Dr Muhammad Babar

Profile

I am a Teaching Fellow in Computer Science at the University of Leeds, School of Computer Science. My background is in robotics and autonomous systems, and my research interest lies in applying machine learning methods to intelligent and autonomous systems, spanning areas such as control, perception, and adaptive task execution.

I lead two postgraduate modules: Mathematical Foundations of AI, and the MSc AI Project module, where I support students across the full research and development lifecycle. My teaching connects the mathematical underpinnings of AI with practical applications in intelligent and autonomous systems.

My research draws on a range of machine learning and AI techniques applied to real-world problems, including autonomous systems, predictive modelling, natural language processing, and optimisation. I have published work in venues including Discover Computing, Scientific Reports, and the International Conference on Artificial Intelligence in Information and Communication (ICAIIC), among others. Several of my publications have grown directly out of collaborative work with students, reflecting my commitment to research-informed teaching.

I supervise graduate and undergraduate students on research projects across AI, machine learning, and autonomous systems. I maintain active international research collaborations with colleagues internationally and am a member of the Industrial Advisory Board at Namal University, Pakistan. I am a Fellow of the Higher Education Academy (FHEA), having completed the full Postgraduate Certificate in Academic Practice (PGCAP) at the University of Leeds.

Responsibilities

• Module Leader for Mathematical Foundations of AI
• Module Leader for MSc AI Project
• Project Supervisions

Research interests

My research sits at the intersection of autonomous systems, machine learning, and human-robot interaction. I am broadly interested in making intelligent systems more capable, more adaptive, and more accessible - bridging the gap between classical robotics and modern AI.

A central thread of my current research is integrating large language models (LLMs) into autonomous systems to enable natural, intuitive interaction between humans and machines. I am interested in how conversational AI can be embedded within autonomous platforms - allowing users to communicate needs in natural language while the system interprets intent and responds intelligently in real time. Realising this requires close integration of LLMs with computer vision, sensor fusion, and classical control systems, and represents a step towards autonomous systems that are not only technically capable but genuinely user-friendly.

I have worked on motion planning algorithm development for legged robots as part of the MEMMO: Memory of Motion project (https://www.research.ed.ac.uk/en/projects/memmo-memory-of-motion/) at the University of Edinburgh. This project explored how robots can learn and reuse motion strategies to move more efficiently and robustly across complex environments.

As part of an EPSRC-funded project in collaboration with Heriot-Watt University, I worked on the design and development of a fully autonomous system for the precise stacking of 2D material layers, a process that was previously performed manually. The system combined computer vision, physics-based modelling, and feedback control to automate this delicate task without human intervention, significantly improving precision, repeatability, and efficiency.

Across these areas, I apply a range of machine learning techniques to real-world problems, including deep learning, optimisation, predictive modelling, and natural language processing. My work spans both theoretical contributions and practical implementations, often in close collaboration with graduate and undergraduate students.

Qualifications

• PhD in Robotics and Autonomous Systems
• MSc in Electronics Engineering (Control Engineering)
• BS Electronics (control systems)

Professional memberships

• IEEE Member
• IET Member
• FHEA
• MIoL

Student education

My teaching sits at the intersection of mathematical foundations, artificial intelligence, and autonomous systems. I aim to equip students with both the theoretical grounding and the practical skills needed to work confidently with modern AI and machine learning technologies, with a strong emphasis on connecting core concepts to real-world applications in robotics and intelligent systems.

I supervise graduate and undergraduate students across a range of topics in AI, machine learning, and autonomous systems. I am committed to involving students in active research where possible, and a number of my students have gone on to produce work of publishable quality. I take pride in offering an engaging, supportive, and research-informed learning environment.