BigData 2024 Panels
Panel A: Big Data and AI: How they benefit each other
December 16, 14:30-16:00 pm; Regency Ballroom
Panelists: Aidong Zhang (https://engineering.virginia.edu/faculty/aidong-zhang), University of Virginia
Karuna Pande Joshi (https://karuna.informationsystems.umbc.edu/), University of Maryland, Baltimore County
Charles Kamhoua, Army Research Laboratory, Adelphi, Maryland
Moderator: Bhavani Thuraisingham, The University of Texas at Dallas
https://personal.utdallas.edu/~bhavani.thuraisingham/
Abstract:
Big data technologies have been developed over the past 15 years to collect, store, manage and analyze massive amounts of data for a variety of applications including in bioinformatics, cyber security, finance and manufacturing. At the same time developments in deep learning and generative AI techniques have exploded in recent years to benefit society. Big data technologies have played a role in these developments as massive amounts of data are needed to develop accurate AI algorithms. At the same time, AI techniques are being applied for managing big data including determining the appropriate optimization techniques for big data processing. The panel will explore the development in both Big Data and AI and discuss how they benefit each other. We will focus on a variety of applications including in Bioinformatics and Cyber Security.
Panel B: Big Data and AI - Shaping the Next Frontier of Data-Driven Intelligence
December 17, 10:30am-12:00pm; Regency Ballroom
Moderator: Prof. Dr. Lindi Liao, George Mason University (https://mason.gmu.edu/~dliao2/)
Abstract: This panel will bring together experts from government, industry, and academia to collaboratively explore the future of Big Data and AI, with a particular emphasis on the dynamic interplay between Big Data for AI and AI for Big Data in reshaping technology, innovation, and decision-making. Through this cross-sector dialogue, panelists will explore cutting-edge strategies, emerging innovations, and key challenges in leveraging Big Data to accelerate AI advancements, while also exploring how AI technologies are poised to transform Big Data infrastructures and applications across diverse sectors.
By focusing on the intersection of Big Data and AI, the panel will further highlight how these converging forces are driving the emergence of new paradigms of intelligence that fundamentally reshape industries, government, and academia. The discussion will offer valuable insights into how Big Data and AI together will drive transformative change, opening new frontiers of innovation and advancing the future of data-driven intelligence.
Confirmed Panelists:
-
Dr. Phillip Regalia, Program Director, NSF (https://new.nsf.gov/staff/pregalia)
Short Bio: Phil Regalia received his PhD in Electrical and Computer Engineering in 1988 from UC Santa Barbara. He was the founding Editor-in-Chief of the EURASIP J. Wireless Communications and Networking, and has served as Editor-in-Chief of the EURASIP J. Advances in Signal Processing, as well as associate editor for the IEEE Trans. Signal Processing, the IEEE Trans. Circuits and Systems, and the Int. J. Adaptive Control and Signal Processing. He was elected Fellow of the IEEE in 2000. He joined the National Science Foundation in 2012, having previously held academic appointments in both the US and France. -
Dr. Haixun Wang, VP of Engineering and Distinguished Scientist, Instacart, USA
(https://haixun.github.io/)
Short Bio: Dr. Haixun Wang is currently an IEEE fellow, editor in chief of IEEE Data Engineering Bulletin, and a VP of Engineering and Distinguished Scientist at Instacart. Before Instacart, he was a VP of Engineering and Distinguished Scientist at WeWork, a Director of Natural Language Processing at Amazon, and he led the NLP team working on Query and Document Understanding at Facebook. From 2013 to 2015, he was with Google Research working on natural language processing. From 2009 to 2013, he led research in semantic search, graph data processing systems, and distributed query processing at Microsoft Research Asia. He had been a research staff member at IBM T. J. Watson Research Center from 2000 to 2009. He received the Ph.D. degree in Computer Science from the University of California, Los Angeles in 2000. He has published more than 200 research papers in international journals and conference proceedings. He serves as a trustee of the VLDB Endowment and has held roles such as PC Chair for conferences like SIGKDD and CIKM, as well as editorial board member for journals like IEEE Transactions on Knowledge and Data Engineering (TKDE). He won the 10-year ICDE influential paper award in 2024, ICDE best paper award in 2015, ICDM10-year best paper award in 2013, and the best paper award of ER 2009. -
Mr. Chengmin Ding, Principal Architect, IBM
(https://www.linkedin.com/in/myuima/)
Short Bio: Chengmin Ding leads the Architecture Squad at IBM Technical Expert Lab, Data and AI Practice for US Government. He has extensive software research, development, architecting and management experience. He led multiple large-scale Data intensive and AI centric projects and is a certified Distinguished Architect from the Open Group. He is a founding editorial board member of the Springer-Nature's AI & Ethics Journal. He published multiple research/white papers and holds numerous granted patents in the field of Data and AI. -
Dr. Chenren Shao, Software Development Manager, AWS, Amazon
(https://www.linkedin.com/in/chenrenshao)
Short Bio: -
Dr. Xubin He, Professor of Computer and Information Sciences, Temple University
(https://cis.temple.edu/~he/)
Short Bio: Dr. Xubin He is a Professor in the Department of Computer and Information Sciences at Temple University. His research interests focus on data storage and I/O systems, including big data, cloud storage, Non-Volatile Storage, and scalability for large storage systems. He has published more than 100 refereed articles in prestigious journals such as IEEE Transactions on Parallel and Distributed Systems (TPDS), Journal of Parallel and Distributed Computing (JPDC), ACM Transactions on Storage, and IEEE Transactions on Dependable and Secure Computing (TDSC), and at various international conferences, including USENIX FAST, USENIX ATC, Eurosys, IEEE/IFIP DSN, IEEE INFOCOM, IEEE IPDPS, etc. Recently he served as the program co-chair for ccGRID'2024, IPCCC'2017, and ICPADS'2016. He is a senior member of the IEEE, a member of the IEEE Computer Society, and the USENIX campus representative.
Panel C: Federal Big Data Initiatives and Activities
December 17, 14:30-16:00pm; Regency Ballroom
Panelists: Jennifer Wei, NASA; Rick Mueller, USDA; Sudhir Shrestha, NOAA; Rui Pereira De Sa, NIH Moderator: Prof. Liping Di, George Mason University; Dr. Zhengwei Yang, USDA
Abstract: The pervasive growth of data in all sectors has propelled Big Data to the forefront of national priorities, driving advancements in science, technology, and industry. Federal agencies play a pivotal role in shaping the Big Data research landscape, fostering innovation, and addressing pressing societal challenges. This panel, titled "Federal Big Data Initiatives and Activities," brings together officers and program directors from leading federal big data agencies to discuss ongoing initiatives, strategies, and opportunities in Big Data research and development.
Panelists will provide insights into how federal agencies are addressing the challenges of data management, security, scalability, and integration across various domains. Topics will include funding priorities, interdisciplinary collaboration, public-private partnerships, and the role of Big Data in advancing artificial intelligence, healthcare, climate science, agriculture and food security, and beyond.
Attendees will gain a unique perspective on how federal investments are shaping the future of Big Data research and how researchers, industry leaders, and policymakers can utilize the federal big data resources and align their efforts with federal initiatives. This panel offers a platform for thought-provoking discussions on the strategic directions of Big Data innovation and its transformative impact on society.
Join us for this engaging session to learn, connect, and contribute to the dialogue on federal Big Data initiatives and activities shaping the next decade of data-driven innovation.