Yining Hua

Yining HuaMy research is mainly focusing on three aspects, which are applied AI, the Internet of Things, and Information Security. Before I joined the University of Aberdeen, I subsequently worked as a postdoctoral researcher at the School of Computing Science, University of Glasgow, and a lecturer at the University of Roehampton (London) and the University of Lincoln, U.K. As an early career researcher, I have 5 flag journal papers (including two i10-index, total Impact factor over 35) and 1 top conference paper published in the last five years. I also serve as the chair/program committee member and reviewer in several international high-quality venues.

Areas of Interest: Applied Artificial Intelligence, Machine Learning, Robotics & Autonomous Systems, Computer Vision, Future Generation Computer Networks, Internet of Things, Distributed Wireless Systems, Information Security, Blockchain

Shouyong Jiang

Shouyong JiangI have a strong interest in computational intelligence approaches for interdisciplinary research, such as cell factories design, digital agriculture, and healthcare. My research focuses on unlocking the potential of computer approaches to address pressing challenges in various disciplines/applications. I have more than 45 publications in the relevant fields. I was honored to receive the IEEE Computational Intelligence Society Outstanding PhD Dissertation Award (one per year) in 2021, in recognition of my significant contributions to computational intelligence for decision making in complex systems. As a PI/Co-I, I have delivered multiple research projects funded by EU-Interreg, BBSRC, Royal Society, and Scottish Research Council. I am a member of EPSRC/NERC Peer Review College, an editorial board member of several journals, and an active reviewer for top-tier publication venues. 

Areas of Interest: Artificial Intelligence, Nature-inspired Optimisation, Evolutionary Computation, Machine Learning, Systems Biology, Biotechnology, Digital Twin, Carbon Modelling

Jari Korhonen

Jari KorhonenMy past research experience covers both telecommunications and signal processing aspects of multimedia communications. Currently, my research focus is primarily on visual quality assessment, and I am studying deep learning techniques applied to image and video quality prediction, as well as other related computer vision problems. Before joining the University of Aberdeen, I worked at Shenzhen University in China, and I was principal investigator (PI) for a project funded by National Natural Science Foundation of China (NSFC) from 2018 to 2021. During my career, I have published over 80 papers in peer-reviewed journals and conference proceedings.

Areas of Interest: Machine learning, Deep learning, Computer vision, Visual quality assessment

Tryphon Lambrou

Tryphon LambrouInterim Vice-Dean for the Joint Institute of Data Sciences and Artificial Intelligence. Senior Lecturer in Computing Science with the School of Natural and Computing Sciences; joined the School in January 2022. Tryphon was a Senior Lecturer with the School of Computer Science, University of Lincoln; where he was Programme Leader for Computer Science undergraduate degrees as well as for the MSc in Intelligent Vision, and Lead for the PGR research degrees.

Tryphon is an Honorary Lecturer with the Department of Medical Physics & Bioengineering, University College London. He was a Senior Research Associate with the Centre of Medical Image Computing (CMIC) at UCL, and previously a Research Fellow with the Department of Medical Physics and Bioengineering, UCL, employed under the Interdisciplinary Research Consortium scheme - “From Medical Images and Signals to Clinical Information” funded jointly by EPSRC and MRC.

Areas of Interest: Artificial Intelligence, Machine Learning, Computer Vision, Diagnostic Imaging

Georgios Leontidis

Georgios Leontidis giving a TED TalkI am the University's Interdisciplinary Academic Director of Data and AI, and a Reader in Machine Learning, working closely with the Vice Principal for Research. I have a strong interest in both theoretical aspects of Machine/Deep Learning, e.g. capsule networks, domain adaptation, self-supervised learning etc., as well as applications in the environment, agri-food, healthcare, industry, and energy. I have been a Principal/Co-Investigator in grants with a value of more than £7M for the host institution, funded by EPSRC, NERC, BBSRC-CTP, ESRC SGSSS, Innovate UK, Siemens Energy, EU-H2020, EU-ERDF, Data Lab, Angus Soft Fruits, and others.   I am a member of the Full College of EPSRC (ranked in the top 4%) and have been a panel college member of the UKRI FLF scheme. I am also a Senior Expert with the NERC Constructing a Digital Environment Expert Network and was recently invited to participate as a panellist at the 2022 inaugural Scottish AI summit in a panel that discussed and debated the topic “Why is Explainable AI still a Challenge?”. Finally, I am currently supervising several PhD students and Post-docs.

Areas of Interest: Machine Learning, Deep Learning, Capsule Neural Networks, Transformers, Multimodal Processing, Self-Supervised Learning, Domain Adaptation, Privacy Technologies, Efficient Machine Learning, Applications of Deep Learning

Ruizhe Li

Ruizhe LiMy research covers NLP/NLG, especially on dialogue systems from open domains to task-oriented domains. In addition, my interest also includes exploring different ML models and methods in NLP/NLG, such as deep latent variable models (VAE), reinforcement learning (online/offline RL), graph-based models (GCN), etc. In these areas, I have published 10+ papers on the top tier conferences. I am also the program committee member for many top tier conferences, e.g., AAAI, ACL, EMNLP, NAACL, EACL, etc.

Areas of Interest: Natural Language Processing/Generation, Dialogue Systems, XAI for NLP, Deep Latent Variable Models, Reinforcement Learning, Graph-based Models

Xiao Li

Xiao LiMy research focuses on Artificial Intelligence and Machine Learning models. While studying the theory of how the models work, I'm also interested in the application of these models in different domains. My main research interests include computer vision, natural language processing and quantitative finance. And published multiple papers on the top tier conferences.

Areas of Interest: Natural Language Processing/Generation, Computer vision, Autoencoder, Metaphor

Arabella Sinclair

Arabella SinclairI am very interested in exploring how humans adapt to one another as they interact or communicate and how we can model this. To that end, my work involves analysing human properties of language in a dialogue setting, evaluating to what extent machine generated text contains human-like linguistic properties, and, more recently, whether modern language models exhibit similar patterns to humans when exposed to certain linguistic phenomena. I am interested in applying my work to an educational setting, as well as exploring more general communicative properties of language. My research interests include Natural Language Processing, Computational Linguistics, Cognitive Science and Education.

Areas of Interest: Natural Language Processing, Computational Linguistics, Dialogue Systems, Artificial Intelligence for Educational applications, Natural Language Generation

Yaji Sripada

Yaji SripadaMy research is primarily focused on the intersection of data science and natural language generation (NLG). An earlier work related to automated weather reports was mentioned on BBC news. I am one of the founders of Arria, a company that specializes in building data-to-text applications. I am a named inventor on several US patents. I am also interested in applying NLG to improve human-AI interaction particularly in the context of responsible/ethical AI to achieve fairness, accountability, and transparency (XAI). I am a member of the EPSRC Peer Review College. I also review research proposals for ERC (EUROPEAN RESEARCH COUNCIL).

Areas of Interest: Natural Language Generation, Data-to-Text, Data Storytelling, Human-centered Data Science, Data Engineering, Responsible/ethical AI, Natural Language Processing, Question-Answering, Bias in language models, Language-Vision, Machine Learning and Deep Learning

Jinya Su

Jinya SuMy main research interests include intelligent autonomous systems: situational awareness and robust control under uncertainties; and AI applications. In these areas, I have published 60+ journal and conference papers. Recently, as a PI or CI, I have delivered a few research council projects funded by STFC, Innovate UK in the areas of AI and UAV for smart farming and solar farm inspection. 

Areas of Interest: Autonomous systems, UAV inspection, Applied artificial intelligence

Dewei Yi

Dewei YiMy research mainly focuses on computer vision and robotics,  including human-centric autonomous systems, medical image processing, vision-based scene perception, and their applications on AV, UAV, and underwater vision systems. In these areas, I have published 20+ high-quality journal papers. Recently, as a PI or CI, I have delivered a few research council projects funded by BBSRC, FIS in the areas of AI and computer vision for autonomous robots and smart fishing. In addition, I am a co-chair (senior) in various top venues, such as VTC2022 etc. and I am guest editor for Electronics Journal.

Areas of Interest: Hybrid intelligent Systems and Robotics, Applied Machine Learning, Intelligent Vehicles, Advanced Driver Assistance Systems (ADAS), Personalised Systems, Vision-based Semantic Understanding, Remote Sensing, Precision agriculture

Mingjun Zhong

Mingjun ZhongMy research interests are mainly (application-driven) machine learning and computational statistics. My research mission is to understand patterns and phenomena observed in real-world data by devising probabilistic and statistical machine learning methodologies. Currently I am working on various application domains and inferential machine learning algorithms including causal inference, weak-supervised learning, computational statistics, healthcare, and energy.

Areas of Interest: Health care data, Non-intrusive load monitoring (energy disaggregation), Spectroscopy data, EEG and fMRI, Variational inference, Markov chain Monte Carlo, High-dimentional inference, Variance reduction, Intractable likelihood models, Bayesian matrix factorization, Deep learning, Causal inference, Weak-supervised learning