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Brock Institute for Scientific Computing

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Brock University, 500 Glenridge Avenue, St.Catharines, 
Ontario, Canada L2S 3A1 (905)688-5550 

The Brock Institute for Scientific Computation (BISC) was established this year to promote collaborative relations between computationally-based research groups within Brock University, in addition to collaborations with faculty at other Universities (presently McMaster) and industry. We have formed interdisciplinary teams of researchers with expertise in information science, biotechnology, computational biology, mathematics, and physics, experts in the use of numerical analysis, mathematical modelling, computer simulation, and artificial intelligence, to tackle major research problems demanding innovative solutions. These have practical technological implications across a broad spectrum of important Canadian industries.

The current director of BISC is Prof. Stuart M. Rothstein.

Computational Physics [insert link here]
S. Bose
H. Fuks
B. Mitrovic
S. Rothstein
J. Vrbik

Computational Mathematics and Combinatorics [insert link here]
S. Anco
W. Craig
H. Fuks
S. Houghten
B. Ombuki
T. Wolf

Modelling, Simulation and Artificial Intelligence [insert link here]
J. Barchanski
D. Bruce
H. Gordon
B. Ross
S. Rothstein
T. Wolf

Statistical and Data Analysis [insert link here]
M-L. Huang
E. Sternin

Bioinformatics [insert link here]
H. Gordon
M. Richards
A.. Skandalis
R. Nadon

   Computational physicists use high-performance computers to explore physical phenomena, from those involving the most fundamental objects such as electrons and asteroids, to those important in applications to areas such as materials science and medical technology. This new approach to physics opens avenues to problems whose solution is otherwise impossible and to gain physical insight needed to make those advances.
   Most physical properties of gases, solids and liquids are dictated by the behaviour of the electrons in the atoms that form the building blocks of these systems. It is important that we study and understand these electrons as accurately as possible, so that we can not only describe the physical properties better, but also predict new phases and new types of materials and fabricate solids and liquids with tailored problems. This is a complex problem, residing at the heart of current materials science technology, is being addressed by the research programs of BOSE, MITROVIC and ROTHSTEIN.
   VRBIK's reseach is focused on investigating the perturbed Kepler equation to understand the resonances frequently found in our solar system, and to investigate the solar system's stability and long-time behaviour.

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------------------------- individual blurb (BOSE; include link to his home page------
   The research of BOSE and co-workers has the objective of providing a fundamental understanding of the electronic structure of non-crystalline materials and of transport properties of liquids and amorphous metals, alloys, and semiconductors. In addition they do theoretical studies of the spectroscopy of solids, collision-induced absorption, and vibrational and magnetic properties of amorphous materials.
   Recently Monte Carlo simulations of localized spin models on decagonal (Penrose) and octagonal quasiperiodic lattices were performed. Many interesting results, including the possibility of a gauge spin glass transition in two dimensional systems were obtained. This last result, albeit controversial, has aroused much interest and definitely needs to be explored further. Unfortunately this work is currently on hold, since progress can be made only via extensive computational work requiring much larger memory and computing speed than currently available to BOSE at Brock, needed even to get the algorithms to the developmental stage.
    In collaboration with European colleagues BOSE investigated the ordering tendency in some random states of alloys using density functional theory. Effective pair (EPI) and triplet interactions, calculated via the coherent potential approximation generalized perturbation method, were used to predict both primary and secondary transitions in the alloys.
    A logical extension would be to use the EPIs calculated via the density functional generalized perturbation method in an effective Ising Model Monte Carlo simulation to describe the details of the transition, including an accurate determination of the transition temperature and the critical point exponents. Almost all numerical simulations of phase diagrams in alloys are based on empirical parameters; studies based on electronic structure have not as yet gone beyond the mean field approximation. Extension along these lines is highly desirable, and improved computing resources at Brock will enable them to develop the codes necessary to undertake this research.
   BOSE and co-workers recently studied the effect of topological disorder on the magnetism of Fe and Co by using the linear muffin-tin orbitals Green’s function method. This was the first ab initio study of the exchange interactions in magnetic glasses. A future goal is to use the calculated exchange interactions in a Heisenberg model Monte Carlo simulation to study the magnetic phase transitions in Fe- and Co-based glasses. This calculation would be time consuming as the exchange interactions are dependent on local spin configurations, demanding new calculation of the exchange interaction with every Monte Carlo move. However, this approach is valuable and novel, as it is free from adjustable parameters and would provide the first application of the knowledge of electronic structure in the study of magnetic transitions.
   For more details on Prof. Bose's research, please consult his home page. [insert link]
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---------------------individual blurb (FUKS); include link to his home page--
   RESEARCH AREAS: discrete dynamical systems, mathematical models of growth and diffusion phenomena in nonhomogeneous media (such as bacterial growth in contaminated food products).
   Traditional models of phenomena with complex spatial dynamics are usually based on partial differential equations (PDE). They are quite inadequate in situations where local stochasticity plays a prominent role, and when the modeled system consists of many locally-interacting discrete units, such as individuals in biological populations. To model such systems, FUKS uses a novel class of tools, spatially extended dynamical systems (SEDDS), including cellular automata and lattice gas automata. SEDDS often exhibit simplicity expected from a good mathematical model, and at the same time provide realism expected from a good  simulation.
   For more details on Prof. Fuks' research, please consult his home page. [insert link]
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-------------------individual blurb (MITROVIC); include link to his home----
   There are very strong physical arguments that in systems with low superfluid density (e.g., high-T_c copper oxide superconductors and the organic superconductors) the fluctuations in phase of the superconducting order parameter play a significant role. Indeed, measurements of the ab-plane magnetic field penetration depth gave unambiguous evidence for three-dimensional XY critical scaling behavior below T_c. It is further argued that in the case of poor screening, as indicated by a low conductivity, phase fluctuations might influence the superconducting properties over a wide range of temperatures below T_c. In this scenario a low-temperature order parameter would vary linearly with T as found experimentally for the ab-plane magnetic field penetration depth.
   If, indeed, the phase fluctuations in the order parameter are responsible for the observed linear temperature dependence of the ab-plane superfluid density at low T, then recent experiments on c-axis electrodynamics in YBCO are puzzling and have to be explained. Namely, it was found that the c-axis penetration depth never has the linear temperature dependence observed in ab-plane.
   MITROVIC is examining the charging effects (i.e., the effect of the Coulomb interaction between the charge density fluctuations) on the temperature dependence of the c-axis penetration depth. Using a quantum Monte Carlo algorithm developed for this purpose, he will compute this quantity for the XY-model augmented to include the charging effects. This calculation is fundamental as it will elucidate which processes determine the electromagnetic response of high-T_c copper oxide superconductors.
   For more details on his research, please consult Prof. Mitrovic's  home page. [insert link here]
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----------------start of individual (ROTHSTEIN) blurb-------------------------
    Traditionally there is a strong interest in energy-related physical properties, such as the force constant and other spectroscopic constants. It is a challenge to estimate physical properties other than the energy, because the electron distribution obtained in quantum Monte Carlo is not sufficiently accurate for these properties. In principle, the exact electron distribution is required, the square of the unknown exact wavefunction.
   Although we do not have an analytic form for the exact wavefunction, ROTHSTEIN and others have shown how to sample it by Monte Carlo methods. Recently he developed a quantum Monte Carlo algorithm, practical for properties represented by non-differential operators, where indeed the "exact" electron distribution is sampled, with only a small error due to the incorrect nodes of the guiding function.
   His goal now is to derive within the framework of quantum Monte Carlo a systemized methodology to estimate the non-trivial electrical properties of molecules, such as high order polarizabilities and hyperpolarizabilities. He is building on previous work in his laboratory, promising higher accuracy for these properties, eventually employing methodologies to minimize the nodal error. Polarizabilities are potentially fundamental in determining the molecular geometry of products of chemical reactions, and they are basic for understanding the electrical properties of advanced electronic and photonic materials and devices, such as nanotubes.
   Accurate low-order hyperpolarizabilities are available for small systems (molecules up to 10 electrons) using analytical methods, such as coupled cluster theory. It is quantum Monte Carlo which promises an accurate and easy extension to higher-order properties for much larger systems.
   For more details on Prof. Rothstein's research, please consult his home page. [insert link here]
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   VRBIK works on new techniques of Celestial Mechanics (the perturbed Kepler problem in particular), utilizing an older idea of regularizing and linearizing the corresponding equation by means of Quaternion algebra. This approach was extensively explored about 25 years ago, but only one of its many advantages was clearly demonstrated when the new equation was numerically integrated (little progress in solving it analytically). He has developed an analytical (even though perturbative) approach to solving this problem, which relies heavily on symbolic-algebra computations. One can also extract a semi-analytic solution (Fourier series with numerical coefficients); this approach being as computationally demanding as the symbolic one (thousands of terms required for each quantity).
   VRBIK has demonstrated the technique's feasibility and usefulness in a variety of problems (lunar theory being the standard benchmark); its advantages becoming apparent mainly for resonant forces (e.g. creation of Kirkwood gaps), since it alleviates the issue of small divisors.
   For more details on Prof. Vrbik's research, please consult his home page [insert link here]

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   As symmetries of a Riemannian space are associated with conservation laws of geodesic motion in this space, the investigation of Killing vectors and Killing tensors is a central task in General Relativity
in classifying and understanding a spacetime.
   Killing tensors are important too for polynomial first integrals of motion described by Hamiltonian functions and for the separability of
Hamilton-Jacobi equations in classical mechanics. An algorithm
developed by WOLF allows to write the integrability conditions of the
partial differential equations which determine Killing tensors as a
system of linear algebraic equations. The solution of these algebraic
systems was prevented so far by their size. New algorithms and
implementations to solve such linear algebraic systems shall make the effective computations of Killing vectors and tensors possible.
   For more details on Prof. Wolf's research, please consult his web page. [insert link here; under construction]

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   Fundamental physical phenomena have mathematical descriptions in terms of equations. The objective of computational mathematics is to study certain mathematical aspects of these equations and their solutions. The results are intended to deepen the mathematical knowledge of such equations and thereby carry across to physical understanding of phenomena that the equations describe. In addition to applications in the physical sciences, such as in material sciences, computational mathematics has been applied to finance, biology, and system analysis.
   A Webster-type definition of combinatorics is the study of the arrangement of, operations on, and selection of discrete mathematical elements belonging to finite sets or making up geometric configurations. Combinatorical methods have been used to solve problems in telecommunications and networking, cryptography, and in the design of computers.
   Waves, light, gravity, and elementary particle interactions are described mathematically in terms of wave equations and physical field equations. The purpose of ANCO's, CRAIG's, and WOLF's research is to study these kinds of equations, focusing in particular on symmetries, conservation laws, and properties of their solutions.
   When transmitting data, errors can occur due to noise. To deal with this, error-correcting codes are used in many areas including for satellite transmissions and to store data on compact discs. These codes do not exist for all possible sets of parameters, so we may have to perform a search to determine if that code exists, and if so, determine its structure. This process is called a combinatorial search. The objective of HOUGHTEN and OMBUKI research is to develop efficient algorithms and programs to use in those searches.
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   It is widely recognized that symmetries and conservation laws are central to many aspects of analysis of differential equations. ANCO's work involves both development of calculational techniques in this area, and applications of the techniques to particular ordinary and partial differential equations arising in applied mathematics. In general the calculation of symmetries and conservation laws is a very non-trivial computational problem, but in recent years the development of powerful computer algebra programs has opened the door to tackling the necessary calculations.
   For more details on Prof. Anco's research, please consult his home page. [insert link here]
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--------------start of individual (CRAIG) link--------------------------------

   CRAIG is the director of the Applied and Industrial Mathematical Sciences (AIMS) Laboratory at McMaster University. One major undertaking of the AIMS Lab is to establish a resource centre with infrastructure for scientific calculations, providing a high performance computing environment, state of the art scientific computational facilities, and a flexible laboratory space for collaborative scientific work.  The research activities of the AIMS Lab will consist of mathematical modelling of processes of interest to the physical and engineering sciences, most often in terms of partial differential equations, and the subsequent mathematical analysis, asymptotic analysis, and numerical simulations of solutions of these differential equations.
   For more details on Prof. Craig's research, please consult his web page. [insert link here; @mcmaster]

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---------------start of individual (HOUGHTON) link--------------------------
   In a combinatorial search HOUGHTON is attempting to determine the existence of, or enumerate all, combinatorial objects with a given set of parameters. A major difficulty in these searches arises from the potentially huge search space and thus the use of computers is generally a necessity if one wishes to complete such a search accurately and in a reasonable amount of time. Due to the mathematical nature of these objects, one must employ mathematics to understand the nature of the problem, and thus sufficiently restrict the search space. The use of efficient algorithms is not only of great importance, but also their design is of interest in its own right.
   For more details on Prof. Houghton's research, please consult her home page. [insert link here]
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-------------------start of individual (OMBUKI) link-------------------------------------------
PARALLEL COMBINATORIAL OPTIMIZATION TECHNIQUES In real-life, we encounter combinatorial optimization problems in various situations. The potential of providing innovative solutions for combinatorial optimization problems by using modern meta-heuristics such as simulated annealing, tabu search and genetic algorithms (GAs) has been examined. However, due to the complexity due to conflicting constraints and huge search spaces by combinatorial optimizations problem instances, there is need to consider high-performance combinatorial optimization based on parallel and distributed environments.
   The recent advancement of parallel processing allows one to execute efficiently with low cost optimization algorithms on parallel processing platforms (multi-processor systems). OMBUKI is investigating parallel cooperative searching with meta-heuristics as an efficient approach for solving complex combinatorial optimization problems.
   For more details on Prof. Ombuki's research, please consult her web page. [insert link here; it will be under construction]
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---------------------start of individual (WOLF) link--------------------------------------

   A major part of WOLF's work is the development of computer algebra algorithms which are applicable for the solution and study of systems of ordinary and partial differential equations. The algorithms are designed for solving over-determined systems of partial differential equations, often very large, as arise in the calculation of symmetries, conservation laws and variational principles, and linearization integrability for differential equations. This work is helping to provide a powerful, general-purpose tool useful in many areas of differential equations. The development of algorithms goes hand in hand with work on  specific applications, which on one hand specify requirements for the algorithms and which are also a good test bed for the algorithms and the programs developed. It is planned to collaborate with S. ANCO on such problems.

   An interesting new area is the use of stochastic optimization techniques for solving combinatorial problems. Such techniques like genetic algorithms and simulated-annealing are known to be useful to find approximate solutions for very hard optimization problems but so far they have not been used to solve combinatorial problems with singular discrete exact solutions.
   For more details on Prof. Wolf's research, please consult his web page. [insert link here]
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----------------------start of SM&AI team link here-----------------------
   The term "simulation" applies narrowly to molecular dynamics, molecular mechanics, and the Monte Carlo method, but more broadly also to virtual environments and to animation. Through simulation one can visualize concepts which are not obvious by using traditional methods; through modelling one can experiment with physical models to arrive at desired outcomes quickly, safely, and inexpensively. Related to this is the field of artificial intelligence, which is concerned with developing software that accomplishes tasks normally identified as requiring human intelligence, but are infeasible to accomplish in reasonable time using human effort. These fields have the potential to play a central role in the design of new materials and industrial processes and in advancing our understanding of physical and biological processes.
    Complex natural systems may be modelled as dynamical systems. These models find many practical applications ranging from fluid dynamics to biology and social sciences. The goal of FUK's and GRAIG's research is the development of mathematical tools to help understand these dynamics. This will allow better prediction and control methods for complex systems, such as traffic flow, data networks, or growth and self-organization of semi-conduction surfaces.
   Mobile cooperative robotic teams can be considered to be a kind of distributed computing system cooperating in execution of some common tasks, reacting dynamically to changes in their environment. This is a multidisciplinary topic involving wireless communication, mobile computing and robotics. Using simulation methods, BARCHANSKI will discover team performance in different circumstances, and moreover to find the robots' optimal communication range, optimal number of robots for different tasks, and the influence of robot learning algorithms on a team performance.
   Antibodies are an important defense mechanism in the body. GORDON's research will contribute important knowledge for the successful engineering of novel antibodies for the purposes of medical diagnostics or clinical applications. Her goal is to better understand how the three-dimensional shape of antibodies contributes towards their ability to recognize, bind, and thus target antigens for destruction. She is doing this by using computer simulations to study how the inherent flexibility of the active site of the antibody, called the hypervariable loop region, influences the antibody's binding capabilities.
   Photosynthesis provides the energy required for life on earth via the conversion of absorbed sunlight into chemical energy, a process called photochemistry. Although much of the mechanism of photosynthesis is understood, some of the important details of light capture and energy conversion are still missing. BRUCE is modelling one of the most important regulatory mechanisms in photosynthesis, the one responsible for determining the efficiency of photosynthetic energy conversion under natural conditions.
   Multiprocessing and distributed computer systems are becoming increasingly common. Unfortunately, software intended for such systems is difficult to write, since multiprocessing systems are inherently complex to control. ROSS's research focuses on the application of an aspect of artificial intelligence technology, called genetic programming, to the complex domain of concurrent computation.
   NONE of the known techniques in Artificial Intelligence or the whole Computer Science (like genetic learning, temporal difference learning, machine learning of any form, pattern matching, tree searching, parallel computing (even with say 10^6 computers), purpose build hardware,...) can offer any breakthrough, the problem of Computer Go. This demands the development of new methods and techniques. This is principally different from other games, like chess. WOLF's research has the objective of designing computer Go programs, which currently perform only at the beginners level, which overcome the challenges presented by this game.
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   BARCHANSKI is interested in mobile cooperative robotic teams. Such teams can be considered a kind of distributed computing systems cooperating in execution of some common tasks and reacting dynamically to changes in their environment. This is a multidisciplinary topic involving wireless communication, mobile computing and robotics.
While experimentation with real mobile robots would be his preference, it is very expensive and time consumming. He is preceding it therefore with extensive simulation study.
   In his previous work BARCHANSKI proposed some algorithms for inter-robot communication which he would like to study using simulation. It would give him an idea about their performance in different circumstances. He would like moreover to use simulation to find an optimal communication range of robots' transmission when using single-hop or multi-hop routing, optimal number of robots for different tasks and influence of robot learning algorithms on a team performance.
   For more details on Prof. Barchanski's research, please consult his web page. [insert link here]
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-------------------------start of individual (BRUCE) link----------------------
   BRUCE's research which is most relevant BISC is a novel implementation of genetic algorithms in the construction of a model for water oxidation in photosynthesis.  He has also modelled excited state dynamics amongst the 36 chromophores of photosystem II whose positions and orientations have recently been identified by X ray crystallography.  His laboratory is embarking on molecular dynamics modelling and further excited state modelling of site directed mutants of photosystem II which will affect specific chromophore position and orientation.  Promising “model mutants” will then be constructed and experimental data taken to test against the molecular dynamic and excited state models.  The goal is to identify key chromophores in the regulation of photosynthetic efficiency.
   For more details on Prof. Bruce's research, please consult his web page. [insert link here]
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----------------------------start of individual (GORDON)-------------------------------------
   Antibodies constitute an important part of the defense mechanism in the body. GORDON's research is contributing important knowledge for the successful engineering of novel antibodies for the purposes of medical diagnostics or clinical applications. Her goal is to better understand how the three-dimensional shape of antibodies contributes towards their ability to recognize, bind, and thus target foreign substances (antigens) for destruction. She is doing this by using computer simulations to study how the inherent flexibility of the active site of the antibody, called the hypervariable loop region, influences the antibody's binding capabilities. Most theoretical and molecular modelling studies of uncomplexed antibodies have focussed on locating single low energy conformations of the six hypervariable loops comprising the antigen recognition and binding site.  However, experimental results show that the antigen binding site is inherently flexible and cast doubts that antibody selectivity and specificity is attributable to a single low energy conformation of the hypervariable region.
   While some computational work has been done to describe local dynamical behaviour of hypervariable loops using molecular dynamics (MD) simulations, the simulation lengths required for a complete description of the equilibrium conformational distribution are not achievable.  GORDON's efforts are focussed on developing Monte Carlo sampling algorithms that will more efficiently sample conformational space of peptide loops, such that the conformation distribution of the hypervariable region will be described.  This will be necessary in order to correlate hypervariable loop flexibility with antibody specificity and selectivity.  The groundwork is then prepared for computational experiments that will evaluate the effectiveness of point or multiple residue 'virtual' mutations on antibody activity.  Thus the computational methods developed by her research will be of tremendous use to the emerging Canadian biotechnology sector.
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-------------------------------start of individual link (ROSS)----------------------------------
   ROSS' main area of interest in evolutionary computation is formal language induction. He is interested in synthesizing formal grammatical systems from examples of their intended behavior. For example, he has done work in evolving concurrent systems implemented as process algebra. He has also investigated the induction of stochastic regular expressions from probabilistic examples of their behavior. Recently, he used this stochastic regular expression language to evolve motifs for protein sequences, as obtained from databases such as PROSITE.

In all this work, the machine learning paradigm ROSS is using is genetic
programming (GP). GP is an evolutionary computation technique in which computer programs are evolved using an algorithm inspired by Darwinian evolution. GP has a proven track record of being highly applicable to a variety of different nontrivial problems.

ROSS' future research plans entail continuing the application of GP towards language induction. He is interested in refining the evolution of stochastic regular motifs. Preliminary results are very promising, and he feels they can be improved further by enhancing the methods for optimization, training, and motif language definition. I also intend to study the evolution of a variety of concurrent systems, using specialized process algebra.
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-------------------------start of individual (ROTHSTEIN)---------------
   Recent computational and experimental biological research underline the importance of characterizing the ensemble of protein structures corresponding to local minima in the potential energy surface. New Monte Carlo algorithms are being developed to sample a wider phase space than conventional methods with the objective of identifying non-global minimum energy structures. It has been shown that protein models which accommodate a multiple-conformation native state with substantial energy fluctuations well below the unfolding transition temperature are consistent with experimental measurements. This underscores the need for refined interpretations of theoretical models taking into account native-state conformational diversity.
   Recently ROTHSTEIN and co-workers published a novel pattern recognition technique, “histogram filtering”, with which to optimize parameters in wavefunctions for use in quantum Monte Carlo simulations. Its extension to optimization problems involving Monte Carlo-generated data in computational biology is immediate and obvious. ROTHSTEIN's research is exploiting histogram filtering in conjunction with cluster analysis to a) characterize the low-energy local minimum energy structures, and b) to arrive at a complete description of the distribution of conformations for proteins, without having to take recourse to a very large number of simulated annealing runs. While currently completing this task “by hand” for a simple, yet highly-relevant model protein, he will computer-automate the procedure to make feasible its implementation on much more complicated structures, eventually with thousands of structural parameters.
   Notwithstanding advances in computer technology and geometry optimization software, due to the complexity of the problem a theoretical description of protein structure will inevitably be incomplete. The success of this research promises a method to both uncover classes of low-energy structures from a modest-sized data set and to characterize them. The resulting gain in both computational efficiency and theoretical insight will extend the frontier of computational biology problems amenable to investigation.
   For more details on Prof. Rothstein's research, please refer to his home page.[insert link here]
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-------------------------start individual (WOLF) link-------------------------------------------------

   In computer Go, local search (tactics) and global thinking (strategy) are interrelated; the game of Go  does not represent a single fight but involves a number of local fights  going on in parallel, usually interfering with each other. Thus the local  tactical search needs global strategic information, which in turn depends on the outcome of local fights. This is why Go-programs still play only at a beginners level, despite monumental effort spent by the artificial  intelligence community: genetic learning, temporal difference learning,  all forms of machine learning, pattern matching, tree searching, parallel computing, purpose build hardware, etc. The situation is in distinct contrast to chess where speed (purpose built hardware and massive parallelism) can compensate human intelligence.
   The problem of Computer Go therefore requires the development of new methods and techniques. A promising new concept WOLF would like to pursue is to characterize a game position as a dynamical system and analyze this system of equations numerically. This is a novel approach, requiring large computing power.
   In Japan Go is played by the economic, financial and political elite of the country as well as by about 7 million other people, and at least as many in Korea and China. A computer Go program with the strength of a professional player would not only represent a very large economic asset, it would even have considerable political value.
   For more details on Prof. Wolf's research, please refer to his home page. [insert link here]
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--------------------------start B team link-------------------------------------------------------------------

   Biomedical research is an information-based discipline. There is a major revolution in progress as novel experimental approaches are yielding unprecedented amounts of data. The fields of medicine, biology, and biotechnology are increasingly dependent on accessing this information.
   Bioinformatics is an interdisciplinary field at the intersection of life and information sciences which provides the tools and resources for this endeavour. Development of innovative methodologies and practical applications in this important field (such as gene discovery and genomics) are objectives of the research programs of GORDON, RICHARDS, and SKANDALIS

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   NADON is currently the Director of Informatics at Imaging Research Inc.  He has lead a team of scientists and programmers who have developed a software package designed for statistical analysis of gene expressions arrays.  This work required that novel methods be developed to address various issues in array genomics data, notably very small sample sizes, ubiquitous presence of outliers, and various sources of systematic error.
a) Nadon, R., Shi, P., Skandalis, A., Woody, E., Hubschle, H., Susko, E., Rghei, N., & Ramm, P. (2001). Statistical inference methods for gene expression arrays. BIOS 2001 International Biomedical Optics Symposium. San Jose, CA.
   b) Nadon, R., Woody, E., Shi , P., Rghei, N., Hubschle, H., Susko, E., & Ramm, P. (in press). Statistical inference in array genomics. In Daniel Geschwind & Jeffrey Gregg Microarrays for the Neurosciences: The Essential Guide. Cambridge, MA: MIT Press.
   Click here for Imaging Research's homepage. [insert link here]

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------------------------start of individual (RICHARDS) link------------------------------

   Correlating changes in patterns of gene expression that lead to different types of behaviour is the essential goal of the field of behavioural genetics, a field that is evolving from simple genetic mapping of behavioural mutants to sophisticated investigations of the influence of genes on behaviour, and of behaviour on genes.
   Learning how genes influence the expression of altruism in social sweat bees is the fundamental goal of RICHARD's research.  Halictine sweat bees are the most socially variable of any animal group, including species ranging from completely solitary to strongly eusocial, and even including facultatively social species.  In all social species, newly emerged females are ‘totipotent’, that is, capable of acting as either queens or workers.  Which caste they eventually join is strongly influenced by larval nutrition and genetic relationships among nestmates.  By observing female sweat bees from larval to early adult stages, she can investigate how changes in gene expression influence and are influenced by the development of caste-specific behaviour.  Particularly crucial is to investigate how different patterns of gene expression are associated with the expression of altruism, behaviour that typically is expressed only by workers.  Recent technological advances in honeybee genomics have created molecular tools that can be used for sweat bee studies.  At the DNA sequence level, strong similarities between honeybees and sweat bees allow her to use tools such as honeybee DNA chips to analyze gene expression patterns in sweat bees using microarray analysis.  This means that elucidating genetic mechanisms determining behaviour, especially those underlying the specialized caste behaviours of queens and workers, is an achievable and worthwhile goal.
   A second avenue of research is the use of DNA sequences for analyzing phylogenetic relationships among closely related sweat bee species, and in fact, for identification of cryptic species and subspecies, the existence of which greatly complicates the interpretation of behavioural data unless the species are confidently delineated.  DNA sequence data are also being generated for molecular evolutionary studies of evolutionary rates (in terms of DNA and amino acid substitution rates) and mutational rates of haplodiploid vs diploid insects, a new line of research recently opened up in her research group.
   RICHARDS is dealing with the mountains of microarray and DNA sequence data she is currently generating and studying.  She is focusing on the development and expression of caste-specific behaviour in social sweat bees, using microarray analyses for both intra- and inter-specific comparisons of gene expression patterns.  In honeybees and sweat bees, differences in larval nutrition appear to trigger a developmental switch that leads to the expression of the caste-specific behavioural repertoires. The timing of this switch and the nature of the genes that are turned off or on in queen- and worker-destined females will be investigated using microarray analysis of queen and worker-destined bees from early larvae to 3 days post-eclosion.  Once candidate loci have been identified, the study will be enlarged to compare pairs of solitary vs. social sibling species to determine whether particular loci may be responsible for facultative variation in sociality.
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    Nonparametric regression and ANOVA are important research directions in last 20 years with many applications. HUANG's research is developing new kinds of quantile, regression estimation, prediction and ANOVA methods. The mathematical properties of these estimators, predictors and test statistics are being studied: consistency, rate of convergence, efficiencies. She is building stochastic models based on these methods and applying them to economics, sciences, quality control, telecommunication network and biostatistics.
      Studies of truncated and censored data have important applications in biostatistics, health studies, industrial engineering and other fields. Her research will develop new nonparametric, Bayesian and likelihood methods appropriate for such data. The work also links to combinatorial occupancy models and theory.
      The above research utilizes computer simulations, bootstrap re-sampling in large data bases, and spectral analysis of time series.
   For more details on Prof. Huang's research, please consult her homepage. [insert link here]
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