Residence Baltimore, Maryland Nationality American | Name Justin Jacobs | |
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Thesis Nonparametric Bayesian Density Estimation on Riemannian Manifolds (2014) Doctoral advisors John Zweck, Anindya Roy Notable awards National Intelligence Medallion, PECASE | ||
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Justin Jacobs is a United States statistician, applied research mathematician at the National Security Agency, an independent sports analytics researcher, and a member of the American Statistical Association. Noted for his research into geolocation, geospatial statistics and spatio-temporal statistics, Jacobs was awarded a National Intelligence Medallion from the ODNI in January 2014 by the Director of National Intelligence as well as the Presidential Early Career Award for Science and Engineering (PECASE) in April 2014 by President Barack Obama.
Contents
- Your sky purple lighter official video justin jacobs
- Justin jacobs hittting
- Life
- Career and achievements
- Sports analytics
- References
Justin jacobs hittting
Life
Jacobs earned his Bachelor of Science (B.S.) degrees in mathematics and software engineering at Carroll University in Waukesha, Wisconsin in 2003; his Master of Science (M.S.) degree in mathematics at University of Wisconsin–Milwaukee in 2005, and his Ph.D in statistics at University of Maryland Baltimore County in 2014. Jacobs is a former NCAA basketball player, is married and has two children.
Jacobs' M.S. research involved development of asymptotic confidence interval estimation for exponential distribution and Pareto distribution, with applications into insurance coverage for wind damage data. Jacobs' Ph.D dissertation is in the field of statistics and differential geometry, titled "Nonparametric Bayesian Density Estimation on Riemannian Manifolds" and has applications in the fields of geolocation and geostatistics. Jacobs has also served in an advisory and support role for scientists who have been trying to recover the ill-fated MH370 Malaysian airlines crash in the South Indian Ocean.
Career and achievements
Jacobs currently serves as an applied research mathematician in the National Security Agency with a main focus on geolocation and spatio-temporal analysis on the WGS 84 manifold. Much of Jacobs' work has involved nonparametric statistics and analysis of electromagnetic wavefront propagation analysis for RF geolocation in the presence of degraded Geospatial Navigation Satellite System (GNSS) signals. This includes signature building using other RF methods, which has resulted in a patent, an ODNI medallion award, and the PECASE award.
Jacobs graduated Summa Cum Laude at UMBC, earning a Top 30 graduate of UMBC's Class of 2014. Other awards from Jacobs' academic time include the Citizenship Award and Top Mathematics student award at Stoughton High School in Stoughton, WI in 1999; Top Graduate Student Instructor from UMBC in 2007; and UMBC's Alumni Award in 2014.
Sports analytics
Jacobs also independently develops prediction models on data obtained from the National Football League NFL, the National Collegiate Athletic Association NCAA, and the National Basketball Association NBA. These methods range from score predictions of NFL and NBA games based on several factors such as recent player performance, past positional match-ups and referee assignments to ranking algorithms based on win-loss models, player injuries, locations, and times of day. In his 2015 recent rankings algorithm, Jacobs' predicted 65 of 68 NCAA Division One Men's Basketball Teams that would make the field of 68 for the annual March Madness National Championship tournament. In contrast, Joe Lunardi of ESPN predicted 65 correct. In 2016, Jacobs' model correctly selected 65 of the 68 NCAA teams selected to the NCAA Tournament, again tying Lunardi. In 2017, Jacobs' model correctly selected all 68 of the 68 NCAA teams selected to the NCAA Tournament, this time beating the ESPN results that had 67 selected.
Jacobs also develops spatio-temporal models that predicts player tendencies on either the field (NFL) or court (NBA) of play. One such model identifies the ability for NBA post players to rotate from weak side to strong side relative to other players' and the basketball's movements; this then predicts the ability for coaches to identify optimal ways to get players to break to the basket or locate entry passes into the post.