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Through teaching, research and outreach, ILR generates and shares knowledge to solve human problems, manage and resolve conflict, establish best practices in the workplace and inform government policy.

ILR Welcoming Two Professors

New faculty welcome card

Y. Samuel Wang and Dana Yang will join the Department of Statistics and Data Sciences during the 2021-22 academic year.

“Statistics and Data Sciences has had a very successful faculty search this year with two great new hires, said Alex Colvin, Ph.D. ’99, the Kenneth F. Kahn ’69 Dean and the Martin F. Scheinman’75, MS ’76, Professor of Conflict Resolution. “Dana Yang and Sam Wang are excellent scholars who will add to the strength of the department and of ILR. We look forward to welcoming them to our community.”

Y. Samuel Wang, Department of Statistics and Data Sciences
• Ph.D., Statistics, University of Washington, 2018
• B.A., Applied Math, Rice University, 2010

Wang has broad interests across statistics, machine learning and data science, but much of his work is in the subfield of "graphical models." In this area, researchers consider how each variable in a complex system might be dependent or independent of the other variables.

He primarily works in theory and methods, however, the methods he works on can be applied to functional magnetic resonance imaging data to discover how different regions of the brain interact, or applied to financial data to see how the performance of some stocks affect the performance of other stocks, or to systems biology data to see how certain proteins might regulate other proteins. Wang also enjoys connecting statistics and data science to social science questions, as one of his current projects seeks to measure gender bias in co-authorship team formation.

Dana Yang, Department of Statistics and Data Sciences
• Ph.D., Statistics & Data Science, Yale, 2019
• M.A., Statistics, Yale, 2014
• B.S., Mathematics, Tsinghua University, 2013

Yang works in the broad area of high-dimensional statistics and machine learning. One of her focuses is large-scale network analysis, more specifically, learning hidden network structures from noisy observations. She primarily works on determining the fundamental statistical limit for recovery – a threshold beyond which the data becomes too “noisy” and the hidden structure cannot be recovered reliably. Designing algorithms that attain the statistical limit is important for practitioners working on real network data.

Besides the natural applications in social networks, Yang’s work can also be applied to a wide class of other problems including genome sequencing and particle tracking, given the versatility of network models. She is also interested in the ethics and safety of machine learning. Some of her recent works have involved the design of learning frameworks that protect the learner against eavesdropping attacks, for example, in federated learning.