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
Towards a unified understanding of uncertainty quantification in traffic flow
forecasting
Weizhu Qian, Yan Zhao, Dalin
Zhang, Bowei Chen, Kai Zheng, Xiaofang Zhou
IEEE Transactions on Knowledge
and Data Engineering, 36(5), pp. 2239-2256, 2024
SRNI-CAR: A comprehensive dataset for analyzing the Chinese automotive market
Ruixin Ding, Bowei Chen, James M Wilson, Zhi Yan, Yufei Huang
Proceedings of 2023 IEEE International Conference on Big Data, pp. 3405-3412, 2023
GEO: A computational design
framework for automotive exterior facelift
Jingmin Huang, Bowei Chen, Zhi
Yan, Iadh Ounis, Jun Wang
ACM Transactions on Knowledge
Discovery from Data, 17(6), No. 82, 2023
DVM-CAR: A large-scale automotive
dataset for visual marketing research and
applications
Jingmin Huang, Bowei Chen, Lan
Luo, Shigang Yue, Iadh Ounis
Proceedings of 2022 IEEE International Conference on Big Data, pp. 4130-4137, 2022
A Bayesian graph embedding model
for link-based classification problems
Yichao Zhang, Huangxin Zhuang,
Tiantian Liu, Bowei Chen, Zhiwei Cao, Yun Fu,
Zhijie Fan, Guanrong Chen
IEEE Transactions on Network
Science and Engineering, 9(2), pp.
716-727,
2022
Incorporating prior financial
domain knowledge into neural networks for
implied
volatility surface prediction
Yu Zheng, Yongxin Yang, Bowei
Chen
Proceedings of the 27th ACM
SIGKDD Conference on Knowledge Discovery and
Data Mining, pp. 3968-3975, 2021
Index tracking with cardinality
constraints: a stochastic neural networks
approach
Yu Zheng, Bowei Chen, Timothy
Hospedales, Yongxin Yang
Proceedings of the AAAI
Conference on Artificial Intelligence,
pp. 1242-1249, 2020
A hybrid model for predicting human
physical activity status from lifelogging data
Ji Ni, Bowei Chen, Nigel
Allinson, Xujiong Ye
European Journal of Operational
Research, 281(3), pp.
532-542, 2020
Combining guaranteed and spot
markets in display advertising: selling
guaranteed page views with stochastic demand
Bowei Chen, Jingmin Huang, Yufei
Huang, Stefanos Kollias, Shigang Yue
European Journal of Operational
Research, 280(3), pp. 1144-1159, 2020
Pricing average price advertising
options when underlying spot market prices are
discontinuous
Bowei Chen, Mohan Kankanhalli
IEEE Transactions on Knowledge
and Data Engineering, 31(9), pp.
1765-1778, 2019
MM2RTB: bringing multimedia metrics
to real-time bidding
Xiang Chen, Bowei Chen, Mohan
Kankanhalli
Proceedings of the 10th
International Workshop on Data Mining for
Online
Advertising, No.4, 2017
Optimizing trade-offs among
stakeholders in real-time bidding by
incorporating
multimedia metrics
Xiang Chen, Bowei Chen, Mohan
Kankanhalli
Proceedings of the 40th
International ACM SIGIR Conference on
Research and
Development in Information Retrieval,
pp. 205-214, 2017
Risk-aware dynamic reserve prices
of programmatic guarantee in display
advertising
Bowei Chen
Proceedings of the 16th IEEE
International Conference on Data Mining
Workshops, pp. 511-518, 2016
A lattice framework for pricingPhD Comics display advertisement options with the
stochastic volatility underlying model
Bowei Chen, Jun Wang
Electronic Commerce Research and
Applications, 14(6), pp.
465-479, 2015
Multi-keyword multi-click
advertisement option contracts for sponsored
search
Bowei Chen, Jun Wang, Ingemar
Cox, Mohan Kankanhalli
ACM Transactions on Intelligent
Systems and Technology, 7(1), No. 5,
2015
A dynamic pricing model for
unifying programmatic guarantee and real-time
bidding in display advertising
Bowei Chen, Shuai Yuan, Jun Wang
Proceedings of the 8th
International Workshop on Data Mining for
Online
Advertising, pp. 1-9,
2014
Best Paper Award
An empirical study of reserve price
optimisation in real-time bidding
Shuai Yuan, Jun Wang, Bowei Chen,
Peter Mason, Sam Seljan
Proceedings of the 20th ACM
SIGKDD Conference on Knowledge Discovery and
Data Mining, pp. 1897-1906, 2014
To personalize or not: a risk
management perspective
Weinan Zhang, Jun Wang, Bowei
Chen, Xiaoxue Zhao
Proceedings of the 7th ACM
Conference on Recommender Systems,
pp. 229-236, 2013
Selling futures online advertising
slots via option contracts
Jun Wang, Bowei Chen
Proceedings of the 21st
International World Wide Web Conference,
pp. 627-628, 2012
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