{"id":1813,"date":"2023-07-31T06:55:42","date_gmt":"2023-07-31T06:55:42","guid":{"rendered":"https:\/\/ijcb2023.ieee-biometrics.org\/?page_id=1813"},"modified":"2024-09-15T18:38:34","modified_gmt":"2024-09-15T18:38:34","slug":"keynote-talks","status":"publish","type":"page","link":"https:\/\/ijcb2024.ieee-biometrics.org\/keynote-talks\/","title":{"rendered":"Keynote Talks"},"content":{"rendered":"\n

Keynote 1:<\/strong><\/h3>\n\n\n\n

Title:<\/strong> AI Risk Management: Enabling Trust<\/p>\n\n\n\n

Abstract:<\/strong> Artificial intelligence (AI) technologies have significant potential to transform society and people\u2019s lives \u2013 from commerce and health to transportation and cybersecurity to the environment and our planet. AI technologies, however, also pose risks that can negatively impact individuals, groups, organizations, communities, society, the environment, and the planet. This talk will cover NIST AI Innovation Lab\u2019s effort to cultivate trust in design, development, deployment and use of AI systems. Responsible for a variety of NIST\u2019s AI efforts and headquartered within the agency\u2019s Information Technology Laboratory, NAIIL advances AI measurement methods and guidelines, including their incorporation into international standards.<\/p>\n\n\n\n

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Elham Tabassi<\/strong>
Chief AI Advisor at NIST
Associate Director for Emerging Technologies Information Technology Laboratory
National Institute of Standards & Technology<\/p>\n\n\n\n

In addition to serving as NIST Chief AI Advisor and Associate Director for Emerging Technologies in NIST\u2019s Information Technology Laboratory (ITL), Elham Tabassi leads NIST\u2019s Trustworthy and Responsible AI program that aims to cultivate trust in the design, development, and use of AI technologies by improving measurement science, standards, and related tools in ways that enhance economic security and improve quality of life.<\/p>\n\n\n\n

She has been working on various machine learning and computer vision research projects with applications in biometrics evaluation and standards since she joined NIST in 1999. Tabassi is the principal architect of NIST Fingerprint Image Quality (NFIQ), an international standard for measuring fingerprint image quality which has been deployed in many large-scale biometric applications worldwide. Among her other roles at NIST, Tabassi has served as ITL Chief of Staff.<\/p>\n\n\n\n

She is a member of the National AI Resource Research Task Force, the US Government\u2019s AI Standards Coordinator, a senior member of IEEE, and a fellow of Washington Academy of Sciences. In September 2023, Tabassi was named by TIME magazine as one of the “100 Most Influential People in AI.”<\/p>\n\n\n\n

Keynote 2:<\/h3>\n\n\n\n

Title: <\/strong>Amazon One – Technology Behind the Experience
Abstract:<\/strong> Amazon One is a fast, convenient, contactless way for people to use their palm to enter, identify, and pay. The service is highly secure and uses custom-built algorithms and hardware to create a person\u2019s unique palm signature. In this talk, I will discuss pieces of the technology behind Amazon One, including the image acquisition system and the recognition engine. I will also share insights into components beyond recognition such as generative AI for synthetic data generation, cross-modality recognition to enable customer to enroll using their smartphones, and model quantization that are key to scaling the service and to achieve a good customer experience.<\/p>\n\n\n\n

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Rui Zhao is currently a Senior Applied Scientist at Amazon. He is a core member of the team that developed the Amazon One palm recognition system. He leads the effort in developing multiple algorithmic components that ensure a highly accurate and secure identification solution used by hundreds of thousands of customers every day. Prior to Amazon, he received his Ph.D. degree in Computer and Systems Engineering from Rensselaer Polytechnic Institute in 2018. Part of his work on computer vision-based human behavior analysis was reported by media outlets such as MIT Technology Review and The New York Times. He was selected to attend the 2018 CVPR Doctoral Consortium and is a member of Morningside Scholars, an honorary program from Zhejiang University in China. He has served as a reviewer or an area chair for numerous conferences such as CVPR, ICCV, NeurIPS, ECCV, AAAI, ICPR, etc.<\/p>\n\n\n\n

Keynote 3:<\/h3>\n\n\n\n

Title<\/strong>: New Challenges and Opportunities in the Age of Generative AI

Abstract:<\/strong> The global financial system depends on real-time decisions, advanced biometrics, and a complex web of dependencies, powered by AI and Machine Learning. Many financial institutions such as Capital One deploy sophisticated AI\/ML models, ranging from identity verification to fraud detection. However, with the emergence of Generative AI, particularly Foundation Models, the industry has a new set of challenges and opportunities ahead.\u00a0 In this keynote, we will explore the evolution of AI and ML in banking, and reflect on the opportunities and risks for where it goes from here, with some examples from biometrics and fraud. We\u2019ll share insights and best practices from Capital One\u2019s technology and AI\/ML journey, including examples of how we\u2019re taking a well-managed approach to state-of-the-art advances in this space.\u00a0<\/p>\n\n\n\n

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Dr. Abhijit Bose<\/strong>
Senior Vice President Enterprise AI\/ML Platforms (MLX),
Capital One, 1680 Capital One Drive, McLean, VA 22102-3491<\/p>\n\n\n\n

Abhijit Bose is the Senior Vice President for Capital One\u2019s Enterprise AI\/ML Platforms & Engineering. Utilizing his extensive experience, he leads the development, execution, and future direction of enterprise AI\/ML platforms and infrastructure at the company. Abhijit and his team have been responsible for building much of the modern AI\/ML infrastructure including the most recent GenAI Platform at Capital One in the public cloud. He is currently focused on building the foundational GenA capabilities, such as LLM pre-training, fine-tuning, guardrails, RAG pattern, multi-agentic workflows etc, including a number of human-in-the-loop applications in various stages of development.<\/p>\n\n\n\n

Before joining Capital One in 2020, Abhijit was with Facebook AI Research as their East Coast Head of Engineering, working on large-scale graph neural networks, early transformer models, and computer vision. Abhijit\u2019s career journey also expanded to JP Morgan, IBM TJ Watson Research, Google, and American Express. He has close to 3500 citations on his research publications and over 30 patents issued.<\/p>\n\n\n\n

Abhijit holds MS and Ph.D in Computer Science from the University of Michigan, Ann Arbor. He also holds a Ph.D in Engineering Mechanics with a specialization in High Performance Computing (HPC) from the University of Texas at Austin.<\/p>\n\n\n\n

<\/p>\n\n\n\n

Keynote 4:<\/h3>\n\n\n\n

Title:<\/strong> Facing the Future: Navigating the Promise and Pitfalls of Automated Face Recognition<\/p>\n\n\n\n

Abstract:<\/strong> As face recognition technology becomes increasingly integrated into national security and law enforcement operations, its role in shaping public safety and personal freedoms has never been more critical. Drawing on the findings from the NASEM report, “Facial Recognition: Current Capabilities, Future Prospects, and Governance,” this keynote will explore the current state and future trajectory of face recognition technologies, focusing on their use as investigative tools by law enforcement. Therefore, we will also examine instances where face recognition has contributed to wrongful arrests, discussing the technological limitations and the systemic issues in its application. In addition, we will explore the prospects of future face recognition research, highlighting the need for improved accuracy, transparency, and robust governance frameworks to mitigate risks and ensure ethical use. By addressing both technological advancements and ethical challenges, the aim is to foster a deeper understanding of the necessity of balancing innovation with responsible deployment.<\/p>\n\n\n\n

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Dr. Michael King<\/strong>
Associate Professor, Florida Institute of Technology<\/p>\n\n\n\n

Michael King is an Associate Professor in the Department of Electrical Engineering and Computer Science, and a Research Scientist for the L3-Harris Institute for Assured Information at the Florida Institute of Technology and has served in this role since August 2015. Before joining academia, Dr. King served fourteen years as a scientific research\/program management professional in the United States Intelligence Community. While in government, Dr. King created and managed research portfolios covering various topics related to biometrics and identity. He crafted and successfully led the Intelligence Advanced Research Projects Activity\u2019s Biometric Exploitation Science and Technology Program to transition technology deliverables to over 40 Government organizations. As a subject matter expert in biometrics and identity intelligence, Dr. King has been invited to brief the Director of National Intelligence, Congressional staffers and science advisers, the Defense Science Board, the Army Science Board, and the Intelligence Science Board. Also, he served as the Intelligence Community Department Lead to the White House Office of Science and Technology Policy’s (OSTP) National Science and Technology Council Subcommittee (NSTC) on Biometrics and Identity Management. Dr. King received his Ph.D. in Electrical Engineering from North Carolina Agricultural and Technical State University.<\/p>\n","protected":false},"excerpt":{"rendered":"

Keynote 1: Title: AI Risk Management: Enabling Trust Abstract: Artificial intelligence (AI) technologies have significant potential to transform society and people\u2019s lives \u2013 from commerce and health to transportation and cybersecurity to the environment and our planet. AI technologies, however, also pose risks that can negatively impact individuals, groups, organizations, communities, society, the environment, and […]<\/p>\n","protected":false},"author":2,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-1813","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/ijcb2024.ieee-biometrics.org\/wp-json\/wp\/v2\/pages\/1813"}],"collection":[{"href":"https:\/\/ijcb2024.ieee-biometrics.org\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/ijcb2024.ieee-biometrics.org\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/ijcb2024.ieee-biometrics.org\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/ijcb2024.ieee-biometrics.org\/wp-json\/wp\/v2\/comments?post=1813"}],"version-history":[{"count":3,"href":"https:\/\/ijcb2024.ieee-biometrics.org\/wp-json\/wp\/v2\/pages\/1813\/revisions"}],"predecessor-version":[{"id":2780,"href":"https:\/\/ijcb2024.ieee-biometrics.org\/wp-json\/wp\/v2\/pages\/1813\/revisions\/2780"}],"wp:attachment":[{"href":"https:\/\/ijcb2024.ieee-biometrics.org\/wp-json\/wp\/v2\/media?parent=1813"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}