Projects
Digital twins for multiphase flow systems
Integrating data, physics and machine learning to develop rapid predictive models for engineered multiphase flow systems
Optimising hypertension management strategies
Using smart algorithms to optimise treatment decisions in blood pressure management and reduce economic burden on healthcare providers
Streaming data modelling for real-time monitoring and forecasting
Developing scalable algorithms for extracting useful information from large, complex and ever-growing data sets in near real time
Bringing the social city to the smart city
Using AI and machine learning to understand and simulate cities
Safe AI for surgical assistance
Developing methods for assistive robots to learn correct actions from human experts, in the domain of surgical assistance in operating rooms
Nocell: Probabilistic programming for spreadsheet experts
Developing a programming language for probabilistic model-building, for organisations that produce spreadsheets as output
People
Christina Last
Research Data Scientist
Professor Samuel Kaski
Turing Fellow
Dr Grigorios Mingas
Senior Research Data Scientist
Lorenzo Rimella
Enrichment Student
Torty Sivill
Doctoral Student
Alex Campbell
Doctoral Student
Dr Oscar Giles
Research Data Scientist
Professor Alison Heppenstall
Turing Fellow
Bilal Mateen
Clinical Data Science FellowEvents
Masterclass: Probabilistic Programming
Monday 08 May 2017
Time: 18:37
Probabilistic Programming
Monday 27 Mar 2017
Time: 18:37
Articles
Publications and software
Efficient Programmable Random Variate Generation Accelerator from Sensor Noise
We introduce a method for non-uniform random number generation based on sampling a physical...
J. T. Meech and P. Stanley-Marbell, "Efficient Programmable Random Variate Generation Accelerator from Sensor Noise," in IEEE Embedded Systems Letters, doi: 10.1109/LES.2020.3007005
Probabilistic Planning by Probabilistic Programming
Automated planning is a major topic of research in artificial intelligence, and enjoys a...
Belle, V. 2 Feb 2018 4 p.
Planning in hybrid relational MDPs
We study planning in relational Markov decision processes involving discrete and continuous states and...
@inproceedings{Nitti2018. Nitti, Davide and Belle, Vaishak and De Laet, Tinne and De Raedt, Luc. ICAPS (Journal Track). Planning in hybrid relational MDPs. 2018.
Interest groups
