Latent Force Models
Speaker: Neil Lawrence, Univeristy of Manchester
Location: Warren Weaver Hall 1302
Date: October 23, 2009, 11:30 a.m.
Host: Rich Bonneau
Physics based approaches to data modeling involve constructing an accurate mechanistic model of data, often based on differential equations. Machine learning approaches are typically data driven--- perhaps through regularized function approximation.
These two approaches to data modeling are often seen as polar opposites, but in reality they are two different ends to a spectrum of approaches we might take.
In this talk we introduce latent force models. Latent force models are a new approach to data representation that model data through unknown forcing functions that drive differential equation models. By treating the unknown forcing functions with Gaussian process priors we can create probabilistic models that exhibit particular physical characteristics of interest, for example, in dynamical systems resonance and inertia. This allows us to perform a synthesis of the data driven and physical modeling paradigms. We will show applications of these models in systems biology and modelling of human motion capture data.
Neil Lawrence received his bachelour's degree in Mechanical Engineering from the University of Southampton in 1994. Following a period as an field engineer on oil rigs in the North Sea he returned to academia to complete his PhD in 2000 at the Computer Lab in Cambridge University. He spent a year at Microsoft Research in Cambridge before leaving to take up a Lectureship at the University of Sheffield where he was subsequently appointed Senior Lecturer in 2005. In January 2007 he took up a post as a Senior Lecturer at the School of Computer Science in the University of Manchester where he works in the Machine Learning and Optimisation research group. His main research interest is machine learning through probabilistic models. He focuses on both the algorithmic side of these models and their application in areas such as bioinformatics, speech, vision and graphics.
In-person attendance only available to those with active NYU ID cards.