Upcoming Department Events

To submit an event for consideration, or sign up a speaker, please see this page.
Tuesday, October 21, 2014 - 4:30pm to 5:30pm
CS Department Colloquium Series
Computer Science Small Auditorium (Room 105)
Host: Ed Felten

Decentralized Anonymous Credentials and Electronic Payments from Bitcoin [POSTPONED]

Matthew Green (Johns Hopkins University)
[ View Event Abstract ]
Traditionally, making statements about identity on the Internet, whether literal assertions of identity or statements about one’s identity, requires centralized providers who issue credentials attesting to the user’s information. These organizations, which include Certificate Authorities, DNS maintainers, or login providers like Google and Facebook, play a large role in securing internet infrastructure, email, and financial transactions. Our increasing reliance on these providers raises concerns about privacy and trust. 
 
Anonymous credentials represent a powerful solution to this privacy concern: they deprive even colluding credential issuers and verifiers of the ability to identify and track their users. Although credentials may involve direct assertions of identity, they may also be used for a large range of useful assertions, such as “my TPM says my computer is secure”, “I have a valid subscription for content”, or “I am eligible to vote.” Anonymous credentials can also be used as a basis for constructing untraceable electronic payment systems, or “e-cash".

Unfortunately most existing anonymous credential and e-cash systems have a fundamental limitation: they require the appointment of a central, trusted party to issue credentials or tokens. This issuer represents a single point of failure and an obvious target for compromise. In distributed settings such as ad hoc or peer-to-peer networks, it may be challenging even to identify parties who can be trusted to play this critical role.
 
In this talk I will discuss new techniques for building anonymous credentials and electronic cash in a fully decentralized setting. The basic ingredient of these proposals is a "distributed public append-only ledger", a technology which has most famously been deployed in digital currencies such as Bitcoin. This ledger can be employed by individual nodes to make assertions about a user’s attributes in a fully anonymous fashion — without the assistance of a credential issuer. One concrete result of these techniques is a new protocol named “Zerocash”, which adds cryptographically unlinkable electronic payments to the Bitcoin currency.

Prof. Matthew Green is a Research Professor at the Johns Hopkins University Information Security Institute. His research focus is on cryptographic techniques for maintaining users’ privacy, and on technologies that enable the deployment of privacy-preserving protocols. From 2004-2011, Green served as CTO of Independent Security Evaluators, a custom security evaluation firm with a global client base. Along with a team at Johns Hopkins and RSA Laboratories, he discovered flaws in the Texas Instruments Digital Signature Transponder, a cryptographically-enabled RFID device used in the Exxon Speedpass payment system and in millions of vehicle immobilizers.
Wednesday, October 22, 2014 - 4:30pm to 5:30pm
Distinguished Colloquium Series Speaker
Computer Science Small Auditorium (Room 105)
Host: Jianxiong Xiao

The Unreasonable Effectiveness of Deep Learning

Yann LeCun (Facebook AI Research and Center for Data Science, New York University)
[ View Event Abstract ]

 

Yann LeCun

The emergence of large datasets, parallel computers, and new machine learning methods, have enabled the deployment of highly-accurate computer perception systems and are opening the door to a wide deployment of AI systems.

A key component in AI systems is a module, sometimes called a feature extractor, that turns raw inputs into suitable internal representations. But designing and building such a module requires a considerable amount of engineering efforts and domain expertise.

Deep Learning methods have provided a way to automatically learn good representations of data from labeled or unlabeled samples. Deep architectures are composed of successive stages in which data representations are increasingly global, abstract, and invariant to irrelevant transformations of the input. Deep learning enables end-to-end training of these architectures, from raw inputs to ultimate outputs.

The convolutional network model (ConvNet) is a particular type of deep architecture somewhat inspired by biology, which consists of multiple stages of filter banks, interspersed with non-linear operators, and spatial pooling. ConvNets have become the record holder for a wide variety of benchmarks, including object detection, localization and recognition in image, semantic segmentation and labeling, face recognition, acoustic modeling for speech recognition, drug design, handwriting recognition, biological image segmentation, etc.

The most recent systems deployed by Facebook, Google, NEC, IBM, Microsoft, Baidu, Yahoo and others for image understanding, speech recognition, and natural language processing use deep learning. Many of these systems use very large and very deep ConvNets with billions of connections, trained in supervised mode. But many new applications require the use of unsupervised feature learning. A number of such methods based on sparse auto-encoder will be presented.

Several applications will be shown through videos and live demos, including a category-level object recognition system that can be trained on the fly, a scene parsing system that can label every pixel in an image with the category of the object it belongs to (scene parsing), an object localization and detection system, and several natural language processing systems. Specialized hardware architectures that run these systems in real time will also be described.

Yann LeCun is Director of AI Research at Facebook, and Silver Professor of Data Science, Computer Science, Neural Science, and Electrical Engineering at New York University, affiliated with the NYU Center for Data Science, the Courant Institute of Mathematical Science, the Center for Neural Science, and the Electrical and Computer Engineering Department.

He received the Electrical Engineer Diploma from Ecole Supérieure d'Ingénieurs en Electrotechnique et Electronique (ESIEE), Paris in 1983, and a PhD in Computer Science from Université Pierre et Marie Curie (Paris) in 1987. After a postdoc at the University of Toronto, he joined AT&T Bell Laboratories in Holmdel, NJ in 1988. He became head of the Image Processing Research Department at AT&T Labs-Research in 1996, and joined NYU as a professor in 2003, after a brief period as a Fellow of the NEC Research Institute in Princeton. From 2012 to 2014 he directed NYU's initiative in data science and became the founding director of the NYU Center for Data Science. He was named Director of AI Research at Facebook in late 2013 and retains a part-time position on the NYU faculty.

His current interests include AI, machine learning, computer perception, mobile robotics, and computational neuroscience.  He has published over 180 technical papers and book chapters on these topics as well as on neural networks, handwriting recognition, image processing and compression, and on dedicated circuits and architectures for computer perception. The character recognition technology he developed at Bell Labs is used by several banks around the world to read checks and was reading between 10 and 20% of all the checks in the US in the early 2000s.  His image compression technology, called DjVu, is used by hundreds of web sites and publishers and millions of users to access scanned documents on the Web. Since the mid 1980's he has been working on deep learning methods, particularly the convolutional network model, which is the basis of many products and services deployed by companies such as Facebook, Google, Microsoft, Baidu, IBM, NEC, AT&T and others for image and video understanding, document recognition, human-computer interaction, and speech recognition.

LeCun has been on the editorial board of IJCV, IEEE PAMI, and IEEE Trans. Neural Networks, was program chair of CVPR'06, and is chair of ICLR 2013 and 2014. He is on the science advisory board of Institute for Pure and Applied Mathematics, and Neural Computation and Adaptive Perception Program of the Canadian Institute for Advanced Research. He has advised many large and small companies about machine learning technology, including several startups he co-founded. He is the lead faculty at NYU for the Moore-Sloan Data Science Environment, a $36M initiative in collaboration with UC Berkeley and University of Washington to develop data-driven methods in the sciences. He is the recipient of the 2014 IEEE Neural Network Pioneer Award.

Friday, October 24, 2014 - 9:00am to 4:45pm
CITP event (Event Website)
Friend Center Convocation Room
(Hosted by: Center for Information and Technology Policy)

Web Privacy and Transparency Conference

[ View Event Abstract ]

Everything we do on the web is tracked and profiled. What types of data are companies collecting? Who are they trading it with? And how is this data used for personalizing our online experience and treating different users differently? What are the algorithms used for targeting ads, as well as prices, news recommendations, and so forth? A quickly emerging area of computer science research aims to bring transparency to privacy-impacting practices on the web via empirical measurement. This conference will discuss the state of the art in this field and the research agenda for the next few years as well as questions of policy — how should laws utilize the results of measurement, and what new laws do these studies suggest? Can self-regulation be effective, and how can web services work together with transparency researchers to foster a healthy public dialog?

Please RSVP on event website.

 

Monday, November 3, 2014 - 4:30pm to 5:30pm
CS Department Colloquium Series
Computer Science Small Auditorium (Room 105)
Host: Sebastian Seung

Machine Learning for Robots: Perception, Planning and Motor Control

Daniel Lee (University of Pennsylvania )
[ View Event Abstract ]
Daniel LeeMachines today excel at seemingly complex games such as chess and Jeopardy, yet still struggle with basic perceptual, planning, and motor tasks in the physical world.  What are the appropriate representations needed to execute and adapt robust behaviors in real-time?  I will present some examples of learning algorithms from my group that have been applied to robots for monocular visual odometry, high-dimensional trajectory planning, and legged locomotion. These algorithms employ a variety of techniques central to machine learning: dimensionality reduction, online learning, and reinforcement learning.  I will show and discuss applications of these algorithms to autonomous vehicles and humanoid robots.
Daniel Lee
 
 
 
 
 
 
 
 
 
 
 
 
Daniel Lee is the Evan C Thompson Term Chair, Raymond S. Markowitz Faculty Fellow, and Professor in the School of Engineering and Applied Science at the University of Pennsylvania. He received his B.A. summa cum laude in Physics from Harvard University in 1990 and his Ph.D. in Condensed Matter Physics from the Massachusetts Institute of Technology in 1995.  Before coming to Penn, he was a researcher at AT&T and Lucent Bell Laboratories in the Theoretical Physics and Biological Computation departments.  He is a Fellow of the IEEE and has received the National Science Foundation CAREER award and the University of Pennsylvania Lindback award for distinguished teaching. He was also a fellow of the Hebrew University Institute of Advanced Studies in Jerusalem, an affiliate of the Korea Advanced Institute of Science and Technology, and organized the US-Japan National Academy of Engineering Frontiers of Engineering symposium.  As director of the GRASP Robotics Laboratory and co-director of the CMU-Penn University Transportation Center, his group focuses on understanding general computational principles in biological systems, and on applying that knowledge to build autonomous systems.
Tuesday, November 4, 2014 - 10:00am to 5:00pm
CITP event (Event Website)
Friend Center Convocation Room
(Hosted by: Center for Information and Technology Policy)

Trusting Human Safety to Software: What Could Possibly Go Wrong?

[ View Event Abstract ]

The conference will focus on the need for affirmative, preventative measures to be put in place to prevent physical harm from code-based machines and systems. Planned topics include medical devices and automotive software.

 

Please RSVP on event website.