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 centers 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 also aims to build connections to the broader data science community in a radial manner: starting from more fundamental areas such as machine learning, statistical modeling and geometric/topological data analysis, to other data analytic areas and application domains.

The cluster will focus on new fundamental methodologies, theories 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 neuroscience, GIS, material science, and medical imaging, etc., to tackle fundamental questions arising 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.

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Why we are doing it

Many challenges in data science and artificial intelligence require interdisciplinary efforts in foundational research from developments of methodologies, theories, and effective and efficient algorithms to related applications. The CoP seeks to foster such cross disciplinary research efforts by bridging researchers from different data science fields and providing a platform for collaboration. Further, the CoP aims to build bridges between foundational research and applied aspects of artificial intelligence.

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Why at Ohio State

OSU has a strong team in Topological and Geometric Data Analysis [please include a weblink to TGDA group page https://tgda.osu.edu/ under “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.