Seminars: Spring 2018
Optimizing The Network Edge For Flexible Service Provisioning
Speaker: Leandros Tassiulas, Yale University
Time: 11:00 am - 12:00 pm Feb 1, 2018
Location: 2 MetroTech Center, 10th floor, Room 10.099, Brooklyn, NY
Abstract: The virtualization of network resources provides unique flexibility in service provisioning in most levels of the network stack. Softwarization of the network control and operation (SDN) is a key enabler of that development. Starting from the network core, SDN is a dominant trend in the evolution of network architectures with increased emphasis recently on the network edge. I will present some recent results in this area starting with a study on migration from legacy networking to SDN enabled network modules. The trade-off between the benefits of SDN upgrades and the cost of deployment is addressed and captured by an appropriate sub-modular function that allows to optimize the penetration pace of the technology. Validation on some real world network topologies and traffic matrices will be presented as well. Then we move our attention to the network periphery. A wireless multi-hop extension at the network edge is considered and the problem of enabling SDN is addressed via replication of SDN controllers. The delay constraints of the controlled data-path elements is appropriately modeled and the problem of locating the controllers is addressed via optimization and a proof-of concept implementation. An alternate approach is considered then for the wireless network where we assume coexistence of SDN enabled components with network islands operating under distributed adhoc routing protocols. The trade-off of the coexistence is studied and the impact of SDN penetration is evaluated. Some paradigms of collaborative network services are presented finally as they are enabled by the above architectural evolution.
About the Speaker: Leandros Tassiulas is the John C. Malone Professor of Electrical Engineering at Yale University. His research interests are in the field of computer and communication networks with emphasis on fundamental mathematical models and algorithms of complex networks, architectures and protocols of wireless systems, sensor networks, novel internet architectures and experimental platforms for network research. His most notable contributions include the max-weight scheduling algorithm and the back-pressure network control policy, opportunistic scheduling in wireless, the maximum lifetime approach for wireless network energy management, and the consideration of joint access control and antenna transmission management in multiple antenna wireless systems. Dr. Tassiulas is a Fellow of IEEE (2007). His research has been recognized by several awards including the IEEE Koji Kobayashi computer and communications award (2016), the inaugural INFOCOM 2007 Achievement Award "for fundamental contributions to resource allocation in communication networks," several best paper awards including the INFOCOM 1994, 2017 and Mobihoc 2016, a National Science Foundation (NSF) Research Initiation Award (1992), an NSF CAREER Award (1995), an Office of Naval Research Young Investigator Award (1997) and a Bodossaki Foundation award (1999). He holds a Ph.D. in Electrical Engineering from the University of Maryland, College Park (1991). He has held faculty positions at Polytechnic University, New York, University of Maryland, College Park, University of Ioannina and University of Thessaly, Greece.
Models, Algorithms and Applications of Convex Optimal Power Flow
Speaker: Zhao Yuan, NYU Shanghai Presents Engineering
Time: 3:00 pm - 5:00 pm Feb 13, 2018
Location: 2 MetroTech Center, 10th floor, Room 10.099, Brooklyn, NY
Abstract: Optimal power flow (OPF) is the fundamental mathematical model to optimize the operation of power system. We propose second-order cone programming (SOCP) convex OPF models which are reformulated from the original nonconvex OPF model. The advantage of convex OPF over nonconvex OPF in terms of global optimality over local optimality is obvious. We then propose a modified Benders decomposition algorithm (M-BDA) to efficiently solve large-scale OPF model by decomposition and parallelization. The feasibility and optimality of the proposed M-BDA are analytically and numerically proved. Finally the applications of convex OPF in distribution locational marginal pricing, wind power integration and super grid coordinated energy dispatch are demonstrated.
About the Speaker: PhD Candidate, Erasmus Mundus Joint PhD Degree: KTH Royal Institute of Technology, Sweden Comillas Pontifical University, Spain Delft University of Technology, Netherlands.
Data Analytics Approach for Topology Change Detection and Identification in Power Systems
Speaker: Maggie Cheng, NJIT
Time: 11:00 am - 12:00 pm Feb 15, 2018
Location: 2 MetroTech Center, 10th floor, Room 10.099, Brooklyn, NY
Abstract: An undetected topology change caused by overgrown trees is the root cause for the 2003 large scale blackout. The discrepancy between the actual topology of the power grid and the graph model used in state estimation is considered a topology error. Topology error was a reason that the WLS-based state estimation failed to converge and report alarms. This presentation will cover a non-WLS approach for topology error detection, and a learning framework to further identify the line outage. Time series analysis of multi-stream PMU data is used for feature extraction, and several statistical learning algorithms are trained based on these features. The line outage identification process is not tightly coupled with power flow analysis and state estimation as these tasks require detailed and accurate information about the system matrices, but can achieve a high detection rate comparable to the previous work that involves solving power flow and state estimation equations. Both single line outage and multiple simultaneous line outages are considered.
About the Speaker: Maggie Cheng is an associate professor in the Martin Tuckman School of Management in New Jersey Institute of Technology. She received a Ph.D. degree in Computer Science from the University of Minnesota at the Twin Cities in 2003. She was a faculty member in the department of Computer Science in Missouri University of Science and Technology before she joined NJIT in 2016. Her research focuses on data analytics methodology and optimization in complex network systems, and has a wide spectrum of topics including data analytics for cyber-physical systems, intrusion and attack detection in communication networks, user cyber behavioral analysis, network tomography, fault diagnosis, system level vulnerability analysis, as well as social network analysis on link formation, information propagation, and community detection. Her research has been sponsored by National Science Foundation, Department of Energy, Department of Education, Department of Transportation, and UM-System Research Board. She served as a guest editor of the journal of Combinatorial Optimization, associate editor of Nano Communication Networks Journal, and is currently on the editorial board of the International Journal of Sensor Networks. She served as an organizer and member of technical program committees in multiple international conferences.
Obstacles to Progress in Deep Learning and AI
Speaker: Yann LeCun, Facebook AI Research & New York University
Time: 10:00 am - 11:00 am Feb 20, 2018
Location: Pfizer Auditorium, 5 MetroTech Center, Brooklyn, NY, US
Abstract: Deep learning is causing revolutions in computer perception and natural language understanding. But almost all these successes largely rely on supervised learning, where the machine is required to predict human-provided annotations. For game AI, most systems use model-free reinforcement learning, which requires too many trials to be practical in the real world. But animals and humans seem to learn vast amounts of knowledge about how the world works through mere observation and occasional actions. Good predictive world models are an essential component of intelligent behavior: with them, one can predict outcomes and plan courses of actions. One could argue that prediction is the essence of intelligence. Good predictive models may be the basis of intuition, reasoning and "common sense", allowing us to fill in missing information: predicting the future from the past and present, the past from the present, or the state of the world from noisy percepts. After a brief presentation of the state of the art in deep learning, some promising principles and methods for predictive learning will be discussed.
Toward Minimalistic and Learning-Enabled Autonomous Navigation
Speaker: Brendan Englot, Stevens Institute of Technology
Time: 11:00 am - 12:00 pm Feb 23, 2018
Location: 2 MetroTech Center, 9th floor, Room 9.011, Brooklyn, NY
Abstract: This seminar will describe recent research efforts to produce lightweight and robust autonomous navigation solutions for range-sensing mobile robots at opposite ends of the spectrum: (1) unmanned ground vehicles (UGVs) equipped with dense, volumetric scanning lidar, and (2) underwater remotely operated vehicles (ROVs) supported by sparse, noisy sonar data. In both cases, these robots must efficiently and safely navigate complex, unstructured environments in the presence of clutter. For range-sensing UGVs, I will discuss efficient techniques for perception and decision-making over dense and voluminous data. This includes a navigation package that relies solely on a Velodyne "Puck" lidar and an Nvidia Jetson TX2 embedded computer to achieve (1) computationally lightweight, low-drift lidar odometry, and (2) real-time scan-by-scan terrain traversability mapping. These techniques in turn support learning-enabled decision-making that guides efficient autonomous exploration of unknown environments. For sonar-equipped ROVs, I will discuss techniques that leverage probabilistic inference to support decision-making over sparse and noisy data. This includes a pipeline for accurate localization and mapping with small quantities of features, occupancy mapping that leverages probabilistic inference to produce predictive 3D grid maps of submerged structures, and an active-SLAM exploration algorithm that uses virtual landmarks to produce improved-accuracy maps. Finally, in support of robust underwater maneuvering and control, I will discuss approaches for efficient motion planning under localization uncertainty, and for robot reinforcement learning under heteroscedastic noise.
About the Speaker: Dr. Brendan Englot received S.B., S.M. and Ph.D. degrees in Mechanical Engineering from the Massachusetts Institute of Technology in 2007, 2009 and 2012, respectively. At MIT, he studied motion planning for surveillance and inspection applications, deploying his algorithms on an underwater inspection robot that is now being produced in quantity for the US Navy. During 2012-2014, Brendan was with United Technologies Research Center in East Hartford, Connecticut, where he was a Research Scientist and Principal Investigator in the Autonomous and Intelligent Robotics Laboratory (AIRLab) and a technical contributor to the Sikorsky Autonomous Research Aircraft (SARA). At Stevens Institute of Technology, he directs the Robust Field Autonomy Lab, which focuses on robust autonomous navigation solutions for robots operating in harsh and unstructured environments. Brendan is the recipient of a 2017 NSF CAREER award.
Continuously Learning Robots
Speaker: Franziska Meier, Max-Planck Institute for Intelligent Systems
Time: 11:00 am - 12:00 pm Feb 26, 2018
Location: 6 MetroTech Center, JAB674, Brooklyn, NY
Abstract: With recent advances in machine learning for intelligent agent design, the robotics community has made immense progress towards autonomous skill learning. However, the day of deploying robots to assist people in their homes still seems far away. One of the key challenges towards this goal is to enable a robot to robustly and safely deal with unknown situations and unexpected events.
Current robot learning approaches assume that one can prepare a robot for every possible task and environment variation. With this assumption learning becomes a large data collection effort, followed by a one-time training phase. Once training has converged, learning of the system is terminated and the robot is left to execute it’s skills in the real world, without the ability to further adapt and improve them. Yet, to be truly autonomous, robots need to be able to react to unexpected events and then update their models to include the just encountered data points. Furthermore, a robot’s ability to adapt to new situations should improve over time. In short, true autonomy requires continuous learning. However, continuously updating models without forgetting previously learned mappings remains a fundamental open research problem. In this talk, I will present learning algorithms, based on localized inference schemes, that alleviate the problem of forgetting when learning continuously. Furthermore, I will introduce our recent advances on learning-to-learn for robotics, which accelerates learning of novel task variations. I will demonstrate the effectiveness of our learning algorithms on the challenging task of learning the dynamics of a 7-DOF torque-controlled manipulator, which operates at a 1000Hz. Finally, I will conclude this talk by presenting my vision on how to enable life-long learning for robotics.
About the Speaker: Franziska Meier is a research scientist at the Max-Planck Institute for Intelligent Systems since August 2016. She is also a postdoctoral researcher with Dieter Fox at the University of Washington, Seattle, since February 2017. Before that she was a PhD student at the University of Southern California. She defended her thesis on “Probabilistic Machine Learning for Robotics” in 2016, under the supervision of Prof. Stefan Schaal. Prior to her PhD studies, she received her Diploma in Computer Science from the Technical University of Munich and attended the Georgia Institute of Technology as graduate student in Computer Science. Her research focuses on machine learning for robotics, with a special emphasis on continuous learning for robotics.
Flying Robots: Agile Autonomous Navigation and Physical Interaction
Speaker: Giuseppe Loianno, University of Pennsylvania
Time: 11:00 am - 12:00 pm Mar 2, 2018
Location: 2 MetroTech Center, 9.011, Brooklyn, NY
Abstract: Flying robots are starting to play a major role in several tasks such as search and rescue, interaction with the environment, inspection, patrolling and monitoring. Unfortunately, their dynamics make them extremely difficult to control and this is particularly true in absence of external positioning systems, such as GPS and motion-capture systems. Additionally, autonomous maneuvers based on onboard sensors are still very slow compared to those attainable with motion capture systems. Agile navigation of Micro Aerial Vehicles (MAVs) through unknown environments poses a number of challenges in terms of perception, state estimation, planning, and control. To achieve this, MAVs have to localize themselves and coordinate between each other in unstructured environments. This in turn requires the MAV to use a combination of absolute or relative asynchronous measurements provided by different noisy sensors at different rates which have to be fused to obtain a reliable state estimate at rates of above 200 Hz. In this talk, I will present recent research results on the pose estimation and planning problems for agile flights, transportation and physical interaction with the environment using a minimal onboard sensor suite composed mainly by a single camera system and an Inertial Measurement Unit (IMU). For truly autonomous agile navigation, the perception, planning and control problems have to be tightly integrated and solved concurrently. I will demonstrate how these different technologies can be combined to design new on-board algorithms, which allow a robust solution enabling aggressive and agile flight maneuvers with MAVs in different scenarios including the ability to interact with the environment.
About the Speaker: Dr. Giuseppe Loianno is a lecturer, research scientist, and team leader at the General Robotics, Automation, Sensing and Perception (GRASP) Laboratory at the University of Pennsylvania. He received his BSc and MSc degrees in automation engineering, both with honors, from the University of Naples "Federico II" in December 2007 and February 2010, respectively. He received his PhD in computer and control engineering focusing in robotics in May 2014 in the PRISMA Lab Group, led by Prof. Dr. Bruno Siciliano. He has been involved in the EU FP7 project AIRobots (www.airobots.eu) in sensor fusion and visual control. Dr. Loianno has published more than 40 conference papers, journal papers, and book chapters. From April 2013 he worked for 14 months with the GRASP Lab at the University of Pennsylvania, supervised by Prof. Dr. Vijay Kumar. From June 2014 to July 2015, he was a postdoctoral researcher in his lab, where he is currently a research scientist, lecturer and team leader. His research interests include visual odometry, sensor fusion, and visual servoing for micro aerial vehicles. He is worldwide recognized for his expertise in autonomy for small aircraft. He received the Conference Editorial Board Best Reviewer Award at ICRA 2016. His work has been featured in a large number of renowned news and magazines.
Methods and Systems for Understanding Large-Scale Internet Threats
Speaker: Paul Pearce, University of California, Berkeley
Time: 11:00 am - 12:00 pm Mar 5, 2018
Location: 5 MetroTech Center, LC400, Brooklyn, NY
Abstract: The value and power mediated by the global, interconnected systems of today’s Internet attract adversaries who seek to exploit these systems for economic, political or social gain. Yet, the underlying complexity of Internet infrastructure, the layering of its services, and the indirect nature of its business relationships can make it challenging to identify even the existence of adversaries manipulating systems for their benefit.
In this talk I present systems and methods to uncover and explore two such large-scale adversarial activities, Internet-wide cybercrime and censorship. I begin with an in-depth exploration of ZeroAccess, a complex peer-to-peer botnet which served as a delivery platform for advertising abuse malware for more than four years and impacted millions of users. I identify innovative attacks and fraudulent business relationships within the advertising ecosystem stemming from complex multi-hop ad reseller chains, resulting in millions of dollars in fraud per month. These relationships and explorations were used as a focal point for fraud remediation and a takedown of the botnet.
Next I present Augur, a measurement technique and accompanying system that uses highly noisy TCP/IP side channels to measure reachability between two Internet locations without access to the endpoints or the path between them. Augur uses sequential hypothesis testing to provide statistical confidence in the face of network and side channel noise. I then use Augur to perform a global censorship measurement study of the blocking practices of more than 180 countries.
About the Speaker: Paul Pearce is a PhD Candidate at UC Berkeley advised by Vern Paxson and a member of the Center for Evidence-based Security Research (CESR). By developing Internet-scale measurement platforms and new empirical methods, his research brings grounding and understanding to the study of large-scale, hidden Internet security problems. His work spans the areas of cybercrime, censorship, and "advanced persistent threats" (APTs). His work has been distinguished at the IEEE Symposium on Security and Privacy, and he has been recognized as an EECS Distinguished Graduate Student Instructor.
Generating, Processing, and Distributing 3D Point Cloud Data for Robotic Perception Tasks
Speaker: William J. Beksi, University of Minnesota, Twin Cities Department of Computer Science and Engineering
Time: 11:00 am - 12:00 pm Feb 27, 2018
Location: 2 MetroTech Center, Room 9.009, Brooklyn, NY
Abstract: 3D point cloud datasets are becoming more common due to the availability of low-cost sensors. Light detection and ranging (LIDAR), stereo, structured light, and time-of-flight are examples of sensors that capture a 3D representation of the environment. These sensors are increasingly found in mobile devices and machines such as smartphones, tablets, robots, and autonomous vehicles. This talk will cover novel techniques for generating, processing, and distributing 3D point cloud data for robotic perception tasks. First, topological methods for 3D hole boundary point detection, segmentation, and object detection and classification will be introduced. Next, a 3D perception architecture for robots connected to a remote computing infrastructure will be shown.
Then, a framework to generate synthetic 3D scenes for the purpose of training data-driven machine learning methods such as deep learning will be presented. Finally, the talk will conclude with a future roadmap for each of these emerging technologies.
About the Speaker: William J. Beksi is a PhD candidate in the Department of Computer Science and Engineering at the University of Minnesota. He is a research assistant at the Center of Distributed Robotics led by Professor Nikolaos Papanikolopoulos. His research focuses on developing algorithms and data structures for fundamental robotic perception tasks such as 3D reconstruction, segmentation, and object detection and classification.
He also works on problems in networked and cloud robotics where the goal is to allow under-resourced robots remote access to computing, data, and learning resources. During the past summer at iRobot, William spearheaded a project on synthetic 3D scene generation for training data-driven machine learning methods. His work has been published in top-tier robotics and computer vision venues. William is also the recipient of the MnDRIVE PhD fellowship through the University of Minnesota Informatics Institute for his research on topological methods for 3D point cloud processing.
Design, Development, and Evaluation of a Meso-Scale Robotic System for Deep Intracranial Tumor Removal
Speaker: Shing Shin Cheng, Georgia Institute of Technology
Time: 11:00 am - 12:00 pm Mar 6, 2018
Location: 2 MetroTech Center, 9.009, Brooklyn, NY
Abstract: Most of the existing surgical robotic systems have bulky footprint, employ straight rigid surgical tools, and are not MRI-compatible. The Minimally Invasive Neurosurgical Intracranial Robot (MINIR-II) project aims at combining the flexible robotic technology, minimally invasive approach, rapid prototyping, and MRI imaging modality to achieve more precise and complete removal of brain tumor. MINIR-II is a spring-based 3-D printed flexible robot that is tendon-driven and equipped with electrocautery, suction and irrigation capabilities. A novel central tendon routing mechanism has been employed to enable independent segment control of the multi-DoF robot in a tight workspace. To improve stability of the surgical procedure, a stiffness-tunable MINIR-II with shape memory alloy (SMA) spring segments has also been developed and characterized to investigate the effect of tendon locking and SMA segment stiffening on the stiffness of the individual segment. SMA springs have been used as the initial proof-of-concept MRI-compatible actuator for MINIR-II. To improve the actuation bandwidth of the SMA actuators, which is part of our effort to confirm the motion capability of the robot, cooling module-integrated SMA springs have been developed together with a new actuation mechanism involving the alternate passage of water and compressed air. In our most recent work, ultrasonic motors have replaced the SMA springs to provide a reliable remote actuation solution that improves the MR image quality. A complete set of MRI-compatible transmission mechanisms, including a switching mechanism, a linkage mechanism, and a quick-connect mechanism, has been developed. The robotic system is currently being evaluated in terms of its precision, force output, repeatability, and effect on the MRI signal-to-noise-ratio (SNR). Our work hopefully lays a solid foundation towards the future development of more MRI-compatible surgical robotic systems that deploy miniaturize, multi-DoF, meso-scale flexible robots, that are potentially customizable.
About the Speaker: Shing Shin Cheng received his B.S. degree in Mechanical Engineering from the Johns Hopkins University in 2013; M.S. degree in Mechanical Engineering from University of Maryland, College Park in 2016; and is currently a Ph.D. candidate in Robotics at Georgia Institute of Technology, Atlanta. His research interests include design and control of flexible surgical robots and medical devices, as well as exploration of smart materials, microfabrication, and artificial intelligence for medical applications.
Security and Privacy Challenges in Internet of Things
Speaker: Arsalan Mosenia, Princeton University
Time: 11:00 am - 12:00 pm Mar 26, 2018
Location: 6 MetroTech, JAB674, Brooklyn, NY
Abstract: Internet of Things (IoT) is envisioned as a holistic and transformative approach for providing numerous services. Smart things, that can sense, store, and process electrical, thermal, optical, chemical, and other signals to extract user- /environment-related information, have enabled services only limited by human imagination. Despite picturesque promises of IoT-enabled systems, the integration of smart things into the standard Internet introduces several security and privacy challenges because the majority of Internet technologies, communication protocols, and sensors were not designed to support IoT. In this presentation, I will shed light on fundamental security challenges in IoT paradigm and argue that we need to rethink the development of multiple IoT-enabled systems while taking security requirements into account. Bridging concepts from information security, machine learning, and network science, I will demonstrate that the threat of unintended private information leakage from seemingly non-critical data is far beyond what is currently thought possible. In particular, I will describe PinMe, a novel user- location mechanism that exploits non-sensory/sensory data collected from smartphones or Internet-connected vehicles, along with publicly-available auxiliary information, e.g., elevation maps, to estimate the user's location when all location services, e.g., Global Positioning System (GPS), are turned off. Next, I will present a novel framework that integrates programmability, connectivity, and security into isolated vehicles and enables rapid development of new vehicular applications for already-in- market vehicles, significantly enhancing the vehicle security, passenger safety, and driving experience. The proposed framework is formed around a security/privacy-friendly programmable dongle (known as SmartCore) and a middlware that enables developers to interact with the vehicle's built-in components in a safe and secure manner, preventing numerous potential threats against Internet-connected vehicles.
About the Speaker: Arsalan Mosenia is currently a postdoctoral research associate, jointly working with Profs. Mung Chiang (Purdue University) and Prateek Mittal (Princeton University). He received the B.Sc. degree in Computer Engineering from Sharif University of Technology in 2012, and the M.A. and Ph.D. in Electrical Engineering from Princeton University, in 2014 and 2016, respectively, under the supervision of Prof. Niraj K. Jha. He is broadly interested in investigating and addressing emerging security and privacy challenges in Internet of Things (IoT) and cyber-physical systems. His interests lie at the intersection of information security, IoT, embedded systems, and machine learning. His work has uncovered fundamental security/privacy flaws in the design of multiple widely- used Internet-connected systems. His research impact includes several publications that are among the most popular papers of top-tier IEEE Transactions, multiple prestigious awards (including Princeton X, Princeton Innovation Fund, French-American Doctoral Exchange Fellowship, and Princeton IP Accelerator Fund), and extensive press coverage. Furthermore, at OpenFog Consortium, he is actively collaborating with Security Work Group, where he defines domain-specific security standards for fog computing, and Testbed Work Group, where he designs, builds, and examines novel fog-inspired real-world systems.
Securing Smart, Connected Systems through Systematic Problem Analysis and Mitigation
Speaker: Qi Alfred Chen, University of Michigan, Ann Arbor
Time: 11:00 am - 12:00 pm Mar 20, 2018
Location: 5 MetroTech Center, LC400, Brooklyn, NY
Abstract:The world is increasingly connected through a series of smart, connected systems such as smartphone systems, smart home systems, and the emerging smart transportation and autonomous vehicle systems. While leading to improved services, such transformation also introduces new security challenges. To address these challenges, in contrast to existing defense mechanisms that are mostly ad hoc and reactive, my research aims at developing proactive defense approaches that can systematically discover, analyze, and mitigate new security problems in smart, connected systems.
In this talk, I will focus on my research efforts in securing two most basic components in any smart, connected system: network stack and smart control. For network stack security, I will describe our discovery of a new attack vector (US-CERT alert TA16-144A) that was unexpectedly brought by the recent expansion in DNS, and our subsequent systematic analysis at both network and software levels for its defense. For smart control security, I will describe my most recent work that performed the first security analysis of the next-generation Connected Vehicle (CV) based traffic signal control, which discovers new vulnerabilities at the traffic signal control algorithm level. I will conclude by discussing my future research plans in securing existing and future smart, connected systems, especially those in critical domains such as transportation and automobile.
About the Speaker: Qi Alfred Chen is a Ph.D. candidate in the EECS department at University of Michigan advised by Professor Z. Morley Mao. His research interest is network and systems security, and the major theme of his research is to address security challenges through systematic problem analysis and mitigation. His research has discovered and mitigated security problems in various systems such as next-generation transportation systems, smartphone OSes, network protocols, DNS, GUI systems, and access control systems. His work has impact in both academia and industry with over 10 top-tier conference papers, news coverage and interviews, vulnerability disclosures, and industry discussions and responses. His current research focuses on smart systems and IoT, e.g., smart home, smart transportation, and autonomous vehicle systems.
ECE Seminar Series on Modern Artificial Intelligence: GANs and Unsupervized Representation Learning
Speaker: Yoshua Bengio, University of Montreal, Canada
Time: 10:00 am - 11:00 am Mar 19, 2018
Location: MakerSpace EventSpace, 6 MetroTech Center, Brooklyn, NY, US
Abstract: GANs and Unsupervized Representation Learning
One of the central questions for deep learning is how a learning agent could discover good representations in an unsupervised way. First, we consider the still open question of what constitutes a good representation, with the notion of disentangling the underlying factors of variation. Second, we discuss issues with the maximum likelihood framework which has been behind our early work on Boltzmann machines as well as our work on auto-regressive and recurrent neural networks as generative models. These issues motivated our initial development of Generative Adversarial Networks, a research area which has greatly expanded recently. We summarize recent GAN work aiming at better dealing with discrete data, as well as work aiming at generalizing GAN ideas to generative models which are iterative, like Boltzmann machines and denoising auto-encoders. We discuss how adversarial training can be used to obtain invariances to some factors in the representation, and a way to make training with such an adversarial objective more stable by pushing the discriminator score towards the classification boundary but not past it. Finally, we discuss applications of GAN ideas to estimate, minimize or maximize mutual information, entropy or independence between random variables.
About the Speaker: Yoshua Bengio (computer science, 1991, McGill U; post-docs at MIT and Bell Labs, computer science professor at U. Montréal since 1993): he authored three books, over 300 publications (h-index over 100, over 100,000 citations), mostly in deep learning, holds a Canada Research Chair in Statistical Learning Algorithms, is Officer of the Order of Canada, recipient of the Marie-Victorin Quebec Prize 2017, he is a CIFAR Senior Fellow and co-directs its Learning in Machines and Brains program. He is scientific director of the Montreal Institute for Learning Algorithms (MILA), currently the largest academic research group on deep learning. He is on the NIPS foundation board (previously program chair and general chair) and co-created the ICLR conference (specialized in deep learning). He pioneered deep learning and his goal is to uncover the principles giving rise to intelligence through learning, as well as contribute to the development of AI for the benefit of all.
Using Time as a Security Measure in Cyber-Physical Systems
Speaker: Sibin Mohan, University of Illinois at Urbana-Champaign
Time: 11:00 am - 12:00 pm Mar 29, 2018
Location: 2 MetroTech Center, 10th floor, Room 10.099, Brooklyn, NY
Abstract: Applications in the cyber-physical systems (CPS) domain are increasingly using commodity-off-the-shelf (COTS) components and are being interconnected for efficiency, better monitoring, and improved functionality. Traditionally, such systems were immune to software security attacks, but the increase in COTS components and interconnectivity are opening up new attack surfaces. Recent events, such as the Stuxnet incident, have shown the serious damage that can result from successful attacks. This can be particularly destructive for systems that have safety-critical constraints (e.g. avionics, automobiles, UAVs, power grids, etc.). In this talk, I intend to demonstrate how timing information can be crucial in the security of CPS especially when such systems have real-time properties.
In the first piece of work, I will demonstrate how timing information can be used to attack such systems – to leak critical information (e.g. the precise schedule and timing properties of tasks). In the second part of the talk, I will demonstrate multiple techniques that can deter attackers, both from the aforementioned attacks and also zero-day attacks that modify behavioral properties of such systems. I will also touch upon other timing-based defensive mechanisms that improve the overall security of safety-critical cyber-physical systems – both at design time, as well as retroactively (for legacy systems).
About the Speaker: Sibin Mohan is a Research Assistant Professor in both, the Dept. of Computer Science as well as the Information Trust Institute (ITI) at the University of Illinois. He completed his Ph.D. and M.S. in Computer Science from North Carolina State University in 2008 and 2004 respectively. His undergraduate degree was in Computer Science and Engineering from Bangalore University, India in 2001.
Sibin’s research interests are in the area of systems and security. His current research efforts include the integration of security in real-time embedded systems, intrusion detection in cyber-physical systems with real-time properties, secure cloud computing and the use of software defined networking (SDN) in safety-critical systems. In the past, he has done extensive work on the analysis of real-time and embedded systems and the development of system composition and safety techniques for avionics and medical devices.
He was previously a postdoctoral scholar in the Computer Science department at UIUC. In the past, he has also worked in Hewlett Packard’s India Software Operations.
ECE Seminar Series on Modern Artificial Intelligence: The Information Knot Tying Sensing and Action; Emergence Theory of Representation Learning
Speaker: Stefano Soatto
Time: 11:00 am - 12:00 pm Apr 5, 2018
Location: Pfizer Auditorium, 5 MetroTech Center, Brooklyn, NY, US
Abstract: Representations are functions of past data useful to accomplish future decision or control tasks. Ideally, they should be as informative as the data (sufficient), unaffected by nuisance factors in future data (invariant), as simple as possible (minimal), and easy to work with (disentangled). Such ideal representations are what one should store in memory in lieu of past data. But do they exist? If so, can they be computed? or learned? Minimality and sufficiency can be achieved by optimizing the Information Bottleneck Lagrangian, but how to do so? And what about invariance and disentanglement?
At face value, these classical principles from statistical decision and information theory have little to do with Deep Learning, where an empirical decision criterion is optimized with respect to a biologically-inspired (parametric) family of functions using stochastic gradient descent (SGD). Despite its simplicity, however, SGD has some surprising properties: First, it does not converge in the classical sense, but instead exhibits limit cycles that can be far from the critical points of the empirical loss. Second, it induces a bias - or regularization - in the learning process that is reminiscent of the Information Bottleneck Lagrangian, but not the usual one: This new one measures the information the parameters of the network (weights) contain about past data. The ideal properties we want, however, pertain to future data. What is the relation between these two information bottlenecks, past and future?
The Emergence Theory shows that minimizing the information the weights of a deep neural network contain about past data bounds minimality, invariance and disentanglement of the resulting representation of future data (activations). The resulting bound can be derived equivalently using Information Theory, or from PAC-Bayes theory. So, (explicit or implicit) regularization of the empirical loss used in Deep Learning provably induces the emergence of desirable properties of the representation implemented. I will discuss examples in visual recognition and control.
About the Speaker: Stefano Soatto is Professor of Computer Science and Electrical Engineering, and Director of the UCLA Vision Lab, in the Henry Samueli School of Engineering and Applied Sciences at UCLA. He is also Director of Applied Science at Amazon AI - AWS. He received his Ph.D. in Control and Dynamical Systems from the California Institute of Technology in 1996; he joined UCLA in 2000 after being Assistant and then Associate Professor of Electrical and Biomedical Engineering at Washington University, and Research Associate in Applied Sciences at Harvard University. Between 1995 and 1998 he was also Ricercatore in the Department of Mathematics and Computer Science at the University of Udine - Italy. He received his D.Ing. degree (highest honors) from the University of Padova- Italy in 1992. Dr. Soatto is the recipient of the David Marr Prize for work on Euclidean reconstruction and reprojection up to subgroups. He also received the Siemens Prize with the Outstanding Paper Award from the IEEE Computer Society for his work on optimal structure from motion. He received the National Science Foundation Career Award and the Okawa Foundation Grant. He was Associate Editor of the IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI) and a Member of the Editorial Board of the International Journal of Computer Vision (IJCV) and Foundations and Trends in Computer Graphics and Vision, Journal of Mathematical Imaging and Vision, SIAM Imaging. He is a Fellow of the IEEE.
ECE Seminar Series on Modern Artificial Intelligence
Speaker: Vladimir Vapnik
Time: 10:00 am - 11:00 am May 4, 2018
Location: Pfizer Auditorium, 5 MetroTech Center, Brooklyn, NY, US
Abstract: The talk considers Teacher-Student interaction in learning processes. It introduces a new learning paradigm, called Learning Using Statistical Invariants (LUSI), which is different from the classical one. In the classical paradigm, learning machine constructs, using data, a classification or regression function that minimizes the expected loss; it is data-driven learning. In the LUSI paradigm, in order to construct the desired classification or regression function using both data and Teacher's input, learning machine computes statistical invariants that are specific for the problem, and then minimizes the expected loss in a way that preserves these invariants; it is both data and intelligence-driven learning.
From a mathematical point of view, methods of the classical paradigm employ mechanisms of strong convergence of approximations to the desired function, whereas methods of the new paradigm employ both strong and weak convergence mechanisms. This can significantly increase the rate of convergence.
About the Speaker: Professor Vapnik gained his Masters Degree in Mathematics in 1958 at Uzbek State University, Samarkand, USSR. From 1961 to 1990 he worked at the Institute of Control Sciences, Moscow, where he became Head of the Computer Science Research Department. He then joined AT&T Bell Laboratories, Holmdel, NJ, having been appointed Professor of Computer Science and Statistics at Royal Holloway in 1995.
Professor Vapnik has taught and researched in computer science, theoretical and applied statistics for over 30 years. He has published 6 monographs and over a hundred research papers. His major achievements have been the development of a general theory of minimizing the expected risk using empirical data and a new type of learning machine called Support Vector machine that possesses a high level of generalization ability. These techniques have been used to solve many pattern recognition and regression estimation problems and have been applied to the problems of dependency estimation, forecasting, and constructing intelligent machines. His current research is presented in his latest books "Statistical Learning Theory", Wiley, 1998, and "The Nature of Statistical Learning Theory", second edition, Springer, 2000.
He was one of the invited speakers at the Colloquium "The Importance of being Learnable" hosted by the Computer Learning Research Centre at Royal Holloway in September 1998.
Highly Available Cloud Services Architecture
Speaker: Mehmet Toy, Verizon
Time: 11:00 am - 12:00 pm May 3, 2018
Location: 2 MetroTech Center, 10th floor, Room 10.099, Brooklyn, NY
Abstract: Cloud Services Architecture has been defined by Open Cloud Connect (OCC) standards organization which was merged with Metro Ethernet Forum (MEF) in 2016. Further work in this area has been initiated in ETSI Network Function Virtualization (NFV) and MEF.
In my talk, I will describe Cloud Services and their architecture, interfaces between a user and Cloud Service Provider, and interfaces between Cloud Service Providers. In addition, I will describe the high availability layers and fault management architecture of virtualized systems and services and relationships between failure recovery timers of the layers.
About the Speaker: Mehmet Toy holds Ph.D degree in Electrical and Computer Engineering from Stevens Institute of Technology, Hoboken, NJ, and M.S and B.S degrees in Electronics and Communications from Istanbul Technical University, Istanbul, Turkey.
He is a Distinguished Member of Technical Staff in Verizon Communications and involved in the implementation, testing and standards for SDN, NFV and Cloud Services.
Prior to this position, Dr. Toy has held senior technical and management positions in several well-known companies and startups including Comcast, Intel Corp., Verizon Wireless, Fujitsu Network Communications, AT&T Bell Labs and Lucent Technologies. He has also been a tenure-track faculty and adjunct professor in several universities including Stevens Institute of Technology, New Jersey Institute of Technology, Worchester Polytechnic Institute, and University of North Caroline at Charlotte.
Dr. Toy contributed to research, development and standardization of Cloud, Overlay, Self-Managed, SDN and Virtualized Commercial Networks and Services, Carrier Ethernet, IP Multimedia Systems (IMS), Optical, IP/MPLS, Wireless, ATM, and Signal Processing technologies. He holds a patent and has six pending patent applications. He has also published numerous articles, seven books and a video tutorial in these areas. Two of his books are being used as college text books and one of them is translated to Turkish.
Dr. Toy has served in the Open Cloud Connect Board, the IEEE Network Magazine Editorial Board, the IEEE Communications Magazine as a Guest Editor, the IEE-USA and the IEEE ComSoc in various capacities. He has received various awards from Comcast, AT&T Bell Labs and IEEE-USA for his accomplishments in these fields. He is currently a Sr. Member of IEEE and chair person of the IEEE ComSoc Cable Networks and Services sub-committee, and serves in MEF and ETSI NFV.
Stochastic-Robust and Robust Programs for the Ramp-Constrained Economic Dispatch Problem with Uncertain Renewable Energy
Speaker: Alberto J. Lamadrid, Lehigh University
Time: 11:00 am - 12:00 pm Apr 6, 2018
Location: 2 MetroTech Center, Room 9.011, Brooklyn, NY
Abstract: The inherent uncertainty of renewable energy sources (RES) makes the solution to the electricity network’s associated economical dispatch (ED) problem with network constraints challenging. In particular, the uncertainty in the power output of RES requires conventional generation units to ramp up and down more frequently to maintain the power balance and the reliability of the system. Typically, the RES power output uncertainty is modeled in ED problems by considering its potential future scenarios. However, this leads to an optimization problem that is difficult to solve for real-sized networks. Here, we present two proposals for this problem.
In the first one, we consider the uncertainty of RES and the consequent ramping of conventional generation via a robust reformulation of the problem. In particular, we show that in typical instances of the ED problem, the associated deterministic formulation of the robust problem can be solved efficiently for medium scale constrained electricity networks even when the underlying uncertainty distribution is not normal. Moreover, by comparing the proposed robust solutions to the ED problem with the typical scenario optimization approach, we show that the former solutions result on dispatch solutions that require less ramping than the later solutions, with little trade-off on the long-term expected costs of the dispatch. These results also provide insights about how RES penetration affects cost and dispatch policies in the electricity network. To illustrate our results, we present relevant numerical experiments on IEEE test networks.
In the second, We present an implementation of a two-stage security constrained unit commitment program with a recourse to dispatch the electricity system using a hybrid method to determine reserves for the System Operator (SO). This works is related to the stochastic optimization literature, with an emphasis on the economic determination and appraisal of different types of balancing reserves. The recourse decision balances the power dispatches, subject to the endogenously determined reserves. We study the implementation of the second settlement over a set of possible realized trajectories, as part of the implementation of a receding (rolling) horizon settlement.
About the Speaker: Alberto J. Lamadrid (Ph.D. Applied Economics and Management, Cornell University, 2012; M.A. Economics NYU, 2004; B.Sc. Electrical Engineering, Universidad de los Andes, Colombia, 2001) is an assistant professor with a joint appointment in the Economics Department and in the Industrial and Systems Engineering Department at the P.C. Rossin College of Engineering and Applied Science at Lehigh University. He is also a member of the Integrated Networks for Electricity Cluster at Lehigh. He has participated in NSF funded grants, as well as other awards funded by the Department of Energy, The Pennsylvania Infrastructure Technology Alliance and EPRI. His research interests are in electricity markets, power systems, and energy economics. He has worked on topics involving multi-period stochastic optimization in electrical networks, adoption of renewable energy sources and the valuation of power infrastructure assets.
Past, Present and Future of Wireless Communications
Speaker: Nambi Seshadri, University of California, San Diego
Time: 11:00 am - 12:00 pm Apr 12, 2018
Location: MakerEvent Space in 6MTC, Rogers Hall
Abstract: Major strides have been made in the eld of wireless communications over the last century. In this work, I will present my personal take (and invite the audience to participate) on the key technical contributions that have impacted the practice of modern wireless communica- tions over the last ve decades. I am fortunate to have worked in this eld since my graduation and and will discuss my journey as well.
About the Speaker: Nambi Seshadri is currently a Professor in the School of Electrical Engineering at the University of California, San Diego, Chief Technology Of cer (Consulting) at Quantenna Communications and an advisor to several start ups in California and India. Prior to his current appointments, he was Chief Technology Of cer for Mobile and Wireless at Broadcom Corporation. From 1986-1999, Nambi was at AT&T Bell Labs and AT&T Shannon Labs where he conducted research on various aspects of wireless communications. He received his M.S. and Ph.D. from RPI in 1984 and 1986 respectively and his B.E. in Electronics and Communications from the Regional Engineering College, Trichy, India in 1982. He is a member of the US National Academy of Engineering and Foreign Member of Indian National Academy of Engineering. He was a co-recipient of the 1999 IEEE Information Theory Society Best Paper Award (with Vahid Tarokh and Rob Calderbank) for his work on Space-Time Trellis codes. He is a Fellow of IEEE and recipient of the 2018 IEEE Alexander Graham Bell Medal.
Energy Efficient Adaptive beam alignment & beam design for 5G Millimeter-wave networks
Speaker: Nicolo Michelusi, Purdue University
Time: 11:00 am - 12:00 pm Apr 16, 2018
Location: 2 MetroTech Center, Room 9.101, Brooklyn
Abstract: Mobile data traffic is expected to increase tremendously over the next decade, and cannot be accommodated by the limited bandwidth availability below 6GHz. On the other hand, the so called millimeter wave frequencies in the 28-100 GHz range promise to overcome these limitations. However, millimeter wave systems require narrow beam communication to achieve high throughput, thus mobile users need to be tightly tracked to provide seamless communication. Such requirement may pose severe challenges in mobile environments and may entail a significant performance degradation due to the associated signaling overhead. In the first part of the talk, we address the energy efficient design of the beam alignment protocol, with the goal of minimizing power consumption under communication constraints. We prove the optimality of a fractional search method, which senses a given fraction of the beam in each slot during beam alignment, and derive the fractional value in closed form. We also investigate the trade-off among beam-alignment, communication and user mobility. Beam alignment is achieved via a proper beam sensing protocol, which specifies how to allocate amplitude and phase at each antenna array element (a codeword) to sense the mobile user's position, through appropriate beam pointing. However, beam imperfections -- such as the presence of side-lobes -- and noise may cause errors in the detection process. Thus, in the second part of the talk, we investigate a Neyman-Pearson codebook design with optimal detection performance. We show that the optimal codebook is the principle eigenvector of a weighted array response matrix, and the dual problem can be solved via semidefinite programming. We show numerically that the proposed design outperforms a state-of-the art algorithm, with improvement up to 33% in detection performance.
About the Speaker: Dr. Nicolo Michelusi received the B.Sc. degree with honors, M.Sc. degree with honors and Ph.D. degree in Electrical Engineering from University of Padova, Italy, in 2006 and 2009, and 2013 respectively, and the M.Sc. degree in Telecommunication Engineering from Technical University of Denmark in 2009. In 2013-2015, he was a postdoctoral research fellow at the Ming-Hsieh Department of Electrical Engineering, University of Southern California, USA. He is currently an Assistant Professor at the School of Electrical and Computer Engineering, Purdue University, IN, USA, Associate Editor for the IEEE Transactions on Wireless Communications, and Senior Member of IEEE. His research interests lie in the areas of wireless communications, cognitive networks, energy harvesting IoT, 5G millimeter-wave networks, distributed algorithm over wireless networks, and machine learning applied to wireless communications. His research is currently funded by the National Science Foundation under grant CNS-1642982 and by DARPA to compete on the Spectrum Collaboration Challenge.
Research Challenges in High Performance VLSI Circuits
Speaker: Eby G. Friedman
Time: 11:00 am - 12:00 pm Apr 19, 2018
Location: MakerEvent Space in 6MTC, Rogers Hall
Abstract: The intention of this presentation is to provide an overview of the different projects of current focus in the high performance integrated circuit design research laboratory at the University of Rochester. Each of these topics considers different aspects of the systems integration process, with a focus on lower physical and circuit level aspects. Emphasis is placed on those fundamental challenges in delivering performance to high speed, high complexity heterogeneous integrated circuits. Technologies range from deeply scaled CMOS to emerging devices and circuits such as spintronic, photonic, and superconductive behaviors.
Delivering high quality power to on-chip circuitry with minimum energy loss is a fundamental objective of all modern integrated circuits (ICs). To supply sufficient power on-chip, an unregulated DC voltage is usually stepped down and regulated within the power delivery system. Power conversion and regulation resources should be efficiently managed to supply high quality power with minimum energy losses within multiple on-chip power domains. To satisfy challenging power efficiency and regulation requirements, hundreds of power regulators will be co-designed with many thousands of decoupling capacitors, distributing the power locally to billions of on-chip loads. Circuits, algorithms, and design methodologies are being developed to fundamentally change the manner in which power is delivered on-chip.
Three-dimensional (3-D) integration is changing the path for device scaling, supporting the delivery of multi-faceted heterogeneous systems. A variety of different design techniques and methodologies are under development to better design, model, architect, and build 3-D systems. Several test circuits have been developed to evaluate some of the key issues in 3-D system integration. These efforts will be reviewed and trends discussed.
Spintronic circuits have the potential to enhance CMOS in several dimensions, particularly as non-volatile memory and novel non-von Neumann structures. A variety of models and circuits will be described and placed within a CMOS perspective.
The energy expended in server farms has become an issue of seminal significance. CMOS simply expends too much energy to scale the size and number of these farms to support expected needs. An ultra-low energy technology is needed. One possible technology is superconductive single flux quantum (SFQ) circuits. This technology will be briefly reviewed, and novel design methodologies will be described to support the development of large scale Josephson junction based integrated systems.
About the Speaker: Eby G. Friedman received the B.S. degree from Lafayette College in 1979, and the M.S. and Ph.D. degrees from the University of California, Irvine, in 1981 and 1989, respectively, all in electrical engineering.
From 1979 to 1991, he was with Hughes Aircraft Company, rising to the position of manager of the Signal Processing Design and Test Department, responsible for the design and test of high performance digital and analog integrated circuits. He has been with the Department of Electrical and Computer Engineering at the University of Rochester since 1991, where he is a Distinguished Professor, and the Director of the High Performance VLSI/IC Design and Analysis Laboratory. He is also a Visiting Professor at the Technion - Israel Institute of Technology. His current research and teaching interests are in high performance synchronous digital and mixed-signal microelectronic design and analysis with application to high speed portable processors, low power wireless communications, and power efficient server farms.
He is the author of more than 500 papers and book chapters, 16 patents, and the author or editor of 18 books in the fields of high speed and low power CMOS design techniques, 3-D design methodologies, high speed interconnect, and the theory and application of synchronous clock and power distribution networks. Dr. Friedman is the Editor-in-Chief of the Microelectronics Journal, a Member of the editorial boards of the Journal of Low Power Electronics and Journal of Low Power Electronics and Applications, and a Member of the technical program committee of numerous conferences. He previously was the Editor-in-Chief and Chair of the Steering Committee of the IEEE Transactions on Very Large Scale Integration (VLSI) Systems, the Regional Editor of the Journal of Circuits, Systems and Computers, a Member of the editorial board of the Proceedings of the IEEE, IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing, IEEE Journal on Emerging and Selected Topics in Circuits and Systems, Analog Integrated Circuits and Signal Processing, and Journal of Signal Processing Systems, a Member of the Circuits and Systems (CAS) Society Board of Governors, Program and Technical chair of several IEEE conferences, and a recipient of the IEEE Circuits and Systems Charles A. Desoer Technical Achievement Award, a University of Rochester Graduate Teaching Award, and a College of Engineering Teaching Excellence Award. Dr. Friedman is a Senior Fulbright Fellow and an IEEE Fellow.
Assessment and Measurement of Port Disruptions
Speaker: Gabriel Weaver, University of Illinois- Urbana-Champaign
Time: 1:30 pm - 2:30 pm Apr 19, 2018
Location: 2 MetroTech Center,Room 10.099, Brooklyn, NY
Abstract: Modern shipping ports require computer systems in order to accommodate an increasing number of port calls, larger vessel sizes, and tighter supply chains. Therefore, disruptions to assets on these networks have the potential to propagate to other critical infrastructures at great economic cost. Such disruptions may be introduced accidentally (e.g. by misconfiguration), or intentionally by adversaries that include nation states, organized crime, pirates, hacktivists, and trusted insiders. Recently, cyber-threats to the Maritime Transportation System (MTS) have become more relevant. The NotPetya malware attack affected Maersk and caused disruptions to operations estimated at over $200 million by Forbes. As a result, legislation was recently introduced within the US that would require cybersecurity information sharing among maritime stakeholders and building a model for maritime cybersecurity risk assessment. The intent of our research is to extend models used within shipping port simulations with dependencies on the Communications/IT sector, develop threat models resulting from these interactions, and measure/rank their impact. In this talk, we present models of cyber-physical interactions within container and petroleum operations at shipping ports that have been validated by practitioners in the field. We then explain how such models can be used to simulate cyber-originating disruptions to the MTS.
About the Speaker: Gabriel Weaver is a Research Scientist at the Coordinated Science Laboratory at the University of Illinois at Urbana- Champaign. During his research career, Weaver has served at MIT’s Lincoln Laboratory and as a non-residential fellow at Harvard where he designed an XML vocabulary to encode Ancient Greek Mathematical Diagrams. Currently, Weaver is PI on a project via the Critical Infrastructure Resilience Institute (CIRI) to look at the economic impacts of cascading disruptions to shipping port infrastructure. This project, in combination with his work as the Inaugural Dieckamp Postdoctoral Fellow at UIUC’s Information Trust Institute, and in coordination with National Laboratories such as INL and PNNL, is being used to develop a Cyber-Physical Topology Language (CPTL) to encode and analyze interdependencies across critical infrastructure systems.
Spatiotemporal Event Detection: from Model-Based Inference to Data Driven Learning
Speaker: Mei Chen, State University of New York
Time: 11:00 am - 12:00 pm Apr 20, 2018
Location: 2 MetroTech Center,Room 9.011, Brooklyn, NY
Abstract: Recent years have seen a revolution in computer vision which has come from embracing data as a primary source of information in solving complex inference problems. The spatiotemporal structure of a class of images, such as microscopy images of live cells in a Petri dish, can be implicitly constrained and defined by a well-chosen annotated dataset. This paradigm has led to impressive gains in a number of key areas, due in part to the power of modern machine learning methods when applied to big data. In this talk I will discuss my work on model-based inference and data-driven learning for cell mitosis event detection. In particular, I will present the rationale behind our design of data descriptors and classifiers and attempt at a principled thought process. A consistent thread in my work is the incorporation of key insights from the problem domain which constrain and bias the learning problem, and lead to effective performance.
About the Speaker: Mei Chen is an Associate Professor in the Electrical and Computer Engineering Department at the State University of New York, Albany. From 2011 to 2014, she led the Intel Science and Technology Center on Embedded Computing at Carnegie Mellon University, driving collaborations across four research themes involving seven universities. Previously she held researcher and research lead positions at Intel Labs, Hewlett Packard Labs, and SRI Sarnoff Corporation. Mei’s work in computer vision and biomedical image analysis were nominated finalists for 6 Best Paper Awards and won 3. While at HP Labs, she successfully transferred her research in computational photography to 5 hardware and software products that went to market. She earned a Ph.D. in Robotics from the School of Computer Science, Carnegie Mellon University, and a M.S. and B.S. from Tsinghua University, Beijing, China.
Practical Millimeter Wave Research on Custom Testbeds and Commercial Off-The-Shelf Devices
Speaker: Joerg Widmer
Time: 10:00 am - 11:00 am May 23, 2018
Location: 9.101 Conference Room, 9th floor, 2 MetroTech Center, Brooklyn
Abstract: The high bandwidth available millimeter-wave frequencies allows for very high data rates, and the latest wireless technologies, such as IEEE 802.11ad, are already starting to exploit this part of the radio spectrum to achieve rates of several GBit/s. Communication at these frequencies typically uses directional antennas which brings about interesting challenges to align antenna beams. Given the high penetration loss, most obstacles (e.g., a person) also completely block the signal. This results in a very dynamic radio environment and channels may appear and disappear over very short time intervals. This talk will give an overview of some of our recent works that deal with these networking challenges, focusing in particular on practical testbed research.
The talk covers low overhead continuous beam tracking on an 802.11ad compliant system with a phased array and an implementation of compressive beam training on commercial off-the-shelf devices. The very large bandwidth at mm-wave frequencies also allows, in principle, to design highly accurate location systems. Such location information can in turn be used to facilitate beam training, optimize access point association, predict (and counteract) future blockage, etc. However, implementing a location system based standard protocols and current mm-wave consumer hardware is challenging. The talk will present a location system we implemented on off-the-shelf 802.11ad devices, that only uses coarse angle information extracted from the beam training process. We further present a system (based on a custom hardware platform) that provides simultaneous localization and mapping of the environment, again based only on angle information.
About the Speaker: Joerg Widmer is Research Director as well as Research Professor at IMDEA Networks in Madrid, Spain. His research focuses on wireless networks, ranging from extremely high frequency millimeter-wave communication and MAC layer design to mobile network architectures. From 2005 to 2010, he was manager of the Ubiquitous Networking Research Group at DOCOMO Euro-Labs in Munich, Germany, leading several projects in the area of mobile and cellular networks. Before, he worked as post-doctoral researcher at EPFL, Switzerland on ultra-wide band communication and network coding. He was a visiting researcher at the International Computer Science Institute in Berkeley, USA, University College London, UK, and TU Darmstadt, Germany. Joerg Widmer authored more than 150 conference and journal papers and three IETF RFCs, and holds 13 patents. He serves or served on the editorial board of IEEE Transactions on Mobile Computing, IEEE Transactions on Communications, Elsevier Computer Networks and the program committees of several major conferences. He was awarded an ERC consolidator grant, the Friedrich Wilhelm Bessel Research Award of the Alexander von Humboldt Foundation, a Mercator Fellowship of the German Research Foundation, a Spanish Ramon y Cajal grant, as well as seven best paper awards. He is senior member of IEEE and ACM.
Incorporating Geometry Images in the retrieval process of 3D models
Speaker: Christodoulos Chamzas
Time: 11:00 am - 12:00 pm July 12, 2018
Location: 10.099, 10th floor, 2 MetroTech Center, Brooklyn
Abstract: The aim of this work is to extend the way 3D content-based retrieval is usually being performed and hence proposes the utilization of Geometry Images. We will describe two cases.
Texture, the neglected companion of 3D objects. We propose the generation of a spatially-consistent UV map by exploiting computational geometry and planar mesh parameterization. Having the texture of a 3D object depicted on a completely 2-dimensional structure and without inconsistencies, enables us to exploit well-known algorithms derived from the image processing domain and apply them on the object’s texture map.
Curveture-Geometry Images: We propose a method to represent a 3D model’s surface on a 2D regular grid and encode its k1curvature, thus producing a new 3D geometry feature, the CurvMaps, that may be used for 3D model classification. The feature creating transformation relies on the identification of a 3D model’s geometrical “extrema”, the computation of k1 principal curvature and its encoding into a 2D regular grid. The applicability of CurvMaps and a convolutional neural network architecture both in 3D model classification and retrieval is being discussed through the experimentation with a number of classical methods.
About the Speaker: Christodoulos Chamzas was born in Komotini, Greece. He received the Diploma Degree in Electrical and Mechanical Engineering from the National Technical University of Athens, Athens, Greece, in 1974 and the M.S. and Ph.D degrees in Electrical Engineering in 1975 and 1979 from the Polytechnic Institute of New York, Farmingdale.
From 1979 to 1982 Dr. Chamzas was an Assistant Professor with the Department of Electrical Engineering at Polytechnic Institute of New York. In September 1982 he joined AT&T Bell Laboratories, Holmdel, NJ, where he was a member of the Visual Communications Research Department until 1990, where he worked on adaptive systems, mobile communications, multimedia image databases and image coding. Since September 1990, he is a member of the Faculty of the Electrical Engineering Department at Democritus University of Thrace, where he is Director of the Electric Circuits Analysis Lab. (1991-2005), of the Sector of Electronics and Information Technology Systems (1997-1998 and 2012-2014) and of the Image Processing and Multimedia Unit ( http:\\ipml.ee.duth.gr ) and of the CULTURAL AND EDUCATIONAL TECHNOLOGIES INSTITUTE (www.ceti.gr) (1998-2012) . During 2001-2003, he was elected to the position of the Department Head of the Electrical and Computer Engineering Department at Democritus University of Thrace (www.ee.duth.gr ). He has been a major player in the definition, design and implementation of the CCITT/ISO (JBIG, JPEG, etc), standards for coding, storage and retrieval of images (color & bilevel) an area where he holds six (6) international patents. In 1985-86, he was a visiting professor with the Department of Computer Science at the University of Crete, Iraklion, Greece. From 1994-1998, he was a member of the Telematics for Knowledge Working Party of EE, representing Greece. He has held summer positions in Greece, England, Portugal, as well as at Bell Laboratories. His primary interests are in cultural technologies, digital signal processing, image coding, multimedia and communications systems. He is currently interested in the implementation of 3D Digitization of Cultural objects and their dissemination with new technologies, multimedia image data base algorithms and schemas as well as content retrieval. He has over 800 citations to his work (over 2000 in Google Scholar).
Dr. Chamzas is a member of the Technical Chamber of Greece, Sigma Xi, an Editor in the IEEE Transactions of Communications (1989-1996) a Distinguished Member of the Technical Staff of AT&T Bell Laboratories and a Senior Member of IEEE.
Electromagnetic Time Reversal and its Application to Fault Detection: Experimental Validations
Speaker: Farhad Rachidi
Time: 11:00 am - 12:00 pm July 25, 2018
Location: 10.099, 10th floor, 2 MetroTech Center, Brooklyn
Abstract: Time reversal has emerged as a very interesting technique with potential applications in various fields of engineering. It has received a great deal of attention in recent years, essentially in the field of acoustics, where it was first developed by Prof. Fink and his team in the 1990s. In the past decade, the technique has also been used in the field of electromagnetics and applied to various other areas of electrical and computer engineering. In particular, the technique has been successfully applied in the fields of electromagnetic compatibility (EMC) and power systems, leading to mature technologies in source-location identification with unprecedented performance compared to classical approaches. It is expected that the fields of application of electromagnetic time reversal (EMTR) will continue to grow in the near future.
This talk presents recent advances in the application of the time reversal theory to the problem of fault location. After a brief review of the theoretical basis of the electromagnetic time reversal and the concept of time reversal cavity, we present recent full-scale experimental validations of the method carried out in China and in Switzerland.
About the Speaker: Farhad Rachidi (M’93–SM’02–F’10) received the M.S. degree in electrical engineering and the Ph.D. degree from the Swiss Federal Institute of Technology, Lausanne, Switzerland, in 1986 and 1991, respectively. He was with the Power Systems Laboratory, Swiss Federal Institute of Technology, until 1996. In 1997, he joined the Lightning Research Laboratory, University of Toronto, Toronto, ON, Canada. From 1998 to 1999, he was with Montena EMC, Rossens, Switzerland. He is currently a Titular Professor and the Head of the EMC Laboratory with the Swiss Federal Institute of Technology, Lausanne, Switzerland. He has authored or co-authored over 160 scientific papers published in peer-reviewed journals and over 350 papers presented at international conferences.
Dr. Rachidi is currently a member of the Advisory Board of the IEEE TRANSACTIONS ON ELECTROMAGNETIC COMPATIBILITY and the President of the Swiss National Committee of the International Union of Radio Science. He has received numerous awards including the 2005 IEEE EMC Technical Achievement Award, the 2005 CIGRE Technical Committee Award, the 2006 Blondel Medal from the French Association of Electrical Engineering, Electronics, Information Technology and Communication (SEE), and the 2016 Berger Award from the International Conference on Lightning Protection. In 2014, he was conferred the title of Honorary Professor of the Xi’an Jiaotong University in China. He served as the Vice-Chair of the European COST Action on the Physics of Lightning Flash and its Effects from 2005 to 2009, the Chairman of the 2008 European Electromagnetics International Symposium, the President of the International Conference on Lightning Protection from 2008 to 2014, and the Editor-in-Chief of the IEEE TRANSACTIONS ON ELECTROMAGNETIC COMPATIBILITY from 2013 to 2015.
Cooperative Unmanned Aerial Systems for Area Coverage
Speaker: Anthony Tzes
Time: 11:00 am - 12:00 pm Aug 10, 2018
Location: 9.007, 9th floor, 2 MetroTech Center, Brooklyn
Abstract: The development of collaborative control schemes for mobile robots for convex and planar area coverage purposes is the subject of this keynote speech. Starting from ground robots and evolving to aerial ones, we will assume point omnidirectional agents with heterogeneous circular sensing patterns. Using information from their spatial neighbors, each robot (agent) computes its cell relying on the Power diagram partitioning. If there is uncertainty in inferring the locations of these robots, the Additively Weighted Guaranteed Voronoi scheme is employed resulting in a rather conservative performance. The noted controllers are applied to Unmanned Aerial Systems, where the notion of visual coverage using downward facing cameras is illustrated. The aforementioned schemes are enhanced by using a Voronoi-free coverage scheme that relies on the knowledge of any arbitrary sensing pattern employed by the agents. The talk concludes with experimental studies for highlighting the efficiency of the suggested control laws. Future challenges taking into account the energy stored in an aerial system are outlined and methods to compute near optimal paths are offered
About the Speaker: Prof. Anthony Tzes is Professor of the Electrical & Computer Engineering (ECE) at New York University Abu Dhabi (NYUAD), United Arab Emirates. Prior to this, he was a Member of the Board of Regents of University of Patras (UPAT) in Greece and Professor of the ECE Department. He is a graduate of UPAT (1985) and has received his doctorate from the Ohio State University in 1990. From 1990 till 1999 he was NYU Tandon School of Engineering (known as Polytechnic University). He has been a Visiting Professor (2009-13) at U. of Loughborough, UK. His research interests include Mechatronics, and Control engineering applications, Cooperative Control of Networked Controlled Systems, and Surgical Robots. Prof. Tzes has received research funding from various organizations including NASA, the National (U.S.) Science Foundation, the European Union (Horizon2020), and the European Space Agency (ESA). He has authored more than 80 (250) papers published in international journals (conferences). He has received various awards as co-author of published articles including the best paper published in 2014 in IET Control, Theory & Applications and the ECC 2003 IST Prize Award. He has served in the editorial board of several journals (e.g. IEEE Control Systems Magazine, Circuits Systems and Computers, Journal of Intelligent and Robotic Systems, International Journal of Distributed Sensor Networks, and others).
From Asymptotic to Finite-Time to Fixed-Time to Prescribed-Time Stabilization
Abstract: It is generally well known that, while linear feedback laws ensure asymptotic/exponential stabilization over finite time intervals, sliding mode control is capable of achieving finite-time stabilization, however, the convergence time is larger when the initial condition is larger. To make the convergence time invariant of the initial condition, certain “homogeneous” time-invariant feedback laws are capable of ensuring that the convergence time not exceed a desired value but are complex, conservative, and exhibit nonsmooth behavior not only as the state arrives at zero but also during the transient. As an alternative, and inspired by the most ubiquitous feedback laws in missile guidance, we propose time-varying feedback laws that achieve stabilization in exactly the prescribed time. We also design observers with time-varying gains which achieve state estimation in prescribed time. Such controllers and observers can be combined into prescribed-time output feedback laws.
About the Speaker: Miroslav Krstic is Distinguished Professor of Mechanical and Aerospace Engineering, holds the Alspach endowed chair, and is the founding director of the Cymer Center for Control Systems and Dynamics at UC San Diego. He also serves as Senior Associate Vice Chancellor for Research at UCSD. As a graduate student, Krstic won the UC Santa Barbara best dissertation award and student best paper awards at CDC and ACC. Krstic has been elected Fellow of seven scientific societies - IEEE, IFAC, ASME, SIAM, AAAS, IET (UK), and AIAA (Assoc. Fellow) - and as a foreign member of the Academy of Engineering of Serbia. He has received the ASME Oldenburger Medal, Nyquist Lecture Prize, Paynter Outstanding Investigator Award, Ragazzini Education Award, Chestnut textbook prize, the PECASE, NSF Career, and ONR Young Investigator awards, the Axelby and Schuck paper prizes, and the first UCSD Research Award given to an engineer. Krstic has also been awarded the Springer Visiting Professorship at UC Berkeley, the Distinguished Visiting Fellowship of the Royal Academy of Engineering, and the Invitation Fellowship of the Japan Society for the Promotion of Science. He serves as Senior Editor in IEEE Transactions on Automatic Control and Automatica, as editor of two Springer book series, and has served as Vice President for Technical Activities of the IEEE Control Systems Society and as chair of the IEEE CSS Fellow Committee. Krstic has coauthored thirteen books on adaptive, nonlinear, and stochastic control, extremum seeking, control of PDE systems including turbulent flows, and control of delay systems.
Can machine learning trump theory in communication system design?
Speaker: Andrea Goldsmith
Time: 11:00 am - 12:00 pm Aug 16, 2018
Location: 10.099, 10th floor, 2 MetroTech Center, Brooklyn
Abstract: Design and analysis of communication systems have traditionally relied on mathematical and statistical channel models that describe how a signal is corrupted during transmission. In particular, communication techniques such as modulation, coding and detection that mitigate performance degradation due to channel impairments are based on such channel models and, in some cases, instantaneous channel state information about the model. However, there are propagation environments where this approach does not work well because the underlying physical channel is too complicated, poorly understood, or rapidly time-varying. In these scenarios we propose a completely new approach to communication system design based on machine learning (ML). In this approach, the design of a particular component of the communication system (e.g. the coding strategy or the detection algorithm) utilizes tools from ML to learn and refine the design directly from training data. The training data that is used in this ML approach can be generated through models, simulations, or field measurements. We present results for three communication design problems where the ML approach results in better performance than current state-of-the-art techniques: signal detection without accurate channel state information, signal detection without a mathematical channel model, and joint source-channel coding of text. Broader application of ML to communication system design in general and to millimeter wave and molecular communication systems in particular is also discussed.
About the Speaker: Andrea Goldsmith is the Stephen Harris professor in the School of Engineering and a professor of Electrical Engineering at Stanford University. She also serves on Stanford’s Presidential Advisory Board, University Budget Group, and Faculty Senate. She previously served as Chair of Stanford’s Faculty Senate and as a member of Stanford’s Commission on Graduate Education, Commission on Undergraduate Education, Committee on Research, Planning and Policy Board, and Task Force on Women and Leadership. She co-founded and served as Chief Technical Officer of Plume WiFi (formerly Accelera, Inc.) and of Quantenna (QTNA), Inc. She has also held industry positions at Maxim Technologies, Memorylink Corporation, and AT&T Bell Laboratories, and she currently chairs the Technical Advisory Boards of Interdigital Corp., Quantenna Communications, Cohere Communications, and Sequans. In the IEEE Dr. Goldsmith served on the Board of Governors for both the Information Theory and Communications societies. She has also been a Distinguished Lecturer for both societies, served as President of the IEEE Information Theory Society in 2009, founded and chaired the student committee of the IEEE Information Theory society, and chaired the Emerging Technology Committee of the IEEE Communications Society. She currently chairs the IEEE TAB committee on diversity and inclusion, and the Women in Technology Leadership Roundtable working group on metrics.
Dr. Goldsmith is a member of the National Academy of Engineering and the American Academy of Arts and Sciences, a Fellow of the IEEE and of Stanford, and has received several awards for her work, including the IEEE ComSoc Edwin H. Armstrong Achievement Award as well as Technical Achievement Awards in Communications Theory and in Wireless Communications, the National Academy of Engineering Gilbreth Lecture Award, the IEEE ComSoc and Information Theory Society Joint Paper Award, the IEEE ComSoc Best Tutorial Paper Award, the Alfred P. Sloan Fellowship, the WICE Technical Achievement Award, and the Silicon Valley/San Jose Business Journal’s Women of Influence Award. She is author of the book ``Wireless Communications'' and co-author of the books ``MIMO Wireless Communications'' and “Principles of Cognitive Radio,” all published by Cambridge University Press, as well as an inventor on 28 patents. Her research interests are in information theory and communication theory, and their application to wireless communications and related fields. She received the B.S., M.S. and Ph.D. degrees in Electrical Engineering from U.C. Berkeley.
Recovering lost information in analog-to-digital conversion
Speaker: Yonina Eldar
Time: 2:00 pm - 3:00 pm Aug 16, 2018
Location: 10.099, 10th floor, 2 MetroTech Center, Brooklyn
Abstract: The famous Shannon-Nyquist theorem has become a landmark in analog to digital conversion and the development of digital signal processing algorithms. However, in many modern applications, the signal bandwidths have increased tremendously, while the acquisition capabilities have not scaled sufficiently fast. Furthermore, the resulting high rate digital data requires storage, communication and processing at very high rates which is computationally expensive and requires large amounts of power.
In the context of medical imaging sampling at high rates often translates to high radiation dosages, increased scanning times, bulky medical devices, and limited resolution.
In this talk we consider a general framework for sub-Nyquist sampling and processing in space, time and frequency which allows to dramatically reduce the number of antennas, sampling rates and band occupancy in a variety of applications. Our framework relies on exploiting signal structure and the processing task. We consider applications of these ideas to a variety of problems in communications, radar and ultrasound imaging and show several demos of real-time sub-Nyquist prototypes including a wireless ultrasound probe, sub-Nyquist MIMO radar, cognitive radio, and an analog combiner prototype.
We then show how these ideas can be used to overcome fundamental resolution limits in optical microscopy and ultrasound imaging and demonstrate sub-Nyquist devices operating beyond the standard resolution limits combining high spatial resolution with short integration time.
About the Speaker: Yonina C. Eldar received the B.Sc. degree in Physics in 1995 and the B.Sc. degree in Electrical Engineering in 1996 both from Tel-Aviv University (TAU), Tel-Aviv, Israel, and the Ph.D. degree in Electrical Engineering and Computer Science in 2002 from the Massachusetts Institute of Technology (MIT), Cambridge. From January 2002 to July 2002 she was a Postdoctoral Fellow at the Digital Signal Processing Group at MIT.
She is currently a Professor in the Department of Electrical Engineering at the Technion - Israel Institute of Technology, Haifa, Israel, where she holds the Edwards Chair in Engineering. She is also an Adjunct Professor at Duke University, a Research Affiliate with the Research Laboratory of Electronics at MIT and was a Visiting Professor at Stanford University, Stanford, CA. She is a member of the Israel Academy of Sciences and Humanities (elected 2017), an IEEE Fellow and a EURASIP Fellow.
Dr. Eldar has received numerous awards for excellence in research and teaching, including the IEEE Signal Processing Society Technical Achievement Award (2013), the IEEE/AESS Fred Nathanson Memorial Radar Award (2014), and the IEEE Kiyo Tomiyasu Award (2016). She was a Horev Fellow of the Leaders in Science and Technology program at the Technion and an Alon Fellow. She received the Michael Bruno Memorial Award from the Rothschild Foundation, the Weizmann Prize for Exact Sciences, the Wolf Foundation Krill Prize for Excellence in Scientific Research, the Henry Taub Prize for Excellence in Research (twice), the Hershel Rich Innovation Award (three times), the Award for Women with Distinguished Contributions, the Andre and Bella Meyer Lectureship, the Career Development Chair at the Technion, the Muriel & David Jacknow Award for Excellence in Teaching, and the Technion’s Award for Excellence in Teaching (twice). She received several best paper awards and best demo awards together with her research students and colleagues including the SIAM outstanding Paper Prize and the IET Circuits, Devices and Systems Premium Award, and was selected as one of the 50 most influential women in Israel.
She was a member of the Young Israel Academy of Science and Humanities and the Israel Committee for Higher Education. She is the Editor in Chief of Foundations and Trends in Signal Processing, a member of the IEEE Sensor Array and Multichannel Technical Committee and serves on several other IEEE committees. In the past, she was a Signal Processing Society Distinguished Lecturer, member of the IEEE Signal Processing Theory and Methods and Bio Imaging Signal Processing technical committees, and served as an associate editor for the IEEE Transactions On Signal Processing, the EURASIP Journal of Signal Processing, the SIAM Journal on Matrix Analysis and Applications, and the SIAM Journal on Imaging Sciences. She was Co-Chair and Technical Co-Chair of several international conferences and workshops.
She is author of the book "Sampling Theory: Beyond Bandlimited Systems" and co-author of the books "Compressed Sensing" and "Convex Optimization Methods in Signal Processing and Communications", all published by Cambridge University Press.
Spatially Stationary Electromagnetic Random Fields
Speaker: Andrea Pizzo
Time: 11:00 am - 12:00 pm Aug 17, 2018
Location: 9.099, 9th floor, 2 MetroTech Center, Brooklyn
Abstract: Electromagnetic propagation for wireless systems is often modelled through ray-tracing. Although very accurate, ray-tracing is far too complex to be mathematically tractable. In order to capture some fundamental behaviour of wireless communication systems performance, communication theorists are willing to trade-off model accuracy for mathematical abstraction. Hence, the most common channel model used in wireless communication theory is the independent Rayleigh fading model, which implies statistical independence from one point in space to another.
Close spacing of antennas motivates a more realistic physic-based channel model for studying waves propagation that accounts for spatial correlation. The model takes the form of a superposition of plane-waves having statistically independent amplitudes, each of which satisfies the homogeneous wave equation. A Fourier spectral representation is particularly convenient for generating samples of the random field, and for theoretical inferences.
In particular, we show that the number of degrees of freedom of a MIMO system is proportional to the surface of the array rather than its volume and that the expansion of a planar array into a volume array yields only a two-fold increase in the channel degrees of freedom. This result had previously been established in spherical coordinates. Our derivation is entirely in Cartesian coordinates.
About the Speaker: Andrea Pizzo (S’14) received the M.Sc. degree in telecommunication engineering from the University of Pisa, Italy, in 2013. In 2014, he was with the CAS group at the Delft University of Technology, Delft, The Netherlands. He is pursuing his Ph.D. degree in Information Engineering at the Department of Information Engineering, Pisa, Italy. Currently, he is a visiting research scholar with the NYU wireless group at the New York University, New York. His research interests lie within the area of signal processing for wireless communications with emphasis on Massive MIMO and energy efficiency.