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  1. 01 - Monday December 02, 12222
  2. Statistics in Drug Research: Methodologies and Recent Developments - CRC Press Book
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I work on the theoreticalanalysis of computationally efficient methods for large or otherwise complex prediction problems. One example is structured prediction problems, where there is considerable complexity to the space of possible predictions. Such methods are important in a variety of application areas, including natural language processing, computer vision, and bioinformatics.

A second area of interest is the analysis of prediction methods in a deterministic, game-theoretic setting. As well as being of interest in areas such as computer security, where an adversarial environment is a reasonable model, this analysis also provides insight into the design and understanding of prediction methods in a probabilistic setting.

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A third area of interest is the design of methods for large scale sequential decision problems, such as control of Markov decision processes. Again, computational efficiency is a crucial requirement. This is a common feature in all of these areas: the interplay between the constraint of computational efficiency and the statistical properties of a method.

My main theoretical interest is in understanding why we are able to do statistics as well as we do on very high dimensional datasets without knowing much, even though least favorable malicious God formulations suggest we should not be able to do anything. Currently this has led me to focus on estimation of covariance matrices and their eigenstructures in high dimensions.

01 - Monday December 02, 12222

Parallel applied interests are in:. Random process theory and data analysis, risk analysis, spatial-temporal trajectory modeling, sports statistics, applications to ecology, forestry, marine biology, neuroscience, seismology and engineering. My recent research focuses on statistical methods for random processes, random process data analysis and applications in engineering and science generally. Particular topics include: modelling the motion of animals and other entities and risk analysis for earthquakes, wildfires, floods, and similar phenomena. Statistical design of experiments, originated from agricultural applications, is used extensively in a wide range of scientific and industrial investigations.

Experiments need to be properly designed so that valid information can be extracted at a lower cost. I am interested in efficient experimental designs and the related construction and combinatorial problems. Currently, I work mostly on design of experiments in the situation where the response depends on a large number of factors variables , the so called factorial design.

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When a large number of factors have to be studied, but the experimental runs are expensive, it is not feasible to observe all possible combinations of the factors. One aspect of my research deals with how to choose a "good" small subset of the factor combinations. There are interesting connections with combinatorics, coding theory and finite geometry. My research and teaching activities concern the development and application of statistical and computational methods to address problems in biomedical and genomic research.

The statistical inference questions are truly multivariate and involve the joint analysis of multiple, diverse, and high-dimensional datasets. High-throughput assays such as microarrays and next-generation sequencers allow biologists to monitor expression levels for entire genomes. A challenging task is to relate these genome-wide genotypes to biological and clinical covariates e. Motivated by these biological challenges, my methodological research interests fall in the following two areas: loss-based estimation with cross-validation parametric and non-parametric density estimation and regression, variable selection and resampling-based multiple hypothesis testing.

High-dimensional statistics, random matrices, high-dimensional robust regression, high-dimensional M-estimation, the bootstrap and resampling in high-dimension, limit theorems and statistical inference, applied statistics. Recent interests include auction theory from the bidder standpoint.

I am a probabilist and statistician working in the general area of stochastic processes and their applications. In the past, I have collaborated with Persi Diaconis and others on random matrices and various other aspects of probability on algebraic structures. I have numerous publications with Martin Barlow, Ed Perkins, Klaus Fleischmann, Tom Kurtz, Xiaowen Zhou, and Peter Donnelly on Dawson-Watanabe superprocesses and other measure-valued processes that arise in population biology, as well as with Jim Pitman on various coalescent models that appear in biology, chemistry and astrophysics.

I share an ongoing interest in biodemography with David Steinsaltz and Ken Wachter that has resulted in papers on fitness landscapes, mutation-selection balance, stochastic PDE models of bacteria and yeast aging, and applications of quasistationarity to mortality modeling. I continue research on probability and real trees, particularly applications of ideas from metric geometry such as the Gromov-Hausdorff metric, some of it in collaboration with Tye Lidman, Jim Pitman, and Anita Winter. I am investigating tree statistics and most recent common ancestors in diploid populations with Erick Matsen.

Monty Slatkin and I are researching allele frequency spectra for time-varying population sizes. I am in the middle of an extensive project involving Tandy Warnow, Don Ringe, Luay Nakhleh, and Francois Barbancon on several aspects of phylogenetic inference - particularly applications of computational phylogenetic methods in historical linguistics. I currently have students working on stepping stone models and coalescent sticky flows, the population genetics of hybrid zones, random matrices associated with Coxeter groups, random matrices arising from random trees and random networks, infinite-dimensional dynamical systems applied to mutation-selection balance, and connections between matrix-valued orthogonal polynomials and queuing theory.

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Models of Percolation, Phase transitions in Statistical Mechanics, Mixing time of Markov chains, Random walk on graphs, Counting problems in non-linear sparse settings. Financial economics, statistical evaluation of investment strategies, asset allocation, credit and counterparty risk, socially responsible investing, tax-aware investing, causal inference, random matrix theory, sports statistics.

High dimensional and integrative genomic data analysis; Network modeling;Hierarchical multi-label classification; translational bioinformatics. My research areas are in Computational Biology and Applied Statistics. Particularly, I am interested in solving practical problems in emerging bio data-intensive systems, and in understanding and developing theoretical principles of the practical methods. My current focuses are: 1 develop statistical methods that provide a consistent formulation between the statistical modeling and the biological nature of data, 2 understand and solve the problem of unreliable estimates in analyzing high dimensional structured data, and 3 tackle the challenges posed by the high level of noise and the lack of reproducibility in the datasets from different resources.

Infectious diseases, specifically HIV; chronic disease epidemiology; environmental epidemiology; survival analysis; human rights statistics. As a biostatistician, my research focuses on the application of novel statistical techniques to the design, analysis and interpretation of data arising from public health and human disease studies.

From a statistical point of view I am interested in survival analysis-- particularly current status data, statistical methods for epidemiology--particularly for i infectious diseases and ii environmental exposures, causal inference in intervention studies, survey methods and longitudinal data analysis, and applications of statistics to molecular and cell biology. My work is motivated by application of such statistical techniques most recently to studies of HIV disease and AIDS including intervention trials to reduce HIV transmission in Africa, the impact of pesticide exposure to pregnancy outcomes and infant neurodevelopment, measuring key factors in epidemic growth in situations such as SARS, assessing drug safety with particular interest in the adverse cardiovascular side effects of Cox-2 inhibitors, the measurement of PBDEs in peregrine falcon eggs in California, and the assessment of civilian casualties in times of conflict.

I have interests that span the spectrum from theory to algorithms to applications. I'm most interested in problems that arise when working with non-traditional data types; examples I've worked with include document corpora, graphs, protein structures, phylogenies and multi-media signals. Working with these kinds of data types often leads one to work on problems of an unusually large scale, where classical methods can be infeasible on computational grounds.

I've thus been interested in new computational methods for large-scale problems; specifically I've worked on the development of novel estimators using tools from constrained optimization theory and convex analysis. I'm also interested in the interface between probability theory and nonparametric statistics, particularly in the setting known as "nonparametric Bayes", where the prior distribution is a general stochastic process.

Statistics in Drug Research: Methodologies and Recent Developments - CRC Press Book

Here ideas familiar in modern probability theory, such as the Chinese restaurant process and stick-breaking distributions, yield novel statistical models and novel inference procedures. These methods have numerous applications in areas such as statistical genetics, image processing and natural language processing. I'm quite interested in pursuing these applications, particularly in collaboration with biologists and computer scientists. My research interests are in probability theory. I have done research on sums of independent random variables, laws of the iterated logarithm, approximations of tail probabilities, operator limit theorems for sums of independent random vectors, expectations of functions of sums of fixed and random numbers of random variables, as well as arbitrary self-normalized sums.

My work focuses on the development and application of statistical methods in genomics.

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  • Most of it concentrates on making inferences regarding function and evolution from molecular and genetic data. Some of the projects that I am currently involved in are in the areas of human population genetics, comparative evolutionary genomics, coalescent theory, and statistical methods in molecular ecology. Examples include evolutionary analyses of whole genome data from a diverse set of organisms including bacteria, the Giant Panda, the Rhesus Macaque monkey, humans, and chimpanzees, development of methods for association mapping which can accommodate non-linear interactions, and the development of MCMC methods for inferring demographic parameters in population genetics.

    One of my current interests is in the area of cyber-infrastructure for education. I am also interested in problems in more traditional areas of statistics that are related to high-dimensional modeling and model selection. I have been interested in interfaces between the traditional theory of stochastic processes and other areas of mathematics, especially combinatorics. Theodore Holford, Ph. His scholarly work involves the development and application of statistical methods in public health and the training of individuals developing careers in health research.

    He developed an approach for analyzing temporal trends in disease rates using the age-period-cohort modeling framework that has be used extensively in the analysis of cancer incidence and mortality trends. Ongoing research using this model focuses on quantifying the effects of cigarette smoking policy on lung cancer mortality, as well as effects of screening and treatment. This work provides approaches for analyzing the effect of traffic related air pollution on childhood asthma. Finally, Dr. Holford has played a leading role in training both pre-doctoral and post-doctoral students in biostatistics and epidemiology, including assessment of the effects of environmental exposure on disease risk.

    He received his Ph. For pragmatic trials, he has advanced the covariate-constrained randomization techniques to design cluster randomized trials, and developed the marginal model framework for the design and analysis of complex longitudinal cluster randomized trials, including the crossover and stepped wedge cluster randomized trials.

    For observational studies, he has contributed propensity score weighting methods for causal inference with difference-in-differences designs and multiple treatments. Haiqun Lin has been a faculty member in biostatistics since upon her graduation with a PhD in Biometry and Statistics from Cornell University in Her research expertise includes longitudinal and multilevel data analysis. Prior to her education and career in biostatistics, she obtained a medical degree from Peking University Health Science Center and worked for Chinese Center for Disease Control and Prevention.

    She obtained her Masters degree in cellular and molecular biology prior to her pursuit of the PhD.

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    Ma received his Ph. Prior to arriving at Yale, Dr. He has been involved in developing novel statistical and bioinformatics methodologies for analysis of cancer NHL, breast cancer, melanoma, lung cancer , mental disorders, and cardiovascular diseases. He has also been involved in health economics research, with special interest in health insurance in developing countries.

    He also was heavily involved in HIV research from the mid 80's through the early-mid 90's. He participated on the data monitoring committee for the original AZT vs. He returned to Yale in , and has worked extensively on methodologic issues in clinical trials and large population-based studies since. Another area of current interest involves detection of rare adverse drug events, especially in the post-marketing environment. These areas of methodologic research evolved as a result of his continued interest since the mid s in regulatory affairs science.


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    In addition, Makuch developed a regulatory affairs track at YSPH for graduate and post-doctoral level students, and over the past 10 years has been the leader of more than 25 training programs for senior delegations of the Chinese Food and Drug Administration. His areas of medical application include cancer, HIV, arthritis, and cardiovascular disease.

    In , Makuch received the American Statistical Association Fellow Award for his numerous contributions to the field. He also has been a decades-long member of Phi Beta Kappa. He also developed a 5-year biostatistics training program in Japan, in collaboration with the Japanese government. Design and sample size considerations for Phase IV studies is another active research area, in which a new class of hybrid designs has been proposed for scientific and regulatory purposes to detect rare adverse events.