YINS Distinguished Lecturer Series
“Safeguarding Privacy in Sequential Decision-Making Problems”
Speaker: John Tsitsiklis
Clarence J. Lebel Professor of Electrical Engineering and Computer Science at MIT
Wednesday, April 10, 2019 – 12:00pm
Yale Institute for Network Science | 17 Hillhouse Avenue, 3rd floor | New Haven, CT 06511
Abstract: With the increasing ubiquity of large-scale surveillance and data analysis infrastructures, privacy has become a pressing concern in many domains. We propose a framework for studying a fundamental cost vs. privacy tradeoff in dynamic decision-making problems. More concretely, we are interested in ways that an agent can take actions that make progress towards a certain goal, while minimizing the information revealed to a powerful adversary who monitors these actions. We will examine two well-known decision problems (path planning and active learning), and in both cases establish sharp tradeoffs between obfuscation effort and level of privacy. As a byproduct, our analysis also leads to simple yet provably optimal obfuscation strategies. Based on joint work with Kuang Xu (Stanford) and Zhi Xu (MIT).
Speaker bio: John Tsitsiklis is the Clarence J. Lebel Professor of Electrical Engineering and Computer Science at MIT, and a member of the National Academy of Engineering. He obtained his PhD from MIT and joined the faculty in 1984. His research focuses on the analysis and control of stochastic systems, including applications in various domains, from computer networks to finance. He has been teaching probability for over 15 years.
Amy Justice and Walther Mothes; program leaders of the VOIC Research Program, invite you to the Virus & Other Infection-associated Cancer Program Retreat being held on Tuesday, April 30, 2019 in Hope H216.
Attached is agenda and flyer for the event. SPACE IS LIMITED.
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Joint Biostatistics, CS, and S&DS
BIN YU, University of California, Berkeley
Date: Monday, April 01, 2019
Time: 4:00PM to 5:15PM
Dunham Lab. see map
10 Hillhouse Avenue, Rm. 220
New Haven, CT 06511
Title: Three principles of data science: predictability,
computability, and stability (PCS)
Information and Abstract:
In this talk, I’d like to discuss the intertwining importance and connections of three principles of data science in the title and the PCS workflow that is built on the three principles. The principles will be demonstrated in the context of two collaborative projects in neuroscience and genomics for interpretable data results and testable hypothesis generation. If time allows, I will present proposed PCS inference that includes perturbation intervals and PCS hypothesis testing. The PCS inference uses prediction screening and takes into account both data and model perturbations. Finally, a PCS
documentation is proposed based on Rmarkdown, iPython, or Jupyter Notebook, with publicly available, reproducible codes and narratives to back up human choices made throughout an analysis. The PCS workflow and documentation are demonstrated in a genomics case study available on Zenodo.
3:45 p.m. Pre-talk tea Dunham Lab, Suite 222, Breakroom 228
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