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.
This event will be held in the Peston Lecture Theatre, Graduate Centre, Queen Mary University of London (Mile End Campus).
For directions please see our Queen Mary Campus map here.
Mauricio Barahona (Imperial College, UK) (*)
Jacob Biamonte (Skolkovo Institute of Science and Technology, Russia)
Licoln Carr (Colorado School of Mines, USA)
A.C.C. Coolen (King’s College, UK)
Sergey Dorogovstev (Aveiro University, Portugal)
Andrew Green (UCL, UK)
Kostantin 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 (University of Paris V, France)
Jurky 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)
This event is free but please register your participation by 20th August via our Eventbrite page here.
If you are a member of the NetworkPlus Emergence and Physics Far From Equilibrium you are eligible for funding to participate to the workshop.
If you would like to request NetworkPlus funding or would like to submit an abstract for a contributed talk please send an email to firstname.lastname@example.org.