About

Curtis Neiderer is a skilled Machine Learning Engineer and Data Scientist with over 13 years of relevant experience. He has a strong background in electrical engineering and has worked on projects across the data science spectrum. Curtis has experience ranging from ad-hoc analysis and prototyping through production and deployment. Currently, Curtis works at Systems & Technology Research as a Machine Learning Engineer and Data Scientist. In this role, he focuses on using data science, machine learning, and statistical techniques to develop forecasting, anomaly detection, and classification algorithms. Previously, Curtis worked at Riverside Research as a Research Engineer and Member of Technical Staff. His role focused on using data science, machine learning, and other statistical techniques to evaluate and improve classification algorithms. During his time at Riverside Research, Curtis re-architected a suite of satellite image processing algorithms from Interactive Data Language (IDL) to Python, improving code readability, reducing time, and enhancing quality. He also completed an environmental track characterization analysis and built a Naive Bayes model to suppress unwanted tracks from the air traffic controller interface aboard aircraft carrier. Curtis also worked at Technology Service Corporation (TSC) as a Senior Engineer. His role focused on using statistical modeling and data mining to evaluate and improve the performance of anti-jamming algorithms. While at TSC, Curtis integrated descriptive algorithm models into system simulation to evaluate algorithm performance as well as to inform experiment design for live test events. He also developed a weighted ensemble that combined multiple discrimination algorithms into a single model, resulting in improved performance against a specific jamming threat. Curtis holds a Bachelor of Science in Electrical Engineering from Penn State University. He has also completed graduate coursework in Computer Science from Harvard Extension School and in Systems Architecting and Engineering from the University of Southern California. Curtis is proficient in various technologies, including ML, Data Scientist, Research Scientist, Naive Bayes, Image Processing, Software Engineer, Python, and test.