Maria V. Kulikova 
Research Assistant Professor/Investigador auxiliar
E-mails: Kulikova.Maria(at),
E-mails:  maria.kulikova(at)

Address: CEMAT, Instituto Superior TécnicoUniversidade de Lisboa,  
Address:    Av. Rovisco Pais 1, 1049-001 Lisboa, Portugal. IST scholar


Maria V. Kulikova works at the Center for Computational and Stochastic Mathematics (CEMAT), Instituto Superior Técnico (IST), Universidade de Lisboa, Portugal. She earned her Ph.D. (Russian degree Candidate of Sciences in Physics and Mathematics) in 2006 at Ulyanovsk State University, Russia. She held a post-doctoral position at the University of the Witwatersrand, South Africa (2007-2009) and then joined the CEMAT, IST, Universidade de Lisboa in 2009. She has published widely in international peer-reviewed journals and has received individual research grants from the University of the Witwatersrand (South Africa), CEMAT (Portugal), FCT (Portugal). She has served as a referee for various international peer-reviewed journals and as a reviewer for the Mathematical Reviews of the American Mathematical Society (AMS). In addition, she has an experience to be a part of evaluation panels of the higher degrees committees, serves as an external examiner and she is a research associate of the "African Collaboration for Quantitative Finance & Risk Research" (ACQuFRR). Dr. Kulikova has contributed to teaching undergraduate and graduate courses in computational mathematics and numerical methods. She has also supervised a number of M.Sc. students in the area of her expertise. M.V. Kulikova has been recognized as a TOP 2% cited active researcher (Prior One Year) in the world according to Scopus' data (in 2020, 2021, 2022, 2023). She has been also recognized as #3 Highly Ranked Scholar (Prior Five Years) in the field of "Kalman filter" in 2023 according to ScholarGPS ranking (URL).

Scientific interests/Áreas de Interesse Científico

Her main research interests are in solving mathematical problems related to development of powerful algorithms for control, computing and signal processing. More precisely, the key goals are the derivation of linear/nonlinear Bayesian filtering methods as well as the related adaptive filtering schemes for dynamic state and system parameter estimation with strong emphasis on accuracy and numerical robustness, including the robustness with respect to non-Gaussian uncertainties.
Mathematics Subject Classification (2000):  Primary interest: 93E11, 93E35, 65C60
Mathematics Subject Classification (2000).  Secondary interest: 93E10, 93E12, 93E24, 93E30