Structured prior information in probabilistic machine learning with applications in gene regulation modelling

Lecturer : 
Antti Honkela
Event type: 
HIIT seminar
Event time: 
2010-10-22 10:15 to 11:45
Place: 
Kumpula, Exactum C222
Description: 

Talk announcement:
HIIT Seminar Kumpula, Friday Oct 22, 10:15 a.m., Exactum C222

The next HIIT Seminar Kumpula presenter is Dr. Antti Honkela.
Antti was recently appointed to the position of senior researcher at HIIT.

The talk will include a general introduction.

Cookies are served - welcome!

--Matti Järvisalo

P.S. Please also note the updated HIIT Seminar Kumpula schedule below.

-------------------------------------------------------------------

TITLE:
Structured prior information in probabilistic machine learning with
applications in gene regulation modelling

SPEAKER:
Antti Honkela
Helsinki Institute for Information Technology

ABSTRACT:
Bridging the gap between purely data-driven methods and more
traditional computational modelling with physically based models is an important challenge in machine learning. We address this by combining a differential equation model of gene regulation and a probabilistic Gaussian process model. The resulting model captures the relevant degrees of freedom in the process very effectively and allows making very accurate predictions of potential targets of regulator genes (transcription factors).

This is joint work with Neil D. Lawrence, Magnus Rattray and Michalis Titsias.

BIO:
Dr. Antti Honkela is a docent of statistical machine learning at the
Department of Information and Computer Science at Aalto University. He received his PhD in Computer and Information Science at Helsinki University of Technology (TKK) in 2005. Since then he has been a postdoc at TKK (more recently Aalto University) at the Centre of Excellence in Adaptive Informatics Research and under an Academy of Finland Postdoctoral researcher's project. He has also spent 12 months at the University of Manchester, UK. His research interests include Bayesian machine learning and approximate inference as well as computational systems biology.

 


Last updated on 27 Oct 2010 by WWW administrator - Page created on 15 Oct 2010 by Visa Noronen