SCIENTIFIC ACTIVITIES - Fields Institute - University of Toronto

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Distinguished Lecture Series in Statistical Sciences JIANQING FAN IS WELL-KNOWN FOR HIS WORK in financial econometrics, computational biology, semiparametric and nonparametric modeling, and other aspects of statistical theory and methodologies. He is the Frederick I. Moore Class of 1918 Professor in Finance, Director of the Committee of Statistical Studies at Princeton, the Past President of the Institute of Mathematical Statistics, and winner of the 2000 COPSS Presidents’ Award. In early May, Jianqing gave the Distinguished Lecture Series in Statistical Science at the Fields Institute. His two lectures, titled Vastdimensionality and sparsity and ISIS: A vehicle for the universe of sparsity, focused primarily on high-dimensional statistical modelling and feature selection. These aspects became important with the advent of mass data collection, advances in computation, and the discovery of new interplay between various natural and social sciences. In his public lecture, Fan outlined the problem of high dimensionality in fields Coxeter Lecture Series THE 2010 SUMMER THEMATIC PROGRAM ON THE Mathematics of Drug Resistance in Infectious Diseases was held at the Fields Institute during July and August. In association with this thematic program, Professor Neil Ferguson was invited to the Fields Institute to deliver the Coxeter Lecture Series on Mathematical modelling of emerging infectious disease epidemics and their control. Ferguson, a Professor of Mathematical Biology in the Division of Epidemiology, Public Health, and Primary Care of the Medical School at Imperial College, is a world leader in the use of mathematical models in infectious disease epidemiology. He is the Director of the MRC Centre for Outbreak Analysis and Modelling. In the first lecture, Ferguson reviewed the development of outbreak modelling over the last two decades and discussed the drivers which lead to more complex computational simulations being increasingly used replacing simpler compartmental models of disease transmission. The second lecture discussed ways in which modelling can be optimally used to assist public health policymakers in their planning for and reaction to emerging infectious disease threats—an issue on which Ferguson is an expert, and which was 14 FIELDSNOTES | FIELDS INSTITUTE Research in Mathematical Sciences Vast-dimensionality and Sparsity as diverse as bioinformatics, genetics, physics, and economics, discussing the twin challenges of noise accumulation and spurious correlations. The notion of sparsity or, more generally, homogeneity, was methodically described for feasible inference to high-dimensional problems. After an overview of the penalized likelihood approach for variable selection, Fan communicated the idea of large-scale screening and moderate-scale selection together with conditional inference as an effective solution to highdimensional problems. Using this approach in an analysis of U.S. housing price indices over a 30-year period, Fan demonstrated impressive prediction improvements. His second presentation was meant for a more specialized audience. Exploiting sparsity, Fan outlined a unified framework for solving high-dimensional variable selection problems. He applied iterative vast-scale screening followed by moderate-scale variable selection, resulting in a process called ISIS. Applying this process to multiple regression, generalized linear models, survival analysis, and machine learning, Fan demonstrated its overall reach via marginal variable screening and penalized likelihood methods. With tailored simulation studies and manipulation of empirical data from disease classifications and survival analyses, Fan demonstrated the advantages of a folded-concave over convex penalty method on sure screening properties, false selection sizes and model selection consistency. Elif Fidan Acar (Toronto) Mathematical Modelling of Emerging Infectious Diseases of great interest to the thematic program participants. The third lecture focused on the potential impact of antiviral resistance during an influenza pandemic. He offered several explanations for new findings that show the degree to which previous risk assessments concerning antiviral resistance in influenza pandemics have been over-pessimistic. In the lecture, Ferguson touched on the critical issue of the dependence of the final impact of resistance during a closed epidemic on the transmissibility of a sensitive and resistant virus, the mutation rate from one type to the other, and the level of seeding of both viral types at the beginning of the epidemic. He argued that resistance is not likely to entail a substantial reduction of effectiveness of antivirals during the start of a pandemic, but that intensive drug use in this phase can lead to a higher degree of resistance in later epidemics. His concluding remark that “simple models suggest antiviral resistance could be a major issue in the first wave of a new pandemic, but allowing for spatial heterogeneity reduces speed of resistance” strongly echoed the theme of transmission heterogeneity of the two-week block of this entire thematic program on mathematics for drug resistance. Jianhong Wu (York)
Optimization and Data Analysis in Biomedical Informatics CENTRE FOR MATHEMATICAL MEDICINE DATA MINING AND BIOMEDICAL SCIENCE ARE TWO of the fastest growing areas of engineering and scientific computing. Modern data acquisition protocols have generated vast amounts of data that require fast and rigorous analysis methods. New challenges and problems are being posed for applied mathematicians, statisticians, computer scientists, and engineers. The workshop on Optimization and Data Analysis in Biomedical Informatics explored recent progress in the field. Several challenges and roadblocks in biomedical informatics with reference to the application of data mining were discussed, and new computing technologies and paradigms in optimization and control, as well as systems engineering were presented. During the two days of the workshop distinguished speakers from universities in Canada, the U.S., and Europe gave presentations about the state-of-the-art in data analysis methodologies. Specific talks related to machine learning (e.g. support vector machines, biclustering, generalized eigenvalue classification), optimization, biomedical information systems, biomedical imaging, and signal processing were given. The applications presented at the conference covered a large part of biomedicine and included DNA microarray analysis, Raman spectroscopy, biomarkers, electroencephalogram analysis, and radiation therapy treatment. The conference attracted the interest of many local faculty members and students from both the University of Toronto and the University of Waterloo. The organizers were very satisfied with the high quality of the talks. This workshop provided an excellent chance to meet and interact with scientists from very diverse backgrounds ranging from mathematics, electrical engineering, and industrial and systems engineering, to biology, medicine and information technology. Selected papers based on the talks given at the workshop will be published as a Fields Communications volume by the American Mathematical Society. Tom Coleman (Waterloo) FIELDS FIELDS INSTITUTE INSTITUTE Research Research in in Mathematical Mathematical Sciences Sciences | FIELDSNOTES FIELDSNOTES 15 15
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