Mathew Rynes

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Graduate Student

PhD

University of Minnesota, Biomedical Engineering (Current)

MS

University of Minnesota, Biomedical Engineering (2018)

BS

University of Minnesota, Neuroscience and Biochemistry (2015)

Biosketch

I am interested in neuroengineering and neuroscience research – mainly in the field of advancing technology for neuroscientists so that more complex questions regarding motor and sensory systems, cognition, and decision making can be asked. A long-term research goal of mine is to improve these technologies and advance applications of neuroengineering research such that it has become widely used in the clinical setting. I believe this requires significant advancement in the tools neuroscientists use to perform fundamental research. During my undergraduate, I worked on applying machine learning algorithms to predict movements from offline monkey dorsal-lateral prefrontal cortex local field potentials. After graduation, I helped develop a lyoprotectant matrix for non-cryogenic storage of human serum at the University of Minnesota mechanical engineering department. Following this work on this project, he continued his studies in the biomedical engineering department. I worked on applying highly developed machining tools to perform automated surgical procedures on small animals for neuroscientists.

 

Publications

Ghanbari L *, Rynes M*, Hu J, Schulman D S, Johnson G, Laroque M, Shull G, Kodandaramaiah S B, Craniobot: A computer numerical controlled robot for cranial
microsurgeries,Scientific Reports, 2019
 
Solivio M J, Less R, Rynes M, Kramer M, Aksan A, Adsorbing/dissolving Lyoprotectant Matrix Technology for Non-cryogenic Storage of Archival Human Sera,  Scientific Reports, 6, 24186.
 
Rynes M*, Vattendahl Vidal G W*, Kelliher Z, Goodwin S J, 2016 Review of Brain-Machine Interfaces Used in Neural Prosthetics with New Perspective on Somatosensory Feedback through Method of Signal Breakdown. Scientifica, vol. 2016, 8956432 *Equal contribution

 

HOnors and awards

University of Minnesota Distinguished M.S. Thesis Award 2018