Signal Processing
Methods for extracting structure, patterns, and interpretable information from complex signals.
Data Science Research Center | CMU
Computational Applied Mathematician
I work across applied mathematics, signal processing, machine learning, and software systems for scientific and engineering problems.
About
Sippanon Kitimoon is a computational applied mathematician with a Ph.D. in Mathematics from Claremont Graduate University and more than eight years of industry experience in software development, data analytics, and signal processing.
His work connects mathematical modeling, high-performance software, and data-driven methods for advanced analytics, engineering applications, recommender systems, and scientific computing. He joined the Data Science Research Center, Faculty of Science, Chiang Mai University on April 1, 2026.
Research
The research program centers on computational methods that turn mathematical structure into reliable tools for data analysis and decision support.
Methods for extracting structure, patterns, and interpretable information from complex signals.
Mathematical foundations for data representation, similarity, prediction, and model behavior.
Computational harmonic analysis for scientific, engineering, and image-processing problems.
Predictive models and language systems that support applied analytics and decision-making.
Software and numerical workflows for reproducible computational research and consulting projects.
Experience
Develops big data solutions in R, Python, and C/C++; applies signal processing, machine learning, NLP, and predictive modeling to client projects in engineering and business domains.
Developed a hotel recommendation system using closed-loop customer data, k-nearest neighbors models, engineered features, and TensorFlow-based experiments.
Conducted applied mathematics research in MATLAB and Python for image processing, including implementation of algorithms from academic papers.
Supported courses in Data Structures and Advanced Programming through grading and student tutoring sessions.
Publications
A complete and current publication list is maintained on Google Scholar. Selected publications can be added to this site as the research pages expand.
Open Google Scholar profileTeaching
Teaching interests include data science, signal processing, computational mathematics, machine learning, programming, and applied analytics.
Teaching assistant experience in Data Structures and Advanced Programming.
Mathematics tutoring experience for Calculus I, II, and III.
Contact
For research collaboration, student inquiries, and academic correspondence, email is the best first contact.