Education

Western Michigan University, PhD

Research Interests

Development of statistical and machine learning models for psychiatric disorder research.

Research Description

My research is focused on analyses of large-scale genetic studies by applying statistical genetics and machine learning methods to aid in the interpretation and understanding of the genetic influences on Major Depressive Disorder (MDD) and Alcohol Use Disorder (AUD).

Awards

Post-doctoral fellowship funded by:

  • R21MH126358 Adapting machine learning methods to detect genetic loci specific to strictly-defined MDD (PI Webb)
  • P50AA0225637 Project 5, Research Training: Psychiatric and Statistical Genetics (PI Bacanu)

IEEE EIT Conference Award (2015)
PhD Scholarship at Western Michigan University awarded by the HCED organization.

Selected Publications

M. F. Hassan, Ikhlas Abdel-Qader, Bradley Bazuin, A new method for ensemble combination based on adaptive decision making, Knowledge-Based Systems, Volume 233, 2021,107544, ISSN 0950-7051, https://doi.org/10.1016/j.knosys.2021.107544.

M. F. Hassan and I. Abdel-Qader, “Performance Analysis of Majority Vote Combiner for Multiple Classifier Systems,” 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA), Miami, FL, USA, 2015, pp. 89-95, doi: 10.1109/ICMLA.2015.27.

M. F. Hassan and I. Abdel-Qader, “Analysis of multiple classifier system using product and modified product rules,” 2015 IEEE International Conference on Electro/Information Technology (EIT), Dekalb, IL, USA, 2015, pp. 152-157, doi: 10.1109/EIT.2015.7293334.

M. F. Hassan and I. Abdel-Qader, “Improving pattern classification by nonlinearly combined classifiers,” 2016 IEEE 15th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC), Palo Alto, CA, USA, 2016, pp. 489-495, doi: 10.1109/ICCI-CC.2016.7862081.