Conference: Toward Explainable, Reliable, and Sustainable Machine Learning for Signal and Data Science

The fast-growing areas of artificial intelligence (AI) and machine learning (ML), especially in the area of deep models, are bringing new ways and tools of solving problems beyond the traditional paradigms. They offer new perspectives and also prompt challenges and open questions in the areas of signal processing and data science. This workshop aims at bringing together experts from academia, industry, government agencies, and professional societies to discuss research challenges, opportunities, and potential pathways forward and collaborative efforts needed toward explainable, reliable, and sustainable machine learning for signal and data science. The workshop will be held in College Park, MD, on March 20-21, 2023, to foster convergence research in AI/ML. The workshop will initiate a dialogue and foster open and constructive exchanges of a group of leading experts with complementary expertise and perspectives. It is envisioned that this two-day workshop will feature keynote presentations, panel discussions, as well as break-out and summary sessions; participants from a wide range of background will be engaged, such as signal sensing and processing, data science, machine learning, computer vision, speech and natural language, control theory and system, information theory, and more. Discussions and collaborations facilitated by the workshop will help shape the paradigm of research and graduate education in the new era of AI, and pave a pathway forward to the research advancement and talent training in machine learning that can benefit society in sound, reliable, and sustainable ways. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.