Short Bio
I am an Associate Professor and Bicentennial Research Leader at School of Mathematical and Computing Sciences, Heriot-Watt University, Edinburgh. I am leading the BCML lab at Heriot-Watt. I am also an academic member of Edinburgh Center for Robotics and National Robotarium, a joint effort among Heriot-Watt and Edinburgh Universities.
Previoulsy I was a Honorary Senior Lecturer (2020-2021), Senior Lecturer (2017-2019), and Lecturer (2012-2017) at Department of Computing Science, University of Aberdeen. In this post I was associated with dot.rural, an RCUK Digital Economy Hub（2009-2015）.
Before I was appointed a lectureship at Aberdeen, I was a Research Fellow from 2009 to 2012 in the CRISP consortium, a collaboration between Aberdeen, Imperial, and Exeter. CRISP was one of the largest investments in systems biology by RCUK (£5M) at the time. In this post I worked with Prof Celso Grebogi (Nobel Prize in Physics Nominee) in his group (ICSMB at Aberdeen).
I obtained my PhD in Computing Science in 2009, and my PhD thesis is about qualitative model learning, which falls into the fields of machine learning and qualitative reasoning. Before my PhD, I did my BSc (ranked 5th out of >120 peers) and MEng (by research) from Jilin University, China.
My research has been funded by EPSRC, ESRC, NERC, Royal Society, Royal Society of Edinburgh, and Cancer Research UK. I contributed to securing external funding in total over £10M, of which £3.5M was awarded to my institutions, and £1.14M was awarded to myself. I have authored 150+ papers, my Erdös Number is at most 4, and my Google H-Index is 25 (as of Septembrer 2024). I have supervised 12 PhD students to completion.
My Research Expterise (Theoretical Stream)
(Non-computer scientists please see the next section if you feel this is too technical)
Keywords: Bio-inspired Computing, Machine Learning, Data Clustering, Robust, Fair, Accountable Machine Learning
I am interested in bio-inspired computing and machine learning. I am particularly interested in applying bio-inspired computing approaches to develop novel machine learning and data mining algorithms. For example, I have done the following research:
- Artificial immune system for learning qualitative models
- Swarm Intelligence for optimising deep neural networks
- Swarm Intelligence for data clustering
- Swarm Intelligence for learning quantitative models
Within machine learning, I am also interested in kernel learning, neural architecture search, data clustering, and anomaly detection. I am also interested in robustness, accountability, and fairness in machine learning.
My Research Exptertise (Applied Stream)
Keywords: AI, Machine Learning, Data Mining, Deep Learning, Learning Qualitative Models
I have extensive experiences of applying Artificial Intelligence (AI) and Machine Learning (ML) to solve real-world problems of various fields, for instance, inferring biological pathways and guiding biological experiments (the RSE Robot Systems Biologists project), predicting properties of new materials and facilitating material experiments (the EPSRC MI project), analysing social media (the ESRC Social Media Project), sensor data analysis (river level sensor data analysis) and cancer early detection with deep learning (two CRUK funded projects).
I have rich experience in inter/multi-disciplinary research, and I collaborate with biologists, health scientists, material scientists, biochemists, applied mathematicians, environmental scientists, and social scientists.