About
My research is broadly concerned with the applications of probabilistic modelling and machine learning in business, with special focuses on marketing and finance. For the former, I am particularly interested in digital advertising revenue management, AI-powered product visual design, and data-driven netnography analysis. For the latter, my current research interests include digital financial product pricing, machine learning based computational investing, and supply-chain finance analytics.
Information for Students
I welcome inquiries from prospective students interested in pursuing research related to my areas of interest, as highlighted above. I expect that potential candidates will have a strong passion for and skills in quantitative research, and be highly self-motivated. If you are considering studying for a PhD under my supervision, please get in touch and include your recent CV and transcripts. Also, please review the details (e.g., study options, entry requirements, fees) of the PhD
Programmes offered by the Adam Smith Business School, as well as the
Student Funding Opportunities.
If you are embarking on a PhD journey or have recently begun, the PhD Comics and the PhD Survival Guide by Ronald T. Azuma could be useful in providing you with an introductory insight into PhD studies. Consider reading them prior to commencing your PhD journey. Also, I strongly recommend delving into your reasons for undertaking a PhD. Understanding your motivations can profoundly influence your journey and outcomes.
For students I supervise at the Adam Smith Business School, particularly those undertaking BSc, MSc, MBA, or MRes dissertation projects, this
General Advice
to Dissertation Projects may be beneficial.
I teach data analytics related
courses for undergraduate and postgraduate
students in computer science school (previously)
and business school (now). I mainly use the
materials from the DSML Reading Lists
as the main references in my teaching (also in my research),
which could be useful for students who need advice in directed studies.
Research
Publications
A lattice framework for pricingPhD Comics display advertisement options with the
stochastic volatility underlying model
Electronic Commerce Research and
Applications, 14(6), pp.
465-479, 2015
Datasets
Teaching
Adam Smith Business School, University of Glasgow, UK
Data Science for Marketing Analytics (MSc)
2018-2019 | 2019-2020 | 2020-2021 | 2021-2022 | 2022-2023 | 2023-2024
Business Intelligence (MSc)
2023-2024
Digital Marketing Strategy (MSc)
2019-2020 | 2020-2021 | 2021-2022 | 2022-2023
Business Decision Analysis (Year 2)
2020-2021
Copenhagen Business School, Denmark
Machine Learning for Predictive Analytics in Business (Summer School)
2019-2020 | 2020-2021 | 2021-2022 | 2022-2023
Business Intelligence (Summer School)
2018-2019 | 2019-2020 | 2020-2021 | 2021-2022 | 2022-2023
University of La Sabana, Colombia
Business Intelligence (Winter
School)
2020-2021 | 2021-2022 | 2022-2023 | 2023-2024
Trinity Business School, Trinity College Dublin, Ireland
Business Analytics (MSc)
2021-2022
School of Management, University College London, UK
Mathematics III: Probability Theory (Year 2)
2019-2020
Department of Economics and Business Economics, Aarhus University, Denmark
Data Science for Business
Intelligence (Summer School)
2018-2019
School of Computer Science, University of Lincoln, UK
Algorithms for Data Mining (Year
3 and MComp)
2017-2018
Data Science Tools and
Techniques (Year 3 and MComp)
2017-2018
Business Intelligence (Year 3
and MComp)
2016-2017 | 2017-2018
Data Science (Year 3 and MComp)
2016-2017
Business Processes (Year 2)
2015-2016 | 2016-2017
Information Systems in Practice
(Year 1)
2015-2016
Social Media and Society (Year
1)
2015-2016