Colloquium

Schedule for 2007/2008

date	speaker	title	host
Sep 25	Panos Papadimitratos, EPFL	Securing Vehicular Communications	Kotz
Oct 1
Oct 8	Evdokia Nikolova, MIT	A Strategic Model for Information Markets	Fleischer
Oct 15
Oct 22
Oct 29
Nov 5
Nov 7	Ben Shneiderman, UMD	Information Visualization for Knowledge Discovery	Pellacini
Nov 12	Chris Rose		Kotz
Nov 19	Mark Moir		Jayanti
Nov 26
Dec 3
Dec 10

Abstracts

Panos Papadimitratos, Securing Vehicular Communications Moore B03 at 4:00 pm

Vehicular communications (VCs) and vehicular ad hoc networks (VANETs) lie at the core of several on-going industry and academic research initiatives. Vehicles and roadside infrastructure units equipped with sensors, computers, and wireless transceivers enable a range of applications that enhance transportation safety and efficiency. VCs offer a rich set of tools but also make possible a formidable set of abuses. For example, an adversary could 'contaminate' large portions of the VANET with false information; or, intercept vehicle-originating messages, track the vehicles locations and transactions, and infer sensitive information about their passengers. Without security mechanisms, VCs can make antisocial and criminal behavior easy, essentially jeopardizing the benefits of deploying VCs systems.
In this talk, we discuss this new and uniquely constrained problem: how to secure vehicular communications. First, we discuss design principles and requirements as well as elements of a secure VCs architecture. Then, we present mechanisms to enhance privacy yet provide strong security; evict misbehaving or faulty nodes; and, extend the traditional notion of trust to data-centric trust, that is, attribute trustworthiness to node-reported data per se. The presented results reflect recent work, jointly with researchers of the Univ. of Maryland, the Pol. of Torino, and the SeVeCom and Car-2- Car communication consortia.

Evdokia Nikolova, A Strategic Model for Information Markets Carson L01 at 4:30 pm

This talk is about information (aka prediction) markets--these are markets designed specifically to aggregate traders' information, and are becoming increasingly popular for predicting future events.
Recent research in information markets has resulted in two new designs, market scoring rules and dynamic parimutuel markets. We develop an analytic method to guide the design and strategic analysis of information markets. Our central contribution is a new abstract betting game, the projection game, that serves as a useful model for information markets. We demonstrate that this game can serve as a strategic model of dynamic parimutuel markets, and also captures the essence of the strategies in market scoring rules. The projection game is tractable to analyze, and has an attractive geometric visualization that makes the strategic moves and interactions more transparent. We use it to prove several strategic properties about the dynamic parimutuel market. We also prove that a special form of the projection game is strategically equivalent to the spherical scoring rule, and it is strategically similar to other scoring rules.
Finally, we illustrate two applications of the model to analysis of complex strategic scenarios: we analyze the precision of a market in which traders have inertia, and a market in which a trader can profit by manipulating another trader's beliefs.
Joint work with Rahul Sami.

Ben Shneiderman, Information Visualization for Knowledge Discovery

Interactive information visualization tools provide researchers with remarkable capabilities to support discovery. By combining powerful data mining methods with user-controlled interfaces, users are beginning to benefit from these potent telescopes for high-dimensional data. They can begin with an overview, zoom in on areas of interest, filter out unwanted items, and then click for details-on-demand. With careful design and efficient algorithms, the dynamic queries approach to data exploration can provide 100msec updates even for million-record databases. This talk will start by reviewing the growing commercial success stories such as www.spotfire.com, www.smartmoney.com/marketmap and www.hivegroup.com. Then it will cover recent research progress for visual exploration of large time series data applied to financial, medical, and genomic data (www.cs.umd.edu/hcil/timesearcher ). Our next step was to combine these key ideas to produce the Hierarchical Clustering Explorer 3.0 that now includes the rank-by-feature framework (www.cs.umd.edu/hcil/hce). By judiciously choosing from appropriate ranking criteria for low-dimensional axis-parallel projections, users can locate desired features of higher dimensional spaces. Finally, these strategies of unifying statistics with visualization are applied to network data. Demonstrations will be shown.