Thomas Ferree
About Thomas Ferree
Thomas Ferree is a Principal Signal Processing & Algorithm Development Engineer at Boston Scientific, with extensive experience in algorithm development, signal processing, and machine learning across various industries.
Current Role at Boston Scientific
Thomas Ferree is currently serving as the Principal Signal Processing & Algorithm Development Engineer at Boston Scientific since 2021. He is based in Cambridge, Massachusetts, within the United States. His role involves leveraging his extensive expertise in signal processing and algorithm development, particularly within electrophysiology applications relevant to cardiac mapping and rhythm management.
Previous Roles and Experience
Thomas Ferree has an extensive background in various engineering and scientific roles. Before joining Boston Scientific, he worked at Titan Advanced Energy Solutions as a Principal Algorithm Engineer from 2020 to 2021, and at Veo Robotics, Inc. as a Principal Algorithm Engineer for six months in 2019-2020. His earlier roles include Senior Signal Processing Engineer at NeuroMetrix, Inc. (2011-2019) and Neuroscientist & Laboratory Director at Nielsen NeuroFocus (2009-2011). He has also held academic positions, such as Assistant Professor at The University of Texas Southwestern Medical Center at Dallas and University of California, San Francisco.
Educational Background
Thomas Ferree completed his PhD in Physics from the University of Colorado Boulder, adding to his solid foundation in scientific research. Additionally, he attended the Woods Hole Oceanographic Institution for a specialized course in Methods in Computational Neuroscience. He holds a BS degree in Physics from the University of Florida, underscoring his long-standing commitment to the field of physics and computational sciences.
Technical Skills and Expertise
Thomas Ferree has a diverse skill set tailored towards algorithm development and signal processing. He is proficient in numerical integration methods like forward/backward Euler and Runge-Kutta, and skilled in optimization techniques such as variational calculus and simulated annealing. His expertise extends to machine learning, particularly in feature engineering and dimension reduction using Principal Component Analysis (PCA). He is fluent in programming languages, including Matlab, Python, and C/C++.
Specialization in Human Electrophysiology
Thomas Ferree specializes in human electroencephalography (EEG) and actigraphy, utilizing advanced signal processing methods such as nonlinear system identification and digital filters. His work frequently involves time series analysis techniques including power spectrum, coherence, and Granger causality. In his roles, he has also applied statistical methods and detection theory within machine learning projects, emphasizing experimental design and data acquisition.