About PiFODS

The growing field of data science promises to bring many benefits to society: personalized knowledge and services; improved healthcare; improved decision-making at individual, organizational, national, and international levels; a safer and possibly fairer society; and many others.

The ability to realize these promises, however, depends critically on building the right foundational principles for the field. At 
Penn Institute for Foundations of Data Science (PIFODS), with the goal of bringing together scientists and ideas from multiple disciplines, including computer science, electrical engineering, statistics, and mathematics, to collectively develop long-lasting principles for data science that can serve the field for decades to come.

The main activities of the Institute will include transdisciplinary research, education and training, engagement with the broader research community through invited seminars and workshops, and engagement with applied scientists and practitioners. 

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Principal Investigators

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Research Thrusts

TBA

Publications

3

Department

Research Thrusts

Complex Learning Tasks

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Efficient Optimization

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Streaming, Distributed, and Massively Parallel Data Analysis

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Privacy and Fairness-Preserving Data Analysis

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Reproducible Data Analysis

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People

PiFODS strives to bring together the brightest of minds.

Faculty

Shivani Agarwal

Principal Investigator

Sanjeev Khanna

Co-Principal Investigator

Aaron Roth

Co-Principal Investigator

Weijie Su

Co-Principal Investigator

Hamed Hassani

Co-Principal Investigator

Research Activities

Research at PIFODS addresses important foundational needs 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 various modern computational resources in data science, as well as developing data science algorithms that guarantee meaningful notions of privacy, fairness, and reproducibility.

Funded by the National Science Foundation TRIPODS
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