Massimiliano Di Ventra | |
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| Nationality | American-Italian |
| Alma mater | EPFL |
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| Scientific career | |
| Fields | Physics, Nanotechnology |
| Institutions | UCSD |
Massimiliano Di Ventra is an American-Italian theoretical physicist. Specializing in condensed-matter physics, he is the co-founder of MemComputing, Inc.
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 École Polytechnique Fédérale de Lausanne (Switzerland) in 1993–1997. [1]
He was a visiting scientist at the IBM T.J. Watson Research Center and a research assistant professor at Vanderbilt University before joining the physics department of Virginia Tech in 2000 as assistant professor. [2] He was promoted to associate professor in 2003. In 2004, he moved to the physics department of the University of California, San Diego, where he was promoted to full professor in 2006. [3]
In 2022, Di Ventra was accused of retaliation by a striking graduate student worker in his lab as he gave the student a "U" (unsatisfactory) grade. [4] In response, Di Ventra said he did not threaten the student and that the grade related to the student's lack of performance in classes, saying: "it’s a student class, it’s not related to the strike". [5] After reaching its agreement, the union agreed to drop all charges of unfair labor practices. [6]
Di Ventra has published more than 200 papers in refereed journals (he was named 2018 Highly Cited Researcher by Clarivate Analytics) and has 7 granted patents (3 foreign).[ citation needed ] He is the co-founder of MemComputing, Inc.
Di Ventra has made several contributions to condensed-matter physics, especially quantum transport in atomic and nanoscale systems, [7] non-equilibrium statistical mechanics of many-body systems, [8] DNA sequencing by tunneling, [9] and memelements. [10] [ third-party source needed ]
He suggested the MemComputing paradigm of computation, [11] [12] [13] and with his group derived various analytical properties of memristive networks, including the Caravelli–Traversa–Di Ventra equation, [14] an exact equation for the evolution of the internal memory in a network of memristive devices.
Neuromorphic computing is an approach to computing that is inspired by the structure and function of the human brain. A neuromorphic computer/chip is any device that uses physical artificial neurons to do computations. In recent times, the term neuromorphic has been used to describe analog, digital, mixed-mode analog/digital VLSI, and software systems that implement models of neural systems. The implementation of neuromorphic computing on the hardware level can be realized by oxide-based memristors, spintronic memories, threshold switches, transistors, among others. Training software-based neuromorphic systems of spiking neural networks can be achieved using error backpropagation, e.g., using Python based frameworks such as snnTorch, or using canonical learning rules from the biological learning literature, e.g., using BindsNet.
Phase-change memory is a type of non-volatile random-access memory. PRAMs exploit the unique behaviour of chalcogenide glass. In PCM, heat produced by the passage of an electric current through a heating element generally made of titanium nitride is used to either quickly heat and quench the glass, making it amorphous, or to hold it in its crystallization temperature range for some time, thereby switching it to a crystalline state. PCM also has the ability to achieve a number of distinct intermediary states, thereby having the ability to hold multiple bits in a single cell, but the difficulties in programming cells in this way has prevented these capabilities from being implemented in other technologies with the same capability.
Nanoelectromechanical systems (NEMS) are a class of devices integrating electrical and mechanical functionality on the nanoscale. NEMS form the next logical miniaturization step from so-called microelectromechanical systems, or MEMS devices. NEMS typically integrate transistor-like nanoelectronics with mechanical actuators, pumps, or motors, and may thereby form physical, biological, and chemical sensors. The name derives from typical device dimensions in the nanometer range, leading to low mass, high mechanical resonance frequencies, potentially large quantum mechanical effects such as zero point motion, and a high surface-to-volume ratio useful for surface-based sensing mechanisms. Applications include accelerometers and sensors to detect chemical substances in the air.
A recurrent neural network (RNN) is one of the two broad types of artificial neural network, characterized by direction of the flow of information between its layers. In contrast to the uni-directional feedforward neural network, it is a bi-directional artificial neural network, meaning that it allows the output from some nodes to affect subsequent input to the same nodes. Their ability to use internal state (memory) to process arbitrary sequences of inputs makes them applicable to tasks such as unsegmented, connected handwriting recognition or speech recognition. The term "recurrent neural network" is used to refer to the class of networks with an infinite impulse response, whereas "convolutional neural network" refers to the class of finite impulse response. Both classes of networks exhibit temporal dynamic behavior. A finite impulse recurrent network is a directed acyclic graph that can be unrolled and replaced with a strictly feedforward neural network, while an infinite impulse recurrent network is a directed cyclic graph that can not be unrolled.
Unconventional computing is computing by any of a wide range of new or unusual methods. It is also known as alternative computing.
Resistive random-access memory is a type of non-volatile (NV) random-access (RAM) computer memory that works by changing the resistance across a dielectric solid-state material, often referred to as a memristor. One major advantage of ReRAM over other NVRAM technologies is the ability to scale below 10nm.
A memristor is a non-linear two-terminal electrical component relating electric charge and magnetic flux linkage. It was described and named in 1971 by Leon Chua, completing a theoretical quartet of fundamental electrical components which also comprises the resistor, capacitor and inductor.
Daan Frenkel is a Dutch computational physicist in the Department of Chemistry at the University of Cambridge.
A physical neural network is a type of artificial neural network in which an electrically adjustable material is used to emulate the function of a neural synapse or a higher-order (dendritic) neuron model. "Physical" neural network is used to emphasize the reliance on physical hardware used to emulate neurons as opposed to software-based approaches. More generally the term is applicable to other artificial neural networks in which a memristor or other electrically adjustable resistance material is used to emulate a neural synapse.
Massimiliano Versace is the co-founder and the CEO of Neurala Inc, a Boston-based company building Artificial Intelligence emulating brain function in software and used in automating the process of visual inspection in manufacturing. He is also the founding Director of the Boston University Neuromorphics Lab. Massimiliano Versace is a Fulbright scholar and holds two PhD in Experimental Psychology from the University of Trieste, Italy and Cognitive and Neural Systems from Boston University, USA. He obtained his BSc from the University of Trieste, Italy.
Mark S. Lundstrom is an American electrical engineering researcher, educator, and author. He is known for contributions to the theory, modeling, and understanding of semiconductor devices, especially nanoscale transistors, and as the creator of the nanoHUB, a major online resource for nanotechnology. Lundstrom is Don and Carol Scifres Distinguished Professor of Electrical and Computer Engineering and in 2020 served as Acting Dean of the College of Engineering at Purdue University, in West Lafayette, Indiana.
Quantum machine learning is the integration of quantum algorithms within machine learning programs.
Muthusamy Lakshmanan is an Indian theoretical physicist currently working as Professor of Eminence at the Department of Nonlinear Dynamics of Bharathidasan University. Presently he is the DST-SERB National Science Chair awarded by the Science and Engineering Research Board, Department of Science and Technology. He has held several research fellowships which included Raja Ramanna fellowship of the Department of Atomic Energy, Alexander von Humboldt fellowship, Japan Society for the Promotion of Science fellowship, Royal Society Nuffield Foundation fellowship, and NASI-Senior Scientist Platinum Jubilee Fellowship. In the year 2021, on August 15, he was conferred with the Dr. A. P. J Abdul Kalam Award by the Government of Tamil Nadu.
Pablo Jarillo-Herrero is a Spanish physicist and current Cecil and Ida Green Professor of Physics at Massachusetts Institute of Technology (MIT).
Aleksandra Radenovic is a Swiss and Croatian biophysicist. Her research focuses on the development of experimental tools to study single-molecule biophysics. She is a professor of biological engineering at the École Polytechnique Fédérale de Lausanne (EPFL) and head of the Laboratory of Nanoscale Biology.
Mark McMahon Wilde is an American quantum information scientist. He is an Associate Professor in the School of Electrical and Computer Engineering at Cornell University, and he is also a Fields Member in the School of Applied and Engineering Physics and the Department of Computer Science at Cornell.
Sandip Tiwari is an Indian-born electrical engineer and applied physicist. He is the Charles N. Mellowes Professor of Engineering at Cornell University. His previous roles were Director of National Nanotechnology Users Network, Director of the National Nanotechnology Infrastructure Network, and research scientist at IBM T. J. Watson Research Center. He is best known for his pioneer research in the fields of SiGe transistor and nanocrystal memory.
Computing in Science & Engineering (CiSE) is a bimonthly technical magazine published by the IEEE Computer Society. It was founded in 1999 from the merger of two publications: Computational Science & Engineering (CS&E) and Computers in Physics (CIP), the first published by IEEE and the second by the American Institute of Physics (AIP). The founding editor-in-chief was George Cybenko, known for proving one of the first versions of the universal approximation theorem of neural networks.
The Caravelli-Traversa-Di Ventra equation (CTDV) is a closed-form equation to the evolution of networks of memristive devices, within the simplest model of memristive component. It was derived by F. Caravelli, F. Traversa and Massimiliano Di Ventra to study the exact evolution of complex circuits made of resistances with memory, also known as memristors.
Themistoklis "Themis" Prodromakis, FRSC, FBCS, FInstP, FIET, CEng is an electronic engineer. In 2022 he joined the School of Engineering at the University of Edinburgh as the Regius Professor of Engineering. Prodromakis is also Director of the Centre for Electronics Frontiers and holds an RAEng Chair in Emerging Technologies in AI hardware technologies. He is the founder and director of ArC Instruments that commercialises high-performance testing instruments for semiconductor technologies.
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