Brief Biography

Nick attended Colorado State University where he received his Bachelors degree (2005, summa cum laude) in electrical engineering and subsequently obtained his Masters (2007) in the same and focused on signal processing. He worked at Numerica Corp. from 2007 to 2009 on algorithms for track decorrelation and ambibuity assessment in data association, and on stochastic modeling; working at Numerica again in 2013 on the resolution of tracking ambiguity through use of Dempster-Shafer theory and Smets’ transferable belief model for class identification, as well as for class uncertainty quantification and sensor reliability modeling.

In 2013 he obtained his Phd in electrical engineering from Kansas State University, publishing research on optimization of resource-limited decentralized systems with signal processing joint-objectives.

From 2013 to 2014 he lectured on signal processing and control theory at Universität Paderborn (Germany) and researched low-sample-support methods for correlation analysis of high dimensional data sets. He was employed at ISA from 2014 to 2017 and worked on tracking, optimization, and statistical signal processing algorithms. From winter 2017 to 2018 he worked on anomaly detection and machine learning algorithms as a Senior Data and Algorithms Scientist at Alchemy IoT. From 2019 to 2021, he was a research scientist working for Numerica corporation, designing and improving target tracking and computational sensing algorithms, as well as integrating them into the operational systems. Currently, Nick is the lead data scientist for Revenue Solutions, Inc. (RSI).

His work and research-related interests include statistical signal processing, machine learning, and optimization.