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SUMMARY:Memcomputing: a brain-inspired memory-enabled computing paradigm
DTSTART:20160405T141500
DTEND:20160405T153000
DTSTAMP:20260416T025126Z
UID:2d348d594b2517f4d80cd73da8202294c7c614b30d856ef88ee63537
CATEGORIES:Conferences - Seminars
DESCRIPTION:Prof. Massimiliano Di Ventra\, Department of Physics\, Univers
 ity of California San Diego\, La Jolla\, California\, USA\nBio: Massimilia
 no Di Ventra obtained his undergraduate degree in Physics summa cum laude 
 from the University of Trieste (Italy) in 1991 and did his PhD studies at 
 the Ecole Polytechnique Federale de Lausanne (Switzerland) in 1993-1997. H
 e has been Visiting Scientist at IBM T.J. Watson Research Center and Resea
 rch Assistant Professor at Vanderbilt University before joining the Physic
 s Department of Virginia Tech in 2000 as Assistant Professor. He was promo
 ted to Associate Professor in 2003 and moved to the Physics Department of 
 the University of California\, San Diego\, in 2004 where he was promoted t
 o Full Professor in 2006.\nDi Ventra's research interests are in the theor
 y of electronic and transport properties of nanoscale systems\, non-equili
 brium statistical mechanics\, DNA sequencing/polymer dynamics in nanopores
 \, and memory effects in nanostructures for applications in unconventional
  computing and biophysics. He has been invited to deliver more than 230 ta
 lks worldwide on these topics (including 7 plenary/keynote presentations\,
  7 talks at the March Meeting of the American Physical Society\, 5 at the 
 Materials Research Society\, 2 at the American Chemical Society\, and 1 at
  the SPIE). He serves on the editorial board of several scientific journal
 s and has won numerous awards and honors\, including the NSF Early CAREER 
 Award\, the Ralph E. Powe Junior Faculty Enhancement Award\, fellowship in
  the Institute of Physics and the American Physical Society.\nHe has publi
 shed more than 170 papers in refereed journals (13 of these are listed as 
 ISI Essential Science Indicators highly-cited papers of the period 2003-20
 13)\, co-edited the textbook Introduction to Nanoscale Science and Technol
 ogy (Springer\, 2004) for undergraduate students\, and he is single author
  of the graduate-level textbook Electrical Transport in Nanoscale Systems 
 (Cambridge University Press\, 2008).\nWhich features make the brain such a
  powerful and energy-efficient computing machine? Can we reproduce them in
  the solid state\, and if so\, what type of computing paradigm would we ob
 tain? I will show that a machine that uses memory to both process and stor
 e information\, like our brain\, and is endowed with intrinsic parallelism
  and information overhead - namely takes advantage\, via its collective st
 ate\, of the network topology related to the problem - has a computational
  power far beyond our standard digital computers.\nWe have named this nove
 l computing paradigm “memcomputing”. As an example\, I will show the p
 olynomial-time solution of prime factorization and the NP-hard version of 
 the subset-sum problem using polynomial resources and self-organizable log
 ic gates\, namely gates that self-organize to satisfy their logical propos
 ition. These are examples of scalable digital memcomputing machines that c
 an be easily realized with available nanotechnology components.
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STATUS:CONFIRMED
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