Tatiana Ermolieva
Research Scholar
Cooperation and Transformative Governance Research Group
Advancing Systems Analysis Program
Research Scholar
Integrated Biosphere Futures Research Group
Biodiversity and Natural Resources Program
Biography
Dr. Tatiana Ermolieva is a Research Scholar with IIASA’s Ecosystems Services & Management Program (ESM). She holds the Kjell Gunnarson's Risk Management Prize (Swedish Insurance Society) and an IIASA Peccei Award for her work on optimal decisions for coping with dependent catastrophic risks.Dr. Ermolieva has a master's degree in applied mathematics with specialization in statistics, optimization, and economics from Kiev State University, Ukraine, and a PhD on spatio-temporal estimation and optimization of heterogeneous values and flows in complex dynamic and stochastic systems. Her main research interests are analysis and modeling of complex socioeconomic, agricultural, environmental, and financial systems with explicit treatment of uncertainties, risks, extreme events, and spatial and temporal heterogeneities of regions and agents. She develops quantitative models and methods, including software and practical applications, for designing strategies ensuring robust system performance in the presence of uncertainties and risks, in particular of extreme catastrophic nature.
Dr. Ermolieva's activities with ESM concern GIS-based modeling and optimization of regional developments, methodologies for robust downscaling of land use (including agricultural) valuesfrom incomplete, uncertain, aggregate information; non-parametric approaches to data estimation and recovering. Recent practical studies cover topics of global food security and adaptation under climatic uncertainties and risks; fusion of downscaling within the framework of ESM's GLOBIOM model; treatment of systemic risks with a stochastic version of GLOBIOM; design of robust insurance programs (including cat bonds, contingent credits, mutual funds) against climatic disasters, e.g., floods; analysis of robust economic instruments (emission trading markets) under uncertainties; and discounting under catastrophic risks. She applies her skills in several EU projects, in collaborative research with colleagues from various IIASA programs, joint research with IIASA NMOs and other institutions.
Last update: 26 APR 2016
Publications
Ermolieva, T., Ermoliev, Y., Havlik, P. , Derci Augustynczik, A.L., Komendantova, N. , Kahil, T. , Balkovič, J. , Skalský, R. , Folberth, C. , Knopov, P.S., & Wang, G. (2023). Connections between robust statistical estimation, robust decision making with two-stage stochastic optimization, and robust machine learning problems. Cybernetics and systems analysis 59 (3) 33-47.
Ermoliev, Y., Komendantova, N. , & Ermolieva, T. (2023). Energy Production and Storage Investments and Operation Planning Involving Variable Renewable Energy Sources A Two-stage Stochastic Optimization Model with Rolling Time Horizon and Random Stopping Time. In: Modern Optimization Methods for Decision Making Under Risk and Uncertainty. Eds. Gaivoronski, A., Knopov, P., & Zaslavskyi, V., Taylor & Francis. ISBN 9781003260196 10.1201/9781003260196-13.
Ortiz-Partida, J.P., Fernandez-Bou, A.S., Maskey, M., Rodríguez-Flores, J.M., Medellín-Azuara, J., Sandoval-Solis, S., Ermolieva, T., Kanavas, Z., Sahu, R.K. , Wada, Y. , & Kahil, T. (2023). Hydro-Economic Modeling of Water Resources Management Challenges: Current Applications and Future Directions. Water Economics and Policy 09 (01) e2340003. 10.1142/S2382624X23400039.
Ermolieva, T., Ermoliev, Y., Komendantova, N. , Norkin, V., Knopov, P., & Gorbachuk, V. (2023). Linking Catastrophe Modeling and Stochastic Optimization Techniques for Integrated Catastrophe Risk Analysis and Management. In: Modern Optimization Methods for Decision Making Under Risk and Uncertainty. Eds. Gaivoronski, A., Knopov, P., & Zaslavskyi, V., pp. 15-50 Taylor & Francis. ISBN 9781003260196 10.1201/9781003260196-2.
Ermolieva, T., Ermoliev, Y., Zagorodny, A., Bogdanov, V., Borodina, O., Havlik, P. , Komendantova, N. , Knopov, P., Gorbacuk, V., Norkin, V., & Zaslavskyi, V. (2022). Artificial Intelligence, Machine Learning, and Intelligent Decision Support Systems: Iterative “Learning” SQG-based procedures for Distributed Models’ Linkage. In: XXII International Scientific and Technical Conference Artificial Intelligence and Intelligent Systems, Kiev, Ukraine.