Dr. Alec Koppel, a Research Scientist for the U.S. Army Research Laboratory, as part of the Computational and Information Sciences Directorate (CISD), participates in Risk Roundup to discuss Learning in Autonomous Systems.
Learning in Autonomous Systems
Across nations, enormous amounts of data are created by
consumer devices in all sizes, shapes, and forms. From the connected mobile
phones to the internet of things, each of these data contains valuable
information about users and their personal preferences: what websites they
mostly visited, what social media applications they primarily used, what types
of videos they mostly watched, etc. With such valuable information, these data
become the key to building better and personalized machine learning models to
deliver personalized services to enhance user experiences maximally.
While the federated learning model provides a unique way to build such personalized models without intruding on users’ privacy, there is an alternative to these practices emerging in the form of artificial intelligence at the edge. This model of machine learning that will take place near the user, on their device, home hub, or at a local data-aggregation point holds excellent promise. It is crucial to understand and evaluate:
The time is now to begin a discussion on learning in
autonomous systems.
For more, please watch the Risk Roundup Webcast or hear the Risk Roundup Podcast
About the Guest
Alec Koppel completed his Ph.D. in Electrical and Systems
Engineering at the University of Pennsylvania in June of 2017, after which he
began work as a Research Scientist for the U.S. Army Research Laboratory in
Adelphi, MD as part of the Computational and Information Sciences Directorate
(CISD). His research agenda focuses on the development of new stochastic
optimization, estimation, and approximation algorithms for large-scale or
dynamic learning tasks. These problems are especially common in autonomous
systems, power systems, communication networks, and finance.
His research focuses on developing new optimization-based
methods for learning from streaming data, motivated primarily by autonomous
systems. His recent work has emphasized making nonparametric and Bayesian
methods scalable in the online setting. He also pursues foundational work in
reinforcement learning. These threads are part of the U.S. Army’s Artificial
Intelligence/Machine Learning Essential Research Program. He is based in the United
States.
Apart from research, his educational goals are to mentor
aspiring researchers at the undergraduate and graduate level and develop core
competencies via project-based learning that gives students hands-on experience
with messy real-world engineering problems. He is always hunting for student
contributors.
About
the Host of Risk Roundup
Jayshree Pandya (née Bhatt), Ph.D., is a leading expert at the
intersection of science, technology, and security and is the Founder and Chief
Executive Officer of Risk Group LLC. She has been involved in a wide range of
research, spanning security of and from science and technology domains. Her
work is currently focused on understanding how converging technologies and
their interconnectivity across cyberspace, aquaspace, geospace, and space (CAGS),
as well as individuals and entities across nations: their governments,
industries, organizations, and academia (NGIOA), create survival, security, and
sustainability risks. This research is pursued to provide strategic security
solutions for the future of humanity. From the National Science Foundation to
organizations from across the United States, Europe, and Asia, Dr. Pandya is an
invited speaker on emerging technologies, technology transformation, digital
disruption, and strategic security risks. Her work has contributed to more than
100 publications in the areas of science and commerce. She is the author of the
books, Geopolitics of
Cybersecurity and The Global Age. She writes about Artificial
Intelligence on Forbes.
About Risk Roundup
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