Starts 5 Dec 2022
Ends 16 Dec 2022
Central European Time
ICTP
Budinich Lecture Hall (LB)
Strada Costiera, 11 I - 34151 Trieste (Italy)
An ICTP Hybrid Meeting. You can submit your application for participation in presence or online.

Machine Learning is increasingly being used in biology, as also in other physical and life sciences, to build predictive models of underlying processes.

This school will combine the knowledge of biologists who work with large and heterogeneous data, of physicists who are experts at modelling such complexity, and of machine learning experts who bring in state-of-the-art methods in statistics. This integrated approach is essential to learn how to tackle these issues and gain a better understanding of key phenomena in biology.

Lecturers:
P. CHAUDHARI, University of Pennsylvania, USA
S. MUKHERJEE, MPG Leipzig, Germany
E. PIASINI, SISSA, Italy
G. SCHWEIKERT, University of Dundee, UK
G. TKACIK, IST, Austria
C. TOMASETTI, City of Hope, Los Angeles, USA
C. VALLEJOS, University of Edinburgh, UK

Scientific Advisory Committee:
V. BALASUBRAMANIAN, University of Pennsylvania, USA
A. CELANI, ICTP, Italy
​​S. JAIN, University of Delhi, India
V.K. KRISHNAMURTHY, ICTS, Bangalore, India
M. MARSILI, ICTP, Italy
M. THATTAI, National Centre for Biological Sciences, India

Grants: A limited number of grants are available to support the attendance of selected participants, with priority given to participants from developing countries. There is no registration fee.

In person participation: As regards the COVID-19 policy, we advise to follow the updated rules available on the ICTP page Access Guidelines for Visitors.
Note: the deadline on 15 October 2022 is for applications needing financial support and/or visa and on 15 November 2022 for all other applications.
**DEADLINE: 15/11/2022**

Organizers

Vijay BALASUBRAMANIAN (University of Pennsylvania), Pratik CHAUDHARI (University of Pennsylvania), Guido SANGUINETTI (SISSA), Matteo MARSILI (ICTP), Local Organiser: Antonio Celani (ICTP)

Co-sponsors