STOEAN Catalin – Adjunct Research Associate Professor

 

Affiliation: Romanian Institute of Science and Technology, Cluj-Napoca, Romania / University of Craiova, Romania.

Cătălin is a former Fulbright scholar at the University of Illinois at Urbana-Champaign and two times a DAAD scholar at TU Dortmund University and Cologne University of Applied Sciences. His research interests involve the application of machine and deep learning approaches to real-world tasks. Examples of tasks are classification, regression, time series and the types of data varies from numerical, images to text. Areas of application where he was often involved range from medicine, chemistry to financial data and even text processing for extracting relevant information.

Fields of interest/specialization: Evolutionary Computation, Artificial Intelligence, Multimodal Optimization, Deep Learning.

Google Scholar: https://scholar.google.ro/citations?user=oEdoht8AAAAJ&hl=en

Representative works:

  • Catalin Stoean, Ruxandra Stoean, Miguel Atencia, Moloud Abdar, Luis Velázquez-Pérez, Abbas Khosravi, Saeid Nahavandi, U. Rajendra Acharya, Gonzalo Joya, Automated Detection of Presymptomatic Conditions in Spinocerebellar Ataxia Type 2 Using Monte Carlo Dropout and Deep Neural Network Techniques with Electrooculogram Signals, Sensors, Vol. 20, No. 11, 3032, https://doi.org/10.3390/s20113032, IF 3.031, 2020.
  • Catalin Stoean, Daniel Lichtblau, Author Identification using Chaos Game Representation and Deep Learning, Mathematics, 8, No. 11: 1933, https://www.mdpi.com/2227- 7390/8/11/1933, IF 2.258, 2020.
  • Shachi Mittal, Catalin Stoean, Andre A Kajdacsy-Balla, Rohit Bhargava, Digital Assessment of Stained Breast Tissue Images for Comprehensive Tumor and Microenvironment Analysis, Frontiers in Bioengineering and Biotechnology, section Bioinformatics and Computational Biology, Manuscript ID: 454225, doi: 10.3389/fbioe.2019.00246, IF 5.122, 2019.
  • Catalin Stoean, Wieslaw Paja, Ruxandra Stoean, Adrian Sandita, Deep architectures for long-term stock price prediction with a heuristic based strategy for trading simulations. PLOS ONE 14(10): e0223593. https://doi.org/10.1371/journal.pone.0223593, IF 2.776, 2019.
  • Daniel Lichtblau, Catalin Stoean, Cancer diagnosis through a tandem of classifiers for digitized histopathological slides, PLOS ONE, 14(1): e0209274, https://doi.org/10.1371/journal.pone.0209274, IF 2.776, 2019.