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Special Symposium on Intelligent Systems 2018
September 18 – 20, 2018, in Stuttgart and Tübingen
Cars are becoming increasingly autonomous, bird-like aerial vehicles may soon deliver packages, soft robots have the potential to assist us in our daily activities, and surgery might someday be performed by a fleet of microscopic devices. The digitization of industry and the birth of cyber-physical systems have brought intelligent systems to the forefront of science, technology, and everyday life. Only a deep understanding of natural and artificial systems that can perceive, act, and learn will allow our society to harness the full potential of intelligence in all its various forms.
On September 18, 19, and 20, the Max Planck Institute for Intelligent Systems is holding a Special Symposium on Intelligent Systems in Stuttgart and Tübingen, Germany. Keynote talks by renowned scientists from outside institutions will provide insights into cutting-edge research on intelligent systems worldwide and provide attendees with a glimpse into the future of this field.
Agenda
Tuesday, September 18, 2018, Stuttgart
10:00 - 10:30
Lecture Hall 2R04, Stuttgart
10:30 - 11:15
It is exciting to imagine a future when we can use wearable robots to increase strength or efficiency, restore or repair ability after injury or prevent injuries from happening in the first place. This vision is currently challenging to achieve due to limitations in current technology and a lack of understanding of how humans will respond to physical assistance. This talk will give an overview of our work on developing disruptive soft wearable robot technologies for augmenting and restoring human performance and what we have learned from biomechanical and physiological studies that furthers the scientific understanding of how humans interact with such machines. Our efforts are the result of a multidisciplinary team of students and research staff with backgrounds in engineering, materials science, apparel design, industrial design, biomechanics, and physical therapy, in addition to valuable collaborations with colleagues from Harvard, Boston University, and beyond. Our long-term vision is for ubiquitous soft wearable robots that can be worn all day, every day underneath clothing, in both the community, home, sporting and workplace environments. This would enable a change in the paradigm of how we currently assist mobility for those with physical impairments (i.e. replacing plastic braces with active assistance), and how we rehabilitate those after injury (i.e. training programs outside of a clinic environment) as well as how we protect healthy individuals with physical tasks (i.e. protecting military personnel, athletes or factory workers performing high risk tasks).
Speaker Bio >>
Conor Walsh is the John L. Loeb Associate Professor of Engineering and Applied Sciences at the Harvard John A. Paulson School of Engineering & Applied Sciences and a Core Faculty Member at the Wyss Institute at Harvard University. He directs the Harvard Biodesign Lab, which brings together researchers from the engineering, industrial design, apparel, clinical and business communities to develop disruptive robotic technologies for augmenting and restoring human performance. Example application areas include enhancing the mobility of healthy individuals, restoring the mobility of patients with gait deficits and assisting those with upper extremity weakness to perform activities of daily living. Conor’s multidisciplinary research spans robotics, materials, biology and medicine and has led to over 125 peer reviewed scientific papers. His group’s work is highly translation focused, with multiple technologies already licensed, and one that has entered a clinical trial. Conor is also dedicated to training the next generation of biomedical engineering innovators through his teaching, as well as outreach efforts through the Soft Robotics Toolkit. He is the winner of multiple awards including the IEEE ICRA Best Medical Robotics Paper Award, MIT Technology Review Innovator Under 35 Award, IEEE Early Academic Career Award in Robotics and Automation, the Rolex Award for Enterprise, Popular Science Brilliant 10, National Science Foundation Career Award, the Robotics Business Review Next Generation Game Changer Award and the MIT 100K Entrepreneurship Competition Grand Prize.
11:15 - 11:45
Coffee Break (In front of Lecture Hall 2R04, Stuttgart)
11:45 - 12:30
Many organisms fly in order to survive and reproduce. My lab focusses on understanding bird flight to improve flying robots—because birds fly further, longer, and more reliable in complex visual and wind environments. I use this multidisciplinary lens that integrates biomechanics, aerodynamics, and robotics to advance our understanding of the evolution of flight more generally across birds, bats, insects, and autorotating seeds. The development of flying organisms as an individual and their evolution as a species are shaped by the physical interaction between organism and surrounding air. The organism’s architecture is tuned for propelling itself and controlling its motion. Flying animals and plants maximize performance by generating and manipulating vortices. These vortices are created close to the body as it is driven by the action of muscles or gravity, then are ‘shed’ to form a wake (a trackway left behind in the fluid). I study how the organism’s architecture is tuned to utilize these and other aeromechanical principles to compare the function of bird wings to that of bat, insect, and maple seed wings. The experimental approaches range from making robotic models to training birds to fly in a custom-designed wind tunnel as well as in visual flight arena’s—and inventing methods to 3D scan birds and measure the aerodynamic force they generate—nonintrusively—with a novel aerodynamic force platform. The studies reveal that animals and plants have converged upon the same solution for generating high lift: A strong vortex that runs parallel to the leading edge of the wing, which it sucks upward. Why this vortex remains stably attached to flapping animal and spinning plant wings is elucidated and linked to kinematics and wing morphology. While wing morphology is quite rigid in insects and maple seeds, it is extremely fluid in birds. I will show how such ‘wing morphing’ significantly expands the performance envelope of birds during flight, and will dissect the mechanisms that enable birds to morph better than any aircraft can. Finally, I will show how these findings have inspired my students to design new flapping and morphing aerial robots.
Professor Lentink's multidisciplinary lab studies how birds fly to develop better flying robots—integrating biomechanics, fluid mechanics, and robot design. http://lentinklab.stanford.edu He has a BS and MS in Aerospace Engineering (Aerodynamics, Delft University of Technology) and a PhD in Experimental Zoology cum laude (Wageningen University). During his PhD he visited the California institute of Technology for 9 months to study insect flight. His postdoctoral training at Harvard was focused on studying bird flight. Publications range from technical journals to cover publications in Nature and Science. He is an alumnus of the Young Academy of the Royal Netherlands Academy of Arts and Sciences, recipient of the Dutch Academic Year Prize, the NSF CAREER award, he has been recognized in 2013 as one of 40 scientists under 40 by the World Economic Forum, and he is the inaugural winner of the Steven Vogel Young Investigator Award.
13:45 - 14:30
Soft robotics deals with interaction with environments that are uncertain and vulnerable to change, by easily adapting to the environment with soft materials. However, softness requires controlling large degrees of freedom. Many soft robots use pneumatics which can easily distribute the actuation. If tendons are used for actuating a soft body, the large degrees of freedom of the material either requires large number of tendons or limits the controllability. Tendon drive soft robots can benefit from using the concept of physically embodied intelligence, first proposed by Prof. Rolf Pfeifer. By embodying intelligence into the design, better performance can be achieved with a simpler actuation. In nature, there are few example that exhibit this property. Flytrap, for example, can close its leaves quickly by using bistability of the leaves instead of just relying on the actuation. Inchworm achieves adaptive gripping with its prolegs by using the buckling effect. In this talk, I will give an overview of various soft b robotic technologies, and some of the soft robots with physically embodied intelligence that are being developed at SNU. These examples will show that the concept of physically embodied intelligence simplifies the design and enables better performance by exploiting the characteristics of the material.
Kyu-Jin Cho received B.S and M.S. degrees from Seoul National University, Seoul, Korea and a Ph.D. degree in mechanical engineering from Massachusetts Institute of Technology. He was a post-doctoral fellow at Harvard Microrobotics Laboratory. At present, he is a professor of Mechanical and Engineering and the director of Soft Robotics Research Center and Biorobotics Laboratory at Seoul National University. His research interests include biologically inspired robotics, soft robotics, soft wearable devices, novel mechanisms using smart structures, origami robots and rehabilitation and assistive robotics. He has been exploring novel soft bio-inspired robot designs, including a water jumping robot, flytrap inspired robot and a soft wearable robot for the hand, called Exo-Glove. The work on the water jumping robot was published in SCIENCE and covered by over 300 news media world-wide. He has received the 2014 IEEE RAS Early Academic Career Award for his fundamental contributions to soft robotics and biologically inspired robot design. He has also received the 2014 ASME Compliant Mechanism Award, 2013 IROS Best Video Award, 2015 KROS Hakbo ART (Assistive Robotic Technology) Award and 2013 KSPE Paik Am Award. The Biorobotics Lab has won the 1st RoboSoft Grand Challenge sponsored by European Commission with the robot “SNUMAX” in Livorno, Italy.
14:30 - 15:15
Robots are moving from the factory floor and into our lives (e.g. autonomous cars, package delivery drones, and search-and-rescue devices). However, compared to living systems, locomotion by such devices is still relatively limited, in part because principles of interaction with complex environments are largely unknown. In this talk I will discuss efforts to develop a physics of moving systems -- a locomotion ``robophysics'' -- which we define as the pursuit of the discovery of principles of self-generated motion [Aguilar et al, Rep. Prog. Physics, 2016]. We use the methods of physics to examine successful and failed locomotion in simplified laboratory devices using parameter space exploration, systematic control, and techniques from dynamical systems. Drawing from examples from my group and our collaborators, I will discuss how robophysical studies in terrestrial environments have begun to aid engineers in the creation of devices that begin to achieve life-like locomotor abilities on and within complex environments, have inspired new physics questions in low-dimensional dynamical systems, geometric mechanics and soft matter physics, and have been useful to develop models for biological locomotion in complex terrain. The rapidly decreasing cost of constructing sophisticated robot models with easy access to significant computational power bodes well for scientists and engineers to engage in a discipline which can readily integrate experiment, theory and computation.
Dr. Daniel Goldman is a Dunn Family Professor in the School of Physics at the Georgia Institute of Technology (GT). He received his PhD in 2002 from the University of Texas at Austin, studying nonlinear dynamics and granular media. He did postdoctoral work in locomotion biomechanics at the University of California at Berkeley. Prof. Goldman became a faculty member at GT in the School of Physics in January 2007, and is core faculty of the GT Institute for Robotics and Intelligent Machines (IRIM), on the management team of the GT Soft Matter Incubator, a member of the GT Bioengineering Graduate Program, and is an adjunct member of the School of Biological Sciences.
Prof. Goldman's research program investigates the interaction of biological and physical systems with complex materials. He takes a comparative approach, for example, looking for common principles in the bio and neuromechanics of sand-swimming in lizards and snakes. He has introduced the discipline of ``robophysics'' to discover principles by which self-propelled systems perform work in the real world. His work has been featured popular outlets like the New York Times, Discover Magaziine.
Prof. Goldman awards include a Georgia Power Professor of Excellence, a Dunn Family Professor in the School of Physics at GT, a Fellow of the American Physical Society (2014), and has received an NSF CAREER/PECASE award, a DARPA Young Faculty Award, a Sigma Xi Young Faculty award, and a Burroughs Wellcome Fund Career Award at the Scientific Interface.
Wednesday, September 19, 2018, Stuttgart/Tübingen
10:00 - 10:45
The ultimate goal of any soft robotics system is to have a cohesive solution to improve the human – machine interface. For such an interface, it is critical to realize a versatile and adaptable multi-degrees of freedom robotic design. While the findings in soft robotics have broadened the application of robotics, they are still limited to specific scenarios. The next challenge is in pushing the boundaries of multi-disciplinary science interceptions simultaneously: materials, mechatronics, energy, control, and design. Such efforts will lead to robust solutions in design methodology, novel actuators, and a comprehensive fabrication and integration method of the core robotic components. This talk will highlight on the recent progresses in soft- material robots and origami robots that aim at achieving comprehensive solutions toward diverse soft human – robot applications.
Prof. Jamie Paik is director and founder of Reconfigurable Robotics Lab (RRL) of Swiss Federal Institute of Technology (EPFL) and a core member of Swiss National Centers of Competence in Research (NCCR) Robotics consortium. RRL’s research leverages expertise in multi-material fabrication and smart material actuation. She received her Ph.D. in Seoul National University on designing humanoid arm and a hand. During her Postdoctoral positions in ISIR (Institut des Systems Intelligents et de Robotic) in Universitat Pierre Marie Curie, Paris VI, she developed laparoscopic tools that are internationally patented and commercialized. At Harvard University’s Microrobotics Laboratory, she started developing unconventional robots that push the physical limits of material and mechanisms. Her latest research effort is in soft robotics and self-morphing Robogami (robotic orgami) that transforms its planar shape to 2D or 3D by folding in predefined patterns and sequences, just like the paper art, origami.
10:45 - 11:30
Robots generally excel at specific tasks in structured environments, but lack the versatility and adaptability required to interact-with and locomote-within the natural world. To increase versatility in robot design, my research group is developing robotic skins that can wrap around arbitrary deformable objects to induce the desired motions and deformations. Robotic skins integrate actuation and sensing into a single conformable material, and may be applied-to, removed-from, and transferred-between different objects to create a multitude of controllable robots with different functions to accommodate the demands of different environments. We have shown that attaching the same robotic skin to a deformable object in different ways, or to different objects, leads to unique motions. Further, we have shown that combining multiple robotic skins enables complex motions and functions. During this talk, I will demonstrate the versatility of this soft robot design approach by showing robotic skins in a wide range of applications - including manipulation tasks, locomotion, and wearables - using the same 2D robotic skins reconfigured on the surface of various 3D soft, inanimate objects.
Rebecca Kramer-Bottiglio is an Assistant Professor of Mechanical Engineering and Materials Science at Yale University. She completed her B.S. at the Johns Hopkins University, M.S. at U.C. Berkeley, and Ph.D. at Harvard University. Prior to joining the faculty at Yale, she was an Assistant Professor of Mechanical Engineering at Purdue University for four years. She currently serves as an Associate Editor of Frontiers in Robotics and AI: Soft Robotics, IEEE Robotics and Automation Letters, and IOPscience Multifunctional Materials. She is the recipient of the NSF CAREER Award, the NASA Early Career Faculty Award, the AFOSR Young Investigator Award, the ONR Young Investigator Award, and was named to Forbes’ 2015 30 under 30 list.
11:30 - 12:00
Coffee Break (In front of Lecture Hall 2R04 Lecture Hall 2R04, Stuttgart)
12:00 - 12:45
Robotics has the potential to address many of today’s pressing problems in fields ranging from healthcare to manufacturing to disaster relief. However, the traditional approaches used on the factory floor do not perform well in unstructured environments. The key to solving many of these challenges is to explore new, non-traditional designs. Fortunately, nature surrounds us with examples of novel ways to navigate and interact with the real world. Dr. Tolley’s Bioinspired Robotics and Design Lab seeks to borrow the key principles of operation from biological systems and apply them to robotic design. This talk will give an overview of projects in the lab demonstrating approaches to the design, fabrication, and control of soft robotic systems. These projects seek to develop bioinspired systems capable of navigating the world by walking, digging, and swimming, of interacting directly with humans and delicate objects, and of self-assembly by folding.
Michael T. Tolley is Assistant Professor in Mechanical and Aerospace Engineering, and director of the Bioinspired Robotics and Design Lab at the Jacobs School of Engineering, UC San Diego (bioinspired.eng.ucsd.edu). Before joining the mechanical engineering faculty at UCSD in the fall of 2014, he was a postdoctoral fellow and research associate at the Wyss Institute for Biologically Inspired Engineering and the School of Engineering and Applied Sciences, Harvard University. He received the Ph.D. and M.S. degrees in mechanical engineering with a minor in computer science from Cornell University in 2009 and 2011, respectively. He received the B. Eng. degree in mechanical engineering from McGill University in Montreal in 2005. His research interests include biologically inspired robotics and design, origami-inspired fabrication, self-assembly, and soft robotics. His work has appeared in leading academic journals including Science and Nature, and has been recognized by awards including a US Office of Naval Research Young Investigator Program award and a 3M Non-Tenured Faculty Award.
14:00 - 14:45
Robots today rely on rigid components and electric motors based on metal and magnets, making them heavy, unsafe near humans, expensive and ill-suited for unpredictable environments. Nature, in contrast, makes extensive use of soft materials and has produced organisms that drastically outperform robots in terms of agility, dexterity, and adaptability. The Keplinger Lab aims to fundamentally challenge current limitations of robotic hardware, using an interdisciplinary approach that synergizes concepts from soft matter physics and chemistry with advanced engineering technologies to introduce intelligent materials systems for a new generation of life-like robots. One major theme of research is the development of new classes of actuators – a key component of all robotic systems – that replicate the sweeping success of biological muscle, a masterpiece of evolution featuring astonishing all-around actuation performance, the ability to self-heal after damage, and seamless integration with sensing.
This talk is focused on the labs' recently introduced HASEL artificial muscle technology. Hydraulically Amplified Self-healing ELectrostatic (HASEL) transducers are a new class of self-sensing, high-performance muscle-mimetic actuators, which are electrically driven and harness a mechanism that couples electrostatic and hydraulic forces to achieve a wide variety of actuation modes. Current designs of HASEL are capable of exceeding actuation stress of 0.3 MPa, linear strain of 100%, specific power of 800W/kg, full-cycle electromechanical efficiency of 30% and bandwidth of 100Hz; all these metrics match or exceed the capabilities of biological muscle. Additionally, HASEL actuators can repeatedly and autonomously self-heal after electric breakdown, thereby enabling robust performance for millions of cycles. Modeling results predict the impact of material parameters and scaling laws of these actuators, laying out a roadmap towards future HASEL actuators with drastically improved performance. These results highlight opportunities to further develop HASEL artificial muscles for wide use in next-generation robots that replicate the vast capabilities of biological systems.
Christoph Keplinger is an Assistant Professor of Mechanical Engineering and a Fellow of the Materials Science and Engineering Program at the University of Colorado Boulder, where he also holds an endowed appointment serving as Mollenkopf Faculty Fellow. Building upon his background in soft matter physics (PhD, JKU Linz), mechanics and chemistry (Postdoc, Harvard University), he leads a highly interdisciplinary research group at Boulder, with a current focus on (I) soft, muscle-mimetic actuators and sensors, (II) energy harvesting and (III) functional polymers. The high quality of his work has been recognized by the scientific community, as illustrated by publications in top journals including Science, Science Robotics, PNAS, Advanced Materials and Nature Chemistry, by prestigious US awards such as a 2017 Packard Fellowship for Science and Engineering, and by international awards such as the 2013 EAPromising European Researcher Award from the European Scientific Network for Artificial Muscles. In 2018 his research group has spun out a first company – Artimus Robotics – which will commercialize the labs' HASEL artificial muscle technology to help enable a new generation of life-like robotic hardware.
15:45 - 16:00
Coffee Break (Lounge, Tübingen)
16:00 - 16:30
N0.002, Tübingen
Thursday, September 20, 2018, Tübingen
10:15 - 11:00
Being able to autonomously learn from scratch is a key ability of intelligent systems - and a central focus of my research. A particular challenge in real world robotic scenarios are methods that are in addition highly data-efficient and robust, since data-collection on real robots is time intensive and often expensive. I will discuss two main areas of progress towards this goal - improved off-policy learning and better exploration - and give examples of simulated and real robots that can learn increasingly complex tasks from scratch.
Martin Riedmiller is a research scientist and team-lead at DeepMind, London. Before joining DeepMind fulltime in spring 2015, he held several professor positions in machine learning and neuro-informatics from 2002 to 2015 in Dortmund, Osnabrück and Freiburg University. He has contributed over 20 years in the fields of reinforcement learning, neural networks and learning control systems. He is author and co-author of some early and ground-lying work on efficient and robust supervised learning and reinforcement learning algorithms, and deep reinforcement learning systems.
11:00 - 11:45
The ultimate promise of robotics is to design devices that can physically interact with the world. To date, robots have been primarily deployed in highly structured and predictable environments. However, we envision the next generation of robots (ranging from self-driving and -flying vehicles to robot assistants) to operate in unpredictable and generally unknown environments alongside humans. This challenges current robot algorithms, which have been largely based on a-priori knowledge about the system and its environment. While research has shown that robots are able to learn new skills from experience and adapt to unknown situations, these results have been limited to learning single tasks, and demonstrated in simulation or lab settings. The next challenge is to enable robot learning in real-world application scenarios. This will require versatile, data-efficient and online learning algorithms that guarantee safety when placed in a closed-loop system architecture. It will also require to answer the fundamental question of how to design learning architectures for dynamic and interactive agents. This talk will highlight our recent progress in combining learning methods with formal results from control theory. By combining models with data, our algorithms achieve adaptation to changing conditions during long-term operation, data-efficient multi-robot, multi-task transfer learning, and safe reinforcement learning. We demonstrate our algorithms in vision-based off-road driving and drone flight experiments, as well as on mobile manipulators.
Angela Schoellig is an Assistant Professor at the University of Toronto Institute for Aerospace Studies and an Associate Director of the Centre for Aerial Robotics Research and Education. She holds a Canada Research Chair in Machine Learning for Robotics and Control, is a principal investigator of the NSERC Canadian Robotics Network, and is a Faculty Affiliate of the Vector Institute for Artificial Intelligence. She conducts research at the intersection of robotics, controls, and machine learning. Her goal is to enhance the performance, safety, and autonomy of robots by enabling them to learn from past experiments and from each other. She is a recipient of a Sloan Research Fellowship (2017), an Ontario Early Researcher Award (2017), and a Connaught New Researcher Award (2015). She is one of MIT Technology Review’s Innovators Under 35 (2017), a Canada Science Leadership Program Fellow (2014), and one of Robohub’s “25 women in robotics you need to know about (2013)”. Her team won the 2018 North-American SAE AutoDrive Challenge sponsored by General Motors. Her PhD at ETH Zurich (2013) was awarded the ETH Medal and the Dimitris N. Chorafas Foundation Award. She holds both an M.Sc. in Engineering Cybernetics from the University of Stuttgart (2008) and an M.Sc. in Engineering Science and Mechanics from the Georgia Institute of Technology (2007). More information can be found at: www.schoellig.name.
11:45 - 12:15
12:15 - 13:00
In recent years, deep neural networks have become an ubiquitous tool in a broad range of AI applications, and inspired new models for neural processing in the brain. I will talk about similarities and discrepancies of how decisions are formed in biological and artificial neural networks and how this relates to the ability of domain adaptation, few-shot learning, task transfer and adversarial robustness in the context of visual perception. I will argue that a deeper understanding of the distributed nature of neural decision making will be crucial for more data-efficient, interpretable and robust learning machines and conclude with more speculative ideas on developing social intelligence in artificial neural networks.
Matthias Bethge's research lies at the interface between artificial intelligence and neuroscience and focuses on representation learning, robust decision making and neuro-computational design principles of perceiving neural networks both in brains and machines. A popular outcome of his research has been a new method for creating artistic images (http://deepart.io).
14:15 - 15:00
Intelligent agents need to understand the world and to plan their actions at multiple levels of temporal resolution. In reinforcement learning, the value function is a standard way to model knowledge related to long-term action outcomes. In this talk, I will describe generalized value functions, which are a way of representing knowledge about many behaviours and at many temporal scales. I will describe how such a representation can be learned form experience and describe new open research questions in this area.
Doina Precup splits her time between McGill University, where she co-directs the Reasoning and Learning Lab in the School of Computer Science, and DeepMind Montreal, where she has led the research team since its formation in October 2017. Her research interests are in the areas of reinforcement learning, deep learning, time series analysis, and diverse applications of machine learning in health care, automated control and other fields. She became senior member of the Association for the Advancement of Artificial Intelligence in 2015, Canada Research Chair in Machine Learning in 2016 and Senior Fellow of the Canadian Institute for Advanced Research in 2017. Dr. Precup is also involved in activities supporting the organization of MILA and the wider Quebec AI ecosystem.
15:00 - 15:30
15:30 - 16:15
Implicit generative modeling has recently scratched the surface on how deep learning can be used as a universal simulator. Until recently, deep learning has been used quite successfully to solve long standing discriminative problems in computer vision, speech and natural language processing, basically showing that hand-coded human-engineered features are suboptimal in the presence of: complex problems in which human only have a basic understanding of the variability of the data; and, the availability of large labelled data sets. Recently, Variational Auto-Encoders and Generative Adversarial Networks have shown that the same representation learning can be used for generative modelling. These implicit generative models do not provide an interpretable model for the available data, but a universal simulator that it is able to generate data similar to the one used for training. These tools can be used to simplify complex simulations (e.g. climate models) or limited observations (e.g. cosmology or particle physics), opening the door to Artificial Intelligence powered advances in many different fields of science. In this talk, we will first present these general methods and what are their potential used and current short comings. In the second part of the talk, we will focus on a recent application of GANs for password guessing. This is an ideal application to understand the need for GANs and understand why they work and what are their limitations.
Fernando Pérez-Cruz was born in Sevilla, Spain, in 1973. He received a PhD. in Electrical Engineering in 2000 from the Technical University of Madrid and an MSc/BSc in Electrical Engineering from the University of Sevilla in 1996. He is the Chief Data Scientist at the Swiss Data Science Center (ETH Zurich and EPFL). He has been a member of the technical staff at Bell Labs and an Associate Professor with the Department of Signal Theory and Communication at University Carlos III in Madrid and Computer Science at Stevens Institute of Technology. He has been a visiting professor at Princeton University under a Marie Curie Fellowship and a Research Scientist at Amazon. He has also held positions at the Gatsby Unit (London), Max Planck Institute for Biological Cybernetics (Tuebingen), BioWulf Technologies (New York) and the Technical University of Madrid and Alcala University (Madrid). His current research interest lies in machine learning and information theory and its application to signal processing and communications. Fernando has organized several machine learning, signal processing, and information theory conferences. Fernando has supervised 8 PhD students and numerous MSc students, as well as one junior and one senior Marie Curie Fellow. Fernando has published over 40 papers in leading academic journals, as well as over 60 peer-reviewed conferences.
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