• 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
  • TBD

  • Careers, Recruiting, Internships
  • 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
  • Decoding Pandemic Data: A Series of Interactive Seminars

    This seminar series brings to Brown experts who are directly engaged in COVID-related data-driven research activities. Instead of the usual long-format lecture, each seminar will feature a 20 to 30–minute presentation by the speaker, followed by a moderated question and answer period. All seminars are virtual and open to the public.


    Professor of Mental Health, Health Policy and Management, and Biostatistics and Associate Dean for Education at Johns Hopkins University



    With a variety of local, state, and even national COVID-19 related policies being implemented across the country and world, there is an opportunity to learn about the relative effectiveness of those policies, to guide policymaking during the current pandemic and beyond. However, accurately estimating policy effects is challenging. Beyond the issues that are faced for any policy, such as lack of randomization and difficulties in disentangling underlying time trends from a policy effect, evaluation of COVID-19 policies is complicated by additional challenges related to infectious disease dynamics and lags, lack of direct observation of key outcomes, and a multiplicity of interventions occurring on an accelerated time scale. This session will provide a high-level overview of the key designs and considerations when estimating policy effects, with the goal of helping attendees understand how to weigh the strengths and limitations of particular studies aiming to estimate the effects of policy interventions.


    Please RSVP below. You will then be sent the calendar invite with an attached zoom link and details. 

Decoding Pandemic Data:  A Series of Interactive Seminars:

Taking place during summer 2020, these are lunchtime short talks by experts directly engaged in COVID-related data-driven research activities, with plenty of time for question and answer. Details here.

Faculty for Faculty Research Talks:

Informal opportunities for faculty to present their data science–related research to other faculty. Our goal is to provide a networking venue that promotes research collaborations between faculty across all disciplines; awareness of the breadth of data science–related research at Brown; and a forum for faculty to share their expertise with one another. Details here.

Data Wednesdays:

Our weekly seminar, hosted by DSI, CCMB, COBRE, 4-5 pm, at 164 Angell, 3rd floor

Data Science, Computing and Visualization Workshops:

[On hiatus] Weekly at noon on Fridays; see previous topics.