Mitchell Sueker
- Research Assistant Professor, CEM Research Institute
-
Research Assistant Professor, Biomedical Engineering
- Multimodal Spectroscopy and Data Fusion, Machine Learning, Optical Imaging, Medical/Food Safety Applications
Contact Info
- Email: mitchell.sueker@UND.edu
- Office: 701.777.4331
Office Address
Columbia Hall Room 2370
501 North Columbia Road Stop 8216
Grand Forks, ND 58202-8216
Biography
Dr. Mitchell Sueker is a Research Assistant Professor in Biomedical Engineering at the University of North Dakota. His research and professional work center on multimodal spectroscopy, optical imaging, and machine learning for biomedical and food safety applications.
Dr. Sueker has contributed to the development and validation of several handheld multimodal spectroscopic systems, including the Pressure Injury Prevention Assessment (PIPA) device for early tissue damage detection, and another device for seafood quality assessment supported by NOAA and USDA collaborations. His experience spans prototype development, embedded software integration, optical characterization, and algorithm design.
Working at the intersection of academia and industry, Dr. Sueker focuses on translating spectroscopy-based research into practical, data-driven diagnostic tools through close collaboration with engineers, clinicians, and commercial partners.
BME 380 - Junior Innovation Based Learning I - Pulmonary Systems (Fall)
BME 381 - Junior Innovation Based Learning II - Cardiovascular Engineering (Spring)
- Multimodal Spectroscopy and Data Fusion
- Machine Learning
- Optical Imaging
- Biomedical and Food Safety Applications
1. Sueker, M., MacKinnon, N., Bearman, G., et al. (2025). FISH-SPEC: Fast Identification System for Handheld Spectroscopy and Species Classification. Applied Food Research, 5(2). https://doi.org/10.1016/j.afres.2025.101536
2. Sueker, M., Mackinnon, N., Bearman, G., et al. (2024). Fish fillet species identification and quality assessment using multi-mode spectroscopy and machine learning. Proc. SPIE PC13060, Sensing for Agriculture and Food Quality and Safety XVI. https://doi.org/10.1117/12.3014193
3. Kashani Zadeh, H., Hardy, M., Sueker, M., et al. (2023). Rapid Assessment of Fish Freshness for Multiple Supply-Chain Nodes Using Multi-Mode
Spectroscopy and Fusion-Based Artificial Intelligence. Sensors, 23(11), 5149. https://doi.org/10.3390/s23115149
4. Sueker, M., Daghighi, A., Akhbardeh, A., et al. (2023). A Novel Machine Learning Framework Based on a Hierarchy of Dispute Models for the Identification of Fish Species Using Multi-Mode Spectroscopy. Sensors, 23(22). https://doi.org/10.3390/S23229062
5. Woods, L., Propp, C., Sueker, M., et al. (2023). Efficacy of sanitization in healthcare using deep learning and multiwavelength fluorescence imaging. Proc. SPIE 12545, Sensing for Agriculture and Food Quality and Safety XV. https://doi.org/10.1117/12.2665702
6. Sueker, M., Stromsodt, K., Gorji, H. T., et al. (2021). Handheld multispectral fluorescence imaging system to detect and disinfect surface contamination. Sensors, 21(21). https://doi.org/10.3390/s21217222
2023 Tau Beta Pi Laureate
Ph.D. Biomedical Engineering, 2024
University of North Dakota
M.S. Biomedical Engineering, 2022
University of North Dakota
B.S. Electrical Engineering, 2020
South Dakota School of Mines and Technology