Dr. Michael L. Brodie has 45+ years of research and industry experience in data science, databases, artificial intelligence, and multidisciplinary problem-solving. I apply my knowledge to big-picture opportunities and challenges with philosophical and imminent practical impacts. For five years, my research objective is to better understand and define data science as a field of inquiry – its philosophy and that of data per se, and the data science problem-solving paradigm.
I am a Visiting Scholar in DASlab, School of Engineering and Applied Sciences, Harvard University. For 2013-2019, I was a Research Scientist in the MIT Data Systems Group. I am a Canadian-American with a Ph.D. in databases and AI from the University of Toronto and a Doctor of Science (honoris causa) from the National University of Ireland.
I have authored 200+ articles and seven books on advanced technologies, often collaborating with Turing Laureate Michael Stonebraker, given 100+ keynote addresses, and have an H-factor of 32 and an i10-index of 63. The Association of Computing Machinery has distributed 33,000+ copies of my 2019 book. Fulltime and visiting computer science professorships in Canada, USA, Germany, France, Italy, Australia, and Ireland expanded my knowledge of computer science research and practice. My practical, industrial knowledge comes from 25+ years as Chief Scientist of Verizon, one of the world’s largest enterprises. I was responsible for emerging technologies – architectures, methodologies, and strategies, e.g., the largest installation outside of Europe of SAP’s R3 ERP.
Since 1980, I have served on Scientific Advisory Boards of national and international research organizations including US National Academy of Sciences committees and 20+ startups. I chaired (2013-2019) the Scientific Advisory Committee of Science Foundation Ireland Research Center for Data Analytics, Europe’s first and largest data science research institute, where I contributed to defining the phenomenal new field of data science.
Fortuitously, my Ph.D. vision of AI extending databases and vice versa, prepared me, more than I could have foreseen, for the recent emergence of AI-based data science. I pursue that vison to better understand the miraculous yet inscrutable field of data science to enable knowledge discovery with scope, scale, complexity, and power beyond that of science, our previously most powerful knowledge discovery paradigm.
While what is data science? and what is data? may seem philosophical and far from urgent, practical concerns, they must be understood to realize potential benefits, to identify and minimize risks, and to anticipate our 21st C world. Such data science thinking is required to gain insights into our world through data science problem-solving. My recent papers on these topics have been downloaded 2,000+ times in six months.
