The Leeds Institute for Data Analytics is pleased to present the next seminar in our series showcasing data analytics.
The seminar will be held in Lecture Theatre 2 (3.04) Clothworkers South Building.
The keynote speaker will be Daniel Tang, Improbable Worlds Ltd.
Computational models are powerful tools for the analysis of complex systems. However, when the dimension of a model’s state space becomes large it often becomes difficult to calibrate, to update its state in response to new observations and to account properly for uncertainty. This has created a barrier to the wider use of large-scale models of complex systems in real world applications.
Probabilistic programming provides a natural and powerful framework, based on Bayesian inference, within which we can tackle the problems of data assimilation, calibration and uncertainty quantification in computational models. Many existing tools for Bayesian inference do not scale well, but by combining recent advances in machine learning with techniques developed in numerical weather prediction we are building a set of tools that will allow us to scale up inference techniques and apply them to the problems of modelling very large, complex systems.
In this seminar I will demonstrate Keanu, our open-source probabilistic programming language, and show how we’ve used it to model the service quality of a national telecoms network and model the power distribution requirements of a transition to electric vehicles.
15.30-15.45: Agent-based models and data assimilation- Jon Ward, Lecturer, School of Mathematics
15.45-16.00: Machine Learning Interface to Dynamic Traffic Simulation- Minh Le Kieu, LIDA Researcher
This presentation is about a Machine Learning augmented interface for Traffic Simulation. The interface aims to lower the technological barriers for a non-proprietary, fine-grained transport simulation model of a large-scale urban system that is capable of real-time dynamic simulation and automatic calibration. The interface develops a programming link to automate information exchange between urban data analytics and traffic simulation. The case study shows the development of the interface on a large-scale traffic simulation model of Sydney, Australia, using a widely available software named Aimsun.
16.00-17.00: Towards a probabilistic programming language for modelling complex systems– Daniel Tang, Improbable Worlds
17:00-18:00: Networking reception with drinks and nibbles hosted in University House.
To book please email Hayley Irving with your name, occupation and faculty/organisation.