So much of data science is oriented around dimensionality reduction. Covariance analysis, regression, Principal Component Analysis, and autoencoders all address the curse of dimensionality through reduction. However, if the right tools are used, dimensionality is in fact a blessing when it comes to segmentation and anomaly detection. Several examples will be presented illustrating the main points.

Join us this month as we are thrilled to have Brian Turnquist from BoonLogic present on this subject! Dr. Turnquist has worked in machine learning for the past twenty years developing numerous novel algorithms for automatically clustering biological signals in real-time. Turnquist is CTO of Minneapolis tech start-up, Boon Logic and was a tenured professor at Bethel University. His Ph.D. is in Mathematics from the University of Maryland, and he has long-standing joint projects with researchers at Johns Hopkins University, Yale University, the University of Erlangen-Nürnberg (Germany), and the University of Mannheim-Heidelberg (Germany) including additional collaborations with Harvard University, University of Nagoya (Japan), Hanyang University (Seoul), University of Oslo (Norway), University of Minnesota, Purdue Pharma Pharmaceutical, and Allergan Pharmaceutical. Turnquist was a visiting researcher at the University of Nürnberg (2004-2005) and at the University of Heidelberg (2011-2012) developing algorithms to detect and classify biological signals, control neural stimulators, and automatically classify ultrasonic acoustic signals in real-time. Turnquist has fifteen refereed publications in neuroscience and mathematics and is the author of the C++-based software package Dapsys which is in use by numerous laboratories worldwide.

We'll be doing this event, in person at Lab651. We'll have networking, pizza, and drinks starting at 6:00 pm. See you then!

Justin Grammens
Applied AI