Dr Anthony Constantinou
Senior Lecturer
Email: a.constantinou@qmul.ac.ukRoom Number: Peter Landin, CS 332Website: http://www.constantinou.info/
Teaching
Data Analytics (Undergraduate)
This module focuses on the range of approaches, methodologies, techniques and tools for data analysis, and the use of data analysis findings to inform decision-making in an industrial / business context.
Data Analytics (Postgraduate/Undergraduate)
This module focuses on the range of approaches, methodologies, techniques and tools for data analysis, and the use of data analysis findings to inform decision-making in an industrial / business context. It exposes students to a range of industry-standard statistical and data analysis techniques and tools, and fosters awareness of the challenges associated with working with large datasets. The module also covers topics related to the legal, social, ethical and professional issues associated with data storage and analysis. Students will undertake practical work including empirical data analysis and summarisation / presentation of the results to a range of relevant stakeholders.
Research
Research Interests:
My research interests are in Bayesian Artificial Intelligence for causal discovery and intelligent decision making under uncertainty. I collaborate with academics and industrial organisations world-wide and I apply my research to a wide range of areas including sports, economics, medicine, finance, and gaming.Publications
- Constantinou AC (2021), Investigating the efficiency of the Asian handicap football betting market with ratings and Bayesian networks $nameOfConferenceDOI: 10.3233/jsa-200588
- Constantinou AC, Liu Y, Chobtham K et al. (2021), Large-scale empirical validation of Bayesian Network structure learning algorithms with noisy data. $nameOfConference
- Kitson NK, Constantinou AC (2021), Learning Bayesian networks from demographic and health survey data. $nameOfConference
- Guo Z, Constantinou AC (2020), Approximate learning of high dimensional Bayesian network structures via pruning of Candidate Parent Sets. $nameOfConferenceDOI: 10.3390/e22101142
- Chobtham K, Constantinou AC (2020), Bayesian network structure learning with causal effects in the presence of latent variables. $nameOfConference
- Constantinou A (2021), The importance of temporal information in Bayesian network structure learning $nameOfConference
- Constantinou AC (2020), Learning Bayesian Networks That Enable Full Propagation of Evidence $nameOfConference
- Fenton N, Neil M, Constantinou A (2020), The Book of Why: The New Science of Cause and Effect, Judea Pearl, Dana Mackenzie, Basic Books (2018) $nameOfConference
- Constantinou A (2020), Learning Bayesian Networks with the Saiyan algorithm $nameOfConferenceDOI: 10.1145/3385655
- Guo Z, Constantinou AC (2020), Approximate Learning of High Dimensional Bayesian Network Structures via Pruning of Candidate Parent Sets. $nameOfConferenceDOI: 10.3390/e22101142
- Constantinou AC (2020), Asian Handicap football betting with Rating-based Hybrid Bayesian Networks. $nameOfConference
- Chobtham K, Constantinou AC (2020), Bayesian network structure learning with causal effects in the presence of latent variables. $nameOfConference
- Liu Y, Constantinou AC, Guo Z (2020), Improving Bayesian Network Structure Learning in the Presence of Measurement Error. $nameOfConference
- Constantinou AC (2020), Learning Bayesian Networks That Enable Full Propagation of Evidence. $nameOfConference
- Constantinou AC (2020), Learning Bayesian Networks with the Saiyan Algorithm. $nameOfConferenceDOI: 10.1145/3385655
- Constantinou AC (2019), Evaluating structure learning algorithms with a balanced scoring function. $nameOfConference
- Fenton NE, Neil M, Constantinou AC (2019), Simpson's Paradox and the implications for medical trials. $nameOfConference
- CONSTANTINOU AC (2018), Dolores: A model that predicts football match outcomes from all over the world $nameOfConference
- CONSTANTINOU AC, FENTON N (2018), Things to know about Bayesian networks $nameOfConference
- YET B, NEIL M, FENTON N et al. (2018), An Improved Method for Solving Hybrid Influence Diagrams $nameOfConference
- Yet B, Constantinou A, Fenton N et al. (2018), Expected Value of Partial Perfect Information in Hybrid Models Using Dynamic Discretization $nameOfConference
- CONSTANTINOU AC, Fenton N (2017), The future of the London Buy-To-Let property market: Simulation with temporal Bayesian Networks $nameOfConference
- CONSTANTINOU AC, Fenton NORMAN (2017), Towards Smart-Data: Improving predictive accuracy in long-term football team performance $nameOfConference
- Fenton N, Neil M, Lagnado D et al. (2016), How to model mutually exclusive events based on independent causal pathways in Bayesian network models $nameOfConference
- Yet B, CONSTANTINOU AC, Fenton N et al. (2016), A Bayesian Network Framework for Project Cost, Benefit and Risk Analysis with an Agricultural Development Case Study $nameOfConference
- CONSTANTINOU AC, FENTON N, NEIL M (2016), Integrating expert knowledge with data in Bayesian networks: Preserving data-driven expectations when the expert variables remain unobserved $nameOfConference
- Constantinou AC, Fenton N, Marsh W et al. (2016), From complex questionnaire and interviewing data to intelligent Bayesian network models for medical decision support $nameOfConference
- Constantinou AC, Fenton NE (2016), Improving Predictive Accuracy Using Smart-Data rather than Big-Data: A Case Study of Soccer Teams' Evolving Performance. $nameOfConference
- CONSTANTINOU AC, Freestone M, Marsh W et al. (2015), Causal inference for violence risk management and decision support in forensic psychiatry $nameOfConference
- Constantinou AC, Yet B, Fenton N et al. (2015), Value of information analysis for interventional and counterfactual Bayesian networks in forensic medical sciences $nameOfConference
- Constantinou AC, Freestone M, Marsh W et al. (2015), Risk assessment and risk management of violent re-offending among prisoners $nameOfConference
- Constantinou AC, Fenton NE, Hunter Pollock LJ (2014), Bayesian networks for unbiased assessment of referee bias in Association Football $nameOfConference
- Constantinou AC, Fenton NE, Neil M (2013), Profiting from an inefficient association football gambling market: Prediction, risk and uncertainty using Bayesian networks $nameOfConference
- Constantinou AC, Fenton NE (2013), Profiting from arbitrage and odds biases of the European football gambling market $nameOfConference
- Constantinou A, FENTON NE (2013), Determining the level of ability of football teams by dynamic ratings based on the relative discrepancies in scores between adversaries $nameOfConference
- Constantinou A, FENTON NE, Neil M (2012), pi-football: A Bayesian network model for forecasting Association Football match outcomes $nameOfConference
- Constantinou A, FENTON NE (2012), Solving the problem of inadequate scoring rules for assessing probabilistic football forecasting models $nameOfConference