Research Awards

  • Wide-Aperture Traffic Analysis for Internet Security (Kolaczyk)

    (Supported by NSF grant CNS-0905565.)
    Focus: Development of methods for identifying malicious and unwanted Internet activity, particularly low-volume activity “hidden” among normal activity.

  • Predicting Drug Mechanism via Chemo-Genomic Profiling and Sparse Simultaneous Equation Models of Gene Regulation (Kolaczyk)

    (Supported by NIH award GM078987.)
    Focus: Development of statistical and computational methods for predicting the mechanism of action of a proposed drug using gene expression profiles of drug activity. Methodology is grounded on the use of simultaneous equation models and complexity penalized inference procedures, with the aim to exploit the expected sparseness of these models.

  • Statistical Propagation of Low-Level Uncertainty to High-level Knowledge and Decision-Making in Network Information Environments (Kolaczyk)

    (Supported by ONR award N000140910654.)
    Focus: Creation of a methodological foundation, with accompanying theoretical and computational components, for propagating uncertainty from `low-level’ data sources to `high-level’ network-based knowledge tasks.

  • Functional Data Modeling of Climate-Ecosystem Dynamics (Ray)

    (Supported by  NSF Award No: #0934739)
    Focus: The goal of this project is to develop better methods for analyzing the response of vegetation to changes in climate. Satellite observations of the earth’s surface can be used to track changes in vegetation over the last three decades, and the task of relating these changes to changes in climate is both important and challenging. The tools developed in this project will also be applicable to a variety of problems at the intersection of statistics and earth system science.

  • NSF GK-12 Graduate STEM Fellows in K-12 Education GLACIER-Global Change Initiative-Education & Research (Ray)

    (Supported by  NSF Award No 0947950)
    Global-scale environmental changes are critical to our survival as they may affect the capacity of the Earth to sustain life. From the perspective of education and research, the topic of global change presents a rich domain of inquiry, exploration, and discovery at all grade levels. GLACIER’s primary goal is to provide graduate fellows a strong interdisciplinary perspective on global change research by training them to observe and analyze both physical and anthropogenic processes and their consequences at a variety of spatial and temporal scales.

  • Long and Short Memory Stationary Processes: Prediction and Estimation (Taqqu)

    (Supported by NSF Award No: 0706786)
    Focus: The project focuses on prediction and estimation problems for second order discrete – or continuous-parameter stationary random processes. These processes may display short, intermediate or long memory. The Prediction Problem can be formulated as follows: having observed part of the past, one wishes to predict the future. The goal is to describe the rate of decrease of the prediction error as the number of past observations increases. This rate will depend on the dependence structure of the underlying random process and the smoothness properties of its spectral density function.

  • Statistical Analysis of Neural Spiking Data (Eden)

    (Supported by  NSF Award No. 0643995.)
    Focus: The fundamental objective of this research is to develop a framework for the analysis of neural spike train data that incorporates dynamical state models of neural and other biological signals. This objective will be achieved by a combination of theoretical development of dynamical models and mathematical algorithms, and the application of this theory to the analysis of recorded neural data at multiple scales, including individual neurons, small microcircuits, and larger networks across brain regions.

  • Computational Methods for Transcriptional Mapping of Eukaryotic Genomes (Kon – joint with Harvard Stats, BU Med School))

    (Supported by NIH grant 1R01GM080625-01A1)
    Focus: Study of machine and statistical learning methods for analysis and prediction of DNA binding by proteins.  An intent of the work is to synergize with parallel statistical algorithms developed by project participants at Harvard Statistics and data obtained at BU Medical School – joint with C. DeLisi.

  • New Methods for Cancer Class Discovery and Prediction: Integration, Visualization.(Kon)

    (Supported by NIH grant 1R21CA13582-01)
    Focus: Development of integrative machine learning and other statistical approaches, including support vector machines, random forests, and recently developed hierarchical machine learning methods, for analysis of cancer data.  Project is integrated with data from the NIH Cancer Genome Atlas.  Applications of machine learning methodologies include combined analysis of DNA polymorphism, gene expression, and other biomarker data for prognosis in cancers, and response prediction to chemotherapy agents – joint with C. DeLisi.

  • The Numeraire in Stochastic Finance Theory (Kardaras)

    (Supported by  NSF Award No: #0908461)
    Focus: Investigation of issues related to a remarkable investment – called “the numeraire” – in financial market modeling. Specific areas include (1) deep connections between existence of the numeraire and market viability; (2) ramifications of the numeraire property in limited-information models; (3) enrichment of the list of optimality
    properties of the numeraire; (4) general equilibria in incomplete financial markets with investors possessing heterogeneous beliefs and consumption patterns; (5) well-posedness of optimization problems related to the numeraire.

  • Risk, Ambiguity and the Long Run (Guasoni)

    (Supported by  NSF Award No: #0807994 )
    Focus: optimization problems which combine uncertainty with intertemporal objectives, such as dynamic portfolio choice and derivatives pricing with several assets and state variables, for preference structures which include expected utility, recursive utility, and multiple priors.

BY AWARDING INSTITUTIONS

  • NSF
    • 0505747 Wavelet estimation of long-range dependent processes (Taqqu)
    • 0905565 TC: Medium: Collaborative Research: Wide-Aperture Traffic Analysis for Internet Security (Kolaczyk)
    • 0934739 CMG: Functional Data Modeling of Climate-Ecosystem Dynamics (Ray)
    • 0643995 CAREER: Statistical Analysis of Neural Spiking (Eden)
    • 0908461 DMS: The Numeraire in Stochastic Finance Theory (Kardaras)
    • 0807994 AMC-SS: Risk, Ambiguity and the Long Run (Guasoni)
    • 0947950 GK-12 Graduate STEM Fellows in K-12 Education GLACIER-Global Change Initiative-Education & Research
  • NIH
    • 1R01GM080625-01A1: Computational Methods for Transcriptional Mapping of Eukaryotic Genomes (Kon)
    • 1R21CA13582-01: New Methods for Cancer Class Discovery and Prediction: Integration, Visualization.(Kon)
    • GM078987: Predicting Drug Mechanism via Chemo-Genomic Profiling and Sparse Simultaneous Equation Models of Gene Regulation.(Kolaczyk)
  • ONR
    • N000140910654: Statistical Propagation of Low-Level Uncertainty to High-level Knowledge and Decision-Making in Network Information Environments (Kolaczyk)