Machine Learning for Health: Learning to understand human disease
Machine learning is revolutionizing our understanding of many human health problems from obesity to cancer. With ever increasing amount of data coming from this domain, computational biology and medicine are also transforming the machine learning community by not only providing new applications but also inspiring new modeling frameworks and learning paradigms.
The goal of this workshop is to bring together machine learning scientists and computational biologists. We would like to showcase recent advances in this field and discuss challenges in computational methodology and biomedical application.
Call for submissions
Relevant submission topics include (but are not limited to):
- Supervised/semi-supervised/unsupervised learning for diseases and clinical data
- Spatio/temporal/spatio-temporal analyses of disease progression
- Image analysis of tissue sections
- Large scale integration of omics data from patient samples
- Methods for understanding inter/intra-sample heterogeneity
- Networks analysis of disease related pathways
Please submit one page summary plus references via firstname.lastname@example.org. Accepted submissions will be presented as contributed talks or posters.
- Deadline: Extended to May 15
- Author Notification: May 20
- Workshop: June 29
Confirmed speakers include:
|Olga Troyanskaya||Princeton University|
|Laxmi Parida||IBM Research|
|Gunnar Rätsch (Tentative)||ETH Zurich|
Machine Learning for Health is hosted in conjunction with UAI 2016 at the Westin Jersey City Newport Hotel (located across the Hudson river from Manhattan) on June 29, 2016.
Ke Yuan is a Lecturer at School of Computing Science at University of Glasgow. He received MSc and PhD in Electronic/Electrical Engineering and Machine Learning from University of Southampton in 2008 and 2013. From 09/2012 to 04/2016, He was a postdoctoral fellow in the Cancer Research UK Cambridge Institute at University of Cambridge. He joined University of Glasgow in 05/2016.
Olivier Elemento is an Associate Professor at Weill Cornell Medicine, Associate Director of the Institute for Computational Biomedicine and Head of the Laboratory of Cancer Systems Biology. He holds a PhD in Computational Biology from CNRS/University of Montpellier. From 2003 to 2008, he was a postdoctoral research associate at the Lewis-Sigler Institute for Integrative Genomics at Princeton University. He joined the Weill Cornell Medicine in 2008.
Edoardo M Airoldi is an Associate Professor of Statistics at Harvard University. He received a Ph.D. from Carnegie Mellon University in 2007, Till December 2008, He was a postdoctoral fellow in the Lewis-Sigler Institute for Integrative Genomics and the Department of Computer Science at Princeton University working with Olga Troyanskaya and David Botstein. He joined the Statistics Department at Harvard University in 2009.
Florian Markowetz is a Senior Group Leader at the CRUK Cambridge Institute at the University of Cambridge. He holds degrees in Mathematics and Philosophy from University of Heidelberg and a PhD in Computational Biology from Free University Berlin. He was affiliated with the German Cancer Research Center (DKFZ) in Heidelberg and the Max Planck Institute for Molecular Genetics in Berlin. The Max Planck Society honoured his PhD thesis with an Otto-Hahn medal. He pursued postdoctoral research at Princeton University at the Lewis-Sigler Institute for Integrative Genomics. Since 01/2009 he is a group leader at the CRUK Cambridge Institute at the University of Cambridge. In 2014 he was promoted to tenured senior group leader.