Abstract

The emerging advanced IoT applications in connected healthcare, industrial internet, multi-robot systems, and other areas demand higher intelligence of the connected devices, larger scale of the systems, and better decision making leveraged by analyzing the data being continuously generated and the advancement of AI technologies. In this context, centralized cloud computing would face high data transmission cost, high response time, and data privacy issues. The edge cloud paradigm seeks to alleviate these inefficiencies by moving the computation and analytics tasks closer to the end devices. It facilitates the evolution of IoT from instrumentation and interconnection to distributed intelligence. This talk focuses on future collaborative edge computing where edge nodes share data and computation resources and perform tasks by leveraging distributed intelligence. It covers the major problems in distributed collaboration at the edge we are currently studying, namely collaborative task execution, distributed machine learning, and distributed autonomous cooperation. Solutions need to address the challenging issues such as distributed data sources, conflicting network flows, heterogeneous devices, consistency, and mutual influence during the training.

Biography

Dr. Cao is the Otto Poon Charitable Foundation Professor in Data Science and the Chair Professor of Distributed and Mobile Computing in the Department of Computing at The Hong Kong Polytechnic University. He is the director of the Internet and Mobile Computing Lab and the associate director of University’s Research Facility in Big Data Analytics. He served the department head from 2011 to 2017.

Dr. Cao’s research interests include parallel and distributed computing, wireless networking and mobile computing, big data and machine learning, and cloud and edge computing. He published 5 co-authored and 9 co-edited books, and over 500 papers in major international journals and conference proceedings. He also obtained 13 patents. Dr. Cao received many awards for his outstanding research achievements. He is a member of Academia Europaea, a fellow of IEEE and a distinguished member of ACM. In 2017, he received the Overseas Outstanding Contribution Award from China Computer Federation.

Abstract

The rapid growth, proliferation, and reliance on digital devices has permeated our government, military, and society sectors. Concurrently, there are ongoing concerns from malfeasance, cyber-attacks, and illegal penetration of devices exposing valuable information to our nation’s adversaries. According to the United States Bureau of Labor Statistics – between now the year 2028 – there is a projected 32% rise in the number of digital forensics examiners armed with advanced tools and techniques. Moreover, digital device growth greater than 26% will enable 43 billion active devices by the year 2023.

This talk will incorporate an investigative, synergestic, goal-driven approach to solve data-driven forensic problems by covering the following: (a) providing an overview of the range of technologies developed in the analysis of signature-driven crimes employing “big data”; (b) discussing the use of forensic-fusion models for extracting event signatures; and (c) emphasizing the importance of building educational infrastructure capacity for advancements in digital forensics through training of the next generation of students and researchers.

Biography

Dr. S.S. Iyengar is currently the Distinguished University Professor of Computer Science and Former Director of the Knight Foundation School of Computing and Information Sciences at Florida International University (FIU) in the United States. He is Director of the FIU Forensics Investigations Network in Digital Sciences (FINDS) Center of Excellence funded by the Army Research Office and US Army Development Command. Prior to joining FIU, Dr. Iyengar was the Roy Paul Daniel’s Distinguished Professor and Chairman of the Computer Science department for over 20 years at Louisiana State University. His research interests include High-Performance Algorithms, Biomedical Computing, Sensor Fusion, and Intelligent Systems for the last four decades.

Dr. Iyengar is a Member of the European Academy of Sciences, a Fellow of the Institute of Electrical and Electronics Engineers (IEEE); a Fellow of the Association of Computing Machinery (ACM); a Fellow of the American Association for the Advancement of Science (AAAS); a Fellow of the Society for Design and Process Science (SDPS); a Fellow of National Academy of Inventors (NAI); and Fellow of the American Institute of Medical and Biological Engineering (AIMBE).

He was awarded Satish Dhawan Chaired Professorship at IISc, then Roy Paul Daniels Chaired Professor at LSU. He has received the Distinguished Alumnus Award of the Indian Institute of Science. In 1998, he was awarded the IEEE Computer Society’s Technical Achievement award and recognized as an IEEE Golden Core Member. Additionally, Professor Iyengar is an IEEE Distinguished Visitor, SIAM Distinguished Lecturer, and ACM National Lecturer.

During the last four decades, he has supervised over 60 Ph.D. students, 100 Master’s students, and many undergraduate students. He has published more than 600 research papers, has authored/co-authored and edited 25 books. During the last thirty years, Dr. Iyengar has brought in over $65 million (USD) for research and education initiatives.

Abstract

The spaces of a smart city contains the heterogeneous electronic and data networks, such as social networks, and IoT for city and industry; these spaces form barriers for information extractions and applications. Artificial intelligence research for smart cities must overcome these barriers. This presentation presents our latest AI research: the extraction and correlation of spatial information in smart cities and the application of the AI Internet of Things.

Biography

Weijia Jia is currently a Chair Professor and Director of Joint AI and Future Networking Research Institute of Beijing Normal University (BNU, Zhuhai) and United International College (UIC), Zhuhai, Guangdong, China. He also serves as the VP for Research at UIC and Zhiyuan Chair Professor at Shanghai Jiaotong University, China. Prior joing BNU-UIC, he served as the Deputy Director of State Kay Laboratory of Internet of Things for Smart City at the University of Macau. He received BSc/MSc from Center South University, China in 82/84 and Master of Applied Sci./PhD from Polytechnic Faculty of Mons, Belgium in 92/93, respectively, all in computer science. For 93-95, he joined German National Research Center for Information Science (GMD) in Bonn (St. Augustine) as a research fellow. From 95-13, he worked in City University of Hong Kong as a professor. His contributions have been reconganized as optimal network routing and deployment; vertex cover; anycast and QoS routing, and sensors networking; knowledge relation extractions; NLP and intelligent edge computing. He has over 600 publications in the prestige international journals/conferences and research books and book chapters. He has received the best product awards from the International Science & Tech. Expo (Shenzhen) in 2011/2012 and the 1st Prize of Scientific Research Awards from the Ministry of Education of China in 2017 (list 2) and many provincial science and tech awards. He has served as area editor for various prestige international journals, chair and PC member/keynote speaker for many top international conferences. He is the Fellow of IEEE and the Distinguished Member of CCF.

Abstract

The collection, storage, manipulation, analysis and retention of massive amounts of data have resulted in new technologies including big data analytics and data science. It is now possible to analyze massive amounts of data and extract useful nuggets. However, the collection and manipulation of this data has also resulted in serious security and privacy considerations. Various regulations are being proposed to handle big data so that the privacy of the individuals is not violated. Furthermore, the massive amounts of data being stored may also be vulnerable to cyber attacks. Furthermore, Artificial Intelligence Techniques including machine learning are being applied to analyze the massive amounts of data in every field such as healthcare, finance, retail and manufacturing.

Artificial Intelligence techniques are being integrated to solve many of the security and privacy challenges. For example, machine learning techniques are being applied to solve security problems such as malware analysis and insider threat detection. However, there is also a major concern that the machine learning techniques themselves could be attacked. Therefore, the machine learning techniques are being adapted to handle adversarial attacks. This area is known as adversarial machine learning. In addition, privacy of the individuals is also being violated through these machine learning techniques as it is now possible to gather and analyze vast amounts of data and therefore privacy enhanced data science techniques are being developed.

With the advent of the web, computing systems are now being used in every aspect of our lives from mobile phones to smart homes to autonomous vehicles. It is now possible to collect, store, manage, and analyze vast amounts of sensor data emanating from numerous devices and sensors including from various transportation systems. Such systems collectively are known as the Internet of Transportation, which is essentially the Internet of Things for Transportation, where multiple autonomous transportation systems are connected through the web and coordinate their activities. However, security and privacy for the Internet of Transportation and the infrastructures that support it is a challenge. Due to the large volumes of heterogenous data being collected from numerous devices, the traditional cyber security techniques such as encryption are not efficient to secure the Internet of Transportation. Some Physics-based solutions being developed are showing promise. More recently, the developments in Data Science are also being examined for securing the Internet of Transportation and its supporting infrastructures. Our goal is to develop smart technologies for a Smart World.

To assess the developments on the integration of Big Data, Data Science and Security over the past decade and apply them to the Internet of Transportation, the presentation will focus on three aspects. First it will examine the developments on applying Data Science techniques for detecting cyber security problems such as insider threat detection as well as the advances in adversarial machine learning. Some developments on privacy aware and policy-based data management frameworks will also be discussed. Second it will discuss the developments on securing the Internet of Transportation and its supporting infrastructures and examine the privacy implications. Finally, it will describe ways in which Big Data, Data Science and Security could be incorporated into the Internet of Transportation and Infrastructures that would result in a Smart World.

Biography

Dr. Bhavani Thuraisingham is the Founders Chair Professor of Computer Science and the Executive Director of the Cyber Security Research and Education Institute at the University of Texas at Dallas (UTD). She is also a visiting Senior Research Fellow at Kings College, University of London and an elected Fellow of the ACM, IEEE, the AAAS, the NAI and the BCS. She was a Cyber Security Policy Fellow at the New America Foundation for 2017-2018 and focused on engaging rural America in cyber security. Her research interests are on integrating cyber security and artificial intelligence/data science for the past 35 years (where it used to be computer security and data management/mining). She has received several awards including the IEEE CS 1997 Technical Achievement Award, ACM SIGSAC 2010 Outstanding Contributions Award, the IEEE Comsoc Communications and Information Security 2019 Technical Recognition Award, the IEEE CS Services Computing 2017 Research Innovation Award, the ACM CODASPY 2017 Lasting Research Award, the IEEE ISI 2010 Research Leadership Award, the 2017 Dallas Business Journal Women in Technology Award, and the ACM SACMAT 10 Year Test of Time Awards for 2018 and 2019 (for papers published in 2008 and 2009). She was named one of 500 most influential business leaders in North Texas for 2021 by the D Magazine’s D CEO Magazine. She co-chaired the Women in Cyber Security Conference (WiCyS) in 2016 and delivered the featured address at the 2018 Women in Data Science (WiDS) at Stanford University serves as the Co-Director of both the Women in Cyber Security and Women in Data Science Centers at UTD. She has spent around 20 years promoting Diversity, Equity and Inclusion in Cyber Security and Data Science and has chaired multiple panels including her recent panel at IEEE ISI 2020 (Intelligence and Security Informatics). Her 40-year career includes industry (Honeywell), federal research laboratory (MITRE), US government (NSF) and US Academia. Her work has resulted in 130+ journal articles, 300+ conference papers, 170+ keynote and featured addresses, seven US patents, fifteen books as well as technology transfer of the research to commercial products and operational systems. She received her PhD from the University of Wales, Swansea, UK, and the prestigious earned higher doctorate (D. Eng) from the University of Bristol, UK.

Abstract

Rapid advancements in biotechnologies such as wearable sensors, –omic (genomics, proteomics, metabolomics, lipidomics etc.), next generation sequencing, bio-nanotechnologies, and molecular imaging etc. accelerate the data explosion in biomedicine and health wellness. Multiple nations around the world have been seeking novel effective ways to make sense of “big data” with AI for evidence-based, outcome-driven, and affordable 5P (Patient-centric, Predictive, Preventive, Personalized, and Precise) health care. The goal is develop multi-modal and multi-scale (i.e. molecular, cellular, whole body, individual, and population) biomedical data analysis with AI for discovery, development, and delivery. Ultimately, the goal is to promote healthy aging, improve quality of patient care, and reduce healthcare cost. However, during the COVID-19 pandemic, many AI tools have been developed, but a few have shown true impact. First, I will discuss a recent MIT Technology Review on AI tools impact in combating COVID-19 pandemics. Second, I will provide a systematic review regarding the causes. Next, I will present opportunities in harnessing data by using HL7 Fast Health Informatics Resource (FHIR) to integrate genomics, imaging, physiological, and clinical EMR for improving 5P health. Then I will highlight the progresses made and opportunities in improving data quality. Also, I will share what needs to be done to make multi-modality data analysis outcome usable by clinicians. Last, I will present an emerging intelligent reality to increase impact of AI and BHI in post-pandemic era.

Biography

Dr. Wang is a Wallace H. Coulter Distinguished Faculty Fellow and a full professor in the Departments of Biomedical Eng. and Electrical and Computer Eng. at Georgia Institute of Technology and Emory University. Her research is in Biomedical Big Data Analytics with a focus on Biomedical and Health Informatics (BHI) and Artificial Intelligence (AI) for predictive, personalized, and precision health (pHealth). She published over 260 peer-reviewed articles in referred journals and conference proceedings with Google Scholar citations of over 12,800+ times, and delivered over 240 invited and keynote lectures. Dr. Wang is the Director of Biomedical Big Data Initiative, a Kavli Fellow, a Georgia Distinguished Cancer Scholar, a Petit Institute Faculty Fellow, an AIMBE Fellow, an IAMBE Fellow, and a member of Board of Directors in American Board of AI in Medicine. Dr. Wang received BEng from Tsinghua University China, and MSCS, and PhD EE degrees from Georgia Institute of Technology. She is a recipient of Georgia Tech Outstanding Faculty Mentor Award for Undergraduate Research, and a recipient of Emory University MilliPub Award (for a high-impact paper that is cited over 1,000 times). Dr. Wang currently serves as the Senior Editor for IEEE Journal of Biomedical and Health Informatics, an Associate Editor for both IEEE Transactions for BME and IEEE Reviews for BME, a standing panelist for NIH CDMA study section, a multi-year NSF Smart and Connect Health panelist, and a panelist for Brain Canada and multiple European countries. She has been helping grow the large bioinformatics and health informatics technical communities in IEEE EMBS, ACM, and Gordon Research Conferences. In 2021, Dr. Wang is a member of Georgia Tech Provost’s Emerging Leader’s Program and is elected into IAMBE Executive Committee. She is currently Chair of Biomedical and Health Informatics Technical Community/Committee, and chair of ACM Special Interest Group in Bioinformatics. During 2018-2020, Dr. Wang was Carol Ann and David Flanagan Distinguished Faculty Fellow at Georgia Tech. During 2017-2019, she served as Vice President of IEEE EMBS and AIMBE Bioinformatics Nomination Committee Chair. During 2015-2018, she was Georgia Tech Biomedical Informatics Program Co-Director in Atlanta Clinical and Translational Science Institute (ACTSI). During 2015-2016, she was IEEE Engineering in Medicine and Biology Society (EMBS) Distinguished Lecturer and an Emerging Area Editor for PNAS. Before 2016, Dr. Wang was Director of Bioinformatics and Biocomputing Core in NIH/NCI-sponsored U54 Center for Cancer Nanotechology Excellence, and Co-Director of Georgia-Tech Center of Bio-Imaging Mass Spectrometry for over 10 years. Dr. Wang’s research has been supported by NIH, NSF, CDC, Georgia Research Alliance, Georgia Cancer Coalition, Shriners’ Hospitals for Children, Children’s Health Care of Atlanta, Enduring Heart Foundation, Coulter Foundation, Microsoft Research, HP, UCB, and Amazon.

Abstract

Smart mobile devices and mobile apps have been rolling out at swift speeds over the last decade, turning these devices into convenient and general-purpose computing platforms. Sensory data collected from smart devices are important resources to nourish mobile services, and they are regarded as innocuous information that can be obtained without user permissions. However, such seemingly innocuous information could cause serious privacy issues. In this talk, we first demonstrate that users’ tap positions on the screens of smart devices can be identified based on the collected sensory data by employing some deep learning techniques. Then we show that tap stream profiles can be collected, so that a lot of private information can be accurately inferred.

Biography

Dr. Yingshu Li received her PhD and MS degrees from the Department of Computer Science and Engineering at University of Minnesota-Twin Cities. Dr. Li is currently a Professor in the Department of Computer Science at Georgia State University. Her research interests include Privacy-aware Computing, Internet of Things, Social Networks, and Wireless Networking. Dr. Li is the recipient of an NSF CAREER Award. Dr. Li’s research has been being supported by the National Science Foundation, U.S. Department of State, and some other academic and industrial sponsors. Dr. Li regularly publishes in scholarly journals, conference proceedings, and books, and her publications have received more than 10,000 citations. Dr. Li has been serving as an associate/guest editor for many prestigious journals including ACM Transactions on Sensor Networks, IEEE Transactions on Computers, IEEE Transactions on Network Science and Engineering, IEEE Internet of Things Journal, etc. She has also been serving as a General/Program Chair and TPC member for many international conferences such as CIKM, ICDCS, INFOCOM, IPCCC, WASA, etc.