In this episode of the Neil Ashton podcast, Professor Michael Mahoney discusses the intersection of machine learning, mathematics, and computer science. The conversation covers topics such as randomized linear algebra, foundational models for science, and the debate between physics-informed and data-driven approaches. Prof. Mahoney shares insights on the relevance of his research, the potential of using randomness in algorithms, and the evolving landscape of machine learning in scientific disciplines. He also discusses the evolution and practical applications of randomized linear algebra in machine learning, emphasizing the importance of randomness and data availability. He explores the tension between traditional scientific methods and modern machine learning approaches, highlighting the need for collaboration across disciplines. Prof Mahoney also addresses the challenges of data licensing and the commercial viability of machine learning solutions, offering insights for aspiring researchers in the field.

Prof. Mahoney website: https://www.stat.berkeley.edu/~mmahoney/
Google scholar: https://scholar.google.com/citations?user=QXyvv94AAAAJ&hl=en
Youtube version: https://youtu.be/lk4lvKQsqWU

Chapters

00:00 Introduction to the Podcast and Guest
05:51 Understanding Randomized Linear Algebra
19:09 Foundational Models for Science
32:29 Physics-Informed vs Data-Driven Approaches
38:36 The Practical Application of Randomized Linear Algebra
39:32 Creative Destruction in Linear Algebra and Machine Learning
40:32 The Role of Randomness in Scientific Machine Learning
41:56 Identifying Commonalities Across Scientific Domains
42:52 The Horizontal vs. Vertical Application of Machine Learning
44:19 The Challenge of Common Architectures in Science
46:31 Data Availability and Licensing Issues
50:04 The Future of Foundation Models in Science
54:21 The Commercial Viability of Machine Learning Solutions
58:05 Emerging Opportunities in Scientific Machine Learning
01:00:24 Navigating Academia and Industry in Machine Learning
01:11:15 Advice for Aspiring Scientific Machine Learning Researchers

Keywords

machine learning, randomized linear algebra, foundational models, physics-informed neural networks, data-driven science, computational efficiency, academic advice, numerical methods, AI in science, engineering, Randomized Linear Algebra, Machine Learning, Scientific Computing, Data Availability, Foundation Models, Academia, Industry, Research, Algorithms, Innovation