Wenbin Li

Dr Wenbin Li

Wenbin is interested in developing unified autonomous systems across various robots and their applications in manufacturing, professional filming, and search-and-rescue. Such research involves a range of topics in machine learning theory and engineering efforts, including reinforcement learning and its inverse problems, graph and network optimisation, and continuous state approximation.

Research interests: 

  • Reinforcement learning and its inverse problems;
  • Graph and network optimisation;
  • Continuous state approximation.

 

University of Bath Research Portal:

https://researchportal.bath.ac.uk/en/persons/wenbin-li

Dr Georgios Exarchakis

Georgios finds great satisfaction in seeing well-defined theories validated through real-world observations. With a background in mathematics, he became fascinated with developing models to understand brain function. Over the past decade, machine learning has grown rapidly, driven by breakthroughs in deep learning that solved problems once thought unsolvable. This raises the critical question: Why were our expectations so misaligned? In machine learning, complex models often mimic human processes, but their function cannot be fully specified. Georgios’ research focuses on uncovering the underlying conditions that make these algorithms work—particularly why neural networks generalize so effectively across diverse domains. His work centers on representation learning and probabilistic modelling,  and it has found application in diverse areas such as astronomy, medical imaging, and quantum chemistry, with an emphasis on understanding the mathematical principles behind these models’ success.

Research Topics:

  • Learning Invariant Representations of High Dimensional Data
  • Efficient Inference and Learning in Probabilistic Models
  • Deep Learning

 

University of Bath Research Portal:

Georgios Exarchakis — the University of Bath’s research portal

Dr Rohit Babbar

Rohit is a Senior Lecturer in the department of Computer Science at the University of Bath. Before this, he was an Assistant Professor at Aalto University in Finland, and a post-doc at Max-Planck Institute for Intelligent Systems, Germany.

The focus of his current research is on : (i) supervised learning problems with large label spaces, (ii) learning with long-tailed data, and under label/input noise, (iii) sparse neural networks for energy & memory efficient training/fine-tuning with LLMs, and (iv) generalisation/robustness in deep learning. Some of his works on developing memory efficient algorithms on commodity GPUs have been published in ECML 2023 and NeurIPS 2024. Other works on proposing evaluation metrics for long-tail data in large output space, their statistical properties and optimal classifiers have been published in ICLR 2024, NeurIPS 2023, and KDD 2022.

Research interests :

  • supervised learning problems with large label spaces,
  • learning with long-tailed data, and under label/input noise,
  • sparse neural networks for energy & memory efficient training/fine-tuning with LLMs,
  • generalisation/robustness in deep learning.

 

University of Bath Research Portal:

Rohit Babbar — the University of Bath’s research portal

Dr Michael Yang

Michael’s research is in the fields of Visual Computing and Computer Vision with specialisation on Scene Understanding, Multimodal Learning, Deep Generative Models. Scene understanding involves enabling computers to interpret and analyse visual data, such as images or videos, to recognise objects, people, and activities within complex environments. One recent research focus is on developing AI models for 3D scene synthesis, a rapidly growing field driven by the demand for realistic virtual environments in applications such as gaming, AR/VR, and robotics. The goal is to push the boundaries of current techniques to improve the realism, diversity, and practical usability of generated 3D scenes for real-world applications.

Research interests: 

  • 3D scene synthesis
  • Scene graph generation
  • Multimodal learning

 

University of Bath Research Portal:

Michael Yang — the University of Bath’s research portal

Dr Ali Uncu

I work on developing and applying formal and human-verifiable symbolic computation algorithms to prove novel mathematical results. I am particularly interested in enumerative and algebraic combinatorics and number theory, especially from the problems arising from q-analogs and the theory of partitions. I am just as interested in the utilization of SAT/SMT (satisfiability and satisfiability modulo theories) methods and applied algebraic geometry to solve/simplify mathematical problems regardless of the source.

Research interests

  • Applications of Computer Algebra
  • Combinatorics and q-series
  • Developing new algorithms for mathematics

 

University of Bath Research Portal:

Ali Uncu — the University of Bath’s research portal

Dr Da Chen

I am currently a Lecturer in the Department of Computer Science at the University of Bath. Prior to this, I was a Postdoc researcher in a joint project between Alibaba Group and The Institute of Automation, Chinese Academy of Sciences.

My research focuses on computer vision, machine learning, multimodal learning, and related areas. I am particularly interested in solving complex practical tasks under “limited” data conditions, such as learning with limited labelled data, out-of-distribution data, cross-domain learning, incremental learning, etc. To this end, I have proposed multiple solutions for few-shot learning image classification, out-of-distribution detection, and other tasks. I am also interested in video-related computer vision and multimodal learning tasks such as video summarization, video object detection, dense video captioning, etc.

Research interests:
  • Computer vision, deep learning, multimodal learning: general CV/ML tasks in practical scenarios.
  • Learning with limited data: Learning with imbalanced data, long-tailed data, open-set data, out-of-distribution data, etc; incremental learning, few-shot learning, etc.
  • Video processing: multimodal learning with video with visual, textual, and audio information.

 

University of Bath Research Portal:

https://researchportal.bath.ac.uk/en/persons/chen-chen-2

Professor Özgür Şimşek

Özgür’s research is on artificial intelligence and machine learning, with emphasis on reinforcement learning. She is particularly interested in open-ended learning in complex, dynamic, uncertain environments. Areas of interest include: 1) Statistical properties of natural environments that enable fast, effective learning, 2) Autonomous construction of hierarchies of reusable skills, 3) Intrinsic motivation and curiosity.

 

University of Bath Research Portal: 

Özgür Şimşek — the University of Bath’s research portal

 

Dr Julian Padget

Julian’s main research focus is the use of formal approaches to validation and verification with computational logic-based models. Application domains include legal reasoning, checking and monitoring security policies, investigating interactions between policies and their automatic revision (inductive logic programming), gaming, virtual and mixed environments and agent-based modelling.

 

University of Bath Research Portal: 

https://researchportal.bath.ac.uk/en/persons/julian-padget/

Professor Eamonn O’Neill

Eamonn’s research has the overarching goal of developing an applied science of human-computer interaction (HCI). This involves developing a sound theoretical footing for HCI and deriving design principles for the development of human-computer systems that are theoretically well-founded, empirically tested and operationalised for people’s use.

 

University of Bath Research Portal:

https://researchportal.bath.ac.uk/en/persons/eamonn-oneill/