COP - Foundations of Data Science and AI

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What we are doing

This CoP aims to build a community at OSU focusing on artificial intelligence and the foundation of data science. It will center around the theoretical and algorithmic foundation of data science and use of AI across the data spectrum, with core members spanning across Computer Science, Statistics and Mathematics. It will also build connections to the broader data science community in a radial manner: starting from more fundamental areas such as machine learning and geometric/topological data analysis, to other data analytic areas and application domains.

The cluster will focus on new fundamental methodologies as well as new analytics algorithms for analyzing diverse data. To achieve this goal, this CoP also aims to reach out and bridge researchers from application areas, such as neuron science, GIS, material science, and medical imaging, etc., so as to tackle fundamental questions arised from the application domains. It also aims to connect to researchers from other data analytic and data management areas, such as cybersecurity and data governance. These goals also align with those of the TRIPODS center TGDA@OSU that has been funded by NSF for Phase I, as well as the theme for potential Phase II of this TRIPODS center.

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Why we are doing it

The CoP will produce effective and efficient algorithms for modern data analysis, as well as understanding for the structures behind data analysis problems. These fundamental algorithms can potentially be used to a broad range of application domains, from science to engineering, such as neuron science, material science, GIS, biomedical image analysis and so on. These algorithms have potentially important impacts on several aspects for societal challenges in fields such as cybersecurity, health, manufacturing, and engineering.

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Why at Ohio State

OSU has a strong team in Topological and Geometric Data Analysis, consisting of faculty members from Computer Science, Mathematics and Statistics. OSU also has strong researchers in (statistical) machine learning, data privacy, security, and optimization. Through this cluster, we hope to combine our strength. The methodologies developed can be applied to a broad range of application domains, including material science, neuroscience, medical images analysis, GIS, and so on. This CoP envisions to utilize the strength of research and developmental activities already existing at OSU both at the technical and organizational fronts. OSU hosts strong groups in algorithm design, topological and geometric data analysis, machine learning, and optimizations which can form the core of this CoP. There is no dearth of application domains at OSU which can connect to this core. For example, OSU has strong presence in material science (MRI center), medical science, and geography among others. This CoP along with the initiative of NSF funded TRIPODS at OSU is a timely undertaking. The presence of various centers such as TDAI itself, MBI, MRI can help shaping the outreach activities.