Benjamin M. Rodriguez has a background in statistical signal processing with a focus on data science, intelligent systems and machine learning. His current work duties include research and development in algorithms development, data processing, information retrieval, intelligent system design, recognition techniques, and fusion of multiple data sources, including sensor data for pattern association, decision making and tracking. He has worked on projects related to target identification using SAR, Hyperspectral and Panchromatic imagery along with facial recognition, fingerprint matching, voice recognition, web crawling, and breaking encoded messages within transmitted signals. He also has conducted research in radar, lidar, and optical sensors for target recognition/tracking using generated features, feature preprocessing techniques, classification models and fusion methods. Other areas of his research include pattern recognition using image, signal, and video processing techniques for face recognition, finger print matching, anomaly detection and voice recognition. His software engineering experience includes Unix, Linux, and Window operating systems and programming using assembly, C/C#/C++, ENVI IDL, Java, Matlab, Python and R. Dr. Rodriguez is also a full time Johns Hopkins University – Applied Physics Laboratory (JHU-APL) Principal Professional Staff since 2008 and an Assistant Group Supervisor.
- Ph.D Electrical and Computer Engineering – Statistical Signal Processing, Air Force Institute of Technology
Principal Professional Staff, JHU Applied Physics Laboratory
Amir Saeed, Francisco Holguin, Jonathon Gabriel, Alhassan S. Yasin, Benjamin M. Rodriguez. (2023). Reinforcement Learning Application to Satellite Constellation Sensor Tasking, SPIE, SPIE Defense + Commercial Sensing Program, Orlando, FL, May, 2023.
Casey J. Richards, Nawal Valliani, Benjamin A. Johnson, Nelson Ka Ki Wong, Angelo Pennati, Amir K. Saeed, Benjamin M. Rodriguez. (2023). Multimodal Data Fusion using Signal/Image Processing Methods for Multi-Class Machine Learning, SPIE, SPIE Defense + Commercial Sensing Program, Orlando, FL, May, 2023.
Cindy Gonzales, Benjamin M. Rodriguez. (2022). An Empirical Study of Digital Signal Filtering to Improve Alzheimer’s Disease Detection, SPIE, Applications of Machine Learning 2022, San Diego, California, August, 2022.
Tau Beta Pi Honor Society
Eta Kappa Nu Honor Society
The Society of Mexican American Engineers and Scientists