Abstract: The last few years, robots have moved from the pages of science fiction books into our everyday reality. Currently, robots are utilized in entertainment, scientific exploration, manufacturing, and household maintenance. While the above advances were made possible by recent improvements in sensors, actuators, and computing elements, the research of today is focused on the computational aspects of robotics. In particular, methodologies for utilizing the vast volumes of data that can be generated by a robotic mission, together with techniques that would allow a robot to respond adequately in unforeseeable circumstances are the challenges of tomorrow. This talk presents an overview of algorithmic problems related to marine robotics, with the particular focus on increasing the autonomy of robotic systems in challenging environments. I will talk about vision based state estimation and mapping of underwater caves. Motion planning algorithms for covering an aquatic environment will be discussed with a focus on rivers and lakes. In addition I will talk about several vehicles used at the University of South Carolina such as drifters, underwater, and surface vehicles. In addition a short overview of current projects will be discussed. The work that I will present has a strong algorithmic flavour, while it is validated in real hardware. Experimental results from several testing campaigns will be presented.
Bio: Ioannis Rekleitis is an Associate Professor at the Computer Science and Engineering Department, University of South Carolina. He was an Adjunct Professor at the School of Computer Science, McGill University 2007-2020. Between 2004 and 2007 he was a visiting fellow at the Canadian Space Agency. During 2004 he was at McGill University as a Research Associate in the Centre for Intelligent Machines with Professor Gregory Dudek in the Mobile Robotics Lab (MRL). Between 2002 and 2003, he was a Postdoctoral Fellow at the Carnegie Mellon University in the Sensor Based Planning Lab with Professor Howie Choset. He was granted his Ph.D. from the School of Computer Science, McGill University, Montreal, Canada in 2002 under the supervision of Professors Gregory Dudek and Evangelos Milios. Thesis title: “Cooperative Localization and Multi-Robot Exploration”. His Research has focused on mobile robotics and in particular in the area of cooperating intelligent agents with application to multi-robot cooperative localization, mapping, exploration and coverage. Field deployments of the robotics systems provide valuable feedback for the developed algorithms. His interests extend to computer vision, machine learning, and sensor networks. He has worked with underwater, surface, terrestrial, aerial, and space robots. Ioannis Rekleitis has published more than ninety journal and conference papers. His work can be found online at: http://www.cse.sc.edu/~yiannisr/.
Abstract: Combination puzzles, such as the Rubik’s cube, pose unique challenges for artificial intelligence. Furthermore, solutions to such puzzles are directly linked to problems in the natural sciences. In this talk, I will present DeepCubeA, a deep reinforcement learning and search algorithm that can solve the Rubik’s cube, and six other puzzles, without domain specific knowledge. Next, I will discuss how solving combination puzzles opens up new possibilities for solving problems in the natural sciences. In particular, I will describe how we are using DeepCubeA to tackle problems in chemistry. Finally, I will show how problems we encounter in the natural sciences motivate future research directions. A demonstration of our work can be seen at http://deepcube.igb.uci.edu/.
Bio: Forest Agostinelli is an assistant professor at the University of South Carolina. He received his B.S. from the Ohio State University, his M.S. from the University of Michigan, and his Ph.D. from UC, Irvine. His research interests include deep learning, reinforcement learning, search, bioinformatics, neuroscience, and chemistry. His homepage is located at https://cse.sc.edu/~foresta/.
Abstract: Intelligent decision making is at the heart of Artificial Intelligence (AI). A large number of real-world domains, such as autonomous vehicles, delivery robots, cyber security, and so many others, involve multiple AI decision makers, or agents, that cooperate with collective efforts in a distributed manner, where each agent’s decisions are based on its local information, with often limited communication with others. This distributed nature makes it challenging to design efficient and reliable multiagency. Issues like failure to coordinate, unsafe interactions, and resource misallocation can easily arise.
In part one of the talk, I will be introducing our work on utilizing the notion of social commitments to achieve reliable and trustworthy multiagent coordination. Intuitively, a commitment regularizes an agent’s behavior so that it can be well anticipated and exploited by another. I will build up a formalism of this intuition, and discuss how multiagent commitments can be efficiently identified and faithfully fulfilled. In part two of the talk, I will be describing our ongoing research on automatically discovering multiagent learning algorithms, with a focus on learning to communicate.
Bio: Qi Zhang was educated at University of Michigan and received a PhD in Computer Science and Engineering in August 2020; previously, he had earned a Bachelor of Engineering in Electrical Engineering from Shanghai Jiao Tong University in China (2015). During his graduate studies, he served as a graduate research assistant contributing to multi-agent coordination and reinforcement learning research (2015-20) and was nominated for the UM Towner Prize for outstanding doctoral research (2019). As a summer intern with IBM, he conducted multi-agent reinforcement learning research (2018). Qi joined UofSC in August 2020.
Specialization and Heterogeneity in Computer Architecture
Abstract: Processor technology has reached an impasse. While Moore’s Law remains alive for the moment, traditional general-purpose computer architectures have reached efficiency limits that have effectively stalled their performance growth. In response, processor designers have shifted their focus to designing specialized processors to deliver real-time performance of increasingly intensive workloads and achieving new levels of energy efficiency for low power mobile platforms. In this talk, I will describe how our research group is contributing to this effort by highlighting two of our current projects involving domain-specific processor architectures. The first is a reconfigurable overlay architecture for high-performance pattern-matching, which outperforms the state-of-the-art CPU- and GPU-based implementations for benchmark datasets. The second is a run-time optimization framework for computer vision applications designed for an embedded Digital Signal Processor architecture.
Bio: Jason D. Bakos is a professor of Computer Science and Engineering at the University of South Carolina. His research focuses on applications, abstractions, and development tools for domain-specific processing technologies at both the high-performance and embedded scales, including reconfigurable, massively-parallel, and processor-in-memory architectures. He received the U.S. National Science Foundation (NSF) CAREER award in 2009 and won DAC design contests in 2002 and 2004. He is currently serving as associate editor for ACM Transactions on Reconfigurable Technology and Systems (TRETS).
Wireless Sensing: Material identification and Localization
Abstract: Wireless communication has truly transformed the world. It has enabled us to connect the entire globe, and made it simple to reach people separated by thousands of miles. However, what receives less attention are other interesting properties of these wireless signals. The fact that wireless signal spread out in all directions and bounce off objects, make them a power lens to look at our world through. This facilitates sensing of the world through wireless signals. In this talk I present two main ideas: Wireless localization which measures the time wireless signals take to travel between two devices, and wireless material identification which analyzes the effect of a liquid on wireless signals, in order to identify the liquid.
Bio: Dr. Ashutosh Dhekne is an assistant professor in the School of Computer Science at Georgia Tech. He received his Ph.D. from the University of Illinois at Urbana Champaign, his MTech from IIT Bombay, and bachelors from University of Pune. His research interests include Mobile Computing, Wireless Networking, Wireless Sensing, and Internet of Things.
Abstract: The pursuing of safer self-driving cars, smarter robots, and cell phone apps is accelerating, in which deep learning techniques are playing a major role. However, one of the major bottlenecks of deep learning in these edge computing devices is their limited computing power and severe energy constraints. As alternatives to von Neumann architectures, neuromorphic systems have big potential to address these issues by its avoidance of the processor-memory bottleneck, reduced energy consumption, and area-sparing computation. In this talk, we will introduce the “Neuro-Edge” project which proposes leveraging neuromorphic computing algorithms and hardware to address four important needs in machine learning (ML) at edge devices: (1) Energy-Efficient Computing: One of the major bottlenecks of ML in edge devices is their severe energy constraints. (2) Incremental Learning: in most of the edge devices data arrives in form of a stream rather than batches, therefore an ML model is required to learn continuously and incrementally upon the arrival of each sample data. (3) Capture Temporal Information: data streams produced by edge devices often exhibit temporal patterns and dependencies, which are important to be captured to distinguish the relationship between input features over time. (4) Drift Tolerance: edge devices normally operate in non-stationary environments, and are prone to concept drifts that are induced by the appearance and/or disappearance of features and/or classes in the incoming data stream.
Bio: Dr. Ramtin Zand is an Assistant Professor of Computer Science and Engineering at the University of South Carolina (UofSC), and the director of Intelligent Circuits, Architectures, and Systems (iCAS) Lab. He received his Ph.D. in Computer Engineering from the University of Central Florida (UCF), Orlando, FL in 2019. He was the Graduate Research Assistant in the Computer Architecture Lab (CAL), where he worked on a National Science Foundation (NSF) and Semiconductor Research Corporation (SRC) jointly-supported project on Probabilistic Spin Logic for Low-Energy Boolean and Non-Boolean Computing under the supervision of Prof. Ronald F. DeMara. He has authored or co-authored 40+ conference proceedings papers and journal articles, as well as a book chapter, and received research recognitions from ACM/IEEE including the best paper recognition at ACM GLSVLSI in 2018, and Featured Paper of the Oct-Dec 2018 Issue in IEEE Transactions on Emerging Topics in Computing.
Pushing the Boundaries of Millimeter-Wave Networking and Imaging
Abstract: Millimeter-wave (mmWave) technology plays a central role in next-generation wireless networking, sensing, and imaging. In this talk, we will present an overview of our work on pushing the performance boundaries of millimeter-wave systems. We will present results on fast beamforming and alignment algorithms as well as medium access protocols for enabling dense spatial reuse in mmWave networks. We will also highlight our recent work on millimeter wave wireless networks on-chip. Finally, we will discuss our work on enabling through fog high-resolution mmWave imaging for self-driving cars using generative adversarial networks.
Bio: Haitham Hassanieh is an assistant professor in the ECE and CS departments at UIUC. He received his Ph.D. in Electrical Engineering and Computer Science from MIT. His areas of expertise are wireless networks, mobile systems, and algorithms. He has won multiple awards including MobiSys Best Paper Award, SIGCOMM Best Paper Award, the Sprowls award for best thesis in computer science at MIT, the TR10 award for Technology Review Top 10 Breakthrough Technologies, the NSF Career Award, the Alfred Sloan Foundation Fellowship, and the ACM Doctoral Dissertation Award.
Marketing Analytics: Problem Spaces and Potential Solutions
Abstract: Businesses large and small face a common challenge around attracting new customers and retaining existing ones, with marketing as a core component in tackling this challenge. As customers and businesses move online, the amount of data available to inform and improve marketing decisions has grown significantly. In this talk we’ll look at a high level overview of some of the technical challenges involved in making use of this growing set of data to improve marketing decisions and optimize toward business goals, as well as a sample of solutions explored.
Bio: Chao Cai is the engineering lead for Google’s product efforts helping small and medium businesses (SMBs) grow by providing them with simple and effective advertising solutions. Previously, Chao led Google’s conversion measurement, reporting, and attribution efforts across a number of advertiser products. In this role, he had led development in the past on products and features within Google Ads, Google Analytics, DoubleClick, and Google Tag Manager. Prior to this, Chao had focused on various display advertising efforts within AdSense and YouTube. He holds numerous patents across the areas of online advertising, web analytics, and conversion analysis.
AI and the Changing Landscape of Privacy Notice and Choice
Abstract: For more than two decades since the rise of the World Wide Web, the “Notice and Choice” framework has been the governing practice for the disclosure of online privacy practices. The emergence of new forms of user interactions, such as voice, and the enforcement of new regulations, such as the EU’s recent General Data Protection Regulation (GDPR) promises to change this privacy landscape drastically. In this talk, I will discuss the challenges towards providing the privacy stakeholders with privacy awareness and control in this changing landscape. I will also present our recent research on utilizing AI to analyze privacy policies and settings.
Bio: Kassem Fawaz is an Assistant Professor in the Electrical and Computer Engineering department at the University of Wisconsin – Madison. He earned his Ph.D. in Computer Science and Engineering from the University of Michigan. His research interests include the security and privacy of the interactions between users and connected systems. He was awarded the Caspar Bowden Award for Outstanding Research in Privacy Enhancing Technologies in 2019. He also received the National Science Foundation CAREER award in 2020. His research is funded by the National Science Foundation, Federal Highway Administration, and the Defense Advanced Research Projects. His work on privacy has been featured in several media outlets, such as Wired, the Wall Street Journal, the New Scientist, and ComputerWorld.
Abstract: This talk presents the challenges and opportunities of building a city-scale low-power wireless Internet-of-Things. We build upon low-power wide-area networking (LP-WAN), a technology that enables low-cost devices with a 10-year battery to communicate at few kbps to a base station, kilometers away. We address the challenges in deploying LP-WANs in large urban environments, given the power limits of the clients and attenuation from buildings that limit signal range. We further show how LP-WANs at shorter ranges can eliminate the need for a battery altogether. Beyond communication, the talk also discusses novel applications and sensing opportunities of an omnipresent low-power Internet.
Bio: Swarun Kumar is an assistant professor at CMU where he heads the laboratory for emerging wireless technologies (WiTech lab). He designs and builds novel systems to enable faster wireless networks and new services. Swarun is a recipient of the NSF CAREER award and Google Faculty Research Award.
T2Pair: Secure and Usable Pairing for Heterogeneous IoT Devices
Abstract: Secure pairing is key to trustworthy deployment and application of Internet of Things (IoT) devices. However, IoT devices lack conventional user interfaces, such as keyboards and displays, which makes many traditional pairing approaches inapplicable. Proximity-based pairing approaches are very usable, but can be exploited by co- located malicious devices. Approaches based on a user’s physical operations on IoT devices are more secure, but typically require inertial sensors, while many devices do not satisfy this requirement. A secure and usable pairing approach that can be applied to heterogeneous IoT devices still does not exist. We develop a technique, Universal Operation Sensing, which allows an IoT device to sense the user’s physical operations on it without requiring inertial sensors. With this technique, a user holding a smartphone or wearing a wristband can finish pairing in seconds through some very simple operations, e.g., pressing a button or twisting a knob. Mor over, we reveal an inaccuracy issue in original fuzzy commitment and propose faithful fuzzy commitment to resolve it. We design a pairing protocol using faithful fuzzy commitment, and build a prototype system named Touch-to-Pair (T2Pair, for short). The comprehensive evaluation shows that it is secure and usable.
Bio: Dr. Qiang Zeng is an Assistant Professor at University of South Carolina. He obtained his PhD degree from Penn State University. His main research interest is Computer Systems Security, with a focus on Internet of Things and Mobile Systems. He published in CCS, USENIX Security, NDSS, MobiCom, MobiSys, PLDI, TMC, TDSC, TKDE, etc. He looks for students interested in Security research to join his lab.
Wireless and Mobile Sensing problems in IoT: Sports, Drones, and Material Sensing
Abstract: Motion tracking and RF sensing is a broad area with classical problems that dates back many decades. While significant advances have come from the areas of robotics, control systems, and signal processing, the emergence of mobile and IoT devices is ushering a new age of embedded, human-centric applications. Fitbit is a simple example that has rapidly mobilized proactive healthcare; medical rehabilitation centers are utilizing wearable devices towards injury diagnosis and prediction. In this talk, I will discuss a variety of (new and old) IoT applications that present unique challenges at the intersection of mobility, multi-modal sensing, and indirect inference. For instance, I will discuss how inertial sensors embedded in balls, racquets, and shoes can be harnessed to deliver real-time sports analytics on your phone. In a separate application, I will show how GPS signals can be utilized to track the 3D orientation of an aggressively flying drone, ultimately delivering the much needed reliability against crashes. Finally, I will discuss sensing liquid materials by passing WiFi-like signals through containers holding liquids. In general, I hope to show that information fusion across wireless signals, sensors, and physical models can together deliver motion-related insights, useful to a range of applications in IoT, healthcare, and cyber physical systems.
Bio: Mahanth Gowda is an Assistant Professor in Computer Science and Engineering at Penn State. His research interests include wireless networking, mobile sensing, and wearable computing, with applications to IoT, cyber physical systems, and human gesture recognition. He has published across diverse research forums, including NSDI, MobiCom, WWW, Infocom, Hotnets, ASPLOS, etc.
Internet of Acoustic Things (IoAT): Challenges, Opportunities, and Threats
Abstract: The recent proliferation of acoustic devices, ranging from voice assistants to wearable health monitors, is leading to a sensing ecosystem around us — referred to as the Internet of Acoustic Things or IoAT. My research focuses on developing hardware-software building blocks that enable new capabilities for this emerging future. In this talk, I will sample some of my projects. For instance, (1) I will demonstrate carefully designed sounds that are completely inaudible to humans but recordable by all microphones. (2) I will discuss our work with physical vibrations from mobile devices, and how they conduct through finger bones to enable new modalities of short range, human-centric communication. (3) Finally, I will draw attention to various acoustic leakages and threats that arrive with sensor-rich environments. I will conclude this talk with a glimpse of my ongoing and future projects targeting a stronger convergence of sensing, computing, and communications in tomorrow’s IoT, cyber-physical systems, and healthcare technologies.
Bio: Nirupam Roy is an Assistant Professor in Computer Science at the University of Maryland, College Park (UMD). He received his Ph.D. from the University of Illinois, Urbana-Champaign (UIUC) in 2018. His research interests are in wireless networking, mobile computing, and embedded systems with applications to IoT, cyber-physical-systems, and security. His recent projects include low-power sensing techniques to enable self-defense in robots and drones. His doctoral thesis was selected for the 2019 CSL Ph.D. thesis award at UIUC. Nirupam is the recipient of the Valkenburg graduate research award, the Lalit Bahl fellowship, and the outstanding thesis awards from both his Bachelor’s and Master’s institutes. His research received the MobiSys best paper award and was selected for the ACM SIGMOBILE research highlights. Many of his research projects have been featured in news media such as the MIT Technology Review, The Telegraph, and The Huffington Post.