Bozenna Pasik-Duncan received M.S. degree in mathematics from University of Warsaw (Poland), and Ph.D. and D.Sc. (Habilitation Doctorate) degrees from Warsaw School of Economics (Poland). She is Professor of Mathematics; Courtesy Professor of EECS & AE; Investigator at ITTC; Affiliate Faculty at Computational Biology Center, and Chancellors Club Teaching Professor at University of Kansas (Lawrence, KS). She is 2017-2018 IEEE Women in Engineering (WIE) Global Chair and 2019-2020 Past Chair, founder of IEEE Control Systems Society (CSS) Women in Control, founder and faculty advisor of Student Chapters of Association for Women in Mathematics (AWM) and Society for Industrial and Applied Mathematics (SIAM) at KU, founder and coordinator of KU and IEEE CSS, American Automatic Council (AACC) and International Federation of Automatic Control (IFAC) Outreach Programs, a Life Fellow of IEEE, and Fellow of IFAC. She is recipient of many awards that include IREX Fellow, NSF Career Advancement Award, AWM Louise Hay Award, Polish Ministry of Higher Education Outstanding Research and Teaching Award, H.O.P.E. (Honor to Outstanding Progressive Educator) Award, Kemper Fellowship, Max Wells Award, IFAC Outstanding Service Award, Service to Kansas Award, IEEE Educational Activities Board Meritorious Achievement Award "for innovative developments in teaching control systems and inspiring STEM education", the IEEE Third Millennium Medal and IEEE CSS Distinguished Member Award. She is inducted to the KU Women's Hall of Fame, the Phi Beta Delta Honor Society for International Scholars, and to the IEEE HKN. Her broad research interests are primarily in stochastic adaptive control and its applications to science, engineering and economics, and in STEM education.
Advances in noise modeling in stochastic systems and control.
Many continuous time stochastic systems that are modeled by stochastic differential equations (SDE) and stochastic partial differential equations (SPDE) have been limited to noise processes being Brownian motions. Brownian motion models have a well developed stochastic calculus and limiting behaviors that reflect the martingale, Markov and Gaussian properties of Brownian motion. However for many physical systems the empirical data do not justify the use of Brownian motion as the model for random disturbances. In fact Brownian motions provide models that are often far from the physical data. Thus it is necessary to find more general noise models and tractable methods to solve the associated problems of control or adaptive control. These other noise models include more general Gaussian processes and non-Gaussian processes.The talk focuses on new developments and new challenges in noise models for stochastic control and adaptive control problems.