Virginia Military Institute, USA
Dimple Chalishajar has been working in the field of "Mathematical Control Theory and Applications" for the last 20 years. He did his Ph.D. from one of India's leading research institutions in 2005 and the post-doctoral work at Virginia Tech, USA in 2008. He has 60 international peer-reviewed journal publications with one monograph. He has delivered 65 lectures at international conferences including 10 invited talks. He has been serving as an editorial board member of 10 peer-reviewed journals and as a reviewer in 20 journals. Two students pursued their Ph.D. under his supervision, five independent research students, and one student for the honor thesis at the graduate level. He has a teaching experience of almost 25 years and taught several pure and applied mathematics courses with statistics.
Speech Title: "Exact Controllability of Third-Order Nonlinear Dispersion (KdV) System"
Abstract: In this talk, we will discuss the exact controllability of a third-order dispersion system using monotone operator theory and a fixed point approach. The existence and the uniqueness of the problem will be discussed in three different ways. Then we will discuss the controllability of the KdV equation by using monotone operator theory. We will weaken the "Lipschits continuity" by the method of "Integral Contractor" and prove the controllability result. Examples will be provided to illustrate the theoretical results.
Xian Jiatong-Liverpool University, China
Sergey Bocharov has recently joined the Mathematics Department of Xian Jiatong-Liverpool University as an Assistant Professor. Prior to that he has worked for seven years as an Associate Researcher (special terms Associate Professor) at the Mathematics Department of Zhejiang University, China, where he taught a number of graduate and undergraduate courses and served as a supervisor for Masters’ students. His main area of expertise is in Probability Theory including topics like Levy processes, local times, martingales, etc. His research is mostly centred around studying spatial branching processes. A typical such process consists of particles moving randomly in space according to the law of some stochastic process (such as a Brownian motion) and reproducing at a spatially-dependent rate. It is then of interest to study things like spatial spread, population growth and genealogies of particles in the limit as time goes to infinity in such models.
Speech Title: "Rightmost Particle in the Catalytic Branching Brownian Motion"
Abstract: Catalytic branching Brownian motion (BBM) is a spatial population model in which individuals, referred to as ‘particles’, move in space according to the law of a standard Brownian motion and reproduce themselves at a spatially-inhomogeneous rate βδ0(·), where β is some positive constant and δ0 is the Dirac delta measure. We shall discuss some new fine asymptotic results about the position of the rightmost particle (the particle whose spatial position at the given instant of time is largest).
Centre for Research and Technology Hellas, Information Technologies Institute, Greece
Aristotle University of Thessaloniki, Greece
Ourania Theodosiadou received the Degree in Mathematics, the MSc in Statistics and Modeling, and the PhD degree in Stochastic Processes-Stochastic Modelling from the Department of Mathematics in the Aristotle University of Thessaloniki (AUTH), Greece. Her main research interests lie on the domain of stochastic modelling, time series analysis, computational statistics, applied probability and econometrics. Currently, she is a postdoctoral research fellow with Centre of Research and Technology Hellas - Information Technologies Institute (CERTH-ITI) participating in European Security research projects, and an adjunct lecturer in the Department of mathematics, AUTH. She has also published original work in scientific journals and participated in several international conferences.
Speech Title: "Estimating the Beta Coefficients of the Hidden Two-Sided Asset Return Jumps using Variance Gamma Distribution"
Abstract: A semi-parametric method is presented for estimating the parameters of a two-sided jump model, where the asset return is defined as the difference between the positive and negative return jump under noise inclusion. The parameters of interest are the jump beta coefficients which measure the influence of the market jumps on the stock returns, and are latent components. The method is based on the use of the Variance Gamma (VG) distribution along with its properties, and it is proved that it provides always a solution in terms of the jump beta coefficients. An application to empirical returns is also illustrated.