical Engineering, 2025
Laeeq Aslam, Runmin Zou, Yaohui Huang, Ebrahim Shahzad Awan, Sharjeel Abid Butt, and Qian Zhou
IEEE Access, 2025
Laeeq Aslam, Runmin Zou, Ebrahim Shahzad Awan, Sayyed Shahid Hussain, Muhammad Asim, and Samia Allaoua Chelloug
Energies, 2025
Laeeq Aslam, Runmin Zou, Ebrahim Shahzad Awan, Sayyed Shahid Hussain, Kashish Ara Shakil, Mudasir Ahmad Wani, and Muhammad Asim
Traitement du Signal, 2020
Laeeq Aslam, Ahmad Saeed, Ijaz Mansoor Qureshi, Muhammad Amir, and Waseem Khan
Journal of Mechanics of Continua and Mathematical Sciences, 2020
Laeeq Aslam, Fawad Ahmad, Sohail Akhtar, Ebrahim Shahzad Awan, and Fatima Yaqoob
Wireless Communications and Mobile Computing, 2020
Hamid Ali Mirza, Laeeq Aslam, Muhammad Asif Zahoor Raja, Naveed Ishtiaq Chaudhary, Ijaz Mansoor Qureshi, and Aqdas Naveed Malik
International Journal of Computer Science and Network Security, 2017
Muhammad Zaheer, IM Qureshi, Zeeshan Muzaffar, and Laeeq Aslam
2024 14th Asian Control Conference (ASCC), 2024
Laeeq Aslam, Runmin Zou, Ebrahim Awan, and Sharjeel Abid Butt
2024 IEEE 13th Data Driven Control and Learning Systems Conference (DDCLS), 2024
Abdullahi Mohamed Hassan, Xiaojun Zhou, and Laeeq Aslam
2024 IEEE 8th Conference on Energy Internet and Energy System Integration (EI2), 2024
Qian Zhou, Yun Wang, Guang Wu, and Laeeq Aslam
Cutting-edge implementations of novel AI architectures and methodologies.
This project implements a machine learning framework for time series forecasting using Long Short-Term Memory (LSTM) networks and Temporal Convolutional Networks (TCN). The aim is to enhance predictive accuracy through Integrating Physics Informed Vectors as an input and part of loss function. The block diagram of the proposed work is given below
Bridging AI and sustainability for Industry 5.0 advancements through innovative research and practical applications.
Developing techniques to deploy complex AI models on resource-constrained devices, focusing on model compression and hardware-aware optimizations.
Creating energy-efficient AI solutions that minimize environmental impact while maintaining high performance for critical applications.
Combining neural networks with symbolic AI and physics-based models to create more interpretable and robust AI systems.