2021-2022 Virtual Seminar Series
November 5, 2021 - May 6, 2022
Fridays 9 - 10 am PT / 12 - 1 pm ET
Data Activism and the Imagination of Biomedical Data Science
November 5, 2021 at noon ET
To kick-off the 2021-2022 Biomedical Data Science Seminar Series, Renée Cummings of UVA School of Data Science will discuss ethical AI, algorithmic justice, and advancing social justice in biomedical research. This lecture will touch on the importance of enhancing diversity, equity and inclusion in healthcare through data activism, as well as showcase some of Renée's expertise from being on the frontline of ethical AI and data science. We are looking forward to Renée's seminar and are excited to have her serve as a 2021-2022 Biomedical Data Science Innovation Lab Mentor!
Renée Cummings is an AI ethicist and the first Data Activist in Residence, at the School of Data Science, at the University of Virginia. She is also a criminologist, criminal psychologist, and therapeutic jurisprudence specialist and a community scholar at Columbia University. Advocating for AI we can trust, accountable, transparent, explainable, responsible and principled as well as more diverse, equitable, and inclusive, Renée is on the frontline of ethical AI, generating real time solutions to many of the consequences of AI and the impacts of data on society. Renée specializes in AI leadership, AI policy development, AI governance, AI risk management, AI crisis communication, building ethical AI and using AI to save lives. She is committed to using AI to empower and transform by helping governments and organizations navigate the AI landscape and develop future AI leaders. Renée is on the Board of Advisors of the Carnegie Council for Ethics in International Affairs. She’s a founding board member of the Springer journal AI Ethics. She is also on the board of Women in Voice as well as on the board of Inspired Minds, producers of the World Summit AI. Renée lectures extensively on AI and Data Ethics and contributed significantly to the creation of the first Ethical Emerging Technologist certification. A thought-leader, motivational speaker, and mentor, Renée has mastered the art of creative storytelling, science communication and deconstructing complex topics into critical everyday conversations that inform and inspire.
Full Seminar Series Schedule:
Click on the title of each seminar to add it to your calendar!
- 11/5: Data Activism and the Imagination of Biomedical Data Science by Ms. Renée Cummings, University of Virginia
- 11/12: Predictive Analytics Monitoring at the Bedside by Dr. Randall Moorman, University of Virginia
- 11/19: Introduction to use of Structured Medical Record Data by Ms. Johanna Loomba, University of Virginia
- 12/3: Ethics, Equity and Transparency in Biomedical Research and the Role of the Good Pharma Scorecard by Dr. Jennifer Miller, Yale
- 12/10: Practical Tools for Developing Ethical AI (And Why Principles Are Not Enough) by Dr. David Banks, University of California, San Diego
- 1/7: Deep Learning Techniques to Create Summary Outcome Metrics by Dr. Mark Albert, University of North Texas
- 1/14: The Secret Lives of Predictive Models by Dr. Claudia Perlich, Two Sigma
- 1/21: On the Necessity of Relational Ethics and Empathic Attunement for Data-Centric Technologies by Dr. Jarett Zigon, University of Virginia
- 1/28: Single Cell Analysis & Practice by Dr. Lana Garmire, University of Michigan
- 2/4: Ethical AI Requires Ethical Collaboration by Dr. Caitlin Donahue Wylie, University of Virginia
- 2/11: The Moral Machine?: The Bioethical Implications of Algorithmic Intelligence in Health Care by Dr. LaTonya Trotter, University of Washington
- 2/18: Developing a Data Sharing Framework that Meets Ethical and Legal Requirements by Dr. Yann Joly, McGill University
- 2/25: On Collective Wisdom When AI is Involved by Dr. Colin Allen, University of Pittsburgh
- 3/4: A Beginner's Mind by Dr. Donna Chen, University of Virginia
- 3/18: Team Science in Data Science: Bridging the Divide by Dr. Maritza Salazar Campo, University of California, Irvine
- 3/25: Challenges for Research Ethics Governance in the Era of AI and Data Science Research by Dr. Edward Dove, University of Edinburgh School of Law
- 4/1: Biophysics Informed Machine Learning by Dr. Elie Alhajjar, United States Military Academy
- 4/15: Ethical Principles for the All Data Revolution by Dr. Sallie Keller & Dr. Stephanie Shipp, University of Virginia
- 4/22: Data-driven Analysis of the Digital Marketplace for Unproven and Unlicensed Stem Cell Interventions by Dr. Leigh Turner, University of California, Irvine
- 4/29: Interdisciplinarity in the Health Sciences and the Pursuit of Health Equity by Dr. Sean Valles, Michigan State University
- 5/6: Instilling Ethical Data Science into the Undergraduate Curriculum by Dr. Brian Wright, University of Virginia
Biomedical Data Science Innovation Lab: Ethical Challenges of AI in Biomedicine
Virtual Events: October 1, 2021; April 8, 2022; May 13, 2022; June 3, 2022
In-Person Event: June 13-17, 2022
An Intensive Five-Day Workshop
Involving around 30 competitively-selected, early-career (post-doc, assistant to early associate professor level) biomedical and data science investigators, each year’s Biomedical Data Science Innovation Lab strives to develop new and bold approaches to address challenging biomedical questions for topics that could benefit from a fresh or divergent quantitative perspective. The Biomedical Data Science Innovation Lab involves an academic year-long series of stimulating online “microlab” activities aimed at examining the scale and scope of the targeted biomedical research domain, reviewing potential data science solutions, and for sparking creative thinking. Guest lectures throughout provide context, insight, and challenge participants to think deeply about how data can drive new thinking about biomedical problems. Prior topics have included mobile health, single cell dynamics, the microbiome, rural health and environmental exposures, and in 2021, the brain. Activities culminate in an intensive five-day residential workshop facilitating interdisciplinary teams toward the generation of multidisciplinary research programs. Peer and mentored feedback serve to provide critical input on projects, rigor, and polish. Prior knowledge of research at the interface of the biomedical and data science is not required; rather, candidates with either quantitative (i.e. AI, data science, mathematics, etc.) or biomedical (i.e. ethics, medical informatics, population-science, etc.) expertise who demonstrate their willingness to engage in collaborative multidisciplinary research are highly encouraged to apply. Women scientists and researchers from under-represented minorities are particularly welcome. Exemplar areas of quantitative interest are suggested in the document Quantitative Topics of Expertise Needed and biomedical interest are suggested in the document Biomedical Topics of Expertise Needed.
Facilitators and Scientific Mentors
During the Biomedical Data Science Innovation Lab, professional facilitators assist the participants and accelerate the idea formulation process, while a team of scientific experts serve as mentors and impartial referees. For a detailed definition of each specific role of the Biomedical Data Science Innovation Lab, please read section Definitions and Roles below.
Working under the guidance of these mentors, participants will form teams during the workshop to develop interdisciplinary projects to address ethics of artificial intelligence (AI) in biomedicine. The lab will include opportunities for learning about NIH and NSF funding through interaction with program officers. Teams are supported in the development of proposals for submission to funding agencies at the conclusion of the workshop.
Activities, Outcomes, and Success Stories
People often ask us ‘What is the Biomedical Data Science Innovation Lab? What happens at one? What are the outcomes?’ To provide you with answers to these questions and more, we have produced a book, which provides an overview of our Biomedical Data Science Innovation Lab activities and features the attendees and outcomes from our 2018 event on the mathematics of single cell dynamics.
List of Publications from Past Participant Projects or Teams:
- A Multipollutant, longitudinal analysis of the associations between urinary tungsten and incident diabetes in a rural population
- Bayesian model selection reveals biological origins of zero inflation in single-cell transcriptomics
- Clustering of Largely Right-Censored Oropharyngeal Head and Neck Cancer Patients for Discriminative Groupings to Improve Outcome Prediction
- Cohort-based T-SSIM Visual Computing for Radiation Therapy Prediction and Exploration
- Continuous Pain Assessment Using Ensemble Feature Selection from Wearable Sensor Data
- Development and application of an evidence-based directed acyclic graph to evaluate the associations between metal mixtures and cardiometabolic outcomes
- Each patient is a research biorepository: informatics-enabled research on surplus clinical specimens via the living BioBank
- Emerging Priorities for Microbiome Research
- Evaluating the Effect of Right-Censored End Point Transformation for Radiomic Feature Selection of Data From Patients With Oropharyngeal Cancer
- Hybrid Statistical and Mechanistic Mathematical Model Guides Mobile Health Intervention for Chronic Pain
- Improving Pain Management in Patients with Sickle Cell Disease from Physiological Measures Using Machine Learning Techniques
- Interdisciplinary data science to advance environmental health research and improve birth outcomes
- Measuring Pain in Sickle Cell Disease using Clinical Text
- Mobile Monitoring of Traumatic Brain Injury in Older Adults: Challenges and Opportunities
- Periphery Plots for Contextualizing Heterogeneous Time-Based Charts
- Precision Risk Analysis of Cancer Therapy with Interactive Nomograms and Survival Plots
- Precision toxicity correlates of tumor spatial proximity to organs at risk in cancer patients receiving intensity-modulated radiotherapy
- Precision VISSTA: Bring-Your-Own-Device (BYOD) mHealth Data for Precision Health
- Sex as an Effect Modifier of the Association Between Co-Exposure to Multiple Toxic Metals and the Risk of Type 2 Diabetes
- Understanding patterns and correlates of daily pain using the Sickle cell disease Mobile Application to Record Symptoms via Technology (SMART)
- Use of Mobile Health Apps and Wearable Technology to Assess Changes and Predict Pain During Treatment of Acute Pain in Sickle Cell Disease: Feasibility Study
Review of applicants will be rigorous and not all candidates will be selected to attend. A particular emphasis will be placed on candidates demonstrating a strong commitment to collaborative/team science and those with a high impact track record of peer reviewed publications. A committee will select approximately 30 of the applicants to take part in this one-of-a-kind event. Travel and lodging expenses are fully covered for all participants. All participants must commit to engaging in all of the virtual "microlabs", as well as staying for the entire duration of the 5-day event in June, 2022.
Applicants will be accepted on a rolling basis with final applications due by 11:59PM ET, January 31, 2022.
Please note that your application submission cannot be saved and returned to at a later date. Click here to preview application questions.
2021 Biomedical Data Science Innovation Lab: Challenges in Brain Analytics and Data Integration
Virtual program starting October, 2020 - Culminating in a 5-day virtual event from June 21-25, 2021
The goal of the 2020-2021 Biomedical Data Science Innovation Lab was to foster the formation of new interdisciplinary collaborations which will generate creative strategies for addressing data science challenges arising from the use of large-scale data collected from the in vivo and ex vivo brain. Such challenges arise from multifaceted data structures like networks, maps, and gaps (e.g. missing data) or sparsity of data. The brain is recognized as a major source of microscopic, systems-level, spatial, and temporal datatypes in health as well as in disease. This Biomedical Data Science Innovation Lab aimed to highlight the challenges of working with such datatypes and how data might be integrated to gain insights into brain form, function, and connectivity and the understanding of major clinical disorders. It is anticipated that inter-disciplinary collaborations formed during the Data Science Innovation Lab will result in new NIH and/or NSF grant proposals to further develop, refine, and test hypotheses and projects ideas.
Meet the Mentors of the 2021 Biomedical Data Science Innovation Lab
Donald Brown, PhD
Dr. Donald Brown is the Founding Director of the Data Science Institute at the University of Virginia, the Co-Director of the Integrated Translational Health Research Institute of Virginia (iTHRIV), and Quantitative Foundation Distinguished Professor and Professor of Data Science within the School of Data Science. Dr. Brown's research interests includes Data Fusion, Knowledge Discovery, and Simulation Optimization. Watch Dr. Brown discuss Data Science in the kickoff of the 2020-2021 Biomedical Data Science Seminar Series.
Chongzhi Zang, PhD
Dr. Chongzhi Zang is an Assistant Professor at the Center for Public Health Genomics, University of Virginia School of Medicine. Dr. Zang's research focuses on developing computational and statistical methods for analyzing high-throughput data from innovative omics technologies and using integrative data science approaches to study gene regulation in mammalian cell systems. Watch Dr. Zang discuss data science approaches to regulating gene expression.
Aidong Zhang, PhD
Dr. Aidong Zhang is a William Wulf Faculty Fellow and Professor of Computer Science and Biomedical Engineering at University of Virginia and is also affiliated with the Data Science Institute at University of Virginia. Dr. Zhangs research interests focus on data mining, machine learning, bioinformatics and health informatics.