EP18 – Data Scientist : Michael I. Jordan

Hello People,

Here is the eighth article of my series concerning famous Data Scientists. This one the last for the research orientated Data Scientist. Today we will speak about the machine learning maestro Michael I. Jordan.

So we will follow the following steps.
(1) Who is this Data Scientist?
(2) What is his work about?
(3) How can I benefit of such learning insights in my career path & theme selection?

Who is this Data Scientist?

Being a American-born (February 25, 1956), Michael I. Jordan is a scientist, professor and leading researcher in machine learning, statistics, and artificial intelligence. Author of publications dating from early 1990, the latter focus mainly on machine learning.

Shifting from psychology into mathematics and computer science, Jordan started developing recurrent neural networks as a cognitive model.

In 2010 he was named a Fellow of the Association for Computing Machinery “for contributions to the theory and application of machine learning. The latter is also a member of the National Academy of Science, a member of the National Academy of Engineering and a member of the American Academy of Arts and Sciences. In 2016, Jordan was identified as the “most influential computer scientist”, based on an analysis of the published literature by the Semantic Scholar project.

What is his work about?

Popularizing Bayesian networks in the machine learning community, Jordan is mainly known for pointing out links between machine learning and statistics. Although he started with a psychological background, Jordan co-discovered Latent Dirichlet allocation allowing sets of observations to be explained by unobserved groups that explain why some parts of the data are similar.

Jordan received prestigious awards through his career such as: best student paper award at the International Conference on Machine Learning (2004), best paper award at the American Control Conference (1991), the IEEE Neural Networks Pioneer Award, and an NSF Presidential Young Investigator Award. Jordan was also named a Neyman Lecturer and a Medallion Lecturer by the Institute of Mathematical Statistics meanwhile receiving the David E. Rumelhart Prize (2015) and the ACM/AAAI Allen Newell Award (2009).

How can I benefit of such learning insights in my career path & theme selection?

Jordan is referred as a Data Scientist being the maestro of machine learning. Thus his work is dedicated on how to makes machine smarter. Hence, I don’t think that I will study computer science to this extent and get through the artificial intelligence business until developing  self-improving intelligence algorithms for machines. While keeping in mind that I am an marketing-economist profile, I prefer to understand data rather than using it to create artificial intelligence. Nevertheless, I think that considering his approach to data, due to his psychological and mathematics background, I  will consider his insights on my learning track.

Conclusion : Jordan’s work is impressive in the machine learning field. On top of that, the latter do brings in great discoveries while applying his psychological references to his work intelligence related.  However, Jordan is more of a computer engineer and is inclined to do research work related to his passion of intelligence and related applications. Thus, Jordan, could not be considered as a role model for me due to my career goals being more business orientated rather than applying machine learning to the limits. Nevertheless the latter remains an outstanding figure reference in the field of Data Science in his specific sector.



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