Statistics Seminars Series

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Brown Statistical Seminars are hosted collaboratively by the Department of Biostatistics and the Center for Statistical Sciences to provide educational and research opportunities to graduate, undergraduate, and medical students as well as to researchers across the University.  The seminars take place on selected Mondays throughout the academic year and feature leading researchers from the US and internationally.   

To receive our seminar announcements, contact us.

 

  • Despina Kontos, PhD

    Associate Professor of Radiology

    Department of Radiology

    Perelman School of Public Health

    University of Pennsylvania

    Bio:  Dr. Despina Kontos, Ph.D., is an Associate Professor of Radiology and director of the Computational Biomarker Imaging Group (CBIG) in the Center for Biomedical Image Computing and Analytics (CBICA) at the Radiology Department of the University of Pennsylvania. Dr. Kontos received her C.Eng. Diploma in Computer Engineering and Informatics from the University of Patras in Greece and her MSc and Ph.D. degrees in Computer Science from Temple University in Philadelphia. She completed her postdoctoral training in radiologic physics and biostatistics at the University of Pennsylvania. Her research interests focus on investigating the role of quantitative imaging as a predictive biomarker for guiding personalized clinical decisions in cancer screening, prognosis, and treatment. She is leading several research studies, funded both by the NIH/NCI and private foundations, to incorporate novel quantitative multi-modality imaging measures of breast tumor and tissue composition into cancer risk prediction models.

    Title:  “Radiomic Biomarkers for Deciphering Tumor Heterogeneity”

    Abstract - Breast cancer is a heterogeneous disease, with known inter-tumor and intra-tumor heterogeneity. Established histopathologic prognostic biomarkers generally acquired from a tumor biopsy may be limited by sampling variation. Radiomics is an emerging field with the potential to leverage the whole tumor via non-invasive sampling afforded by medical imaging to extract high throughput, quantitative features for personalized tumor characterization. Identifying imaging phenotypes via radiomics analysis and understanding their relationship with prognostic markers and patient outcomes can allow for a non-invasive assessment of tumor heterogeneity. In this study, we identified and independently validated intrinsic radiomic phenotypes of tumor heterogeneity for invasive breast cancer that have independent prognostic value when predicting 10-year recurrence. The independent and additional prognostic value of imaging heterogeneity phenotypes suggests that radiomic phenotypes can provide a non-invasive characterization of tumor heterogeneity to augment personalized prognosis and treatment.

    For more information about the Statistics Seminar Series, click here .

    Biology, Medicine, Public Health, BioStatsSeminar, Graduate School, Postgraduate Education, Mathematics, Technology, Engineering, Research, Training, Professional Development
  • Stephanie Shipp, PhD

    Deputy Director and Professor

    Social and Decision Analytics Division, Biocomplexity Institute

    University of Virginia

    Abstract Title: ”Ethical Principles for the All Data Revolution”

    The data revolution is changing the conduct of research as increasing amounts of internet-based and administrative data become accessible for use. At the same time, the new data landscape has created significant tension around data privacy and confidentiality. To bridge this gap, conversations about ethics, privacy, transparency, and reproducibility need to play a prominent role in both research partnerships and policymaking. At the research level, these conversations must be translated to action. We have created a comprehensive framework that forms the foundation to data science problem solving through defining rigorous, flexible, and iterative processes where learning at each stage informs the other stages. Embedded in this framework is close attention to ethics. The Institutional Review Board structure is well known in parts of academia and industry, but our public and local government partners are not always aware of these processes. The IRB framework could help them think about informed consent and privacy, as well as ethical considerations around the benefits and risks to individuals and communities under study. Through case studies, these principles are demonstrated.

    Keywords: confidentiality, ethics, trust but verify, data science framework

    Brief Bio: Data scientists have the opportunity to use their skills to influence and improve society, especially vulnerable populations who need champions. Stephanie Shipp enthusiastically works with communities, policy makers and other data scientists who have also taken that challenge to heart.

    For more information about the Statistics Seminar Series go here .

    Biology, Medicine, Public Health, BioStatsSeminar, Graduate School, Postgraduate Education, Mathematics, Technology, Engineering, Research, Training, Professional Development
  • Mauricio Sadinle, PhD

    Assistant Professor, Department of Biostatistics

    University of Washington, School of Public Health

    Title and abstract to be posted

     

    For more information about the Statistics Seminar Series go here .

    Biology, Medicine, Public Health, BioStatsSeminar, Graduate School, Postgraduate Education, Mathematics, Technology, Engineering, Research, Training, Professional Development
  • Jared Murray, PhD

    Assistant Professor

    Information, Risk and Operations Management

    University of Texas at Austin, McCombs School of Business

     

    title and abstract to be posted

    For more information about the Statistics Seminar Series go here .

    Biology, Medicine, Public Health, BioStatsSeminar, Graduate School, Postgraduate Education, Mathematics, Technology, Engineering, Research, Training, Professional Development
  • Alekandra Slavković, PhD

    Professor, Departments of Statistics and Public Health Sciences

    Associate Dean for Graduate Education, Eberly College of Science

    Title and Abstract to be posted

    For more information about the Statistics Seminar Series go here .

    Biology, Medicine, Public Health, BioStatsSeminar, Graduate School, Postgraduate Education, Mathematics, Technology, Engineering, Research, Training, Professional Development
  • Thomas Jaki, PhD

    Professor of Statistics

    Medical & Pharmaceutical Statistics Research Unit

    Department of Mathematics and Statistics

    Lancaster University, UK

     

    title and abstract to be posted

    For more information about the Statistics Seminar Series go here .

    Biology, Medicine, Public Health, BioStatsSeminar, Graduate School, Postgraduate Education, Mathematics, Technology, Engineering, Research, Training, Professional Development
  • Juned Siddique, PhD

    Associate Professor

    Biocomplexity Institute

    Northwestern University

     Title:  “Measurement error correction and sensitivity analysis in longitudinal dietary intervention studies using external validation data”

    Abstract to be posted

    For more information about the Statistics Seminar Series go here .

    Biology, Medicine, Public Health, BioStatsSeminar, Graduate School, Postgraduate Education, Mathematics, Technology, Engineering, Research, Training, Professional Development
  • Terrance Savitsky, PhD

    Research Mathematical Statistician

    Mathematical Statistics Research Center

    U. S. Bureau of Labor Statistics

    Title:  Pseudo Posterior Mechanism under Differential Privacy

    Abstract:  We propose a Bayesian pseudo posterior mechanism to generate record-level synthetic datasets equipped with a differential privacy (DP) guarantee from any proposed synthesis model. The pseudo posterior mechanism employs a data record-indexed, risk-based weight vector with weights ∈ [0, 1] to surgically downweight high-risk records for the generation and release of record-level synthetic data. The differentially private pseudo posterior synthesizer constructs weights using Lipschitz bounds for a log-pseudo likelihood utility for each data record, which provides a practical, general formulation for using weights based on record-level sensitivities that we show achieves dramatic improvements in the DP expenditure as compared to the unweighted posterior mechanism. By selecting weights to remove likelihood contributions with non-finite log-likelihood values, we achieve a local privacy guarantee at every sample size. We compute a local sensitivity specific to our Consumer Expenditure Surveys dataset for family income, published by the U.S. Bureau of Labor Statistics, and reveal mild conditions that guarantee its contraction to a global sensitivity result over the space of databases. We further employ a censoring mechanism to lock-in a local result with desirable risk and utility performances to achieve a global privacy result as an alternative to relying on asymptotics. We show that utility is better preserved for our pseudo posterior mechanism as compared to the exponential mechanism (EM) estimated on the same non-private synthesizer due to the use of targeted downweighting. Our results may be applied to any synthesizing model envisioned by the data disseminator in a computationally tractable way that only involves estimation of a pseudo posterior distribution for parameter(s) θ, unlike recent approaches that use naturally-bounded utility functions under application of the EM.   

    (Joint work with Matthew R. Williams and Jingchen Hu)


    Keywords: Differential privacy, Pseudo posterior, Pseudo posterior mechanism, Synthetic data

    For more information about the Statistics Seminar Series go here .

    Biology, Medicine, Public Health, BioStatsSeminar, Graduate School, Postgraduate Education, Mathematics, Technology, Engineering, Research, Training, Professional Development