CS Colloquium

Monday, February 26

4:00 p.m., AKW 200 (coffee & cookies at 3:45)

Speaker: Danqi Chen, Stanford University

Title: Knowledge from Deep Understanding of Language

Host: Dragomir Radev

Abstract:

Almost all of humanity’s knowledge is now available online, but the vast majority of it is principally encoded in the form of human language explanations. In this talk, I explore novel neural network or deep learning approaches that open up increased opportunities for getting a deep understanding of natural language text. First, I show how distributed representations enabled the building of a smaller, faster, better dependency parser for finding the structure of human language sentences. Then I show how related neural technologies can be used to improve the construction of knowledge bases from text. However, maybe we don’t need this intermediate step and can directly gain knowledge and answer people’s questions from large textbases? In the third part, I explore doing this by looking at a simple but highly effective neural architecture for question answering.

Bio:

Danqi Chen is a PhD student in Computer Science at Stanford
University, working with Christopher Manning on deep learning approaches to Natural Language Processing. Her research centers on how computers can achieve a deep understanding of human language and the information it contains. Danqi received Outstanding Paper Awards at ACL 2016 and EMNLP 2017, a Facebook Fellowship, a Microsoft Research Women’s Fellowship and an Outstanding Course Assistant Award from Stanford. She holds a B.E. with honors from Tsinghua University.

CS Colloquium

Tuesday, February 27, 2018

4:00 p.m., AKW 200 (coffee & cookies at 3:45)

Speaker: Kevin Fu, University of Michigan

Title: Analog Cybersecurity and Transduction Attacks

Host: Zhong Shao

Abstract:

Medical devices, autonomous vehicles, and the Internet of Things depend on the integrity and availability of trustworthy data from sensors to make safety-critical, automated decisions. How can such cyberphysical systems remain secure against an adversary using intentional interference to fool sensors? Building upon classic research in cryptographic fault injection and side channels, research in analog cybersecurity explores how to protect digital computer systems from physics-based attacks. Analog cybersecurity risks can bubble up into operating systems as bizarre, undefined behavior. For instance, transduction attacks exploit vulnerabilities in the physics of a sensor to manipulate its output. Transduction attacks using audible acoustic, ultrasonic, or radio interference can inject chosen signals into sensors found in devices ranging from fitbits to implantable medical devices to drones and smartphones.

Why do microprocessors blindly trust input from sensors, and what can be done to establish trust in unusual input channels in cyberphysical systems? Why are students taught to hold the digital abstraction as sacrosanct and unquestionable? Come to this talk to learn about undefined behavior in basic building blocks of computing. I will also suggest educational opportunities for embedded security and discuss how to design out analog cybersecurity risks by rethinking the computing stack from electrons to bits. This work brings some closure to my curiosity on why my cordless phone would ring whenever I executed certain memory operations on the video graphics chip of an Apple IIGS.

Biography:

Kevin Fu is Associate Professor of EECS at the University of Michigan where he directs the Security and Privacy Research Group
(SPQR.eecs.umich.edu) and the Archimedes Center for Medical Device Security (secure-medicine.org). His research focuses on analog cybersecurity—how to model and defend against threats to the physics of computation and sensing. His embedded security research interests span from the physics of cybersecurity through the operating system to human factors. Past research projects include MEMS sensor security, pacemaker/defibrillator security, cryptographic file systems, web authentication, RFID security and privacy, wirelessly powered sensors, medical device safety, and public policy for information security & privacy.

Kevin was recognized as an IEEE Fellow, Sloan Research Fellow, MIT Technology Review TR35 Innovator of the Year, and recipient of a Fed100 Award and NSF CAREER Award. He received best paper awards from USENIX Security, IEEE S&P, and ACM SIGCOMM. He co-founded healthcare cybersecurity startup Virta Labs. Kevin has testified in the House and Senate on matters of information security and has written commissioned work on trustworthy medical device software for the National Academy of Medicine. He is a member the Computing Community Consortium Council, ACM Committee on Computers and Public Policy, and the USENIX Security Steering Committee. He advises the American Hospital Association and Heart Rhythm Society on matters of healthcare cybersecurity. Kevin previously served as program chair of USENIX Security, a member of the NIST Information Security and Privacy Advisory Board, a visiting scientist at the Food & Drug
Administration, and an advisor for Samsung’s Strategy and Innovation Center. Kevin received his B.S., M.Eng., and Ph.D. from MIT. He earned a certificate of artisanal bread making from the French Culinary Institute.

MB&B Dissertation Seminar (Flyer attached)

Speaker: Michael Lacy (Julien Berro, Advisor)
Title: “Single-molecule dynamics in clathrin-mediated endocytosis and membrane remodeling”
Date: Friday, March 2, 2018
Time & 2:00 pm
Place: 305 Bass

Tea at 1:45 pm
Lacy dissertation flyer.pdf

DEPARTMENT OF STATISTICS AND DATA SCIENCE SEMINAR

Date: Monday, February 26, 2018

Time: 4:15pm – 5:15pm

Place: 24 Hillhouse Avenue, Rm. 107

Seminar Speaker: Aaditya Ramdas

University of California, Berkeley, http://people.eecs.berkeley.edu/~aramdas/

Title: Interactive algorithms for multiple hypothesis testing

Abstract: Data science is at a crossroads. Each year, thousands of new data scientists are entering science and technology, after a broad training in a variety of fields. Modern data science is often exploratory in nature, with datasets being collected and dissected in an interactive manner. Classical guarantees that accompany many statistical methods are often invalidated by their non-standard interactive use, resulting in an underestimated risk of falsely discovering correlations or patterns. It is a pressing challenge to upgrade existing tools, or create new ones, that are robust to involving a human-in-the-loop. In this talk, I will describe two new advances that enable some amount of interactivity while testing multiple hypotheses, and control the resulting selection bias. I will first introduce a new framework, STAR, that uses partial masking to divide the available information into two parts, one for selecting a set of potential discoveries, and the other for inference on the selected set. I will then show that it is possible to flip the traditional roles of the algorithm and the scientist, allowing the scientist to make post-hoc decisions after seeing the realization of an algorithm on the data. The theoretical basis for both advances is founded in the theory of martingales : in the first, the user defines the martingale and associated filtration interactively, and in the second, we move from optional stopping to optional spotting by proving uniform concentration bounds on relevant martingales.

This talk will feature joint work with (alphabetically) Rina Barber, Jianbo Chen, Will Fithian, Kevin Jamieson, Michael Jordan, Eugene Katsevich, Lihua Lei, Max Rabinovich, Martin Wainwright, Fanny Yang and Tijana Zrnic. Bio : Aaditya Ramdas is a postdoctoral researcher in Statistics and EECS at UC Berkeley, advised by Michael Jordan and Martin Wainwright. He finished his PhD in Statistics and Machine Learning at CMU, advised by Larry Wasserman and Aarti Singh, winning the Best Thesis Award in Statistics. A lot of his research focuses on modern aspects of reproducibility in science and technology — involving statistical testing and false discovery rate control in static and dynamic settings.

4:00 p.m. Refreshments in Common Room, 24 Hillhouse Avenue

4:15p.m. – 5:15p.m. Seminar, Room 107, 24 Hillhouse Avenue

For more details and upcoming events visit our website at
http://statistics.yale.edu/ .