Bahareh Rahmani, Ph.D.
Associate Professor
Department of Computer Science
Education
- PhD in Computer Science – Bioinformatics, University of Tulsa, Oklahoma, US
- BS in Applied Mathematics, Sharif University of Technology, Iran, Tehran
Practice Areas
Dr. Rahmani received her Ph.D. degree in computer science – Bioinformatics in 2016. She was Assistant Professor in Data Science at Fontbonne University for 4 years and Maryville University for 2 years.
She joined to the Department of Computer Science at Saint Louis University in Fall 2022 to work on a research-based university. She taught and developed 15+ different in-person and online courses in the past 6.5 years including: Machin Learning, Deep Learning, Image Processing, Big Data Mining, Business Intelligence, NoSQL Database, Predictive Modeling, Bioinformatics, C++, Python, and other courses.
Her research interest is exploring and analyzing data using Machine Learning, Deep Learning and Computer Vision. She has published some papers in Springer Nature and other publishers in the past years. Her research has been cited 360+ times.
Research Interests
- Data Science
- Artificial Intelligence
- Bioinformatics
- Image Processing
- Machine Learning
- Deep Learning
Publications and Media Placements
Machine Learning dimensionality reduction to detect EEG channel, differences between PTSD cases and healthy controls, Vu Nguyen, Minh Phan, Tiantian Wang, Payam Norouzzadeh, Eli Snir, Salih Tutun, Brett McKinney, Bahareh Rahmani, Submitted in Scientific Reports, Springer Nature, 2022
Machine Learning Classifiers Help to Manage COVID-19 Distribution in China, J Wei, Y Mingxuan, Z Alsahfi, T Ye, P Norouzzadeh, E Snir, B Rahmani, Scientific Report, Springer Nature, 2022
Precipitation analysis and forecasting weather of Texas, United States, K Miller, G Yi, E Snir, B Rahmani, International Journal of Information Technology, Page 1-8, Springer Nature, 2022
Application of Convolutional Neural Network in lawn measurement, BR J. Wilkins, M. V. Nguyen, Signal & Image Processing: An International Journal, 12 (1), 2021
Application of Machine Learning Methods to Predict Failure of Glaucoma Drainage, Paul Morrison, Maxwell Dixon, Arsham Sheybani, Bahareh Rahmani, Data Mining & Knowledge Management Process, AIRCC 11 (1), 2020
Predicting Failures of Molteno and Baerveldt Glaucoma Drainage Devices Using Machine Learning Models, Paul Morrison, Maxwell Dixon, Arsham Sheybani, Bahareh Rahmani, Computer Science Conference Proceedings in CS & IT, AIRCC 10 (16), 2020
Temperature does not affect on spread of COVID-19, Y Mingxuan, B Rahmani, Business data analytics conference, Waterloo, UK, 2020
Dynamical Hurst analysis identifies EEG channel differences between PTSD and healthy controls, B. Rahmani, K. W. Chung, P. Norouzzadeh, J. Bodurka, B. McKinney, PLoS One, 21,2019
Recursive indirect-paths modularity (RIP-M) for detecting community structure in RNA-Seq co-expression networks, B Rahmani, MT Zimmermann, DE Grill, RB Kennedy, AL Oberg, BC White, ..., Frontiers in genetics 7, 80, 15, 2017
Detecting Community Structure in RNA-SEQ Co-Expression Networks and EEG Networks, B Rahmani, University of Tulsa, Dissertation, 2016
Multifractal features of spot rates in the Liquid Petroleum Gas shipping market, S Engelen, P Norouzzadeh, W Dullaert, B Rahmani, Energy Economics 33 (1), 88-98 46, 2011
Anti-correlation and multifractal features of Spain electricity spot market, P Norouzzadeh, W Dullaert, B Rahmani, Physica A: Statistical Mechanics and its Applications 380, 333-342,7, 2007
Forecasting smoothed non-stationary time series using genetic algorithms, P Norouzzadeh, B Rahmani, MS Norouzzadeh, International Journal of Modern Physics C 18 (06), 1071-1086, 6,2007
A multifractal detrended fluctuation description of Iranian rial–US dollar exchange rate, P Norouzzadeh, B Rahmani, Physica A: Statistical Mechanics and its Applications 367, 328-336, 198, 2006