The virtual Seminar Series and Innovation Lab application will open Fall of 2022! More details coming soon!
2021-2022 Virtual Seminar Series
Click on the title of each seminar below to watch the lecture!
- Data Activism and the Imagination of Biomedical Data Science by Ms. Renée Cummings, University of Virginia
- Predictive Analytics Monitoring at the Bedside by Dr. Randall Moorman, University of Virginia
- Introduction to use of Structured Medical Record Data by Ms. Johanna Loomba, University of Virginia
- Ethics, Equity and Transparency in Biomedical Research and the Role of the Good Pharma Scorecard by Dr. Jennifer Miller, Yale
- Practical Tools for Developing Ethical AI (And Why Principles Are Not Enough) by Dr. David Danks, University of California, San Diego
- Utilizing Deep Learning to Create Tailored Summary Outcome Metrics for Clinical Populations by Dr. Mark Albert, University of North Texas
- The Secret Lives of Predictive Models by Dr. Claudia Perlich, Two Sigma
- On the Necessity of Relational Ethics and Empathic Attunement for Data-Centric Technologies by Dr. Jarrett Zigon, University of Virginia
- Current Practice, Challenges and Perspectives on Single Cell Data Science by Dr. Lana Garmire, University of Michigan
- Ethical AI Requires Ethical Collaboration by Dr. Caitlin Donahue Wylie, University of Virginia
- The Moral Machine?: The Bioethical Implications of Algorithmic Intelligence in Health Care by Dr. LaTonya Trotter, University of Washington
- Addressing Key Challenges in Genomic and Health Data Sharing by Dr. Yann Joly, McGill University
- On Collective Wisdom When AI is Involved by Dr. Colin Allen, University of Pittsburgh
- A Beginner's Mind by Dr. Donna Chen, University of Virginia
- Team Science in Data Science: Bridging the Divide by Dr. Maritza Salazar Campo, University of California, Irvine
- Challenges for Research Ethics Governance in the Era of AI and Data Science Research by Dr. Edward Dove, University of Edinburgh School of Law
- Data Acumen in Action by Dr. Sallie Keller & Dr. Stephanie Shipp, University of Virginia
- 4/22: Ethical Interactions in Scientific Teams as a Foundation for Ethical AI by Dr. Alison Antes of Washington University
- Interdisciplinarity in the Health Sciences and the Pursuit of Health Equity by Dr. Sean Valles, Michigan State University
- Instilling Diversity into a Data Science Undergraduate Curriculum by Dr. Brian Wright, University of Virginia
2022 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
What is the Biomedical Data Science Innovation Lab?
The University of Virginia is organizing a Biomedical Data Science Innovation Lab to foster the development of new interdisciplinary teams via a facilitated and mentored format to tackle data science challenges arising in the ethical use of biomedical artificial intelligence (AI). A more detailed description of the Lab can be found in the document Detailed Information on 2021-2022 Biomedical Data Science Innovation Lab.
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
For Selected Participants
Beginning in the first week of October 2021, our facilitation team will host a Biomedical Data Science Innovation Lab virtual orientation. Follow-up virtual "microlabs" will be held April 8, May 13, and June 3, 2022. These sessions will allow the selected participants to get to know each other, begin to think about team activities and consider the focus topic of AI and ethics. The full details of how the expanded program will take place is outlined in the 2021-2022 Biomedical Data Science Innovation Lab Details.
Application Procedure
To be eligible to apply, candidates must be in the quantitative and data sciences or biomedical fields, specifically involving AI methods or ethics, and at the late-stage post-doctoral and early-stage junior faculty levels. A broad diversity of backgrounds is welcomed with women and underrepresented scientific communities being strongly encouraged to apply. The application will ask candidates to describe their background, research, interests in the intersection of the ethics of AI in biomedicine and data sciences, and their commitment to collaborative/team science within the application. Click here to preview application questions.
Quantitative and data science researchers should provide examples of the types of data science approaches, methods, techniques and the potential to utilize these techniques in diverse research areas wherein AI might be applied.
Biomedical researchers should justify their research focus and be able to leverage data relevant to the ethics of AI in biomedicine.
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 are now closed. Thank you for your submission.
Timeline and Key Dates:
- Selection committee starts reviewing applications on a rolling basis starting August, 2021
- First round of invitations to selected candidates sent out by early September, 2021
- Orientation: October 1, 2021
- Final application deadline: January 31, 2022
- Final round of invitations to selected candidates sent out by early March, 2022
- Microlab: April 8, 2022
- Microlab: May 13, 2022
- Arrange for travel by May, 2022
- Microlab: June 3, 2022
- Arrive at Charlottesville, VA on June 12, 2022
- In-person event: June 13-17, 2022
Definitions and Roles:
Participants: These are persons specifically selected, at the junior faculty level, who are seeking to engage with new, multidisciplinary teams to take on interesting, data-driven challenges in modern biomedicine. We consider advanced post-doctoral fellows, who have a job waiting for them to be eligible. Such folks are the life-blood of a Biomedical Data Science Innovation Lab – they bring ideas, data, creativity, enthusiasm, and team spirit to the workshop. Participants are those, coming from biomedical or data science research, who are interested in collaboration and Team Science.
Mentors: A team of mentors is comprised of people with complimentary skill sets – those more senior investigators who are scientifically established in their own right and who are able to pass on wisdom and insight to the next generation of investigator. They bring their experience, encouragement, and support for the benefit of the Biomedical Data Science Innovation Lab participants. Mentors meet with the teams as they are forming to provide feedback, guidance, and direction. They do not control the teams – they merely orient them, reinforcing the positive aspects of potential team projects while asking clarifying questions about areas in need of improvement. The mentors are a vital element to each and every Biomedical Data Science Innovation Lab.
Facilitators: The facilitators are a combination or tour guides, cruise directors, and ring masters for the Biomedical Data Science Innovation Lab. They run the day-to-day agenda, coordinate our activities, and bring their own positive attitudes to bear on all things we hope to achieve, during the online microlabs as well as at the in-person event.
Provocateurs: At various times during the Biomedical Data Science Innovation Lab it is important to be challenged, pushed, encouraged, and disrupted. Provocateurs are domain experts who give short but disruptive talks, seeking to expand theb bounds of the thinking of Participants to help them envision new directions they haven’t considered. Our past Provocateurs have been inventors, entrepreneurs, astronauts, microbiologists, and neurogeneticists. Each has the goal to throw you off balance but in a good way so that when you recover your footing, it makes you see problems in a new light.
Stakeholders: Many other contributors to our Biomedical Data Science Innovation Lab can be grouped together as stakeholders in the success of the lab and of you. These include NIH and NSF program officials, foundation representatives, Team Science specialists, research ethics specialists, and others seeking to observe our activities. In a special presentation, program officials and foundation representatives will describe how grant applications might be pursued. Nightly sessions on best practices in ethical and reproducible science are provided by experts in research standards. Still other federal government and research leaders take a vested interest in the outcomes of our workshops. You never know who might drop by.
Leadership Team: The UVA-based leadership team provides overall direction, coordination, and assistance throughout each Biomedical Data Science Innovation Lab. Their efforts are more than just to support the 5-day in-person event. Rather, theirs is a year-long level of participation, planning, and promotion. These are your number one points of contact before, during, and after each event.
Questions? See our FAQs below or
Frequently Asked Questions
1. I am scheduled to complete my Ph.D. studies this year, but will not have my degree at the time of the Biomedical Data Science Innovation Lab. Can I still apply? To be considered eligible, applicants will need to have a doctoral level degree (PhD, ScD, MD, DDS, DVM, etc.) at the time of application.
Examples of doctoral level degrees include those obtained in the following disciplines.
- Behavioral and Social Sciences
- Biological, Physical or Earth Sciences
- Bioethics, Social Ethics, or Health Care Ethics
- Computational Sciences, Mathematics, and Statistics
- Engineering
- Health Sciences (Medical, Nursing, Dental, Optometry, or other like fields)
If you have a doctorate-level degree in another discipline, you may still apply, but please specify in your application how your work relates to ethics of AI in biomedicine.
2. I am currently a post-doc. Can I apply? Yes! To be considered eligible, applicants will need to have fully completed their graduate studies and, ideally, be working toward a junior faculty position at the time of application.
3. Can international applicants apply? No, this Biomedical Data Science Innovation Lab is focused on collaborations between investigators with primary appointments at domestic institutions. There are other opportunities at NIH and NSF to develop international partnerships.
4. I have a PhD and have completed my post-doc. I work in industry, where I am involved in research related to AI. Can I apply? No, the focus is on fostering new collaborations between academic researchers.
5. I have a master’s degree and work on AI research, but I do not have doctoral level training. Why can’t I apply to the Innovation Lab? The focus of this Biomedical Data Science Innovation Lab is the development of new teams of academic researchers and scientists who will work on challenges of ethically working with datatypes in support of AI systems and how AI might be made ethical against best practice recommendations. Key outcomes of this Innovation Lab are (1) the academic career paths of Lab participants and teams and (2) the success in obtaining extramural funding for their ethics of AI research. These require doctoral level training.
6. I am actively engaged in ethics of AI research, but am further on in my career. Can I apply? Yes, you can. However, please be mindful that early-career investigators (assistant to newly promoted associate professors) are highly encouraged to apply given the overall goal of the Biomedical Data Science Innovation Lab to generate new interdisciplinary collaborations of biomedical and quantitative researchers to develop collaborative ideas of projects that could be submitted to funding institutions. Those later in their careers would already have established themselves, their research programs, and have a record of successfully applying for funding.
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.
Visit us on Social Media
NIH Grant # R25GM139080