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School of Information Technology

Rosangela Follmann

Assistant Professor
School Information Technology
Office
Williams Hall - WIH 017G
Office Phone
(309) 438-8342office
phone
Email
rfollma@ilstu.eduemail
Website
https://about.illinoisstate.edu/rfo... website
  • About
  • Research

Biography

Rosangela Follmann is an Assistant Professor in the School of Information Technology. She received her Ph.D in Applied Computing from the National Institute for Space Research, Brazil. Before joining School of IT she did two years of postdoctoral research in the Department of Physics at Northwestern University, and two more years as postdoc at the School of Biological Sciences at ISU. Much of her work has been on developing methods and algorithms for data analysis, information processing, pattern recognition, and prediction of behaviors in the fields of nonlinear dynamics and neuroscience.

Current Courses

386.001Introduction to Networking and Parallel and Distributed Computing

386.002Introduction to Networking and Parallel and Distributed Computing

388.001Parallel Processing

487.001Parallel Processing

Research Interests & Areas

Computational Neuroscience,
Reservoir Computing/Machine learning,
High Performance Computing/Parallel processing,
Data analysis

Journal Article

Rutherford, G., Mobille, Z., Brandt-Trainer, J., Follmann, R., & Rosa, E. Analog implementation of a Hodgkin-Huxley model neuron. AMERICAN JOURNAL OF PHYSICS 88.11 (2020): 918-923.
Burek, M., Follmann , R., & Rosa, E. Temperature effects on neuronal firing rates and tonic-to-bursting transitions. BIOSYSTEMS 180 (2019): 1-6.
Follmann , R., & Rosa, Epaminondas, Jr.. Predicting slow and fast neuronal dynamics with machine learning. CHAOS 29.11 (2019)
Follmann , R., & Rosa, E. Predicting slow and fast neuronal dynamics with machine learning. CHAOS 29.11 (2019)
Follmann , R., Goldsmith, C., & Stein, W. Multimodal sensory information is represented by a combinatorial code in a sensorimotor system. PLOS BIOLOGY 16.10 (2018)

Presentations

Reservoir Computing: Structure analysis and dynamics predictability, APS March Meeting 2022, Volume 67, Number 3, March 2022, Chicago, IL.
G. Katuri, E. Rosa, and R. Follmann. 2022. Detecting synchronization in brain activity. In Proceedings of the 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics (BCB '22). Association for Computing Machinery, New York, NY, USA, Article 66, 1. https://doi.org/10.1145/3535508.3545106
Using Reservoir Computing for Predicting Slow and Fast Neuronal Dynamics. SIAM Conference on Applications of Dynamical Systems. Society for Industrial and Applied Mathematics. May 2021

Grants & Contracts

Approach for early detection of seizures using EEG data. College of Science and Applied Technology. Illinois State University. (2022)
Hybrid Approach for Improved Performance in Machine Learning. College of Applied Science and Technology. Illinois State University. (2021)