Vo Ba-Ngu, The University of Western Australia, Australia

Jose Blanchet, Columbia University, New York, USA

Bernard Cazelles, Ecole Normale Superieure, France

Carlos A. Coello Coello, CINVESTAV-IPN, Mexico City, Mexico

Pedro Larrañaga, Technical University of Madrid, Spain

Tony Lelievre, Ecole des Ponts ParisTech, France

Sylvie Méléard, Ecole Polytechnique, Palaiseau, France

Jian-Qiao Sun, University of California, Merced, USA

 



 

vo-ba-ngu      Ba-Ngu Vo, The University of Western Australia, Australia

 

SHORT BIO: Prof. Ba-Ngu Vo received his Bachelor degrees jointly in Science and Electrical Engineering with first class honours in 1994, and PhD in 1997. He had held various research positions before joining the department of Electrical and Electronic Engineering at the University of Melbourne in 2000. Since 2010 he is Winthrop Professor and Chair of Signal Processing in the School of Electrical Electronic and Computer Engineering at the University of Western Australia. Prof. Vo is a recipient of the Australian Research Council’s inaugural Future Fellowship and the 2010 Australian Museum Eureka Prize for Outstanding Science in support of Defence or National Security. His research interests are Signal Processing, Systems Theory and Stochastic Geometry with emphasis on target tracking, robotics and computer vision. 

TITLE: Beyond the PHD filter

ABSTRACT: Since its publication in 2003, Mahler's seminal work on the random finite set approach to multi-target tracking that culminated in the probability hypothesis density (PHD) filter has continued to attract substantial interests from academia and industry alike. The PHD filters have been used in oil pipeline tracking by British Petrolium, ground target tracking in the September 2007 NATO ‘Bold Avenger’ defence exercise and the US space fence program by Lockheed Martin. This seminar presents an overview of the random finite set paradigm to multi-target tracking and outlines recent developments beyond the PHD filter, such as the multi-Bernoulli filters, labeled random finite sets as well as applications in areas such as computer vision, and field robotics.  


 

 

blanchet 1      Jose Blanchet, Columbia University, New York, USA

SHORT BIO: Jose Blanchet is an Associate Professor in IEOR Department at Columbia University. Jose holds a Ph.D. in Management Science and Engineering from Stanford University. Prior to joining Columbia he was a faculty member in the Statistics Department at Harvard University. Jose is a co-recipient of both the 2009 Best Publication Award given by the INFORMS Applied Probability Society and of the 2010 Erlang Prize. He also received a PECASE award given by NSF in 2010. He has research interests in applied probability and Monte Carlo methods. He serves in the editorial board of Advances in Applied Probability, Journal of Applied Probability, Mathematics of Operations Research, QUESTA, and Stochastic Systems. 

TITLE: Stochastic Approximation for Quasi-Stationary Vectors

ABSTRACT: In this presentation we study a method for estimating the quasi-stationary distribution of various interacting particle processes that has been proposed recently. This method improved upon existing methods in eigenvector estimation by eliminating the need for explicit transition matrix representation and multiplication. However, this method has no firm theoretical foundation. We analyze the algorithm by casting it as a stochastic approximation algorithm (Robbins-Monro). In doing so, we prove its convergence and rate of convergence. Based on this insight, we also give an example where the rate of convergence is very slow. This problem can be alleviated by using an improved version of this algorithm that is given in this presentation. Numerical experiments are described that demonstrate the effectiveness of this improved method.  



 

cazelles2      Bernard Cazelles, Ecole Normale Supérieure, France

SHORT BIO: Bernard Cazelles is Professor at the Université Pierre et Marie Curie, he is currently at the head of the department “Ecologie & Evolution” which depends on the Université Pierre et Marie Curie, the CNRS and the Ecole Normale Supérieure. He got a PhD in Biomathematics from the Université Claude Bernard, Lyon I in 1987 and his "Habilitation à Diriger des Recherches" in 2001 at the Université Pierre et Marie. Bernard Cazelles is mainly interested in explaining the complex patterns of populations observed in nature. Different directions have been explored, one concerns theoretical works around the interactions between stochasticity and non-linearity in population dynamics. Another direction concerns the analysis of the evolution and the competition between different pathogen strains that appear very frequent in numerous infectious diseases. Bernard Cazelles also serves as associate editor in “Chaos, Solitons and Fractals” and organizes since 2008 a recurrent international workshop entitled “Chaos, Complexity and Dynamics in Biological Networks”. 

TITLE: Stochastic modeling in epidemiology with applications to human influenza

ABSTRACT: Over the past few decades, our world has experienced the emergence, or the re-emergence, of several infectious diseases in connection with our fast-changing environment. For understanding and predicting the patterns of emerging epidemics in our changing world is of fundamental importance to incorporate stochasticity and evolutionary processes. The presented work is devoted to the mathematical study and statistical inference of stochastic models for the spread of a pathogen in a population. Different aspects will be presented. The first one is related to density-dependent Markov jump processes that are a natural formalism to take into account demographic stochasticity in epidemiological models. We then turn to the phenomenon of multiple waves outbreaks, which is characteristic of influenza pandemics. We formalize different biological mechanisms to account for this phenomenon. Then, using an iterated filtering procedure, we compare these models on the basis of their fit to data. This analysis highlights the role of host heterogeneity in the immune response, a mechanism previously largely underestimated by mathematical-epidemiologists. The last aspect presented concerns the analysis of the patterns of antigenic replacements that are characteristics of the eco-evolutionary dynamics of influenza. We propose stochastic approaches to model the two hypotheses commonly cited to explain these replacements, punctuated or gradual evolution of the influenza’s main antigen.  



 

coello      Carlos A. Coello Coello, CINVESTAV-IPN, Mexico

 

SHORT BIO: Carlos Artemio Coello Coello received a BSc in Civil Engineering from the Universidad Autonoma de Chiapas in Mexico in 1991 (graduating Summa Cum Laude). Then, he was awarded a scholarship from the Mexican government to pursue graduate studies in Computer Science at Tulane University (in the USA). He received a MSc and a PhD in Computer Science in 1993 and 1996, respectively. His PhD thesis was one of the first in the field now called "evolutionary multiobjective optimization". He currently has over 320 publications which report over 6,000 citations (his h-index is 40). Dr. Coello has been a Senior Research Fellow in the Plymouth Engineering Design Centre (in England) and a Visiting Professor at DePauw University (in the USA). He is currently full professor with distinction (Investigador Cinvestav 3F) and Chair of the Computer Science Department at CINVESTAV-IPN in Mexico City, Mexico. He has delivered invited talks, keynote speeches and tutorials at international conferences held in Spain, USA, Canada, Switzerland, UK, Chile, Colombia, Brazil, Uruguay, Argentina, India, Italy, China and Mexico. Dr. Coello has served as a technical reviewer for over 60 international journals and for more than 100 international conferences and actually serves as associate editor of the journals "IEEE Transactions on Evolutionary Computation", "Evolutionary Computation", "Journal of Heuristics", "Soft Computing", "Pattern Analysis and Applications", "Memetic Computing" and "Computational Optimization and Applications", and as a member of the editorial boards of the journals "Engineering Optimization", and the "International Journal of Computational Intelligence Research". He is member of the Mexican Academy of Science, the Association for Computing Machinery, and of Sigma Xi, The Scientific Research Society. He is also a member of the National System of Researchers (Level 3). As of January, 2011, he is also an IEEE Fellow for his "contributions to multi-objective optimization and constraint-handling techniques". He has received the "2007 National Research Award" from the Mexican Academy of Science in the area of exact sciences, the "Medal to the Scientific Merit" from Mexico City's congress and the "Ciudad Capital: Heberto Castillo 2011 Award" in Basic Science. His current research interests are: evolutionary multiobjective optimization and constraint-handling techniques for evolutionary algorithms.

TITLE: Multi-Objective Particle Swarm Optimizers: Past, Present and Future

ABSTRACT: This talk will provide a review of the research that has been conducted on the use of particle swarm optimization for solving multi-objective problems. The talk will emphasize the main algorithmic developments that have been reported in the specialized literature, and will include a brief analysis of some of the most representative multi-objective particle swarm optimizers (MOPSOs) that have been proposed so far. The talk will also include some of the real-world applications of these MOPSOs and will conclude with some of the potential paths for future research in this area.  


 

 

plarranaga

     Pedro Larrañaga, Technical University of Madrid, Spain

 

SHORT BIO: Prof. Pedro Larrañaga received his diploma (Mathematics) degree in 1981 from University of Valladolid, Spain, and a PhD in Computer Science in 1995 from University of the Basque Country, Spain, where he obtained an associate professor level in 1998 and a full professor level in 2004. In 2007 he joined the Technical University of Madrid as full professor at the Department of Artificial Intelligence where he leads the Computational Intelligence group. His research interests are in the fields of probabilistic graphical models and heuristic optimization. In both fields he has proposed methodological advances and successful applications in industry, computer science and biomedicine. He has coauthored two edited books on estimation of distribution algorithms, as well as more than 300 scientific papers in different areas. He has participated in more than 70 research projects at national, European and international levels. During 2007-2010 he has been the expert manager of computer technology area of the Spanish Ministry of Science and Innovation.

TITLE: Multi-objective Optimization with Estimation of Distribution Algorithms

ABSTRACT: Estimation of distribution algorithms (EDAs) are a new paradigm for evolutionary computation, based on modelling the selected pool of individuals by means of a Bayesian network, and its posterior simulation for obtaining a new population of individuals. In this talk we will present ad-hoc EDAs for approaching multi-objective optimization (with many-objectives) based on joint modeling of objectives and variables.


 

 

lelievre small

     Tony LelievreEcole des Ponts ParisTech, France


 

SHORT BIO: Tony Lelièvre is a researcher in applied mathematics and professor at the Ecole des Ponts ParisTech, in Paris. He received his PhD from Ecole des Ponts in June 2004, and held a postdoctoral position in Montreal in 2005-2006. He got his habilitation à diriger des recherches in 2009. He is working on the interplay between probabilistic and deterministic approaches to deal with various modelisation and simulation problems, with applications in molecular modelling and multiscale approaches in materials sciences. He is the co-author of about 40 journal papers, 15 conference proceeding papers, and two books: /Mathematical methods for the Magnetohydrodynamics of liquid metals/, Oxford University Press, 2006 (with J.-F. Gerbeau and C. Le Bris) and Free energy computations: A mathematical perspective, Imperial College Press, 2010 (with M. Rousset and G. Stoltz).

TITLE: Numerical methods to overcome metastability in molecular dynamics.

ABSTRACT: The aim of molecular dynamics simulations is to understand the relationships between the macroscopic properties of a molecular system and its atomistic features. For example, one would like to compute the constitutive relations for materials from molecular models, or predict the most likely conformations of a protein in a solvent from its amino acid sequence. One of the difficulty to reach this aim is related to timescales: the typical timescale of a molecular dynamics simulation is much smaller than the typical timescale at which the crucial events, from a macroscopic viewpoint, occur. This is related to the metastability of a molecular dynamics trajectory: the system stays for a very long time in some metastable state, before hopping to another one, and it is difficult to observe and simulate such rare events. An associated feature is the multimodality of the statistical ensemble (a probability measure) sampled by the molecular dynamics trajectories. Many methods have been proposed in the molecular dynamics community to deal with these difficulties, and we will focus on two prototypical ones for which a mathematical analysis gives useful insights. We will first present adaptive importance sampling techniques, which have been proposed to sample efficiently statistical ensembles. Then, we will propose a mathematical analysis of the parallel replica algorithm which has been introduced by A.F. Voter to generate efficiently metastable dynamics.

References:
- T. Lelièvre, M. Rousset and G. Stoltz, Free energy computations: A mathematical perspective, Imperial
  College Press, 2010.
- C. Le Bris, T. Lelièvre, M. Luskin and D. Perez, A mathematical formalization of the parallel replica
  dynamics, http://arxiv.org/abs/1105.4636, 2011.


 

 

meleard

     Sylvie MéléardEcole Polytechnique, Palaiseau, France


SHORT BIO: Sylvie Méléard received her PhD in 1984 from the Université Pierre et Marie Curie (UPMC, Paris, France) and obtained an Assistant Professor position firstly at the Université du Mans, and then at UPMC. She got her "Habilitation à diriger des recherches" in 1991 and a full Professor position in 1992 at the University Paris Ouest-Nanterre. Since 2006, she is full Professor at Ecole Polytechnique and is Chair of the Applied Mathematics Department. She is a specialist for probability and stochastic processes, interacting particle systems and measure-valued processes. In the last decade, she developed mathematical (and especially stochastic) modeling for ecology and evolution. She is the leader of the French ANR research group on this topics. She is also the leader of a big research program with the Muséum National d'Histoire Naturelle and the Company Veolia-Environment concerning mathematical modeling of the biodiversity. She has been or she is associate editor for several journals (Annals of Probability, Stochastic Processes and their Applications, Maths in Action). She has supervised 10 PhD-students. She is author or co-author of more than 60 refereed papers and she wrote a book entitled "Aléatoire (randomness)". She has given numerous invited talks at universities all over the world.
Her current research mainly concerns the stochastic modeling of Darwinian evolution. The focus is given on the interplay between the ecological properties of the population and the evolution of genetic parameters.

TITLE: Stochastic Modeling of Darwinian Evolution.

ABSTRACT: We study models describing the Darwinian evolution of a population with mutation and selection in the specific scales of the biological framework of adaptive dynamics. We take both into account the demography and the genetics of the population in an individual-based stochastic approach. Each individual is characterized by its own genetic parameters which influence its demographic parameters and are inherited during the reproduction except when a mutation occurs. The individuals compete to survive by sharing a fixed amount of resource. The population is described as a point measure valued process with support on the genotype space and behaves as a birth and death process with mutation and competition. The population size is assumed to be large and the mutation rate small. Under a good combination of these different scales, the population process is approximated in a long time scale by a process jumping from a monomorphic equilibrium to another one. These jumps obtained in an evolutionary time scale represent the successive fixations of successful mutants. When the mutation steps are small, one can observe and rigorously justify the appearance of evolutionary branching.


 

 

sun-1

     Jian-Qiao SunUniversity of California, Merced, USA


 

SHORT BIO:  Dr. Jian-Qiao Sun earned his B.S. degree from Huazhong University of Science and Technology in 1982, M.S. and Ph.D. degrees from the University of California at Berkeley in 1984 and 1988. From 1991 to 1994, he worked for Lord Corporation at their Corporate R&D Center in Cary, North Carolina, and engaged in smart materials research, and acoustic-structural controls since then. In 1994, Dr. Sun joined the faculty of the department of Mechanical Engineering at the University of Delaware as Assistant Professor, was promoted to Associate Professor in 1998 and to Professor in 2003. Currently, he is a Professor of School of Engineering at UC Merced.

He was an Associate Editor of ASME Journal of Vibration and Acoustics since 1994 to 2000, and has been an Associate Editor of Communications in Nonlinear Science and Numerical Simulations since 2001. He is also an Editorial Board Member of Acta Mechanica Solida Sinica, Journal of Sound and Vibration, and Journal of Vibration and Control. His research interests include cell mapping methods, nonlinear random vibrations, nonlinear dynamics and controls, active structural-acoustic control, measurement of mechanical properties of nano-bio-fibers, and physical therapy applications of smart materials and controls. Dr. Sun has supervised 23 graduate students in the past 13 years, 12 of them were doctoral students. He published over 100 refereed journal articles in additional to a large number of conference presentations and technical reports, and has given numerous invited talks at universities all over the world. Dr. Sun authored a book entitled “Stochastic Dynamics and Control” and co-edited several books.

TITLE: Control of Nonlinear Systems with the Cell Mapping Method

ABSTRACT: This talk presents studies of control problems of nonlinear dynamic systems using the cell mapping method. We first present the formulation of optimal control problem of nonlinear systems.Then, we present the cell mapping methods and their application to optimal control problems of deterministic and stochastic nonlinear dynamic systems. Some challenging control problems are studied by using the cell mapping method. Some improvements of the conventional solution algorithm for optimal control problems are discussed. A number of interesting examples are presented including the population dynamics control of two competing species, navigation in a vortex, and stochastic optimal control of nonlinear dynamic systems.