Upcoming Department Events

To submit an event for consideration, or sign up a speaker, please see this page.
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.

Wednesday, November 5, 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

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.
Monday, November 10, 2014 - 4:30pm to 5:30pm
CS Department Colloquium Series
Computer Science Small Auditorium (Room 105)
Host: Sebastian Seung

Google Strength Neural Networks

Greg Corrado (Research at Google)
[ View Event Abstract ]
Greg CorradoIndustrial scale applications of machine learning are surprisingly important in the products and services we enjoy today. Over the last few years classical artificial neural networks have reemerged as one of the most powerful, practical machine learning tools available. More than it was driven by algorithmic advances, this “deep learning” renaissance has been fueled by the availability of ever larger data stores and clever use of vast computational resources. Greg will describe Google's large scale distributed neural network framework and the applications of neural networks to the domains of image recognition, speech recognition, and text understanding.
Greg Corrado is a senior research scientist at Google working in artificial intelligence, computational neuroscience, and scalable machine learning. He has worked for some time on brain inspired computing, and most recently has served as one of the founding members and a technical lead on Google's large scale deep learning project. Before coming to Google, he worked at IBM Research on the SyNAPSE neuromorphic silicon chip. He did his graduate work in Neuroscience and in Computer Science at Stanford University, and his undergraduate in work Physics at Princeton University.
Wednesday, November 12, 2014 - 10:00am to 11:00am
Computer Science 402
Host: Jennifer Rexford

Deep Packet Inspection as a Service

Yaron Koral (Princeton University)
[ View Event Abstract ]

Middleboxes play a major role in contemporary networks, as forwarding packets is often not enough to meet operator demands, and other functionalities (such as security, QoS/QoE provisioning, and load balancing) are required. Traffic is usually routed through a sequence of such middleboxes, which either reside across the network or in a single, consolidated location. Although middleboxes provide a vast range of different capabilities, there are components that are shared among many of them.  A task common to almost all middleboxes that deal with L7 protocols is Deep Packet Inspection (DPI). Today, traffic is inspected from scratch by all the middleboxes on its route. In this paper, we propose to treat DPI as a service to the middleboxes, implying that traffic should be scanned only once, but against the data of all middleboxes that use the service. The DPI service then passes the scan results to the appropriate middleboxes. Having DPI as a service has significant advantages in performance, scalability, robustness, and as a catalyst for innovation in the middlebox domain. Moreover, technologies and solutions for current Software Defined Networks (SDN) (e.g., SIMPLE [41]) make it feasible to implement such a service and route traffic to and from its instances.

This is joint work with Anat Bremler-Barr, Yotam Harchol, and David Hay, and will appear at CoNEXT in December 2014.
Yaron received his PhD at Tel Aviv University and is a new postdoc at Princeton.


Thursday, November 13, 2014 - 4:30pm to 5:30pm
CS Department Colloquium Series
Computer Science Small Auditorium (Room 105)
Host: Barbara Engelhardt

Better Science Through Better Bayesian Computation

Ryan Adams (Harvard University)
[ View Event Abstract ]

Ryan AdamsAs we grapple with the hype of "big data" in computer science, it is important to remember that the data are not the central objects: we collect data to answer questions and inform decisions in science, engineering, policy, and beyond.  In this talk, I will discuss my work in developing tools for large-scale data analysis, and the scientific collaborations in neuroscience, chemistry, and astronomy that motivate me and keep this work grounded.  I will focus on two lines of research that I believe capture an important dichotomy in my work and in modern probabilistic modeling more generally: identifying the "best" hypothesis versus incorporating hypothesis uncertainty.  In the first case, I will discuss my recent work in Bayesian optimization, which has become the state-of-the-art technique for automatically tuning machine learning algorithms, finding use across academia and industry. In the second case, I will discuss scalable Markov chain Monte Carlo and the new technique of Firefly Monte Carlo, which is the first provably correct MCMC algorithm that can take advantage of subsets of data.

Ryan Adams is an Assistant Professor of Computer Science at Harvard University, in the School of Engineering and Applied Sciences. He leads the Harvard Intelligent Probabilistic Systems group, whose research focuses on machine learning and computational statistics, with applied collaborations across the sciences.  Ryan received his undergraduate training in EECS at MIT and completed his Ph.D. in Physics at Cambridge University as a Gates Cambridge Scholar under David MacKay.  He was a CIFAR Junior Research Fellow at the University of Toronto before joining the faculty at Harvard.  His Ph.D. thesis received Honorable Mention for the Leonard J. Savage Award for Bayesian Theory and Methods from the International Society for Bayesian Analysis.  Ryan has won paper awards at ICML, AISTATS, and UAI, and received the DARPA Young Faculty Award.