Silvia Ferrari | |
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Silvia Ferrari is an Italian-American aerospace engineer. She is John Brancaccio Professor at the Sibley School of Mechanical and Aerospace Engineering at Cornell University [2] and also the director of the Laboratory for Intelligent Systems and Control (LISC) at the same university.
Ferrari received her B.S. in Aerospace Engineering from Embry-Riddle Aeronautical University and earned her M.A. and Ph.D. degrees in Mechanical and Aerospace engineering from Princeton University. [3]
Ferrari's research is primarily based on multi-scale adaptive sensor systems. [4] Her research also includes online adaptive critic flight control, [5] and simulations for the beech bonanza fly-by-wire test-bed. [6] She wrote a book called Information-Driven planning [7] and control along with the Thomas A. Wettergren regarding the performance modeling strategies.
Ferrari is currently the director of the laboratory for intelligent systems and controls. [3] Prior to that, she was a professor of mechanical engineering at Duke University. [8] She is the founder and director of NSF Integrative Graduate Education and research trainee-ship. [2] Her teaching interests include optimal control theory, sensor networks, intelligent systems, feedback control of dynamic systems, and multivariable control. She will be the Institute Director for the Veho Institute for Vehicle Intelligence established at Cornell Tech. [9]
Professor Ferrari’s research interests include Robotics, [10] Theory of computation, Statistics and machine learning, systems and Networking, Neuroscience, Signal and Image Processing, Artificial Intelligence, Sensors and Actuators, Complex Systems, Remote Sensing, Algorithms, Nonlinear dynamics, [11] Information theory, and communications.
Prof. Silvia Ferrari moved to Cornell University and focused on the development of new mathematical models of learning and plasticity uncovered from biological brains, [12] design, and analysis of methods and algorithms for computational intelligence and sensorimotor learning and control. She also developed new methods rooted in machine learning and systems theory to design intelligent autonomous systems that are able to learn and discover new information over time. Her Principal research efforts include the Intelligent systems for criminal profiling, [13] approximate dynamic programming, learning in neural and Bayesian networks, [14] reconfigurable control of aircraft, sensor path planning, and Integrated surveillance systems.
She worked on research projects like artificial brains and on the brains of moths with an aim to improve the drone flight for which she has been awarded grants of $2,587,875 and $400,000 respectively. [15] She was also a part of Developing new programming that will make Robobees more autonomous and adaptable to complex environments and her research project on robots development and responding to human gestures. [16] In an effort to win the Popular board game Clue, she along with her team developed a strategy and succeeded in doing so. she Co-led the launch of Veho institute for Vehicle Intelligence along with Cornell engineering. [17]
Ferrari was the recipient of the 2005 Presidential Early Career Award for Scientists and Engineers by the National Science Foundation. [2] Additional awards include the Office of Naval Research Young Investigator Award, [18] international crime analysis association research award, [19] and National Science Foundation Career award. [20] She is a senior member of the IEEE and a past American Society of Mechanical Engineer (ASME) Graduate Teaching Fellow. [21]
She gave a TED talk regarding the new generation of robots and what they can do. [22] She also spoke about the instruments which are capable of unprecedented vision, hearing, Olfaction and about the active sensors. She also gave a speech on how aquatic mammals like dolphins and whales can communicate with each other underwater, and also about hyperspectral cameras object recognition and emotions of humans all the way from space. Other considerations have included how a hyperspectral camera can be used to monitor an industrial plant, what type of parameters robots use for perception, and should these robots perceive the world as humans do—or will humanity perhaps be better served by having a new and different perspective.
Bio-inspired computing, short for biologically inspired computing, is a field of study which seeks to solve computer science problems using models of biology. It relates to connectionism, social behavior, and emergence. Within computer science, bio-inspired computing relates to artificial intelligence and machine learning. Bio-inspired computing is a major subset of natural computation.
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.
Sensor fusion is the process of combining sensor data or data derived from disparate sources such that the resulting information has less uncertainty than would be possible when these sources were used individually. For instance, one could potentially obtain a more accurate location estimate of an indoor object by combining multiple data sources such as video cameras and WiFi localization signals. The term uncertainty reduction in this case can mean more accurate, more complete, or more dependable, or refer to the result of an emerging view, such as stereoscopic vision.
In computer science, programming by demonstration (PbD) is an end-user development technique for teaching a computer or a robot new behaviors by demonstrating the task to transfer directly instead of programming it through machine commands.
Neurorobotics is the combined study of neuroscience, robotics, and artificial intelligence. It is the science and technology of embodied autonomous neural systems. Neural systems include brain-inspired algorithms, computational models of biological neural networks and actual biological systems. Such neural systems can be embodied in machines with mechanic or any other forms of physical actuation. This includes robots, prosthetic or wearable systems but also, at smaller scale, micro-machines and, at the larger scales, furniture and infrastructures.
Cyber–Physical System (CPS) are integrations of computation with physical processes. In cyber–physical systems, physical and software components are deeply intertwined, able to operate on different spatial and temporal scales, exhibit multiple and distinct behavioral modalities, and interact with each other in ways that change with context. CPS involves transdisciplinary approaches, merging theory of cybernetics, mechatronics, design and process science. The process control is often referred to as embedded systems. In embedded systems, the emphasis tends to be more on the computational elements, and less on an intense link between the computational and physical elements. CPS is also similar to the Internet of Things (IoT), sharing the same basic architecture; nevertheless, CPS presents a higher combination and coordination between physical and computational elements.
The Guidance, Control and Decision Systems Laboratory (GCDSL) is situated in the Department of Aerospace Engineering at the Indian Institute of Science in Bangalore, India. The Mobile Robotics Laboratory (MRL) is its experimental division. They are headed by Dr. Debasish Ghose, Full Professor.
Morphogenetic robotics generally refers to the methodologies that address challenges in robotics inspired by biological morphogenesis.
Naomi Ehrich Leonard is the Edwin S. Wilsey Professor of Mechanical and Aerospace Engineering at Princeton University. She is the director of the Princeton Council on Science and Technology and an associated faculty member in the Program in Applied & Computational Mathematics, Princeton Neuroscience Institute, and the Program in Quantitative and Computational Biology. She is the founding editor of the Annual Review of Control, Robotics, and Autonomous Systems.
LAURON is a six-legged walking robot, which is being developed at the FZI Forschungszentrum Informatik in Germany. The mechanics and the movements of the robot are biologically-inspired, mimicking the stick insect Carausius Morosus. The development of the LAURON walking robot started with basic research in field of six-legged locomotion in the early 1990s and led to the first robot, called LAURON. In the year 1994, this robot was presented to public at the CeBIT in Hanover. This first LAURON generation was, in contrast to the current generation, controlled by an artificial neural network, hence the robot's German name: LAUfROboter Neuronal gesteuert. The current generation LARUON V was finished in 2013.
LIONsolver is an integrated software for data mining, business intelligence, analytics, and modeling and reactive business intelligence approach. A non-profit version is also available as LIONoso.
Wassim Michael Haddad is a Lebanese-Greek-American applied mathematician, scientist, and engineer, with research specialization in the areas of dynamical systems and control. His research has led to fundamental breakthroughs in applied mathematics, thermodynamics, stability theory, robust control, dynamical system theory, and neuroscience. Professor Haddad is a member of the faculty of the School of Aerospace Engineering at Georgia Institute of Technology, where he holds the rank of Professor and Chair of the Flight Mechanics and Control Discipline. Dr. Haddad is a member of the Academy of Nonlinear SciencesArchived 2016-03-04 at the Wayback Machine for recognition of paramount contributions to the fields of nonlinear stability theory, nonlinear dynamical systems, and nonlinear control and an IEEE Fellow for contributions to robust, nonlinear, and hybrid control systems.
Bayesian optimization is a sequential design strategy for global optimization of black-box functions that does not assume any functional forms. It is usually employed to optimize expensive-to-evaluate functions.
Naira Hovakimyan is an Armenian control theorist who holds the W. Grafton and Lillian B. Wilkins professorship of the Mechanical Science and Engineering at the University of Illinois at Urbana-Champaign. She is the director of AVIATE Center of flying cars at UIUC, funded through a NASA University Leadership Initiative. She was the inaugural director of the Intelligent Robotics Laboratory during 2015–2017, associated with the Coordinated Science Laboratory at the University of Illinois at Urbana-Champaign.
Sonia Martínez Díaz is a Spanish mechanical engineer whose research applies control theory to the coordinated motion of robot swarms and mobile wireless sensor networks. She is a professor in the Department of Mechanical and Aerospace Engineering at the University of California, San Diego.
Frank L. Lewis is an American electrical engineer, academic and researcher. He is a professor of electrical engineering, Moncrief-O’Donnell Endowed Chair, and head of Advanced Controls and Sensors Group at The University of Texas at Arlington (UTA). He is a member of UTA Academy of Distinguished Teachers and a charter member of UTA Academy of Distinguished Scholars.
Alois Christian Knoll is German computer scientist and professor at the TUM School of Computation, Information and Technology at the Technical University of Munich (TUM). He is head of the Chair of Robotics, Artificial Intelligence and Embedded Systems.
A continuum robot is a type of robot that is characterised by infinite degrees of freedom and number of joints. These characteristics allow continuum manipulators to adjust and modify their shape at any point along their length, granting them the possibility to work in confined spaces and complex environments where standard rigid-link robots cannot operate. In particular, we can define a continuum robot as an actuatable structure whose constitutive material forms curves with continuous tangent vectors. This is a fundamental definition that allows to distinguish between continuum robots and snake-arm robots or hyper-redundant manipulators: the presence of rigid links and joints allows them to only approximately perform curves with continuous tangent vectors.
Javier Andreu-Perez is a British computer scientist and a Senior Lecturer and Chair in Smart Health Technologies at the University of Essex. He is also associate editor-in-chief of Neurocomputing for the area of Deep Learning and Machine Learning. Andreu-Perez research is mainly focused on Human-Centered Artificial Intelligence (HCAI). He also chairs a interdisciplinary lab in this area, HCAI-Essex.
Ali Galip Ulsoy is an academic at the University of Michigan (UM), Ann Arbor, where he is the C.D. Mote Jr. Distinguished University Professor Emeritus of Mechanical Engineering and the William Clay Ford Professor Emeritus of Manufacturing.
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