Anthony Constantinou’s research interests are in Bayesian Artificial Intelligence for causal disclosure and intelligent decision making under uncertainty. Anthony Constantinou works together with scholastics and mechanical associations worldwide and he applies his research to a wide variety of areas including sports, finance, medicine, economics, and gaming.
On August 2018, Anthony Constantinou was appointed as a Turing Fellow. On July 2018, Anthony Constantinou was at Postdoctoral Research Associate position in Bayesian Artificial Intelligence. He has a full Ph.D. studentship in Bayesian Artificial Intelligence. He was awarded EPSRC Fellowship. On May 2018, Anthony Constantinou’s model Dolores which positioned second in the universal rivalry Machine Learning for Soccer has now been distributed in the Machine Learning journal. Find Additional Information Here.
In Apr 2018, The Significance Magazine (of the Royal Statistical Society and the American Statistical Association) had quite recently distributed Anthony Constantinou’s article “Things to think about Bayesian Networks”. On January 2018, Anthony Constantinou had 12 four-year Ph.D. studentships to begin on September 2018, offered through the EPSRC Center for Doctoral Training in Intelligent Games and Game Intelligence (IGGI). The studentships will support full expenses at the Home/EU rate, and give a yearly tax-exempt living stipend somewhere in the range of £14.5 and £16k, depending upon the host university. In December 2017, Anthony Constantinou had completed four 3-year Postdoctoral Fellowships in Health Data Science, supported by the Medical Research Council. In Feb 2017, Anthony Constantinou put forth a full Ph.D. studentship in Bayesian Artificial Intelligence for Decision Making Under Uncertainty.
His Publications include:
Dolores: A model that predicts football match results from everywhere in the world.
- Things to know about Bayesian Networks.
- The future of the London Buy-To-Let property market Simulation with fleeting Bayesian Networks.
- From complex survey and interviews have taken to collect information to intelligent Bayesian models for medical support.
- Incorporating expert information with data in causal probabilistic systems: Preserving information-driven desires when the expert factors remain unobserved.
- Value of Information examination for Interventional and Counterfactual Bayesian systems in Forensic Medical Sciences.
- Risk evaluation and risk management of savage reoffending among prisoners.
- Determining the level of capacity of football groups by powerful evaluations dependent on the relative discrepancies in scores between foes.