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BEGIN:VEVENT
DTSTAMP:20220105T160705Z
DTSTART:20220215T140000Z
DTEND:20220215T150000Z
SUMMARY:Turing Fellow 'Spotlight': 15 February
UID:{http://www.columbasystems.com/customers/uom/gpp/eventid/}luu-kww9zyb
 r-pjl0ui
DESCRIPTION:The Digital Futures team are hosting talks by The University 
 of Manchester's new Turing Fellows for 2021-22. \n\nRichard Allmendinger
 \, Senior Lecturer in Decision Sciences at the Decision and Cognitive Sc
 iences Group\, Alliance Manchester Business School (AMBS).\n\nRichard's 
 research interests are in the field of data science and in particular in
  the development and application of optimization\, learning and analytic
 s techniques to real-world problems arising in areas such as healthcare\
 , manufacturing\, economics\, sports\, music\, and forensics. Much of hi
 s research has been funded by grants from Innovate UK\, the Engineering 
 and Physical Sciences Research Council (EPSRC)\, and industrial partners
 .\n\nRichard's talk is titled 'Experimental challenges in expensive opti
 mization'\n\nEvaluating candidate solutions by conducting an experiment\
 , e.g. a physical\, biological or chemical experiment\, can be expensive
 \, time-consuming and/or resource intense. Drug development\, instrument
  setup optimization\, and robotics are prime examples where optimization
  relies on experiments. This talk will introduce several non-standard ch
 allenges arising due to experiments --- such as non-homogeneous per-obje
 ctive evaluation times in a multi-objective problem\, dynamic resource c
 onstraints\, non-static drug libraries\, and problems with safety concer
 ns ---\, touch on existing techniques to cope with these challenges\, an
 d discuss promising areas of future work.\n\nZhengtao Ding\, Professor o
 f Control Systems\, Department of Electrical and Electronic Engineering.
 \n\nZhengtao's recent research is focused on distributed optimization an
 d control of network-connected dynamic systems\, distributed learning an
 d AI applications. His research projects cover various industrial applic
 ations\, such as distributed optimization in micro grids\, formation con
 trol of mobile robots and UAVs.\n\nZhengtao's talk is titled 'Distribute
 d Algorithms for Network-Based Optimization and Machine Learning'\n\nThe
 re are many challenges and opportunities\, such as internet of things\, 
 big data\, machine learning\, smart grid etc in the area of network-conn
 ected systems and control applications\, in particular\, in the areas re
 lating to distributed learning\, optimisation\, decision making and cont
 rol. Recent advances in distributed networks along with the development 
 of complex and large-scale subsystems have significantly incentivised co
 ordination and cooperation over multi-agent systems. Many distributed al
 gorithms have been developed in the areas relating to distributed machin
 e learning\, optimisation and decision making\, which aim at making deci
 sions in local level\, and achieving certain global objectives through n
 etwork communications. Certain control perspectives such as convergence\
 , nonlinearity\, adaptation and consensus are clearly essential in the d
 esign and analysis of the distributed algorithms. This talk will cover s
 ome recent activities in relation to multi-agent applications carried ou
 t in the speaker’s group in University of Manchester\, including machine
  learning via network consensus\, distributed optimization using algorit
 hms based on multi-agents\, applications of distributed optimization and
  machine learning algorithms to power systems and smart girds such as op
 timal power dispatch etc. The speaker will review some fundamental conce
 pts of network-connected dynamic systems and basic analytic tools for co
 nsensus-based distributed algorithms\, and the presentation will focus o
 n distributed algorithms\, and motivations to the algorithm design from 
 control perspectives.
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CLASS:PUBLIC
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