Peter Cooman
About Peter Cooman
Peter Cooman is a Senior Applied Data Scientist with extensive experience in signal processing, optimization techniques, and data science methodologies. He holds a Ph.D. in Biomedical/Medical Engineering and has a strong background in rehabilitation technologies, particularly for spinal cord injuries and strokes.
Work at Civis Analytics
Peter Cooman has been employed at Civis Analytics as a Senior Applied Data Scientist since 2016. In this role, he applies his expertise in data science techniques to solve complex problems. His work contributes to the company's focus on leveraging data for strategic decision-making in various sectors.
Education and Expertise
Peter Cooman holds a Doctor of Philosophy (Ph.D.) in Biomedical/Medical Engineering from Case Western Reserve University, where he studied from 2006 to 2014. He also earned a Master of Science (M.Sc.) in Systems & Control, Biomedical Engineering from Delft University of Technology from 2003 to 2006. Additionally, he obtained a Bachelor of Science (B.Sc.) in Aerospace Engineering from Delft University of Technology from 1999 to 2003.
Background
Before joining Civis Analytics, Peter Cooman worked as a Post-Doctoral Fellow at the Rehabilitation Institute of Chicago from 2014 to 2016. He also served as a Graduate Research Assistant at Case Western Reserve University from 2006 to 2014. His background includes significant experience in spinal cord injury rehabilitation and stroke rehabilitation, particularly focusing on functional electrical stimulation.
Technical Skills and Knowledge
Peter Cooman is knowledgeable in various signal processing techniques, including the Kalman filter and Unscented Kalman-Bucy Filter. He is experienced in optimization techniques such as Gradient-based methods, Monte Carlo Simulations, and Differential Evolution. His programming skills encompass languages and tools like Matlab, C/C++, R, RStudio, and Python. He is also familiar with artificial intelligence methodologies, including A*, Particle Filters, and SLAM.
Research and Development Expertise
Peter Cooman has a strong foundation in control theory, including techniques such as PID, H2, Hinf, LQR/LQG, Lyapunov-based, Sliding Mode Control, and Adaptive Control. He is skilled in various data science techniques, including Random Forest, Linear Regression, and Artificial Neural Networks, which he applies to his research and development work.