Academic degree and current position in TSI: assistant professor, researcher at the research cluster “Data Analytics and Artificial Intelligence
Previous experience: 4 years of lecturing and research at the Transport and Telecommunication Institute (TSI)
Membership: Reviewer of the Hawaii International Conference on System Sciences (HICSS) and Transport Journal. Member of the scientific committee of the International Scientific Conference “Transbaltica 2021”. Member of a working group European Transport Research Review (ECTRI), thematic group “freight & logistics”.
Academic experience: More than 13 publications indexed by SCOPUS and WoS. Author of such courses as “Risk management in supply chains”, “Modeling Logistic and transport processes”, “Logistic systems and chains”.
Teaching at post- and graduate level: Risk management in supply chains (MSc in Transport and Logistics), Modeling Logistic and transport processes (MSc in Transport and Logistics), Logistic systems and chains (MSc in Transport and Logistics).
Participation in projects: participated as a researcher in several European projects including ALLIANCE (ID: 692426) and ePIcenter (ID: 861584).
Research Interests: Supply chain management, applied machine learning, simulation modelling, operations research and metaheuristics.
Supervised Doctoral, Master and Bachelor Theses (number): 1 master student completed master’s degree under my supervision.
Awards: Young Researcher Award was received at the 20th International Multi-Conference Reliability and Statistics in Transportation and Communication, 2020, Riga, Latvia. Awarded paper: “Neuroevolutionary approach to metamodel-based optimization in production and logistics”
Project Type: Methodological
Taking into consideration the urgent industrial need in metamodeling automation and recent success of neuroevolutionary approaches in neural architecture search and hyperparameter optimization, this aims to examine feasibility and efficiency of the combination of artificial neural network and genetic algorithm for automated metamodeling of inventory control systems. The developed framework is built upon the most prominent state-of-the-art practices and demonstrates solid computational capabilities in classical metamodeling formulated as a regression problem. Additionally, the possibility of using the proposed framework to derive optimal control parameters is discussed with regard to a real-world case study.
Funding: VIAA scholarship for doctoral research, decision N.1.-50.3/3889, decision N.1.-50.3/2978
Deliverables (publications, reports, acknowledgments, etc.)