Adrian M. Peter
Associate Professor | Computer Engineering and Sciences
F.W. Olin Engineering Complex, 311
Doctor of Philosophy in Electrical and Computer Engineering University of Florida 2008
Department of Computer Engineering and Sciences (College of Engineering and Science)
Florida Institute of Technology, Melbourne, FL
Chief Scientist (Advanced Analytics Group)
Northrop Grumman, Melbourne, FL
Harris Corporation - Government Communications Systems Division (GCSD), Melbourne, FL
Communications/Networking Initiative Manager
Intel Corporation, Chandler, AZ
David Elliott, Evan Martino, Carlos E. Otero, Anthony O. Smith, Adrian M. Peter, Benjamin Luchterhand, Eric Lam, Steven Leung, “Cyber-Physical Analytics: Environmental Sound Classification at the Edge,” IEEE World Forum on Internet of Things, April, 2020.
Mark Moyou, Anand Rangarajan, John Corring, Adrian M. Peter, “A Grassmannian Graph Approach to Affine Invariant Feature Matching,” IEEE Transactions on Image Processing, December, 2019.
Kyle B. Johnston, Saida Caballero-Nieves, Veronique Petit, Adrian M. Peter, Rana Haber, “Variable Star Classification Using Multi-View Metric Learning,” Monthly Notices of the Royal Astronomical Society, Volume 491, Issue 3, November, 2019.
Kyle B. Johnston, Rana Haber, Saida Caballero-Nieves, Adrian M. Peter, Veronique Petit, Matt Knote, “A Detection Metric Designed for O'Connell Effect Eclipsing Binaries,” Computational Astrophysics and Cosmology, Volume 6, Issue 1, November, 2019.
Nenad Mijatovic, Rana Haber, Georgios C. Anagnostopoulos, Anthony O. Smith, and Adrian M. Peter, “A Proximal Algorithm for Estimating the Regularized Wavelet-based Density-Difference,” International Conference on Computational Science and Computational Intelligence (CSCI), December, 2019.
Nenad Mijatovic, Rana Haber, Anthony O. Smith, and Adrian M. Peter, “A Majorization-Minimization Algorithm for Estimating the Regularized Wavelet-Based Density-Difference,” International Conference on Computational Science and Computational Intelligence (CSCI), December, 2018.
Mitchell Solomon, Kaleb Smith, Kaylen Bryan, Anthony O. Smith, and Adrian M. Peter, “Infrasound Threat Classification: A Statistical Comparison of Deep Learning Architectures,” SPIE: Chemical, Biological, Radiological, Nuclear, and Explosives (CBRNE) Sensing, April, 2018.
Machine learning, computer vision, time series analysis, wavelet density estimation, shape matching.
Multi-domain, Multi-sensor, Cyber-physical Tactical Exploitation (M2CTE), AFRL, 2020-2023.
Acoustic Edge Analytics, AFRL, 2019
Tactical Infrasound And Seismic Event Classification, 2018