Basharat Hussain (Assistant Professor)

Basharat Hussain received his Ph.D. in Computer Science from COMSATS University Islamabad, Pakistan, and holds an M.S. in Computer Science from the International Islamic University (IIUI), Islamabad. With over 23 years of professional experience, he has developed and architected enterprise-grade software solutions for multinational companies in Europe and the USA, specializing in software design, architecture, and development using C++, C#, ASP.NET Core, Java, HTML, and Python.

Currently, he serves as an Assistant Professor in the Faculty of Computing at FAST National University, Islamabad, where he leads research in deep learning, large language models (LLMs), motion diffusion, multimodal AI, and federated learning. His research further explores Retrieval-Augmented Generation (RAG), LoRA-based model fine-tuning, and the integration of Vector Databases for advanced AI pipelines.

Beyond academia, Basharat provides technical consultancy on real-world and cutting-edge LLM-driven solutions, leveraging tools such as N8N for workflow automation, VectorDB for semantic search, and cloud-native architectures for scalable AI deployments. His work bridges applied research and practical engineering, delivering AI-powered solutions in intelligent transportation, generative AI, and autonomous systems.

His research interests are in “intelligent transportation systems and large language models” and include data modeling, simulation, stability, and control in the domains of smart travel and generative models. In addition, he performs research on the application of software technologies, cloud computing, IoT, federated learning and machine learning for design and operation research systems.

Here’s the latest Nov. 2024 cloud computing seminar short at a university campus in Islamabad.

Research interests

1. Federated Learning in Intelligent Transportation

• Researching in Vertical Federated Learning, I focus on using machine learning to address Intelligent Transportation issues. By exploring this intersection, I aim to develop effective solutions to enhance the model's effectiveness, security, and privacy. My goal is to contribute to the field by conducting innovative research in this area

2. Deep Learning in Intelligent Transportation

• Fascinated by Spatio-Temporal Neural Networks, particularly in utilizing Graph Convolution Networks. Exploring the dynamics of transportation systems, I aim to uncover patterns and relationships crucial for understanding emergent properties and developing effective solutions in Urban Traffic Prediction