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The opinions, findings, and conclusions or recommendations expressed are those of the Center author(s) and do not necessarily reflect the views of the National Science Foundation.

Center Overview

The Center for Data-Driven Drug Development and Treatment Assessment (DATA) advances U.S. competitiveness by working with industry to solve challenges in drug discovery and repurposing and in treatment assessment, optimization, and quantitative pharmacovigilance using novel computational and data science techniques such as metrology, machine learning (ML), and artificial intelligence (AI), and by training the next generation of talent in this field. With new methodologies and infrastructure for industry-wide collaboration, DATA seeks to significantly accelerate drug development to help target the right drug to the right person at a safe and effective dose while reducing R&D costs.

DATA’s mission is to address two principal areas of unmet or underserved computational research needs within the (bio)pharmaceutical and health care sectors:

  1. Challenges related to data, such as silos and skewed research results caused by small, non-diverse databases of patient and pharmaceutical data, or the lack of data standards across the pharmaceutical and healthcare industries.
  2. AI and machine learning algorithmic challenges, including the lack of transparency of AI/ML models, lack of customization of the existing AI/ML methods to address the specific needs of the pharmaceutical and healthcare industries, and the inability of many AI/ML methods to learn from incomplete databases.

The advent of generative AI has created an urgent need for stakeholders in all stages of the drug life cycle to come together, share experiences with this revolutionary technology, and jointly address its implications for health care. Thanks to its cross-industrial and cross-disciplinary nature designed to bring together data scientists, mathematicians, biomedical researchers, pharmaceutical companies, healthcare providers and payers, as well as government agencies, DATA is ideally positioned to catalyze these conversations, identify best practices, and develop solutions to the challenges generative AI brings to the health care sector.

The Center also addresses the growing demand for data science professionals with an expertise in drug design and treatment assessment by a robust education and training program focused on exposing the next generation of data science leaders to real-world needs and by connecting DATA industry partners with emerging talent in this field.

DATA's solutions are applicable at all stages of the drug discovery and treatment optimization pipeline, including during the initial screening for potential candidates, in the mechanistic & machine learning stage, during the testing of in-vitro/in-vivo disease models, in the assessment of clinical trials, through post-market pharmacovigilance and treatment monitoring. The Center will also have a broader impact on the society. For example, privacy-preserving and encryption techniques, while developed in the health care context, have a universal application and are readily transferable to other industries that handle sensitive identifiable information.


  • University of Michigan
View Center Website

Center Personnel

Kayvan Najarian
Center Director
+1 734 763 3924

Ivana Tullett
Managing Director
+1 734 647 4986

Research Focus

DATA's research program focuses on the following areas to advance the design, testing, validation and assessment of drugs and treatments:

  • Developing, testing, and validating novel data science and AI techniques, including generative AI, for drug development, treatment monitoring and patient phenotyping with respect to drugs and treatments.
  • Designing, testing, and validating data science and AI techniques for assessing drugs and treatments through systems for continuous health monitoring.
  • Sharing of unique laboratory resources for drug discovery and the development and validation of patient phenotypes.
  • Creating tools that enable federated machine learning for drug design, health informatics and pharmacovigilance over encrypted databases.
  • Providing an industry-wide and vendor-agnostic secure data hub of pharmaceutical and patient data with third-party private search capabilities for which data owners and providers of computational tools only need to share databases and algorithms in encrypted forms.
  • Developing and enhancing privacy-preserving and transparent machine learning for drug design, health informatics, pharmacovigilance and optimization of drug-based treatments over encrypted databases.


Member Organizations

IUCRC affiliated member organizations are displayed as submitted by the Center. Non-federal organizations are not selected, approved, or otherwise endorsed by the National Science Foundation.
  • Amgen
  • AbbVie
  • Corewell Health
  • DataSpeaks
  • Zero Inc.
  • CardioSounds
  • i2b2 tranSMART Foundation
  • miLEAD
  • PathwaysGI