ABCDEFGHIJKLMNOPQRSTUVWXYZ
1
YearTalkWhereStateCountry
2
2001ESUP accept/reject samplingNC State StatisticsNCUSA
3
2001Monte Carlo exact conditional hypothesis tests for loglinear modelsAT&T Labs, Florham Park, New JerseyNJUSA
4
2001Monte Carlo exact conditional hypothesis tests for loglinear modelsFifth Workshop on Groebner Bases and Statistics (GROSTAT V), Tulane University, New Orleans, LouisianaLAUSA
5
2001Monte Carlo exact conditional hypothesis tests for loglinear modelsJohns Hopkins University Department of Biostatistics, Baltimore, MarylandMDUSA
6
2001Monte Carlo exact conditional hypothesis tests for loglinear models University of Michigan Department of Statistics, Ann Arbor, MichiganMIUSA
7
2001Monte Carlo exact conditional hypothesis tests for loglinear modelsOhio State University Department of Statistics, Columbus, OhioOHUSA
8
2002Model selection and fitting for empirical Bayes analysis of microarray dataJSM New YorkNYUSA
9
2002Ascent-based MCEMYale University Division of Biostatistics, New Haven, ConnecticutCTUSA
10
2002ESUP accept/reject samplingJohns Hopkins University Department of Biostatistics, Baltimore MarylandMDUSA
11
2003A tour of biostatisticsDrexel University Department of Mathematics, Philadelphia, PennsylvaniaPAUSA
12
2003ESUP accept/reject samplingDuke University Institute of Statistics and Decision Sciences, Durham, North CarolinaNCUSA
13
2003Missing data and air pollutionDrexel University Department of Mathematics, Philadelphia, PennsylvaniaPAUSA
14
2003Monte Carlo exact conditional hypothesis tests for loglinear modelsJoint Statistical Meetings, San Francisco, CaliforniaCAUSA
15
2003Monte Carlo exact conditional hypothesis tests for loglinear modelsStatistics and Applied Mathematical Sciences Institute, Workshop on Exact Categorical Methods, Research Triangle Park, North CarolinaNCUSA
16
2004Multilevel models with applications in genomicsUniversity of Minnesota Department of Statistics, MinneapolisMNUSA
17
2004Ascent-based MCEMCornell University Department of Statistics, Ithaca, New YorkNYUSA
18
2005Ascent-based MCEMJohns Hopkins University Department of Applied Math and Statistics, Baltimore, MarylandMDUSA
19
2005ESUP accept/reject samplingPennsylvania State Department of statistics, University, College Station, PennsylvaniaPAUSA
20
2005Discussion of: characterizing experimentally induced neuronal processing by DuBois BowmanDepartment of Biostatistics Grand Rounds, Johns Hopkins University, Department of BiostatisticsMDUSA
21
2005Quantitative characterization of chloroquine and aspirin in the male genital tractwith Craig Hendrix, Johns Hopkins Division of Clinical Pharmacology, Baltimore, MarylandMDUSA
22
2006Ascent-based MCEMDepartment of Statistics, Carnegie Mellon University, Pittsburgh, PennsylvaniaPAUSA
23
2006Is MRI based structure a mediator for lead’s effect on cognitive function MICE meeting, Welch Center for Prevention, Epidemiology and Clinical Research, Baltimore, Maryland
MDUSA
24
2007A Bayesian hierarchical framework for spatial modeling of fMRI dataCenter for Statistics in the Social Sciences, University of Washington, Seattle, WashingtonWAUSA
25
2007 A case study in pharmacologic imaging using single photon emission computed tomography UMBC Prob/Stat Day, Baltimore, Maryland.MDUSA
26
2007Age, lead exposure and neuronal volume ENAR, Atlanta, GeorgiaGAUSA
27
2007Statistical methods for indirect estimation of physiological parameters: case studies in viral kinetics}Department of Statistics University of Minnesota, Minneapolis, MinnesotaMNUSA
28
2007Statistical methods in functional medical imagingDepartment of Biostatistics, University of Florida, Gainesville, FloridaFLUSA
29
2008A Bayesian hierarchical framework for spatial modeling of fMRI dataHuman Brain Mapping, Melbourne, AustraliaASTL
30
2008Conditional and marginal models for binary outcomesDepartment of Statistics University of Minnesota, Minneapolis, MinnesotaMNUSA
31
2008Lead exposure, neuronal volume and cognitive function Department of Biostatistics University of Florida, Gainesville, FloridaFLUSA
32
2008Non-linear curve fitting in the analysis of medical imaging data
Department of Biostatistics Grand Rounds, Johns Hopkins University, Baltimore, Maryland.
MDUSA
33
2008Pharmacologic imaging using principal curves in single photon emission computed tomographyENAR, Arlington, Virginia.VAUSA
34
2008Quantifying the hypnogram and sleep stage transitions: novel approaches and applications to sleep disorders
Annual Meeting of the Associated Professional Sleep Societies, Baltimore, Maryland
MDUSA
35
2008Statistical methods for indirect estimation of physiological parameters: case studies in viral kinetics
Department of Biostatistics, Columbia University, New York, New York
NYUSA
36
2008Statistical methods for indirect estimation of physiological parameters: case studies in viral kineticsDepartment of Biostatistics, Emory University, Atlanta, GeorgiaGAUSA
37
2008Statistical methods for indirect estimation of physiological parameters: case studies in viral kinetics
Department of Biostatistics, Vanderbilt University, Nashville, Tennessee
TNUSA
38
2009Non-linear curve fitting in the analysis of medical imaging data
Center for Imaging Science, Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland
MDUSA
39
2009Non-linear curve fitting in the analysis of medical imaging data
University of Pittsburgh, Department of Biostatistics, Pittsburgh, Pennsylvania
PAUSA
40
2009On the analysis of multiple sleep hypnogramsInternational Statistical Institute, Durban, South AfricaSA
41
2009Statistical methods for studying connectivity in the human brainInternational Workshop on Statistical Modeling, Ithaca, New YorkNYUSA
42
2010Functional principal components for high dimensional brain volumetricsInternational Workshop on Statistical Modeling, Glasgow, ScotlandSCT
43
2010Statistical methods for evaluating connectivity in the human brainENAR, New Orleans, LouisianaLAUSA
44
2010Statistical methods for high dimensional imaging studies of populations
Department of Psychiatry and Behavioral Science, Johns Hopkins Bayview Medical Center, Baltimore, Maryland
MDUSA
45
2011fMRI functional connectivity in subjects at high familial risk for Alzheimer's disease: new approaches to analysisDementia Consortium, Johns Hopkins, Baltimore, MarylandMDUSA
46
2011Indirect estimation of kinetic parameters in dual isotope single photon emission computed tomography studies of microbicide lubricantsENAR, Miami, FloridaFLUSA
47
2011Statistical methods for studying connectivity in the human brainDivision of Biostatistics, University of Maryland, Baltimore, MarylandMDUSA
48
2011Statistical methods for studying connectivity in the human brain
Department of Biostatistics, University of Washington, Seattle, Washington
WAUSA
49
2011Statistical methods for studying connectivity in the human brainDepartment of Statistics, Cornell University, Ithaca, New YorkNYUSA
50
2011Statistical methods for studying connectivity in the human brainDementia Consortium, Johns Hopkins, Baltimore, MDMDUSA
51
2011An overview of EEG research at Hopkins Biostatistics
Regional EEG/ERP Conference, Kennedy Krieger Institute, Baltimore, MD.
MDUSA
52
2011Statistical methods for evaluating (human) brain connectivity
Statistical Methods for Very Large Data Sets Conference, Baltimore, MD.
MDUSA
53
2011Statistical methods for studying connectivity in the human brain
The Brad Efron Honorary Symposium on Large-Scale Inference, Silver Springs, MD.
MDUSA
54
2011Statistical methods for studying connectivity in the human brainISDS, Duke University, Durham, NC.NCUSA
55
2012Predicting neurological disorders using functional and structural brain imaging dataENAR, Washington DC.DCUSA
56
2012Predicting neurological disorders using functional and structural brain imaging dataDepartment of Statistics, University of Virginia, Charlottesville, Va.VAUSA
57
2012Panelist at the 2012 NIH/NIBIB training grantee meetingNational Institutes of Health, Bethesda, MD.MDUSA
58
2012Statistical analysis of functional MRI resting state functional brain connectivity dataSAMSI opening workshop on massive data, Raleigh, NC.NCUSA
59
2012Statistical analysis of functional MRI resting state brain connectivity data
Departments of Statistics and Biostatistics, University of Wisconsin, Madison, Wisconsin
WIUSA
60
2012Statistical analysis of functional MRI resting state brain connectivity data
Departments of Biostatistics, Yale University, New Haven, Connecticut.
CNUSA
61
2013Homotopic group ICA for resting state fMRITalk given at SAMSI 2013NCUSA
62
2013Large scale decompositions for functional imaging studiesTalk given at ENAR 2013
63
2013Measurement in medical imagingLecture given at the ICTR 2013
64
2013Graphical models for analyzing resting state networksTalk given at Penn visit
65
2012Multimodal brain imaging studies for predictionTalk given at JSM 2012 session 56
66
2012A Bayesian Hierarchical Framework for Spatial Modeling of fMRI DataTalk given at the ICSA in 2011
67
2012The Center for Quantitative Neuroscience A core for population neuroanalytics and translational systems neuroscienceTalk given at the BSI
68
2013graphical models for analyzing resting state networksTalk given at KKI in August 2013
69
2013Graphical models for analyzing resting state networksTalk given with KKI and Berkeley
70
2013Analyzing neurological disorders using functional and structural brain imaging dataTalk given at NYU
71
2013Analyzing neurological disorders using functional and structural brain imaging dataTalk given at Microsoft Research
72
2013Analyzing neurological disorders using functional and structural brain imaging dataVirginia Tech 2014
73
2014Teaching statistics for the future The MOOC revolution and beyondMOOC talk given at the Division of Biostat
74
2014Developmental Disorders and Neuroimaging: Tools, Results and IssuesTalk given at ENAR 2014
75
2014Teaching Statistics for the Future: the MOOC Revolution and BeyondTalk given at Brown
76
2014Teaching statistics for the future The MOOC revolution and beyondTalk given at the University of Maryland
77
2014Teaching statistics for the future The MOOC revolution and beyondDean's lecture giving at Johns Hopkins Bloomberg
78
2014Teaching Statistics for the Future: The MOOC Revolution and BeyondTalk given at Rochester
79
2014Analyzing Neurological Disorders Using Functional and Structural Brain Imaging DataTalk given at Duke ISBIS / SLDM meeting
80
2014Statistical methods for the study of human brain functional connectivityTalk given at JSM 2014
81
2014Teaching statistics for the future: The MOOC revolution and beyondTalk given at ISU
82
2015Analyzing Neurological Disorders Using Functional and Structural Brain Imaging DataTalk given at Penn
83
2015Teaching statistics for the future: The MOOC revolution and beyondTalk given at BME
84
2015Discussion of: Statistical Quantitative Magnetic Resonance Imaging by Dr Taki ShinoharaDiscussion of Dr. Shinohara's talk at JHU Biostat
85
2016Bar Codes, Fingerprints and Reproducibility in Functional and Structural Brain Imaging DataTalk given at the Maryland Imaging Retreat
86
2016Barcodes, Fingerprints and Reproducibility in Functional and Structural Brain Imaging Data
87
2017Links for R tutorialMRICloud R tutorial
88
2017Talk given at the malone center mix and mingle
89
2017Executive data scienceTalk given at the National Academy working group
90
2017Radiology research day talkTalk given at the JHU Radiology research dayFile if needed
91
2017Am I my connectome? Fingerprinting with repeated functional connectivity dataTalk given in Vigo
92
2017Am I my connectome? Fingerprinting with repeated functional connectivity dataTalk given at JSM
93
2017Am I my connectome? Fingerprinting with repeated functional connectivity dataTalk given at Michigan State
94
2017Dimension reduction for complex biological phenomenaTalk given at the ASA Biopharm section
95
2017Am I my connectome? Fingerprinting with repeated functional connectivity dataTalk given at the NIH
96
2017SMART group and Data Science Lab
97
2018Fingerprinting and reproducibility in resting state fMRITalk given at BME
98
2018Student recruitment 2018Student recruitment talk
99
2018Is the doctor of the future goig to be a human, robot or cyborg?Talk given at the Mayo Clinic
100
2018Specialized AI in personalized medicineDepartmental retreat presentation