Program

Abstract: Advances in neural networks revolutionized modern machine intelligence, but important challenges remain when applying these solutions in IoT contexts; specifically, on lower-end embedded devices with multimodal sensors and distributed heterogeneous hardware. The talk discusses challenges in offering machine intelligence services to support applications in resource constrained distributed IoT environments. The intersection of IoT applications, real-time requirements, distribution challenges, and AI capabilities motivates several important research directions. For example, how to support efficient execution of machine learning components on embedded edge devices while retaining inference quality? How to reduce the need for expensive manual labeling of IoT application data? How to improve the responsiveness of AI components to critical real-time stimuli in their physical environment? How to prioritize and schedule the execution of intelligent data processing workflows on edge-device GPUs? How to exploit data transformations that lead to sparser representations of external physical phenomena to attain more efficient learning and inference? How to develop foundation models for IoT that offer extended inference capabilities from time-series data analogous to ChatGPT inference capabilities from text? The talk discusses recent advances in edge AI and foundation models and presents evaluation results in the context of different real-time IoT applications.

Biography: Tarek Abdelzaher received his Ph.D. in Computer Science from the University of Michigan in 1999. He is currently a Sohaib and Sara Abbasi Professor and Willett Faculty Scholar at the Department of Computer Science, the University of Illinois at Urbana Champaign. He has authored/coauthored more than 300 refereed publications in real-time computing, distributed systems, sensor networks, and control. He served as an Editor-in-Chief of the Journal of Real-Time Systems, and has served as Associate Editor of the IEEE Transactions on Mobile Computing, IEEE Transactions on Parallel and Distributed Systems, IEEE Embedded Systems Letters, the ACM Transaction on Sensor Networks, and the Ad Hoc Networks Journal, among others. Abdelzaher's research interests lie broadly in understanding and influencing performance and temporal properties of networked embedded, social and software systems in the face of increasing complexity, distribution, and degree of interaction with an external physical and social environment. Tarek Abdelzaher is a recipient of the IEEE Outstanding Technical Achievement and Leadership Award in Real-time Systems (2012), the Xerox Award for Faculty Research (2011), as well as several best paper awards. He is a fellow of IEEE and ACM.

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Abstract: Balancing performance and safety are crucial to deploying autonomous vehicles in multi-agent environments. In particular, autonomous racing is a domain that penalizes safe but conservative policies, highlighting the need for robust, adaptive strategies. Current approaches either make simplifying assumptions about other agents or lack robust mechanisms for online adaptation. In this talk we will explore research themes on perception, planning and control at the limits of performance. We explore: (1) How to generate the most competitive agents who dynamically balance safety and assertiveness by using distributionally robust online adaptation and Game-theoretic planning (2) How to be better-than-the-best using imitation learning with multiple imperfect experts (3) Using invertible neural networks to solve inverse problems in localization and SLAM (4) How to build the most efficient autonomous racecar with Multi-domain optimization across vehicle design, planning and control; We realize all our research in the https://f1tenth.org autonomous racecar platform that is 10th the size, but 10x the fun! The main takeaway from this talk is how you can get involved in very exciting research on safe autonomous systems. I will also present projects on AV Gokart that we are doing in the Autoware Center of Excellence for Autonomous Driving at Pennovation.

Biography: Rahul builds safe autonomous systems at the intersection of formal methods, machine learning and controls. He applies his work to safety-critical autonomous vehicles, urban air mobility, life-critical medical devices, and AI Co-designers for complex systems. He is the Penn Director for the Department of Transportation's $14MM Mobility21 National University Transportation Center [2017-2023] and $20MM Safety21 National UTC [2023-2028] both of which focus on technologies for safe and efficient movement of people and goods. Rahul is the Director of the Autoware Center of Excellence for Autonomous Driving, a consortium of 70+ companies and universities focused on open-source AV software for open-standards EV platforms. Rahul received the 2016 US Presidential Early Career Award (PECASE) from President Obama for his work on Life-Critical Systems. He also received the 2016 Department of Energy’s CleanTech Prize (Regional), the 2014 IEEE Benjamin Franklin Key Award, 2013 NSF CAREER Award, 2012 Intel Early Faculty Career Award and was selected by the National Academy of Engineering for the 2012 and 2017 US Frontiers of Engineering. He has won several ACM and IEEE best paper awards in Cyber-Physical Systems, controls, machine learning, and education.

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Abstract: Mobile and digital healthcare sensing has evolved dramatically over the last decade. We are now experiencing a new change in physiological sensing paradigm where data collection is made ambiently without explicit user effort. This talk will introduce two examples of such systems, where accurate physiological signals captured in a non-intrusive manner. Specifically, this talk will discuss HeartQuake and VitaMon, which are embedded and mobile sensing systems that capture physiological signals (e.g., ECG and HRV) of users in a non-intrusive and contactless way. Both of these sensing systems shed light to new opportunities in capturing human physiological data via a truly ubiquitous approach and can catalyze the development of ambient healthcare systems.

Biography: JeongGil Ko is an associate professor in the School of Integrated Technology, College of Computing and the Department of Biomedical Systems Informatics, College of Medicine, and serves as the Associate Vice President for Information and Communications at Yonsei University. JeongGil received his B.Eng. from Korea University (2007) and received his Ph.D. in Computer Science from the Johns Hopkins University (2012). He was a senior researcher at the Electronics and Telecommunications Research Institute (2012-2015) and was an assistant professor at the Department of Software and Computer Engineering at Ajou University (2015-2019). JeongGil has served on the program committee for many top conferences in the mobile and ubiquitous computing field (ACM MobiCom, MobiSys, SenSys, IEEE PerCom in particular), was the program chair for ACM UbiComp 2022, is an associate editor for renown academic journals including PACM IMWUT and IEEE TMC, and will serve as the general co-chair for ACM MobiSys 2024. His research interests are in the general area of developing mobile/embedded sensing systems with ambient intelligence and systems-related topics for enabling efficient XR applications.

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Abstract: What can data from devices we carry with us in our daily activities really reveal about our health and wellbeing? Considerable research has been conducted into mobile and wearable sensing systems for human health monitoring. This concentrates on either devising sensing and systems techniques to effectively and efficiently collect data about users, and patients or in studying mechanisms to analyse the data coming from these sensors accurately. In both cases, these efforts raise important technical as well as ethical issues. In this talk, I plan to reflect on the challenges and opportunities that mobile and wearable health sensing systems are introducing for the community, the developers as well as the users. I will use examples from my group's ongoing research on on-device machine learning, “earable” sensing and uncertainty estimation for health applications.

Biography: Cecilia Mascolo is a Full Professor of Mobile Systems in the Department of Computer Science and Technology, University of Cambridge, UK. She is director of the Centre for Mobile, Wearable System and Augmented Intelligence. She is also a Fellow of Jesus College Cambridge and the recipient of an ERC Advanced Research Grant. Prior joining Cambridge in 2008, she was a faculty member in the Department of Computer Science at University College London. She holds a PhD from the University of Bologna. Her research interests are in mobile systems and machine learning for mobile health. She has published in a number of top tier conferences and journals in the area and her investigator experience spans projects funded by Research Councils and industry. She has served as steering, organizing and programme committee member of mobile and sensor systems, data science and machine learning conferences. More details at www.cl.cam.ac.uk/users/cm542

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Abstract: Recent advances in machine learning inspire the development of deep neural network-based smart sensing and control applications for cyber-physical systems (CPS). However, due to the nature of the CPS sensing data, the machine learning models are in general subject to poor generalizability due to the scarcity of labeled training data and run-time domain shifts. The existing solutions rely on data-driven approaches and do not consider the physical laws that govern data generation or domain shifts. This talk presents our recent works on utilizing the known physical laws to improve the machine learning model generalizability for CPS sensing and control applications.

Biography: Rui Tan is an Associate Professor at School of Computer Science and Engineering, Nanyang Technological University (NTU), Singapore. He also serves as the Associate Dean (Research), College of Engineering, NTU. Previously, he was a Research Scientist (2012-2015) and a Senior Research Scientist (2015) at Advanced Digital Sciences Center, a Singapore-based research center of University of Illinois at Urbana-Champaign, and a postdoctoral Research Associate (2010-2012) at Michigan State University. He received the Ph.D. (2010) degree in computer science from City University of Hong Kong, the B.S. (2004) and M.S. (2007) degrees from Shanghai Jiao Tong University. His research interests include cyber-physical systems, sensor networks, and pervasive computing systems. He is the recipient of ICCPS'23 Best Paper Award, ICCPS’22 Best Paper Award Finalist, SenSys’21 Best Paper Award Runner-Up, IPSN’21 Best Artifact Award Runner-Up, IPSN’17 and CPSR-SG’17 Best Paper Awards, IPSN’14 Best Paper Award Runner-Up, PerCom’13 Mark Weiser Best Paper Award Finalist, and CityU Outstanding Academic Performance Award. He is currently serving as an Associate Editor of the ACM Transactions on Sensor Networks. He also serves frequently on the technical program committees (TPCs) of various international conferences related to his research areas, such as SenSys, IPSN, and IoTDI. He is the TPC Co-Chair of e-Energy’23. He received the Distinguished TPC Member recognition thrice from INFOCOM in 2017, 2020, and 2022. He is a main contributor of Singapore Standard 697:2023 on data center IT equipment operation under tropical climates.

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Abstract: The previously discrete technologies of IoT and AI have now entered a tight virtuous embrace. IoT allows sensing and actuation in our physical, social, and urban spaces with unimaginable ubiquity. AI allows sophisticated inferences and decisions to be made algorithmically using deep neural networks, even from unstructured and high-dimensional data, with uncanny performance. Together they seek to perform sophisticated perception-cognition-communication-action loops in diverse applications. However, designers of learning-enabled IoT systems face the challenge of extremely resource-constrained edge platforms operating in uncertain environments while assuring performance and trustworthiness. Moreover, in many applications, the systems go beyond taking actions based on rich inferences about the world state to perform long-term reasoning about complex events and obey the underlying physics, rules, and constraints. Based on our experience in designing such systems in applications including mHealth, ocean animal health, agriculture robotics, and military, this talk explores meeting these challenges through a combination of (i) neurosymbolic architectures that allow the incorporation of physics awareness and human knowledge while enhancing user trust, (ii) automatic platform-aware architecture search and code generation, and (iii) techniques to efficiently adapt to the deployment environment.

Biography: Mani Srivastava is Distinguished Professor and Vice Chair at UCLA’s ECE Department with a joint appointment in the CS Department. His research is broadly in human-cyber-physical and IoT systems that are learning-enabled, resource-constrained, and trustworthy. It spans problems across the entire spectrum of applications, architectures, algorithms, and technologies in the context of systems and applications for mHealth, sustainable buildings, smart environments, etc. He is a Fellow of the ACM and the IEEE.

Abstract: AIoT provides opportunities to transcending state-of-the-art technologies in both AI and IoT. On one hand the unprecedented data scale and prevalence from IoT magnifies the AI power; on the other hand the machine intelligence from AI helps excel every aspect of sensing, computing, and communication in nowadays IoT. This talk will introduce our recent research efforts into devising viable AIoT solutions that seek to address fundamental challenges due to (i) massiveness of devices, where hundreds of billions of networked sensors in the physical world may exhaust limited computing and communication resources, (ii) sensing intrusion to people and their things, where improper IoT instrumentation may impairs the harmonious co-existence of people and the machine intelligence, and (iii) plethora of data, where unprecedented scale and prevalence of the IoT data not only contributes to training powerful AI but also sets obstacles for distilling, verifying, adapting, and transferring the machine learning processes across people and different cyber or physically engineered systems.

Biography: Dr. Mo Li is a Professor from Nanyang Technological University. His research focuses on system aspects of wireless sensing and networking, IoT for smart cities and urban informatics. Dr. Li has been on the editorial board of IEEE/ACM Transactions on Networking, IEEE Transactions on Mobile Computing, ACM Transactions on Internet of Things, and IEEE Transactions on Wireless Communications, all leading journals in the field. Dr. Li served the technical program committee member for top conferences in computer system and networking, including ACM MobiCom, ACM MobiSys, ACM SenSys, and many others. Dr. Li is a Distinguished Member of the ACM since 2019, and a Fellow of the IEEE since 2020.

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Abstract: Sensing is an effective means to connect the physical world and digital space. Exploring the intelligent sensing capability has drawn researcher’s great attention. It is quite excited to see in recent years, besides wearable sensing, people have also begun to explore the acoustic, wireless signal, light, and other ambient communication medium’s capability for sensing purpose. In this talk, I will introduce some of our work related to how to leverage the wearables and the communication medium's sensing capability to enable human-centric applications.

Biography: Dr. Zhang joined Hong Kong University of Science and Technology in Sept. 2005 where she is now Tencent Professor of Engineering and Chair Professor of the Department of Computer Science and Engineering. She is also serving as the co-director of Huawei-HKUST innovation lab and the director of digital life research center of HKUST. Before that, she was in Microsoft Research Asia, Beijing, from July 1999, where she was the research manager of the Wireless and Networking Group. Dr. Zhang has published more than 400 refereed papers in international leading journals and key conferences. She is the inventor of more than 50 granted International patents. Her current research interests include Internet of Things (IoT), smart health, mobile computing and sensing, wireless networking, as well as cyber security. She is a Fellow of the IEEE and a Fellow of the Hong Kong Academy of Engineering Science (HKAES). Dr. Zhang has received MIT TR100 (MIT Technology Review) world's top young innovator award. She received the Best Paper Awards in several international conferences. Dr. Zhang servedas Editor-in-Chief of IEEE Trans. on Mobile Computing (TMC) from 2020 to 2022. She is a member of Steering Committee of IEEE Infocom.  Dr. Zhang received the B.S., M.S., and Ph.D. degrees from Wuhan University, China, in 1994, 1996, and 1999, respectively, all in computer science.

Abstract: People constantly touch ambient surfaces in their activities of daily living (ADL), which makes these surfaces natural sensors for acquiring human information. We have explored capturing the human-surface interactions in various forms to infer human information indirectly and non-intrusively. However, the complexity of the physical world poses a challenge when acquiring fine-grain human information with limited (labeled) sensor data. We address this issue from two perspectives. From the data perspective, we exploit the complementarity of co-located multimodal data to minimize the amount of labeled data required. From the data acquisition perspective, we enhance the sensing capabilities of surface sensors by augmenting the physical properties of the target surfaces.

Biography: Dr. Shijia Pan is an Assistant Professor at the University of California Merced. She received her bachelor’s degree from the University of Science and Technology of China and her Ph.D. degree from Carnegie Mellon University. Her research interests include cyber-physical sensing systems (CPS), multimodal learning for CPS/IoT, and ubiquitous computing. She worked in multiple disciplines and published in both top-tier Computer Science ACM/IEEE conferences and high-impact Civil Engineering journals. She received Hellman Fellows Awards, Rising Stars in EECS, Google Anita Borg Scholarship, and Best Paper/Poster/Demo Awards from multiple ACM/IEEE conferences.