Summer 2021
Diversity and Inclusion Seminars
June 18, 2021
“This seminar will survey papers focused on issues of diversity and inclusion within the context of computer science, engineering, and higher education in general. Each week one paper will be assigned for reading and discussion. Meetings will consist of discussion of the papers. The goal of the seminar is to provide an opportunity for attendees to explore current research on the subject of D&I in an informal environment.” https://www.cs.jhu.edu/~misha/DIReadingSeminar/
Institute of Assured Autonomy & Computer Science Seminar Series
June 24, 2021
Abstract: With AI already changing the way in which society addresses economic and national security challenges and opportunities, AI technologies must be developed and used in a trustworthy and responsible manner. That means they need to ensure accuracy, explainability and interpretability, reliability, robustness, security (resilience), privacy, and safety—and that bias is mitigated. Trustworthy data, standards, and integration of machine learning and AI in applications is critical for the successful deployment of new technologies for climate research, genomics, materials, and more. Delivering these needed measurements, standards, and other tools is a primary focus for the National Institute of Standards and Technology’s portfolio of AI efforts. This session provides an overview of the NIST Trustworthy and Responsible AI program.
Speaker Biography: Elham Tabassi is the chief of staff in the Information Technology Laboratory (ITL) at the National Institute of Standards and Technology (NIST). ITL, one of six research laboratories within NIST, supports the institute’s mission to promote U.S. innovation and industrial competitiveness by advancing measurement science, standards, and technology in ways that enhance economic security and improve our quality of life. Tabassi has been working on various machine learning and computer vision research projects with applications in biometrics evaluation and standards since 1999. She is the principal architect of NIST Fingerprint Image Quality, which is now an international standard for measuring fingerprint image quality and has been deployed in many large-scale biometric applications worldwide. Tabassi is a senior member of the Institute of Electrical and Electronics Engineers and a member of the Association for the Advancement of Artificial Intelligence.
Diversity and Inclusion Seminars
June 25, 2021
“This seminar will survey papers focused on issues of diversity and inclusion within the context of computer science, engineering, and higher education in general. Each week one paper will be assigned for reading and discussion. Meetings will consist of discussion of the papers. The goal of the seminar is to provide an opportunity for attendees to explore current research on the subject of D&I in an informal environment.” https://www.cs.jhu.edu/~misha/DIReadingSeminar/
Diversity and Inclusion Seminars
July 2, 2021
“This seminar will survey papers focused on issues of diversity and inclusion within the context of computer science, engineering, and higher education in general. Each week one paper will be assigned for reading and discussion. Meetings will consist of discussion of the papers. The goal of the seminar is to provide an opportunity for attendees to explore current research on the subject of D&I in an informal environment.” https://www.cs.jhu.edu/~misha/DIReadingSeminar/
Diversity and Inclusion Seminars
July 9, 2021
“This seminar will survey papers focused on issues of diversity and inclusion within the context of computer science, engineering, and higher education in general. Each week one paper will be assigned for reading and discussion. Meetings will consist of discussion of the papers. The goal of the seminar is to provide an opportunity for attendees to explore current research on the subject of D&I in an informal environment.” https://www.cs.jhu.edu/~misha/DIReadingSeminar/
Diversity and Inclusion Seminars
July 16, 2021
“This seminar will survey papers focused on issues of diversity and inclusion within the context of computer science, engineering, and higher education in general. Each week one paper will be assigned for reading and discussion. Meetings will consist of discussion of the papers. The goal of the seminar is to provide an opportunity for attendees to explore current research on the subject of D&I in an informal environment.” https://www.cs.jhu.edu/~misha/DIReadingSeminar/
Institute of Assured Autonomy & Computer Science Seminar Series
July 22, 2021
Abstract: The wide-scale adoption of artificial intelligence will require that AI engineers and developers provide assurances to their user base that an algorithm will perform as intended and without failure. High levels of assurance stem from all the planned, systematic activities applied at all stages of the AI engineering lifecycle with the intent of ensuring that an intelligent system is producing outcomes that are valid, verified, data-driven, trustworthy, explainable to a layperson, ethical in the context of its deployment, unbiased in its learning, and fair to its users. In this talk, Laura Freeman will discuss how existing test and evaluation (T&E) processes needed to be updated for systems enabled by AI; she will also show how T&E is critical for the assurance of systems enabled by AI. A point of emphasis is that she focuses on systems conducting missions that leverage AI; the implication is that algorithm performance should be characterized relative to the deployed systems and that assurance should reflect the deployed environment and operating envelope.
Speaker Biography: Laura Freeman is a associate research professor of statistics and the director of the Intelligent Systems Lab at the Virginia Tech Hume Center for National Security and Technology. Her research leverages experimental methods in research that bring together cyber-physical systems, data science, artificial intelligence, and machine learning to address critical challenges in national security. She is a faculty member in the Commonwealth Cyber Initiative and leads research in AI assurance. Freeman develops new methods for test and evaluation processes, focusing on emerging system technology. She is also the assistant dean for research; in that capacity, she works to shape research directions and collaborations in across the College of Science in the National Capital Region. Previously, Freeman was the assistant director of the Operational Evaluation Division at the Institute for Defense Analyses; in that position, she established and developed an interdisciplinary analytical team of statisticians, psychologists, and engineers to advance scientific approaches to Department of Defense testing and evaluation. In 2018, Freeman served as acting senior technical advisor for the Director, Operational Test and Evaluation (DOT&E); as senior technical advisor, Freeman provided leadership, advice, and counsel to all personnel on technical aspects of testing military systems and reviewed test strategies, plans, and reports from all systems with DOT&E oversight. Freeman has a BS in aerospace engineering and an MS and PhD in statistics, all from Virginia Tech. Her PhD research was on the design and analysis of experiments for reliability data.
Diversity and Inclusion Seminars
July 23, 2021
“This seminar will survey papers focused on issues of diversity and inclusion within the context of computer science, engineering, and higher education in general. Each week one paper will be assigned for reading and discussion. Meetings will consist of discussion of the papers. The goal of the seminar is to provide an opportunity for attendees to explore current research on the subject of D&I in an informal environment.” https://www.cs.jhu.edu/~misha/DIReadingSeminar/
IAA & CS Seminar Series
August 17, 2021
Lifelong Learning is at the cutting edge of artificial intelligence, encompassing computational methods that allow systems to learn in runtime and incorporate learning for application in new, unanticipated situations. Until recently, this sort of computation has been found exclusively in nature; thus, Lifelong Learning looks to nature for its underlying principles and mechanisms and transfers them to this new technology. These include, among others, the creation and representation of abstraction [Nature Reports 2015, ICML 2020] and the mechanism of rehearsal [Nature Communication 2020]. Whereas lifelong learning machines are studied to reach next-wave AI capabilities, they are difficult to predict externally, and could thus constitute a virus-like target when under attack.
Speaker Biography: Dr. Siegelmann is a Professor of Computer Science, Core Member of the Neuroscience and Behavior Program, and director of the Biologically Inspired Neural and Dynamical Systems (BINDS) Laboratory at UMass Amherst. Siegelmann recently completed her term as a DARPA PM: L2M, one of her key initiatives, inaugurated “third-wave AI,” pushing major design innovation and a dramatic increase in AI capability. Her program GARD is leading to advancements in assuring AI robustness against attack. Similarly, CSL introduces powerful methods of combined learning and information sharing on AI platforms without revealing private data, and RED reverse engineers attacks to reveal their sources. Other programs include advanced biomedical applications. Siegelmann conducts interdisciplinary research in both next generation machine learning and computational neuroscience, with a variety of applications spanning government and biomedicine. She is a leader in increasing awareness of ethical AI including via the IEEE and INNS. She was the recipient of the Alon Fellowship of Excellence, the NSF-NIH Obama Presidential BRAIN Initiative award, the Donald O. Hebb Award of the International Neural Network Society for “contribution to biological learning”; she was named IEEE fellow and Distinguished Lecturer of the IEEE Computational Intelligence Society. Recently she received the DARPA’s Meritorious Public Service award (2020).