January 6 (13:30-14:30 PST)
Peter Mell is a senior computer scientist in the Computer Security Division at the National Institute of Standards and Technology (NIST). He is an author of the NIST Blockchain Technology Overview publication and has spent the last few years researching managed cryptocurrency systems and blockchain applications for commerce. As a researcher, he is focused on cybersecurity defense and has over 50 research publications. His security research experience includes the areas of network security, intrusion detection, big data, continuous monitoring, cloud computing, security metrics, security automation, and vulnerability databases. He also has started several government initiatives including the National Vulnerability Database, the Federal Risk and Authorization Management Program (FedRAMP), and NIST Security Content Automation Protocol (SCAP) program. He led the technical team in creating version two of the Common Vulnerability Scoring System (CVSS) vulnerability metrics. And he wrote the U.S. government’s definition of cloud computing that was adopted within international standards and that has been cited over 14000 times in scientific publications. Mr. Mell received his undergraduate math and computer science degrees from Vanderbilt University in 1995 and a master’s degree in computer science from the University of California at Davis in 1998.
Stablecoins are cryptocurrencies whose price is pegged to that of another asset (typically one with low price volatility). These coins are being used extensively in newly developing paradigms for digital money and commerce as well as for decentralized finance technology. This presentation provides a technical description of stablecoin technology to enable understanding of the variety of ways in which stablecoins are architected and implemented. This includes a descriptive definition, commonly found properties, and distinguishing characteristics, as well as an exploration of stablecoin taxonomies, descriptions of the most common types, and examples from a list of top stablecoins by market capitalization. The presentation also explores related security, safety, and trust issues.
January 6 (14:30-15:00 PST)
Tim Ken Mackey is a Professor of Global Health in the Global Health Program at UC San Diego, the Director of the Global Health Policy and Data Institute, and the Editor-in-Chief of JMIR Infodemiology. He is also the CEO and co-founder of the data science and research services company S-3 Research. He holds a BA in Political Science-International Relations, a Masters Degree in Health Policy & Law and also earned his PhD in Global Public Health from the joint doctoral program at UC San Diego - San Diego State University. Prof. Mackey’s work has been featured in high-impact journals such as Science, Cell, JAMA, Nature Biotechnology, the Lancet, Nature Reviews Clinical Oncology, Clinical Microbiology Reviews, and BMC Medicine. His research and expertise has also been featured in major news outlets such as CNN, NPR, and the Wall Street Journal. His work focuses on an array of multidisciplinary topics, including the assessment of different types of technology, such as big data, machine learning, and blockchain, to address critical public health challenges. He also has extensive professional experience including over 10 years experience in the private sector and acting as a consultant for the World Health Organization, the US Department of State and others.
This presentation will provide an overview of the “fit-for-purpose” framework and its application to blockchain solutions that address healthcare use cases. It will then pivot to a discussion of several blockchain research projects specific to healthcare challenges, including combating medical fraud and abuse, strengthening health supply chains, and enhancing data governance for indigenous peoples genomic data. These case studies will illustrate the need to have a consumer or patient-centered approach to blockchain design. It will then close with a discussion about educational approaches to teach blockchain in interdisciplinary settings.
January 7 (13:30-14:30 PST)
Kei Sakaguchi is a Dean in Tokyo Tech Academy for Super Smart Society and a Professor in the School of Engineering of Tokyo Institute of Technology. At the same time, he is working for oRo Co., Ltd. in Japan as an outside director. He received the M.E. degree in Information Processing from Tokyo Institute Technology in 1998, and the Ph.D degree in Electrical & Electronics Engineering from Tokyo Institute Technology in 2006. He received the Outstanding Paper Awards from SDR Forum and IEICE in 2004 and 2005 respectively, and three Best Paper Awards from IEICE communication society in 2012, 2013, and 2015. He also received the Tutorial Paper Award from IEICE communication society in 2006. His current research interests are in B5G cellular networks, millimeter-wave communications, wireless energy transmission, V2X for automated driving, and super smart society. He is a fellow of IEICE, and a senior member of IEEE.
At last, 5G commercial services have been launched since year 2020 in Japan. 5G introduced new technologies such as 1) Millimeter-wave, 2) Massive MIMO, 3) Edge computing, and 4) RAN virtualization, however, the performance of these new technologies has not been fully utilized since the commercial 5G still depends on smartphones. If we look back the history of mobile generations, 3G and 4G can be considered as a set, and the set has created the smartphone as a new service platform. Then, what will be created by the set of 5G and 6G? That might be Super Smart Society (SSS). In this lecture, several examples of super smart society and their requirements on Beyond 5G are introduced by referring to the activities of the SSS Promotion Consortium. By the way, commercial service of 6G is expected to start around 2030, however, the 5G technologies will be continuously improved and a new service platform for super smart society will emerge. Such a kind of activities is called Beyond 5G in this lecture.
January 7 (14:30-15:00 PST)
Abhay Ratnaparkhi works at IBM, USA. Abhay does research in Computing in Mathematics, Information Retrieval, Distributed Computing and Machine Learning. He has 15 years of product development experience in the field of information retrieval, big data analytics and enterprise software domains. Currently he leads the engineering team at IBM Chief Information Office at Austin Texas, USA. The team works on developing products and solutions related to the information retrieval and the NLP domain. His prior experience involves working with productizing IBM Watson DeepQA question answering system. He is also a great advocate for open source. His team has recently open sourced the IBM cloud plugin for FEAST feature store for machine learning.
Machine learning is used in various of real-world products from domains search and recommendation engines, chatbots, e-commerce platforms, health care etc. There are many advancements in the field of information retrieval with the use of sophisticated ML techniques. I will be sharing recent advancements in this area and some practical examples like machine learning based ranking etc. Best practices and challenges while building software products/ services will also be discussed. Outline: 1. An overview of machine learning and its potential applications in information retrieval (IR) systems 2. Use of machine learning for the IR tasks like document ranking, query classification, query expansion, nearest neighbor search and recent advancements in this area 3. Neural information retrieval – Recent advancements in using different types of neural network architectures for IR tasks. 4. Strategies for evaluating the performance of a machine learning model in an information retrieval context 5. Best practices for deploying a machine learning-powered information retrieval system 6. The challenges and limitations of using machine learning for information retrieval
CTSoc Administrator Charlotte Kobert charlotte.kobert@ieee.org
