DATA Industry Advisory Board Spring 2023 Meeting (April 13-14, 2023) Call for Student & Post-Doc Research Posters

The Center for Data-Driven Drug Development and Treatment Assessment (DATA), an NSF-supported Industry-University Cooperative Research Center (IUCRC) and a unit of the Michigan Institute for Data Science (MIDAS), is soliciting student and post-doc research project posters to showcase during its inaugural Industry Advisory Board (IAB) meeting on April 13-14, 2023, in Ann Arbor, MI. We are interested in student and post-doc projects that apply computational methods, data science solutions, artificial intelligence (AI) and machine learning (ML) techniques to solve problems in health care, pharmacy, chemistry, and biomedical engineering, in particular in the areas of drug design, drug repositioning, and treatment assessment.

The meeting will host a number of companies from the pharmaceutical, healthcare, IT, and AI startup industries, as well as representatives of the National Science Foundation, offering the presenters ample opportunities to network and discuss their career plans with potential employers.

University of Michigan students of all levels (undergraduate, masters, doctorate) and post-docs are eligible to participate. Interested applicants should submit abstract of their project to Ivana Tullett, DATA Center Managing Director, at by April 1, 2023.

The poster session will take place on Thursday, April 13, 2023, during lunchtime. Selected presenters should plan to be available between 11 am and 2:30 pm for poster setup/breakdown and the session itself.

For additional questions, please contact Ivana Tullett at

About DATA

Established in 2022, the Center for Data-Driven Drug Development and Treatment Assessment (DATA) is part of the Industry-University Cooperative Research Centers (IUCRC) Program of the National Science Foundation and a unit of the Michigan Institute for Data Science (MIDAS) at the University of Michigan. DATA advances U.S. competitiveness by working with industry to solve current, emerging, and industry-relevant challenges in drug design, drug repositioning and repurposing, treatment monitoring, assessment and optimization, patient phenotyping, and quantitative pharmacovigilance using novel machine learning and artificial intelligence techniques. DATA will produce new methodologies and infrastructure for industry-wide collaborative drug discovery and treatment assessment, with the goal of significantly accelerating the pace of drug discovery while reducing the costs. The Center’s cross-industrial and cross-disciplinary nature brings together all stakeholders in this area, including data scientists, mathematicians, biomedical researchers, pharmaceutical companies, healthcare providers, and payers in the healthcare industry. 

The Center seeks to solve multiple challenges related to data (insufficient size and lack of diversity of patient and pharmaceutical data, lack of data standards used across the pharmaceutical and healthcare industries, compliance with privacy requirements applicable to patient and human subject data, curation & integration of the available data), and to algorithms (lack of transparency of AI/ML models, training of AI/ML models on incomplete databases).