We live in an era in which our physical and personal environments are becoming increasingly intertwined and smarter due to the advent of pervasive sensing, wireless communications, computing, and control technologies. Indeed, our daily lives in smart cities and connected communities depend on a wide variety of cyber-physical infrastructures, such as smart city, smart energy, smart transportation, smart healthcare, smart manufacturing, smart agriculture, and so on. Alongside, the availability of wireless sensors, Internet of Things (IoT) and rich mobile devices are empowering humans with fine-grained information and opinion collection through crowdsensing about events of interest, resulting in actionable inferences and decisions. This synergy has led to the cyber-physical-social (CPS) convergence with human in the loop that exhibits complex interactions, inter-dependence and adaptations between the engineered/natural systems and human users with a goal to improve human quality of life and experience in smart living environments. However, huge challenges are posed by the scale, heterogeneity, big data, social dynamics, security, and resource limitations in sensors, IoT and CPS networks. This talk will highlight unique research challenges in smart living , followed by novel frameworks and models for efficient mobility management, data gathering and fusion, security and trustworthiness, and trade-off between energy and information quality in multi-modal context recognition. Case studies and experimental results from smart energy and smart healthcare applications will be presented. The talk will be concluded with directions for future research.

Sajal K. Das, whose academic genealogy includes Thomas Alva Edison, is a professor of Computer Science and Daniel St. Clair Endowed Chair at Missouri University of Science and Technology, where he was the Chair of Computer Science Department during 2013-2017. Prior to 2013, he was a University Distinguished Scholar Professor of Computer Science and Engineering, and founding director of the Center for Research in Wireless Mobility and Networking at the University of Texas at Arlington. During 2008-2011, Dr. Das served the National Science Foundation (NSF) as a Program Director in the Computer and Network Systems Division. His research interests include wireless and sensor networks, mobile and pervasive computing, smart environments (smart city, smart grid, smart transportation, smart healthcare, smart agriculture), CPS, IoT, cloud computing, security and trustworthiness, social and biological networks, applied graph theory and game theory. He has contributed significantly to these areas, and published 350+ research articles in high quality journals, 475+ papers in peer-reviewed conferences, and 54 book chapters. A holder of 5 US patents, Dr. Das has directed numerous funded projects over $20 million. He coauthored four books – Smart Environments: Technology, Protocols, and Applications (John Wiley, 2005); Handbook on Securing Cyber-Physical Critical Infrastructure: Foundations and Challenges (Morgan Kaufman, 2012); Mobile Agents in Distributed Computing and Networking (Wiley, 2012); and Principles of Cyber-Physical Systems: An Interdisciplinary Approach (Cambridge University Press, 2020). According to DBLP, he is one of the most prolific authors in computer science. His h-index is 93 with more than 35,600 citations according to Google Scholar. Dr. Das is the founding Editor-in-Chief of Elsevier’s Pervasive and Mobile Computing journal and serves as Associate Editor of several journals including the IEEE Transactions on Mobile Computing, IEEE Transactions on Dependable and Secure Computing, and ACM Transactions on Sensor Networks. A founder of IEEE PerCom, WoWMoM, SMARTCOMP and ICDCN conferences, he served as General and Technical Program Chair of numerous conferences. He is a recipient of 10 Best Paper Awards in prestigious conferences, and numerous awards for teaching, mentoring and research including IEEE Computer Society’s Technical Achievement award for pioneering contributions to sensor networks and mobile computing, and University of Missouri System President’s Award for Sustained Career Excellence. He graduated 46 PhD, 31 MS thesis students, and 10 postdoctoral fellows. Dr. Das is an IEEE Fellow.

Artificial Intelligence (AI) is a rapidly advancing field. Novel machine learning methods combined with reasoning and search techniques have led us to reach new milestones: from computer vision, machine translation, and Go and Chess world-champion level play using pure self-training strategies, to self-driving cars. These ever-expanding AI capabilities open up new exciting avenues for advances in new domains. I will discuss our AI research for advancing scientific discovery for a sustainable future. In particular, I will talk about our research in a new interdisciplinary field, Computational Sustainability, which has the overarching goal of developing computational models and methods to help manage the balance between environmental, economic, and societal needs for a sustainable future. I will provide examples of computational sustainability problems, ranging from biodiversity and wildlife conservation, to multi-criteria strategic planning of hydropower dams in the Amazon basin and materials discovery for renewable energy materials. I will also highlight cross-cutting computational themes and challenges for AI at the intersection of constraint reasoning, optimization, machine learning, multi-agent reasoning, citizen science, and crowd sourcing.

Dr. Carla Gomes is the Ronald C. and Antonia V. Nielsen Professor of Computing and Information Science and the director of the Institute for Computational Sustainability at Cornell University. Dr. Gomes received a Ph.D. in computer science from the University of Edinburgh, Scotland. Her research area is Artificial Intelligence with a focus on large-scale constraint reasoning, optimization, and machine learning. Recently, she has become deeply immersed in research on scientific discovery for a sustainable future and more generally in research in the new field of Computational Sustainability. Computational Sustainability aims to develop computational methods to help solve some of the key challenges concerning environmental, economic, and societal issues in order to help put us on a path towards a sustainable future. Dr. Gomes is the lead PI of an NSF Expeditions in Computing award. She has (co-)authored over 150 publications, which have appeared in venues spanning Nature, Science, and a variety of conferences and journals in AI and Computer Science, including five best paper awards.

The Internet of Things (IoT) paradigm refers to the network of physical objects or “things” embedded with electronics, software, sensors, and connectivity to enable objects to exchange data with servers, centralized systems, and/or other connected devices based on a variety of communication infrastructures. IoT makes it possible to sense and control objects creating opportunities for more direct integration between the physical world and computer-based systems. Furthermore, the deployment of AI techniques enhances the autonomy of IoT devices and systems. IoT will thus usher automation in a large number of application domains, ranging from manufacturing and energy management (e.g., SmartGrid), to healthcare management and urban life (e.g., SmartCity). However, because of its fine-grained, continuous, and pervasive data acquisition and control capabilities, IoT raises concerns about security, privacy, and safety. Deploying existing solutions to IoT is not straightforward because of device heterogeneity, highly dynamic and possibly unprotected environments, and large scale. In this talk, after outlining key challenges in IoT security and privacy, we will outline a security lifecycle approach to securing IoT data, and then focus on our recent work on security analysis for cellular network protocols and edge-based anomaly detection based on machine learning techniques.

Dr. Elisa Bertino is professor of Computer Science at Purdue University. Prior to joining Purdue, she was a professor and department head at the Department of Computer Science and Communication of the University of Milan. She has been a visiting researcher at the IBM Research Laboratory (now Almaden) in San Jose, at the Microelectronics and Computer Technology Corporation, at Rutgers University, and at Telcordia Technologies. Her main research interests include security, privacy, database systems, distributed systems, and sensor networks. Her recent research focuses on digital identity management, biometrics, IoT security, security of 4G and 5G cellular network protocols, and policy infrastructures for managing distributed systems. Prof. Bertino has published more than 700 papers in all major refereed journals, and in proceedings of international conferences and symposia. She has given keynotes, tutorials and invited presentations at conferences and other events. She is a Fellow member of ACM, IEEE, and AAAS. She received the 2002 IEEE Computer Society Technical Achievement Award “For outstanding contributions to database systems and database security and advanced data management systems,” the 2005 IEEE Computer Society Tsutomu Kanai Award for “Pioneering and innovative research contributions to secure distributed systems,” and the ACM 2019-2020 Athena Lecturer Award.

Pervasive computing applications often assume a distributed sensing substrate, incorporating both personal (mobile & wearable) devices and an IoT infrastructure (including devices such as radar & vision sensors). To support real-time & sustainable machine intelligence that exploits the rapid growth in such sensor-generated data, there is a need to optimize the execution of AI/ML pipelines on such resource-constrained embedded devices. In fact, assuring adequate and ubiquitous availability of power to such devices remains the most serious resource bottleneck and requires advances on both the supply side (new forms of energy harvesting) and the demand side (new forms of ultra-low power inferencing). Through this talk, I shall describe how collaborative machine intelligence (CMI), where the sensing and inferencing pipelines on individual wearable and IoT devices collaborate in real-time, helps tackle this power challenge. First, I will describe work on advances in battery-less, or low-power sensing, enabled by such collaboration. Second, using an exemplar video surveillance application, I will describe how CMI can provide dramatic reductions in energy overheads, as well as improvements in accuracy. I shall conclude by describing the emergence of a “Cognitive & Collaborative Edge”, that evolves from its current focus on mere computation offloading to a platform for enabling such collaborative and trusted sense-making.

Archan Misra is Vice Provost (Research) and Professor of Computer Science at Singapore Management University (SMU). Archan has led a number of multi-million-dollar, flagship research initiatives at SMU, including the LiveLabs research center and SMU’s Center for Applied Smart-Nation Analytics (CASA), that have focused on exploiting pervasive sensing for novel smart-city applications,  Over a 20+ year research career spanning both academics and industry (at IBM Research and Bellcore), Archan has published extensively on, and practically deployed, technologies spanning wireless networking, mobile & wearable sensing and urban mobility analytics. His current research interests lie in ultra-low energy execution of machine intelligence algorithms using wearable and IoT devices. Archan is a current recipient of the prestigious Investigator grant (from Singapore’s National Research Foundation) for sustainable man-machine interaction intelligence. Archan holds a Ph.D. from the University of Maryland at College Park, and chaired the IEEE Computer Society's Technical Committee on Computer Communications (TCCC) from 2005-2007.

Wrist-worn wearable devices provide rich sets of pulsatile physiological data under various modalities and circumstances. An unexploited capability is that the pulsatile physiological time series collected by wrist-worn wearable devices can be used for recovering internal brain dynamics. Two design classes of closed-loop wearable brain-machine interface architectures related to cognitive stress for tracking arousal and fatigue states are presented. The methods are validated by analyzing experimental electrodermal activity and cortisol data as well as simulation studies in the context of cognitive-stress-related arousal and fatigue. Results demonstrate a promising approach for tracking and regulating neurocognitive stress through wearable devices. Since wearable devices can be used conveniently in one's daily life, wearable brain-machine interface architectures have a great potential to monitor and regulate one's neurocognitive stress seamlessly in real-world situations.

Rose T. Faghih is an assistant professor of Electrical and Computer Engineering at the University of Houston where she directs the Computational Medicine Laboratory. She received a bachelor’s degree (summa cum laude) in Electrical Engineering (Honors Program Citation) from the University of Maryland, and S.M. and Ph.D. degrees in Electrical Engineering and Computer Science with a minor in Mathematics from MIT, where she was a member of the MIT Laboratory for Information and Decision Systems as well as the MIT-Harvard Neuroscience Statistics Research Laboratory. She completed her postdoctoral training at the Department of Brain and Cognitive Sciences and the Picower Institute for Learning and Memory at MIT as well as the Department of Anesthesia, Critical Care and Pain Medicine at the Massachusetts General Hospital. Rose is the recipient of various awards including an MIT Technology Review 2020 Innovator Under 35 award, a 2020 National Science Foundation CAREER Award, a 2020 Research Excellence award as well as a 2020 Teaching Excellence Award from the University of Houston's Cullen College of Engineering, a 2016 IEEE-USA New Face of Engineering award, a National Science Foundation Graduate Research Fellowship, an MIT Graduate Fellowship, and the University of Maryland's Department of Electrical and Computer Engineering Chair's Award. Moreover, Rose was selected by the National Academy of Engineering for the 2019 U.S. Frontiers of Engineering Symposium. In 2020, Rose was featured by the IEEE Women in Engineering Magazine as a “Woman to Watch”. Furthermore, she has been inducted into various honor societies including Phi Kappa Phi, Tau Beta Pi, and Eta Kappa Nu. Her research interests include wearable technologies, medical cyber-physical systems, neural and biomedical signal processing, as well as control, estimation and system identification of biomedical and neural systems.

Control flow graphs (CFGs), networks representing possible paths of execution, are used widely in areas of computing such as program analysis, security, and compilation. Visualizations of CFGs are typically used as an exploratory tool to aid understanding or verify, debug, or propose new methods that utilize CFGs. However, general graph drawing approaches can limit the utility of CFG visualizations, producing drawings that are difficult to interpret, especially as the graph grows in size and complexity. To improve the efficacy of CFG visualizations, we develop a layout approach that preserves and emphasizes computing-specific structures such as loops and functions in a manner similar to hand-drawn versions. We design two custom interactive visualization systems that combine these layouts with additional views and features. Finally, we develop a library for filtering and drawing CFGs to support future visualization needs.

Kate Isaacs is an assistant professor at the University of Arizona. Her interests include data visualization and high performance computing. Her work focuses on understanding how people interact with data, how data visualization methodologies can be improved, and developing new methods of representing complex computing processes for exploration and analysis of their behavior, with applications to high performance computing, distributed computing, data science, program analysis, optimization, and environmental planning. She received an NSF CAREER award in 2019 and a DOE Early Career Research Program award in 2021 supporting these efforts.

Digital Twins are a disruptive technology that can automate human health assessment and intervention by creating a holistic, virtual replica of a physical human. The increasing availability of sensing platforms and the maturing of data mining methods support building such a replica from longitudinal, passively sensed data. By creating such a quantified self, we can more precisely understand current and future health status. We can also anticipate the outcomes of behavior-driven interventions. In this talk, I will discuss the challenges that accompany creating human digital twins in the wild, survey emerging data mining methods that tackle these challenges, and describe some of the current and future impacts that technologies have for supporting our aging population.

Diane Cook is Regents Professor and Huie-Rogers Chair in the School of Electrical Engineering and Computer Science at Washington State University, founding director of the WSU Center for Advanced Studies in Adaptive Systems (CASAS), and co-director of the WSU AI Laboratory. She is a Fellow of the IEEE and the National Academy of Inventors. Diane's work is featured in BBC, IEEE The Institute, IEEE Spectrum, Smithsonian, The White House Fact Sheet, Scientific American, the Wall Street Journal, AARP Magazine, HGTV, and ABC News. Her research aims to create smart environments that automate health monitoring and intervention, evaluated via the CASAS Smart Home in a Box installed in over 160 sites across 9 countries. Her research currently focuses on developing machine learning methods that map a human behaviorome as a foundation for constructing a digital twin. She also conducts multidisciplinary research to leverage digital twin technologies for automatically assessing, extending, and enhancing a person's functional independence.

A significant amount of published work in Federated Learning deals with challenge of what can be called consumer federated learning, where federated learning is done on information from consumer mobile phones. Consumer federated learning deals with challenges of learning when data is split into many large number of pieces. When we consider the enterprise scenario, the environment consists of data which is split into a smaller number of locations, usually caused by regulatory concerns. The challenges in federated learning in enterprise scenarios is very different from that of consumer federated learning, and requires a different class of solutions, In this talk, we discuss the challenges in both consumer and enterprise federated learning, and outlines some of the approaches to address those challenges.

Dinesh C. Verma (IBM Fellow, Fellow of Royal Academy of Engineering, IEEE Fellow) is the CTO of Edge Computing at IBM T J Watson Research Center, Yorktown Heights, New York. Dinesh is responsible for defining the IBM strategy is the area of edge computing for IBM world-wide research. He has more than 25 years of professional experience. He has authored ten books, 150+ technical papers and been granted 185+ U.S. patents.  He has chaired/vice-chaired IEEE technical committee on computer communications, as well as IEEE Internet technical committee. He has served on various program committees, editorial boards, and managed large international multi-institutional government programs. He is a member of the IBM Academy of Technology, has been recognized multiple times as an IBM Master Inventor, won several IBM technical awards, including designation as an IBM Fellow, the highest technical recognition within the company.  He has made seminal contributions to several areas in computer networks and is known for his work on Quality of Service management in computer networks and Policy based Networking. He has contributed to several IBM products and service engagements including significant contributions to server networking stack, network management products and customer projects related to cellular network analytics. He has led international research alliances for academia, industry, and government labs for 15 years.  More details about Dinesh can be seen at http://ibm.biz/dineshverma

In this talk, we explore how networked compartmental models of epidemic processes combined with transportation data can be used to model the spread of COVID-19. We first employ a networked SEIR (susceptible-exposed-infected-recovered) model and present necessary and sufficient conditions for identifying the model parameters from data. We illustrate several shortcomings of traditional approaches by applying the identification results to COVID-19 testing and travel data from the Northeastern United States and use these inaccuracies as motivation for the latter parts of the talk. The last second of the talk focuses on the recent development of a networked SEIR model that incorporates population flow as the viral spread mechanism to capture infection transmission between sub-populations. We show, under reasonable assumptions, that the dynamics have a consensus-type behavior where in steady-state each sub-population has the same number of recovered individuals. Employing this model, we present several approaches for using travel restrictions as a control mechanism also coupled with a vaccination effort. We present some preliminary results on validation with real data from the state of Minnesota. We conclude the talk with some recent work that leverages the concept of carrying capacity to account for vaccine hesitancy and shows the negative effects vaccine hesitancy on the threshold behavior of the system.

Philip E. Paré is an Assistant Professor in the School of Electrical and Computer Engineering at Purdue University. He received his Ph.D. in Electrical and Computer Engineering from the University of Illinois at Urbana-Champaign in 2018, after which he went to KTH Royal Institute of Technology in Stockholm, Sweden as a Post-Doctoral Scholar until 2020. He received his B.S. in Mathematics with University Honors and his M.S. in Computer Science from Brigham Young University in 2012 and 2014, respectively. At the University of Illinois, he was the recipient of the Robert T. Chien Memorial Award for excellence in research and named a Mavis Fellow. He has been selected for the Societal Impact Fellows Program at Purdue University for Fall 2021. His research focuses on networked control systems, namely modeling, analysis, and control of virus spread over networks.

The National Science Foundation (NSF) supports a majority of US academic research in the Computer and Information Science and Engineering (CISE) topic areas.  Since February 2020, Dr. Margaret Martonosi serves as the NSF CISE AD, stewarding the CISE directorate’s $1B annual budget on behalf of research, education, workforce and infrastructure funding in CISE topic areas and for science as a whole. Dr. Martonosi is conducting a series of “virtual campus visits” to engage in conversation about a vision for CISE research going forward, and to field Q&A from the CISE community. Please join us for this highly interactive session and please bring your input and questions!

Dr. Margaret Martonosi is the US National Science Foundation’s (NSF) Assistant Director for Computer and information Science and Engineering (CISE). With an annual budget of more than $1B, the CISE directorate at NSF has the mission to uphold the Nation’s leadership in scientific discovery and engineering innovation through its support of fundamental research and education in computer and information science and engineering as well as transformative advances in research cyberinfrastructure. While at NSF, Dr. Martonosi is on leave from Princeton University where she is the Hugh Trumbull Adams '35 Professor of Computer Science. Her research interests are in computer architecture and hardware-software interface issues in both classical and quantum computing systems. Dr. Martonosi is a member of the National Academy of Engineering and the American Academy of Arts and Sciences. She is a Fellow of the Association for Computing Machinery (ACM) and the Institute of Electrical and Electronics Engineers (IEEE). 

I will discuss a recently proposed model of opinion dynamics and its use in decision making for multi-agent cooperative and strategic interactions. The model describes continuous-time opinion dynamics for an arbitrary number of agents that communicate over a network and form real-valued opinions on an arbitrary number of options about which they may receive payoffs or input from the environment. Many models in the literature update agent opinions using a weighted average of their neighbors’ opinions. This model instead applies a sigmoidal saturation function to opinion exchanges, which provides a naturally smooth limit on the influence of neighbors. It also makes the update fundamentally nonlinear: opinions form through a bifurcation yielding multistable agreement and disagreement, tunable sensitivity to input, flexible transition between patterns of opinions, and opinion cascades. I will show how to apply the opinion dynamics to design of efficient dynamic multi-agent task allocation and as a model of reinforcement learning in multi-agent finite games. 

This is joint work with Anastasia Bizyaeva, Alessio Franci, and Shinkyu Park and based on papers https://arxiv.org/abs/2009.04332,  https://link.springer.com/article/10.1007%2Fs11721-021-00190-w, and https://arxiv.org/abs/2108.00966 

Naomi Ehrich Leonard is Edwin S. Wilsey Professor of Mechanical and Aerospace Engineering and associated faculty in Applied and Computational Mathematics at Princeton University.  She is also Director of Princeton’s Council on Science and Technology and Editor of the Annual Review of Control, Robotics, and Autonomous Systems. She received her BSE in Mechanical Engineering from Princeton University and her PhD in Electrical Engineering from the University of Maryland.  She is a MacArthur Fellow, elected member of the American Academy of Arts and Sciences, and Fellow of SIAM, IEEE, IFAC, and ASME.  Her current research focuses on dynamics and control of multi-agent systems on networks with application to distributed decision making, spreading processes, multi-robot teams, and collective behavior.

Cyber-physical systems (CPS) are smart systems that include co-engineered interacting networks of physical and computational components. Prominent examples of CPS include autonomous robots, self-driving cars, smart cities, and medical devices. CPS are increasingly everywhere, providing new capabilities to improve quality of life and transform many critical areas. However, significant challenges are posed for assuring the safety and trustworthiness of CPS. In this talk, I will present some of my recent work to tackle these challenges, including (1) trust in human-automated vehicle interactions, (2) safe multi-agent reinforcement learning via shielding for robotic planning, and (3) predictive monitoring with logic-calibrated uncertainty for smart cities. If time allows, I will also briefly talk about my on-going research on improving the accountability and transparency of CPS.

Dr. Lu Feng is an Assistant Professor at the Department of Computer Science and the Department of Engineering Systems and Environment at the University of Virginia. Previously, she was a postdoctoral fellow at the University of Pennsylvania and received her PhD in Computer Science from the University of Oxford. Her research focuses on assuring the safety and trustworthiness of cyber-physical systems, spanning many different application domains, from autonomous robots, to smart cities, to medical systems. She is a recipient of NSF CAREER Award.

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Cryptography, or the science of secret writing, has a long history going back thousands of years.  Until the wide availability of "computers for the masses" it was the exclusive province of governments and large businesses.  Until the 1970's the standard way for accomplishing this was through a shared secret known as a private key.  Diffie and Hellman changed all that with a system that used a public key, which didn't need to be kept secret for the encoder, and private key only in possession of the recipient.  Their original proposal, however, was discovered to have a flaw which made it less secure than originally thought.  In 1985, Miller and Koblitz independently proposed using a then obscure mathematical object, known as an Elliptic Curve as a means of repairing the flaw in the original proposal.  In the ensuing time, its security has held up, and is now widely used as a basis for secure communications in the Internet.  In this talk we'll introduce elliptic curve and show how they avoid the flaw in the original Diffie-Hellman proposal.

Victor Miller is a Mathematician and Computer Scientist at the Center for Communications Research in Princeton, New Jersey.  He holds a Ph.D. in Mathematics from Harvard University. Previously, he was a Research Staff Member in the Computer Science and Mathematics Department at the IBM Research Center in Yorktown Heights, New York, and an Assistant Professor of Mathematics at the University of Massachusetts, Boston.  He is a Fellow of the IEEE, ACM, and IACR.  He is also the the recipient of the IEEE Third Millenium Medallion, the RSA Excellence in Mathematics Award, the ISSA Hall of Fame Award, the Eduard Rhein Foundation Technology Award, and the Levchin Prize of the Real World Cryptography Conference.  While at IBM he was the recipient of three Outstanding Technical Achievement Awards and a Corporate Technical Achievement Award.