3-4 September 2019, Queen Mary University of London
The last decades have witnessed a surge of activity in network science and machine learning and an unprecedented success in understanding and extracting information from big data.
In Physics Challenges for Machine Learning and Network Science we will explore the interface between Physics, Machine Learning and Network Science.
We'll examine urgent questions including:
The workshop will take place over two days and aims to bring together researchers in the above fields to further explore the fertile grounds for collaboration and cross-fertilization, and address the big questions in the field.
This event is funded by NetworkPlus of the EPSRC Grand Challenge in Emergence and Physics Far From Equilibrium, the Institute of Applied Data Science and Jphys Complexity (IOP).
This event will be held in the Arts Two Lecture Theatre, Queen Mary University of London (Mile End Campus).
For directions please see our Queen Mary Campus map here - the Arts Two building is number 35 on the map.
Adrian Bevan (Queen Mary University of London, UK)
Jacob Biamonte (Skolkovo Institute of Science and Technology, Russia)
Lincoln Carr (Colorado School of Mines, USA)
Sergey Dorogovstev (Aveiro University, Portugal)
Andrew Green (UCL, UK)
Konstantin Klemm (IFISC, Palma de Mallorca, Spain)
Renaud Lambiotte (Oxford University, UK)
Rosario Mantegna (Palermo University Italy and UCL, UK)
Matteo Marsili (ICTP, Italy)
Roger Melko (Perimeter Institute, Canada)
Valentina Parigi (Sorbonne Université, France)
Jyrki Piilo (Turku University, Finland)
Eram Rivzi (Queen Mary University of London)
David Saad (Aston University, UK)
Riccardo Zecchina (Bocconi University, Italy)
Pan Zhang (Chinese Academy of Sciences, China)
Please note that registration for this event has now closed.
If you have any questions about this event, please email email@example.com.