2021-2022 Biomedical Data Science Innovation Lab & Seminar Series Ethical Challenges of Artificial Intelligence in Biomedicine

Applications for the Biomedical Data Science Innovation Lab are still open! We will be accepting applicants on a rolling basis with final applications due by 11:59PM ET, January 31, 2022. We welcome a range of expertise to apply including ethicists, data scientists, and population-level scientists. Women and underrepresented scientific communities are strongly encouraged to apply as well. For more information see Biomedical Data Science Innovation Lab and the Application Procedure sections below.

2021-2022 Virtual Seminar Series

November 5, 2021 - May 6, 2022

Fridays 9 - 10 am PT / 12 - 1 pm ET

Upcoming Talks

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!

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

Boar's Head Resort, Charlottesville, Virginia
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.

2021-2022 Biomedical Data Science Innovation Lab Mentors

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:

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 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.

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.

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