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

人工智能(AI)和机器学习(ML)领域的快速发展，特别是在深度模型领域，正在带来超越传统范式的解决问题的新方法和工具。它们提供了新的视角，也引发了信号处理和数据科学领域的挑战和悬而未决的问题。本次研讨会旨在将来自学术界、工业界、政府机构和专业学会的专家聚集在一起，讨论信号和数据科学中可解释、可靠和可持续的机器学习所需的研究挑战、机会和潜在的前进道路以及合作努力。研讨会将于2023年3月20-21日在马里兰州的College Park举行，以促进AI/ML的融合研究。研讨会将启动对话，并促进一批具有互补专业知识和观点的主要专家进行公开和建设性的交流。预计为期两天的研讨会将包括主旨演讲、小组讨论以及分组和总结会议；与会者将来自广泛背景，如信号传感和处理、数据科学、机器学习、计算机视觉、语音和自然语言、控制理论和系统、信息理论等。研讨会促进的讨论和合作将有助于塑造人工智能新时代的研究和研究生教育范式，并为机器学习方面的研究进步和人才培养铺平道路，从而以健全、可靠和可持续的方式造福社会。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.