Foundations of Data Science & AI Community of Practice bridges connections into foundational and applied research with AI

The Foundations of Data Science & AI Community of Practice at TDAI aims to build a community at Ohio State focused on artificial intelligence, as well as theoretical and algorithmic foundations, applied in data science.

          Yoonkyung Lee

The CoP is led by Yoonkyung Lee, a professor of Statistics and Computer Science and Engineering (by courtesy), and Eric Fosler-Lussier, a professor in Computer Science and Engineering. Fosler-Lussier also maintains courtesy appointments in the Departments of Linguistics and Biomedical Informatics and leads the Speech and Language Technologies Laboratory (SLaTe), focused on issues found within speech and natural language processing.

In order to help address multifaceted issues, this CoP is dependent upon interdisciplinary work stemming from fields such as computer science, mathematics, operations research and statistics. By having these connections present, foundational research and applied AI serve as interdisciplinary bridging points.

On the note of applying interdisciplinarity, Lee provides an insightful example:

“Increase in the data scale has brought new computational challenges to the way we apply data analysis tools in practice, prompting new ideas of approximation through random projections and sketching and distributed learning. These ideas touch on random matrix theory, stochastic optimization, parallel computing, and model aggregation spanning multiple disciplines.”

           Eric Fosler-Lussier

This CoP has its origins in a foundational theoretical background. As it continues to grow, it is likely to focus more on the problems involving the use of AI technology.  In fostering relationships that nurture cross-disciplinary research, this CoP can help provide the backdrop to discuss these ideas and allow for additional prospects, one of which includes creating more integrated programs within educational schemas at Ohio State using machine learning and AI technology. It also amplifies the key goal of connecting foundational and applied research with AI.

For application areas, a core-cluster of CoP members is involved in organizing workshops through the Office of Research to build bridges in human-machine teaming.

Another such cluster involves members interested in computational linguistics and natural language processing as a focused area. Additionally, Foundation members have looked to addressing issues involving AI in medicine, education and social media prediction.

While interdisciplinarity plays a tremendous role in the way Foundations is structured, there are plans to integrate connections between external affiliations. Fosler-Lussier said “Given the requests that come to TDAI and opportunities in the area, one of our planned activities is to build a description of the portfolio of AI research available at the university to facilitate connections between industry and government opportunities.”

While both Lee and Fosler-Lussier are the new leads still transitioning into this CoP, they have various ambitions in what they aim.  They plan to invite speakers to speak on AI and machine learning in the coming spring. During the age of COVID-19, they say the best thing to do is get to know their members a little better and provide a space to connect with them.

To learn more about the CoP or to join, visit the Foundations of Data Science & AI Community of Practice page.


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