08:30 - 08:45




08:45 - 09:00

Opening ceremony



09:00 - 10:00

Keynote lecture 1: The Role of AI in Autonomous Vehicles

Prof. Ragunathan Rajkumar, Carnegie Mellon University

Chair: Chenyang Lu

Abstract: Autonomous vehicles (AVs) have attracted immense interest and investments over many years now. Nevertheless, large-scale AV deployments do not seem feasible in the near future. This talk will address questions like “What went wrong?”, “Is AI the answer?”, “What is the role of AI?”, “Can (and how do) we course-correct?” and “Which other complementary aspects must be brought to bear?”. Finally, challenges that must still be addressed by the research and engineering communities will be raised.

Biography: Prof. Raj Rajkumar is the George Westinghouse Professor of Electrical & Computer Engineering and Robotics Institute at Carnegie Mellon University. At Carnegie Mellon, he directs Mobility21 – the USDOT National University Transportation Center on Mobility, the Metro21 Smart Cities Institute and the Real-Time and Multimedia Systems Laboratory. Raj has served as the Program Chair and General Chair of six international ACM/IEEE conferences on connected vehicles, real-time systems, wireless sensor networks, cyber-physical systems and multimedia computing/networking. He has authored one book, edited another book, holds multiple US patents, and has more than 250 publications in peer-reviewed forums. Nine of these publications have received Best Paper Awards. He is a Fellow of the National Academy of Inventors, an IEEE Fellow, a co-recipient of the IEEE Simon Ramo Medal, and an ACM Distinguished Engineer. He has been given an Outstanding Technical Achievement and Leadership Award by the IEEE Technical Committee on Real-Time Systems. He has given several keynotes and distinguished lectures at several international conferences and universities, and has provided testimony thrice at three US House Sub-Committee hearings. Prof Rajkumar’s work has influenced many commercial operating systems. He was also the founder and CEO of Ottomatika Inc., a company that delivered the software intelligence for self-driving vehicles. Ottomatika was acquired by Delph (becoming Aptiv and then Motional) within 18 months of its founding. His research interests include all aspects of cyber-physical systems with a particular emphasis on connected and autonomous vehicles.

10:00 - 10:30

Coffee break



10:30 - 11:00

Invited talk 1: Towards Resilient Autonomous Cyber-Physical Systems against Adversarial Examples

Prof. Qun Song, Delft University of Technology

Chair: Xianjin Xia

Abstract: Deep learning is shown susceptible to adversarial examples, which are crafted inputs aiming to cause wrong classification outputs for deep models by adding minute perturbations on the clean inputs. Thus, deploying deep learning models on safety-critical cyber-physical systems without incorporating effective countermeasures against adversarial examples raises security concerns. This talk is about the studies on the threat and countermeasures for the adversarial example attack as an ongoing concern for the safety-critical autonomous cyber-physical systems. This talk will introduce the dynamic ensemble-based defenses designed under the strategy of moving target defense that effectively counteract the adaptive adversarial example adversary for embedded deep visual sensing. This talk will also present the systematic requirement investigation and credibility analysis of adversarial example attack against the power grid voltage stability assessment and develops effective countermeasure.

Biography: Qun Song received Ph.D. from Nanyang Technological University, Singapore and B.Eng. from Nankai University, China. She is currently an Assistant Professor in the Embedded Systems Group of the Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS) at Delft University of Technology, the Netherlands. Her research interests include security and robustness for AI systems; resilient AI for autonomous driving; deep learning; and edge computing. She is the recipient of the 2022 SenSys Best Paper Award Finalist, the 2021 IPSN Best Artifact Award Runner-up, and NTU SCALE Best Demo Award.

11:00 - 11:30

Invited talk 2: AIoT for Safer, Healthier, and Smarter Environments

Prof. Xiaofan (Fred) Jiang, Columbia University

Chair: Hongkai Chen

Abstract: The combination of artificial intelligence and internet-of-things, or artificial intelligence of things (AIoT), promises to transform the world. At Columbia Intelligent and Connected Systems Lab, AIoT enables us to sense and understand urban situations and people, and allows us to take actions like never before. In this talk, I will present several projects focused on AIoT for safety, health, and smarter environments. In the urban safety space, we create a mobile AIoT system that uses multichannel audio combined with embedded machine learning to help alert pedestrians and construction workers of dangers from nearby vehicles. We further introduce a generalized audio filtering architecture that enables a wide range of applications. In the health space, we present a low-cost vision-based AIoT system for continuous multi-person fever screening. By using novel algorithms and models that take advantage of multiple frames of information from both thermal and RGB domains, our system achieves better accuracy than commercial solutions at a fraction of the cost. In the wearable AIoT space, we introduce a glasses-based platform for in-situ bio-signal acquisition and emotion recognition, followed by an AR-assisted intelligent stethoscope that enables self-screening at home. In the mental wellness space, we present an AI therapist for daily functioning assessment and intervention using custom-trained GPT models and smart home devices.

Biography: Xiaofan (Fred) Jiang is an associate professor in the Electrical Engineering department at Columbia University and co-Chair of the Smart Cities Center at the Data Science Institute. Fred received his PhD in Computer Science from UC Berkeley in 2010. His research lies at the intersection of systems and data, with a focus on intelligent embedded systems and their applications in mobile and wearable computing, intelligent built environments, Internet of Things, and connected health. His research has been published in top-tier venues and received numerous awards, including Best Paper Award at IPSN ’05 and ITEC '21, Best Demo Award at SenSys ’11, IoTDI '18, IPSN '20, SenSys '21, and IPSN '23, Best Poster Award at BuildSys ’16, and Best Paper Runner-Up Award at BuildSys ’17 and ’19. He has served on technical and organizing committees of leading conferences in the field, including TPC Chair of BuildSys ’14, TPC Chair of e-Energy '23, General Chair of SenSys ’19, General Chair of BuildSys '21, and General Chair of IPSN '23. Fred is the Founding Treasurer and incoming Vice Chair of ACM Special Interest Group on Energy Informatics (SIGEnergy). His research has been featured in many popular media outlets, including The Economist, New York Post, Mashable, Gizmodo, The Telegraph, and Fast Company. He is the recipient of an NSF Graduate Fellowship, a Vodafone-US Foundation Fellowship, and an NSF CAREER Award.

11:30 - 12:45

Panel discussion 1: Enabling Technologies and Infrastructure for Autonomous Driving: The Good, the Bad, and the Ugly

Prof. Guoliang XING (Moderator), Dr. Crystal Fok, Dr. Wang-Hei Ho, Prof. Hongsheng Li, Prof. Ragunathan Rajkumar, Prof. Rahul Mangharam, Prof. Liwei Wang

Abstract: TBD

Biography: TBD

12:45 - 14:45

Lunch time



14:45 - 15:45

Keynote lecture 2: How to achieve strong security guarantees in an IoT-imbued world?

Prof. Adrian Perrig, ETH Zurich

Chair: Chris Xiaoxuan Lu

Abstract: With the increasing connectivity of industrial and personal IoT systems, many security challenges arise. First and foremost, software vulnerabilities inherent in many devices, can be remotely exploited, resulting in privacy, safety, integrity, and availability problems. How can we obtain security for industrial and personal IoT? In this talk, we will discuss several complementary approaches, and discuss the role that AI plays in this context.

Biography: Adrian Perrig is a Professor at the Department of Computer Science atETH Zürich, Switzerland, where he leads the network security group. He is also a Distinguished Fellow at CyLab, and an Adjunct Professor of Electrical and Computer Engineering at Carnegie Mellon University. From 2002 to 2012, he was a Professor of Electrical and Computer Engineering, Engineering and Public Policy, and Computer Science (courtesy) at Carnegie Mellon University. From 2007 to 2012, he served as the technical director for Carnegie Mellon's Cybersecurity Laboratory (CyLab). He earned his MS and PhD degrees in Computer Science from Carnegie Mellon University, and spent three years during his PhD at the University of California at Berkeley. He received his BSc degree in Computer Engineering from EPFL. He is a recipient of the ACM SIGSAC Outstanding Innovation Award. Adrian is an ACM and IEEE Fellow. Adrian's research revolves around building secure systems -- in particular his group is working on the SCION secure Internet architecture.

15:45 - 16:15

Invited talk 3: Towards Efficient and Accurate Edge Video Analytics

Prof. Yuanchao Shu, Zhejiang University

Chair: Nan Guan

Abstract: We are all living in the golden era of AI that is being fueled by game-changing systemic infrastructure advancements. Among numerous applications, video analytics, in particular, has shown tremendous potential to impact science and society due to breakthroughs in ML, copious training data, and pervasive deployment of video sensing devices. Analyzing live video streams is arguably the most challenging of domains for “systems-for-AI”. Live video analytics require high bandwidth, consume considerable compute cycles for processing, necessitate richer query semantics, and demand tight latency, security and privacy guarantees. The talk will touch up on various advances in edge video analytics systems including efficient and accurate inference over edge hierarchies, collaborative analytics on camera networks, and continuous learning of models on edge devices.

Biography: Yuanchao Shu is currently a Qiushi Professor with the College of Control Science and Engineering at Zhejiang University, China. Prior to joining academia, he was a Principal Researcher with Microsoft Azure and Microsoft Research Redmond. His research interests lie broadly in mobile, sensing and networked systems. His previous research results have led to over 60 publications at top-tier peer-reviewed conferences and journals. Dr. Shu currently serves on the editorial board of IEEE Transactions of Wireless Communications, ACM Transactions on Sensor Networks, and was a member of the organizing committee and TPC of conferences including MobiCom, MobiSys, SenSys, etc. He won five Best Paper/Demo (Runner-Up) Awards, and was the recipient of ACM China Doctoral Dissertation Award (2/yr) and IBM PhD Fellowship. Dr. Shu received his Ph.D. from Zhejiang University, and was also a joint Ph.D. student in the EECS Department at the University of Michigan, Ann Arbor. Dr. Shu is a senior member of ACM and IEEE.

16:15 - 17:30

Poster Session



08:45 - 09:45

Keynote lecture 3: Intelligent Edge Services and Foundation Models for Internet of Things Applications

Prof. Tarek Abdelzaher, University of Illinois at Urbana-Champaign

Chair: Xiaofan (Fred) Jiang

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.

09:45 - 10:15

Coffee break



10:15 - 11:15

Keynote lecture 4: Building Safe Autonomous Systems: MAD Games - Multi-Agent Dynamic Games: What can you learn from Autonomous Racing?

Prof. Rahul Mangharam, University of Pennsylvania

Chair: Qixin Wang

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.

11:15 - 12:30

Round table discussion: Next-gen mobile sensing systems: opportunities and challenges

Prof. Zhenyu Yan (Moderator), Prof. Song Min Kim, Prof. Zhenjiang Li, Prof. Weitao Xu, Prof. Yuanqing Zheng

Abstract: TBD

Biography: TBD

12:30 - 14:00

Lunch time



14:00 - 15:15

Panel discussion 2: Ethics of AIoT in the Era of Big Models

Prof. Jie Liu (Moderator), Prof. Tarek Abdelzaher, Prof. Adrian Perrig, Dr. Xian Shuai, Prof. Mani Srivastava

Abstract: TBD

Biography: TBD

15:15 - 15:45

Invited talk 4: Towards Ambient Physiological Sensing for Digital Healthcare

Prof. JeongGil Ko, Yonsei University

Chair: Dan Wang

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.

16:00 - 17:30

Visit to Hong Kong Science Park



09:00 - 10:00

Keynote lecture 5: Sounding Out Audio Data and Wearable Systems for Health Diagnostics

Prof. Cecilia Mascolo, University of Cambridge

Chair: Mo Li

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

10:00 - 10:30

Coffee break/Poster Session



10:30 - 11:45

Panel discussion 3: Future Directions of AIoT

Prof. Chenyang Lu (Moderator), Prof. Tarek Abdelzaher, Prof. Jiannong Cao, Prof. Cecilia Mascolo, Prof. Wei Zhao

Abstract: TBD

Biography: TBD

11:45 - 12:15

Invited talk 5: Physics-informed Machine Learning (PIML) for Sensing and Control in Cyber-Physical Systems

Prof. Rui Tan, Nanyang Technological University

Chair: Qun Song

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.

12:15 - 14:00

Lunch time



14:00 - 15:00

Keynote lecture 6: Enabling Performant and Trustworthy Learning-enabled IoT Systems

Prof. Mani Srivastava, University of California, Los Angeles

Chair: Guoliang Xing

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.

15:00 - 15:45

Distinguished invited talk 1: Building Intelligence into Sensing, Networking, and Data Analytics of IoT

Prof. Mo Li, Nanyang Technological University

Chair: Chenshu Wu

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.

15:45 - 16:15

Coffee break/Poster Session



16:15 - 17:00

Distinguished invited talk 2: Pushing the Limit of Mobile Sensing: Smart Healthcare and Convenient HCI in the Age of AIoT

Prof. Qian Zhang, The Hong Kong University of Science and Technology

Chair: Zimu Zhou

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.

17:00 - 17:30

Invited talk 6: Ubiquitous Intelligent Surfaces for Human Signal Acquisition, Analysis, and Augmentation

Prof. Shijia Pan, University of California, Merced

Chair: Edith Ngai

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.