Digital Twins Initiative

Digital Twins Initiative

Welcome to the Digital Twins Initiative, brought to you by the Northeast Big Data Innovation Hub (NEBDHub) and the National Student Data Corps (NSDC), in collaboration with our partners across industry, government, non-profits, and academia. 

A digital twin, as defined by the National Academies of Sciences, Engineering, and Medicine (NASEM), is a virtual representation of a physical system that is dynamically updated with real-world data, can predict behavior, and informs decisions to create value. By integrating data from sensors, simulations, diagnostic and operational systems, digital twins enable organizations to monitor performance, predict outcomes, test scenarios, and make more informed decisions. From healthcare and manufacturing to smart cities and infrastructure, digital twin technologies are transforming how we understand, manage, and optimize complex systems in real time, leveraging artifical intelligence (AI) and machine learning techniques.

Explore upcoming events, curated resources, and collaboration opportunities for learners, researchers, educators, and professionals who want to dive deeper into the world of digital twins.

NEBDHub Projects & Hands-On Learning

Prefer to build a digital twin rather than just read about one? These are hands-on projects from the NEBDHub and NSDC where you work directly with real data.

  • Data-Driven Digital Twin Simulation for Sleep Quality Prediction — NSDC Data Science Project
    A hands-on Colab project that builds a digital twin of a person’s sleep behavior. Students clean a real-world health dataset, train prediction models in a scikit-learn pipeline, and write a simulate() function to answer counterfactual “what-if” questions — like how lowering stress by 2 points would change predicted sleep quality. ~8–10 hours, self-paced.

Digital Twins Resources

Foundations & Definitions

New to digital twins? Start here. These resources explain what a digital twin actually is. A virtual representation of a physical system that’s continuously updated with real-world data and how the concept has evolved from a manufacturing tool into a technology now shaping healthcare, cities, and infrastructure.

Ethics, Privacy, Legal & Policy

Digital twins raise hard questions the underlying technology can’t answer on its own: who owns the data behind a virtual patient, how much autonomy a model should have, and what “informed consent” means when your digital twin keeps learning after you’ve walked out of the clinic. This section collects the scholarship, government reports, and advocacy work grappling with those questions.

Ethics of Digital Twins in Medicine

Data Privacy, Trust & Security

Bias, Equity & Patient Rights

Governance & Regulatory Reports

Healthcare & Life Sciences

Clinical & Patient-Specific Applications

Hospital Operations & Systems

Clinical Trials & Regulatory (FDA)

Methods & Evidence

Smart Cities & Infrastructure

Digital Twins at Columbia University

The “Digital Twins in Healthcare: Clinical, Ethical and Legal Perspectives” workshop hosted at Columbia University on April 27, 2026 brought together clinicians, bioethicists, radiologists, surgeons, technologists, researchers, engineers, start-ups, legal and regulatory experts from the National Institutes of Health, MD Anderson, Memorial Sloan Kettering Cancer Center, the University of Maryland, the University of Michigan, Harvard University, the Digital Twins Consortium and more. The workshop participants discussed how digital twins, built from patient and environmental data, are moving into clinical care, and the ethical, legal, and regulatory questions that arise. View the agenda and list of speakers here. Watch below to learn more.

Digital Twins Interest Form

Interested in digital twins across domains? Complete the following interest form to receive invitations to upcoming workshops, events, and collaborative opportunities where you can explore digital twin applications and connect with experts across sectors.