Welcome to my page. I am Awol Seid Ebrie.

Academic Works

• PhD Dissertation: Reinfrocement Learning-Based Optimization for Power Generation Scheduling
• MSc Thesis: Joint Modeling of Longitudinal CD4 Cell Counts and Time-to-Default from Highly Active Antiretroviral Therapy (HAART): a Comparison of Separate and Joint Models.

JournalArticles

1. Ebrie, A.S.; Kim, Y.J. (Under Review). Deep Contextual Reinforcement Learning for Scalable Power Scheduling.
2. Ebrie, A.S.; Kim, Y.J. (2024). Reinforcement Learning-Based Optimization for Power Scheduling with Renewable Energy Connected Grid, Renewable Energy, 230, https://doi.org/10.1016/j.renene.2024.120886.
3. Effendi, Y.A. and Sofiah, A. and Hidayat, N.A.; Ebrie, A.S., Hamzah, Z. (2024). Predicting vulnerability for brain tumor: data-driven approach utilizing machine learning, Indonesian Journal of Electrical Engineering and Computer Science, 35(3), http://doi.org/10.11591/ijeecs.v35.i3.pp1579-1589.
4. Ebrie, A.S.; Paik, C.; Chung, Y.; Kim, Y.J. (2024), Reinforcement Learning-Based Multi-objective Optimization for Generation Scheduling in Power Systems, Systems, 12(3), https://doi.org/10.3390/systems12030106.
5. Ebrie, A.S., Kim Y.J. (2024), A Multi-Agent Simulation-Based Optimization Package for Power Scheduling, Software Impacts, 19, https://doi.org/10.1016/j.simpa.2024.100616.
6. Ebrie, A.S.; Paik, C.; Chung, Y.; Kim, Y.J. (2023), Environment- Friendly Power Scheduling Based on Deep Contextual Reinforcement Learning, Energies, 16(16), 5920, https://doi.org/10.3390/en16165920.
7. Angassa, D.; Solomon, S.; Seid A. (2022), Factors associated with dyslipi- demia and its prevalence among Awash wine factory employees, Addis Ababa, Ethiopia: a cross-sectional study, BMC Cardiovascular Disorders, 22(01), 22, https://doi.org/10.1186/s12872-022-02465-4.
8. Ebrie, A.S.; Kim, Y.J. (2022), Investigating Market Diffusion of Electric Vehicles with Experimental Design of Agent-Based Modeling Simulation, Systems, 10(2), 28, https://doi.org/10.3390/systems10020028.
9. Seid A. (2015), Multilevel modeling of the progression of HIV/AIDS disease among patients under HAART treatment, Annals of Data Science, 02(02), 217 – 230, https://doi.org/10.1007/s40745-015-0044-x.
10. Seid A.; Getie M.; Birlie B.; Getachew Y. (2014), Joint modeling of longitudinal CD4 cell counts and time-to-default from HAART treatment: a comparison of separate and joint models, Electronic Journal of Applied Statistical Analysis, 07(02), 292 – 314, https://doi.org/10.1285/i20705948v7n2p292.

ConferenceProceedings

1. 17th International Conference on Innovative Computing, Information and Control (ICICIC2023), Kumamato, Japan, August 29 – 31, 2023.
2. Korea Society of Industrial Science and Technology, and Korea Society of Business Administration Joint Spring Conference, Jeju, South Korea, May 31 – June 03, 2023.
3. Korea Management Science Association and Korean Industrial Engineering Association Spring Joint Academic Conference, Jeju, South Korea, June 01 – 04, 2022.
4. "Joint Modelling of Longitudinal CD4 Cell Counts and Time-to-Default from Highly Active Antiretroviral Therapy (HAART) Treatment: a Comparison of Separate and Joint Models", The 4th ISIbalo Young African Statisticians Conference (IYASC): Young African Statisticians Staking Their Claim in Unleashing the Power of Statistics in Exposing and Disposing of Inequality Post2015, Pretoria, South Africa, July 31 – August 02, 2014.