The Penn Institute for Foundations of Data Science (PIFODS) is based at the University of Pennsylvania and is supported by the National Science Foundation’s (NSF’s) HDR Transdisciplinary Research in Principles of Data Science (TRIPODS) program. The Institute brings together scientists and ideas from multiple disciplines, including computer science, electrical engineering, statistics, and mathematics, in order to collectively develop long-lasting principles for data science that can serve the field for decades to come. The main activities of the Institute include transdisciplinary research, education and training, engagement with the broader research community through invited seminars and workshops, and engagement with selected applications of data science.

On the research side, the PIFODS team develops principles for several transdiciplinary directions, including in particular the following five thrusts: principles for complex learning tasks; principles for efficient optimization (convex, non-convex, and submodular); principles for streaming, distributed, and massively parallel data analysis; principles for privacy-preserving and fairness-preserving data analysis; and principles for reproducible data analysis.

Each of these thrusts addresses an important foundational need in data science; these needs range from designing learning algorithms with stronger performance guarantees, and developing principles for optimization in adaptive settings, to developing a fundamental understanding of the tradeoffs between accuracy and computational resources for various modern computational platforms used in data science, as well as developing data science algorithms that guarantee meaningful notions of privacy, fairness, and reproducibility. Each thrust requires interactions among several disciplines; several of these thrusts also naturally interact with each other.

On the education and training side, the PIFODS team has already initiated several new transdiscplinary courses related to data science that are aimed at developing a common language across disciplines, and continues to further develop and refine these courses together with associated teaching materials/books. The Institute also supports the training of young researchers in a cross-disciplinary manner, so that the next generations of data science researchers from different disciplines will also share a common language.


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Kamalika Chaudhari
Suvrit Sra
Anna Gilbert
Jianqing Fan
Robert Nowark
Soheil Feizi
Phil Long
Clay Scott
Percy Liang
Sham Kakade
Jeannette Wing
Arya Mazumdar
Sujay Sanghavi