Massimiliano Versace

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Not to be confused with Massimiliano Versace.
Massimiliano Versace
Born (1972-12-21) 21 December 1972 (age 51)
Italy
Nationality Italy
Fields Artificial Intelligence
Institutions Boston_University
Alma mater <templatestyles src="Plainlist/styles.css"/>
Thesis Spikes, synchrony, and attentive learning by laminar thalamocortical circuits (2007)
Doctoral advisor Stephen Grossberg
Doctoral students <templatestyles src="Plainlist/styles.css"/>
  • Jasmin Leveille
  • Charles Wong
  • Jesse Palma
  • Byron Galbraith
  • Lena Sherbakov
  • Jeremy Wurbs
Known for SyNAPSE
Notable awards Fulbright Scholar
Website
maxversace.com

Massimiliano Versace (born December 21, 1972 in Monfalcone, Italy) is co-founder and CEO of Neurala Inc. and founding Director of the Boston University Neuromorphics Lab. He is a pioneer in researching and bringing to market large scale, deep learning neural models that allow robots to interact and learn real-time in complex environments. He has authored approximately forty among journal articles, book chapters, and conference papers, holds several patents, and has been an invited speaker at dozens of academic and business meetings, research and national labs, and companies, including NASA, DARPA, The_Pentagon,Los Alamos National Laboratory, Air Force Research Labs, Hewlett-Packard, iRobot, Qualcomm, Ericsson, BAE Systems, Mitsubishi, and Accenture, among others. His work has been featured in over thirty articles, news programs, and documentaries, including IEEE Spectrum, New Scientist, Geek Magazine, CNN, MSNBC and others. Massimiliano is a Fulbright scholar and holds two Ph.Ds: Experimental Psychology, University of Trieste, Italy; Cognitive and Neural Systems, Boston University, USA. He obtained his BS from University of Trieste, Italy.

Professional life

Versace grew up in Monfalcone, Italy and came to the United States in 2001 as a Fulbright scholar. Massimiliano holds a masters in psychology from the University of Trieste and a two PhDs (Experimental Psychology, University of Trieste, Italy -- Cognitive and Neural Systems, Boston University, USA). He is a Research Assistant Professor at Boston University, where he founded and directs the Neuromorphics Lab. In 2009-2011, he was Co-PI of the DARPA SyNAPSE effort in collaboration with Hewlett-Packard, where he led the Boston University neural modeling team. In December 2010, the IEEE Spectrum published a cover page featured article [1] authored by him regarding the roadmap to develop a large scale brain model making use of memristor based technologies. The Modular Neural Exploring Traveling Agent (MoNETA) was the first large-scale neural network model to implement whole-brain circuits to power a virtual and robotic agent compatibly with memristor-based hardware computations. A cover page featured article in IEEE Computer [2] features the software platform and modeling implemented by the joint HP and Boston University teams, and the March 2012 edition of IEEE Pulse [3] features his lab work on brain modeling. His work has also been featured [4] on the Italian national daily business newspaper Il Sole 24 Ore, on the magazine [5] Geek Magazine and on the documentary series [6] Future Tense. In 2006, with two colleagues from Boston University, he co-founded Neurala Inc. to bring this technology to market. He successfully led the company through the TechStars program in 2013, round of financing, and product launches.

Awards

Versace is a recipient of the Fulbright Fellowship in 2001. He was recipient of the CELEST Award for Computational Modeling of Brain and Behavior in 2009, was awarded top cited article 2008-2010 in Brain Research, and has been named Outstanding Researcher at the Global Conference on Business and Finance, Las Vegas (along with Charles Wong).

Neural modeling, Deep Learning, and Robotics

Massimiliano Versace's research interests are focused on neural networks – also called Deep Learning, in particular applied to cortical models of learning and memory, and how to build intelligent machines equipped with low-power, high density neural chips that implement large-scale brain circuits of increasing complexity. His Synchronous Matching Adaptive Resonance Theory (SMART) model shows spiking laminar cortical circuits self-organize and stably learn relevant information, and how these circuits be embedded in low-power, memristor based hybrid CMOS chip and used to solve challenging pattern recognition problems.

See also

References

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External links

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