Prof. Zhiyuan Luo

Tel: +44 1784 443697
Fax: +44 1784 439786
Email: Zhiyuan.Luo@rhul.ac.uk

I am currently a Professor in the Department of Computer Science and a member of the Centre for Reliable Machine Learning which are both at Royal Holloway, University of London.

Research

My main research interests are in machine learning, data analysis, networked systems and agent-based computing, and applications of these algorithms and techniques.

I am currently involved in the following research project:

• Innovate UK: DataSim: A Machine Learning-powered simulation tool for rail timetable optimisation, Co-Investigator (with Dr. Khuong An Nguyen (PI) from Distributed Analytics)

Some of past projects:

• AstraZeneca: Machine Learning for Chemical Synthesis (1 May 2017 to 30 June 2020) Co-Investigator (with Prof. A. Gammerman (PI) and Prof. V. Vovk)
• EU H2020: ExCAPE Compound Activity Prediction Engine (1 September 2015 to 31 August 2018) Co-Investigator (with Prof. A. Gammerman (PI) and Prof. V. Vovk)
• Engineering and Physical Sciences Research Council (EPSRC) Grant (EP/K033344/1) "Mining the Network Behaviour of Bots" (June 2013 to May 2016) Co-Investigator (with Dr. L. Cavallaro (PI, ISG), Prof. A. Gammerman, Prof. V. Vovk and Dr. H. Shanahan)
• Machine learning methods for coal quality analysis based on NIR technology, 2011-2013 (Principal Investigator).
• Royal Society International Joint Project, Explosives Trace Detection with an Odour Capture Hybrid Sensor System, 2009-2011 (Principal Investigator).
• EPSRC EP/E000053/1, Machine Learning for Resource Management in Next-Generation Optical Networks, 2006-2009 (Principal Investigator).
• MRC G0301107, Proteomic Analysis of Human Serum Proteome, 2005-2008 (Principal Co-Investigator).
• EU FP7, Discovery of Novel Serum Biomarkers Based on Aberrant Post-translational Modifications of O-glycoproteins, O-PTM-Biomarkers, and Their Application to Early Detection of Cancer, 2008-2011 (Principal Co-Investigator).
• VLA, Development and Application of Machine Learning Algorithms for the Analysis of Complex Veterinary Data Sets, 2007-2010.

PhD Students

I am always looking to recruit PhD students. I am happy to consider any topic, but for best results, the topic should be in an area close to my current interests (look at my recent publications).

Current PhD students

• Robert Choudhury, "A false sense of security".
• Javier Carreno, "Anomaly detection for digital advertising".

Past PhD students

• Mikhail Dashevskiy, "Prediction with performance guarantees", 2006-2009, PhD awarded in 2010 [Research Engineer, DeepMind Google, London, UK].
• Meng Yang, "Feature handling by conformal predictors", 2010-2014, PhD awarded in 2015 [Lecturer, China University of Mining and Technology, China].
• Chenzhe Zhou, "Conformal and Venn predictors for multi-probabilistic Predictions and Their Applications", 2010-2014, PhD awarded in 2015 [Software development engineer, Amazon China].
• Jiaxin Kou, "Faithful Visualisation of Similarities in High Dimensional Data", 2012-2016, PhD awarded in 2016 [Senior developer at Alibaba China].
• Khuong An Nguyen, "Machine learning based WiFi location fingerprinting", 2012-2016, PhD awarded in Feb. 2017 [Senior lecturer at Dept of Computer Science, Royal Holloway, University of London].
• Andrej Zukov Gregoric, "Towards a question answering view of natural language processing", 2013-2019. PhD awarded in 2019 [Co-Founder and CEO, Corrily (YC W21)]
• Callum Woods, "The Potential for Unknowingly Disclosing Personal Information via Eye Tracking Technology", jointly with Dr. Szonya Durant (Psychology) and Prof. Dawn Watling (Psychology), 2017- 2020. PhD awarded in 2021 [Senior Data Scientist - Biometrics Research , Simprints, UK]
• Nery Riquelme Granada, "Coreset-based Protocols for Machine Learning Prediction", 2018-2022. PhD awarded in 2022 [New Frontiers Fellow at School of Electronics and Computer Science, Southampton University]
• Xu Feng, "Machine Learning Approaches for WiFi Round Trip Time Indoor Positioning Systems", 2021-2025. PhD awarded in 2025 [Teaching Fellow at Dept of Computer Science, Royal Holloway, University of London].

Publications

Edited Books

• B. Scholkopf, Z. Luo, V. Vovk (eds), "Empirical Inference - Festschrift in Honor of Vladimir N. Vapnik", ISBN 978-3-642-41135-9, XXVI, 293 p. 61 illus., 39 illus. in colour, Springer, November 2013.
• A. Gammerman, Z. Luo, J. Vega, V. Vovk, "Conformal and Probabilistic Prediction with Applications", ISBN 978-3-319-33394-6, Springer, April 2016.

Edited Proceedings

• A. Gammerman, V. Vovk, Z. Luo and H. Papadopoulos, Proceedings of Machine Learning Research, Volume 60: Conformal and Probabilistic Prediction and Applications, 13-16 June 2017, Stockholm, Sweden
• A. Gammerman, V. Vovk, Z. Luo, E. Smirnov and R. Peeters, Proceedings of Machine Learning Research, Volume 91: Conformal and Probabilistic Prediction and Applications, 11-13 June 2018, Maastricht, The Netherlands.
• A. Gammerman, V. Vovk, Z. Luo and E. Smirnov (eds), Proceedings of Machine Learning Research, Volume 105: Conformal and Probabilistic Prediction and Applications, 9-11 September 2019, Varna, Bulgaria.
• A. Gammerman, V. Vovk, Z. Luo, E. N. Smirnov, G. Cherubin and M. Christini (eds), Proceedings of Machine Learning Research, Volume 128: Conformal and Probabilistic Prediction and Applications, 9-11 September 2020, Virtual Event, Verona, Italy.
• L. Carlsson, Z. Luo, G. Cherubin and K.A. Nguyen (eds), Proceedings of Machine Learning Research, Volume 152: Conformal and Probabilistic Prediction and Applications, 8-10 September 2021, online.
• U. Johansson, H. Bostrom, K.A. Nguyen, Z. Luo and L. Carlsson (eds), Proceedings of Machine Learning Research, Volume 179: Conformal and Probabilistic Prediction and Applications, 24-26 August 2022, Brighton, UK.

Edited Journal Special Issues

• Special Issue on Conformal and Probabilistic Prediction with Applications, July 2020, Neurocomputing 397:264-265, DOI:10.1016/j.neucom.2019.11.025.
• Special Issue on Artificial Intelligence Applications in Biomedicine, Advances in Artificial Intelligence, Volume 2013, Article ID 219137, DOI: 10.1155/2013/219137

Best Paper Awards

• A.E. Ashby, J.A. Meister, K.A. Nguyen, Z. Luo and W. Gentzke, "Cough-based COVID-19 detection with audio quality clustering and confidence measure based learning", 11th Symposium on Conformal and Probabilistic Prediction with Applications (COPA 2022), Brighton, UK, August 24-26, 2022.
• N. Riquelme-Granada, K. A. Nguyen and Z. Luo, "On generating efficient data summaries for logistic regression: A coreset-based approach", Proceedings of the 9th International Conference on Data Science, Technology and Applications - DATA, 78-89, 2020, (Best Student Paper), ISBN: 978-989-758-440-4, DOI:10.5220/0009823200780089.
• H. Wang, X. Liu, I. Nouretdinov and Z. Luo, "A Comparison of Three Implementations of Multi-Label Conformal Predictor", 3rd International Symposium on Statistical Learning and Data Science (SLDS2015), UK, April 20-23, 2015.
• M. Dashevskiy and Z. Luo, "Guaranteed Network Traffic Demand Prediction Using FARIMA Models", 9th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL2008), South Korea, November 2-5, 2008.
• T. Bellotti, Z. Luo and A. Gammerman, "Reliable Classification of Acute Leukaemia from Gene Expression Data using Confidence Machines", IEEE International Conference on Granular Computing, USA, 10-12 May 2006.

Best Poster Award

• J.A. Meister , K.A. Nguyen and Z. Luo, "Confident COVID-19 cough prediction on imbalanced data", "Machine Learning for Healthcare" conference organised by the Institute of Physics, London, UK, 21 November 2022.

Look here for a list of recent publications.

Teaching

I currently teach one undergraduate course and two MSc Big Data courses.

This is a third year course on main ideas of machine learning with a particular emphasis on kernel methods.

• Nearest Neighbours for classification and regression; interesting distances.
• Discriminant analysis.
• Ridge regression and Lasso.
• Support vector machines for classification and regression.
• Kernel trick and its applications to the algorithms covered so far.
• Practically useful kernels, including string kernels.

The MSc core course teaches fundamental facts and skills in data analysis, including machine learning, data mining, and statistics:

• Supervised learning: classification, regression, and ensemble methods.
• Algorithm-independent machine learning.
• Unsupervised learning and clustering. Exploratory data analysis.
• Bayesian methods. Bayes networks and causality.
• Applications, such as information retrieval and natural language processing.

The MSc course (core course for MSc Machine Learning) addresses the on-line framework of machine learning in which the learning system learns and issues predictions or decisions in real time, perhaps in a changing environment. The course teaches protocols, methods and applications of on-line learning.

This module covers the basics of visualisation and exploratory data analysis and focusses on three aspects of data visualisation:

• What makes a good data visualisation?
• Designing, improving and communicating with visualisations, and
• the technical computing skills needed to create visualisations.

Information about the courses can be reached from the departmental web page.