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( Winter 2009 )
Category Seminar Info
2009/04/24 Software Engineering Place: McConnel 320
Time: 13:00 - 14:00
Speaker: Toheed Aslam
Affiliation: McGill University
Title: Array support for AspectJ's field access pointcuts
Abstract: Join points and advices are two fundamental constructs of aspect-oriented programming languages. AspectJ provides a large set of useful pointcuts that enables aspect-oriented programmers to pick out target join points for advice weaving in a highly flexible manner. However, the field access pointcuts of AspectJ do not support array objects in full. When an element of an array field is set or referenced, the corresponding index values and assigned value are not exposed to the advice. This paper presents an extension of AspectJ's field access pointcuts to arrays that exposes such useful context information. This extension has been implemented using the abc compiler for AspectJ. The core of the implementation is a finite-state machine based pointcut matcher that can handle arrays of multiple dimensions in a uniform way.
2009/04/07 General Place: Krieble Lab, Trottier 3120
Time: 15:30 - 16:30
Speaker: Dirk Schlimm
Affiliation: Department of Philosophy (Faculty Lecturer) and School of Computer Science (Associate Member)
Area: History
Title: On the history of programming languages
2009/04/03 CQIL - Cryptography and Quantum Information Place: MC103
Time: 10:30 - 11:30
Speaker: David Poulin
Area: Quantum information
Title: Iterative quantum coding schemes
Abstract: Error correction is an essential ingredient for quantum communication and computation. To achieve good error suppression, current coding techniques typically encode each logical qubit in a very large number of physical qubits. It is possible to suppress errors with much less resources, for instance by encoding into a random code space, but decoding such codes is typically an NP-complete problem. In this talk, I will present some of our efforts at constructing good, efficiently decodable quantum codes, including turbo and sparse codes. I will also discuss the current bottlenecks in constructing provably good probabilistic quantum coding schemes.
2009/03/27 Faculty Candidate Talk Place: MC437
Time: 10:00 - 11:00
Speaker: Christopher Piekert
Affiliation: Computer Science Laboratory, SRI International (formerly Stanford Research Institute)
Area: Cryptography
Title: Lattice-Based Cryptography
Abstract: Most modern cryptography, and “public-key” crypto in particular, is based on mathematical problems that are conjectured to be infeasible (e.g., factoring large integers). Unfortunately, standard public-key techniques are often too inefficient to be employed in many environments; moreover, all commonly used schemes can in principle be broken by quantum computers.

This talk will review my recent work on developing new mathematical foundations for cryptography, using geometric objects called lattices. Compared to more conventional proposals, lattice-based schemes offer a host of potential advantages: they are simple and highly parallelizable, they can be proved secure under mild “worst-case” hardness assumptions, and they remain unbroken by quantum algorithms. Due to the entirely different underlying mathematics, however, realizing even the most basic cryptographic notions has been a major challenge.

Surprisingly, I will show that lattice-based schemes are also remarkably flexible and expressive, and that many important cryptographic goals can be achieved – sometimes even more simply and efficiently than with conventional approaches. Some of our schemes provide interesting twists on old and cherished cryptographic notions, while others introduce entirely new concepts altogether. Biography of Speaker:

Chris Peikert received his Ph.D. in Computer Science from MIT in 2006, following undergraduate studies in CS and Mathematics (also at MIT). His research interests include cryptography, computational complexity, and algorithms, especially as they relate to lattices and error-correcting codes. He is the P.I. of an NSF CyberTrust grant on lattice-based cryptography.

2009/03/23 Faculty Candidate Talk Place: MC437
Time: 10:00 - 11:00
Speaker: Krishnendu Chatterjee
Affiliation: University of California, Santa Cruz
Area: Gaming
Title: Stochastic Games in Synthesis and Verification
Abstract: Dynamic games played on game graphs with winning conditions specified as automata provide the theoretical framework for the study of controller synthesis and many problems related to formal verification. Besides synthesis and verification, these games have been used in several other contexts such as checking interface compatibility, modular reasoning, checking receptiveness. In this talk we first present different game models, that are suited to different applications, and the canonical winning conditions that can be specified as automata. We first consider the strictly competitive (zero-sum) game formulation that is appropriate for controller synthesis. We present a brief overview of the field, summarizing the classical results, and then present our results that significantly improve the complexity for several classes of games. We also present practical algorithms for analysis of several classes of such games.

We then consider the problem of multi-process verification and argue that the zero-sum formulation is too strong for multi-process verification. This is because the environment for a process is typically other processes with their own specifications. On the other hand, the notion of Nash equilibria, that captures the notion of rational behavior in absence of external criteria, is too weak for multi-process verification. We will present a new notion of equilibrium, which we call secure equilibrium. We will show how the new notion of equilibrium is more appropriate for multi-process verification, discuss the existence and computation of such equilibrium for graph games. Biography of Speaker:

After obtaining a Bachelor of Technology (Honours) from the Indian Institute of Technology, Krishnendu Chatterjee continued his graduate studies at the University of California, Berkeley, where he received a Master in 2004 and a Doctorate in 2007. He is presently a Post-doctoral researcher at the University of California, Santa Cruz.

2009/03/11 Faculty Candidate Talk Place: MC497
Time: 10:00 - 11:00
Speaker: Xin Gao
Affiliation: David R. Cheriton School of Computer Science, University of Waterloo
Area: Protein Structure Determination
Title: Zero in on the Fully Automated NMR Protein Structure Determination
Abstract: High-throughput structural genomics requires parallelizable technologies for high-resolution protein structure determination. Nuclear Magnetic Resonance (NMR) would be such a technology if its tedious and lengthy process could be fully automated. The key road blocks to this goal are peak-picking from noisy experimental spectra, resonance assignment from noisy peaks, and structure generation from incomplete assignments. These steps are inter-related and should be considered only as a whole.
In this talk, I will describe our efforts on a fully automated protocol for NMR protein structure determination. We have developed a singular value decomposition-based peak-picking method, PICKY, which achieves an average of 88% recall and 74% precision over 32 raw NMR spectra extracted from eight proteins. Existing resonance assignment methods, however, do not work well on incomplete and imperfect peak lists. Consequently, we have designed an integer linear programming-based assignment method. It significantly outperforms other existing programs on both perfect peak lists and noisy peak lists. With the partial resonance assignments, FALCON-NMR is developed as a hidden Markov model-based torsion angle sampling method. The entire system, AMR, has been successfully tested on four proteins with weights of approximately 15kDa. Biography of Speaker:

Having received a B.Sc. degree in 2004 from China’s Tsinghua University in Beijing, Xin Gao applied to the University of Waterloo where he began working on his doctoral thesis in the area of bioinformatics, specifically addressing nuclear magnetic resonance (NMR)-based protein structure determination and prediction. He is expecting to graduate next June.

2009/03/10 Faculty Candidate Talk Place: MC437
Time: 10:00 - 11:00
Speaker: Niranjan Nagarajan
Affiliation: Postdoctoral Fellow, University of Maryland
Area: DNS Sequencing
Title: Genomics in Context: Computational Challenges in Microbial Genomics
Abstract: Recent advances in the cost and speed of sequencing DNA have enabled researchers to map ambitious goals for the study of the microbial diversity around us and their role in health and disease. The challenges in achieving these goals are twofold:
Firstly, cheap sequencing does not necessarily translate into cheap genomes. The computational effort of assembling experimentally obtained sequences into a genome most often fails, producing a large collection of unordered sequence fragments. I will present a promising new approach that relies on inexpensive maps of "landmarks" in the genome to produce a much more complete picture of the genome. I will also present results from a more theoretical analysis of the assembly problem that aims to provide insights into why assembling genomes is difficult and what characteristics of the data can make it an easier computational task.
The second challenge is the design of efficient algorithms to extract meaningful information out of the flood of assembled genomes. Undoubtedly, the availability of these genomes has come as a boon for biologists, enabling the joint study of many related microbial species and providing the requisite evolutionary and ecological context to these studies. However, tools to compare the large number of closely related genomes, to answer questions such as "Is there any evidence for rearrangements in the genomes?" or "What makes this species pathogenic?" are still in their infancy, especially in the context of experimental errors and ambiguities in the genomes. I will describe results from a recent work that uses a new biclique mining algorithm to uncover genomic re-assortments in large viral datasets, while accounting for phylogenetic uncertainty in the data.
I will also briefly outline some promising extensions to this work as well as some of the other research questions that interest me in the study of microbial populations.
This is joint work with Mihai Pop, Carl Kingsford, Tim Read and James White. Biography of Speaker:

In 2000, after having received a B.A. in computer Science and Mathematics from the Ohio Wesleyan University of Delaware, Niranjan Nagarajan went on to Cornell University where he first received a Master in Computer Science in 2004, and then a Ph.D. in Computer Science in 2006. He is currently a Postdoctoral Fellow in the Center for Bioinformatics and Computational Biology at the University of Maryland.

2009/03/03 Faculty Candidate Talk Place: MC497
Time: 10:00 - 11:00
Speaker: Jian Ma
Affiliation: Postdoctoral Fellow, University of California (Santa Cruz)
Area: Genome Sequencing
Title: Reconstructing the Evolutionary History of Mammalian Genomes
Abstract: Data generated from numerous mammalian genome sequencing projects have provided an unprecedented opportunity to use comparative genomics to computationally reconstruct the trajectory of all the genetic changes leading to modern placental mammals since their common ancestor living approximately 100 million years ago. However, this task is algorithmically extremely challenging. On a small scale, genomes have undergone point mutations, small insertions and deletions. More dramatically, at a larger scale, rearrangements, duplications, large insertions and deletions have led to varied karyotypes.
In this talk, I will first introduce the method of contiguous ancestral regions, which reconstructs the ancestral karyotype based on modern species. Then I will discuss a recent work on reconstructing evolutionary histories involving complex operations on genomes, called the infinite sites model of genome evolution, which combines rearrangements, large insertions and deletions, and duplications into a single, computationally tractable model. Finally, I will discuss a number of ongoing projects and future plans in order to more efficiently and more accurately document the detailed changes in genome evolution, and to discover how evolution has shaped us at the molecular level Biography of Speaker:

Jian Ma currently works with David Haussler at the Center for Biomolecular Science and Engineering. His research interests lie at the intersection of computer science, genomics and evolutionary biology. In 2006 he received his Ph.D. in Computer Science from The Pennsylvania State University. He has also received B.Sc. and M.Sc. degrees, in 2000 and 2003 respectively, from Fudan University, Shanghai, China

2009/03/02 Faculty Candidate Talk Place: MC437
Time: 10:00 - 11:00
Speaker: Hamed Hatami
Affiliation: University of Toronto
Area: Discrete Mathematics
Title: Two Problems in Discrete Mathematics with Analytic Nature
Abstract: First, I will prove some results in the direction of answering a question of Lovasz about the norms defined by certain combinatorial structures.

Inspired by the similarity of the definitions of $L_p$ norms, trace norms, and Gowers norms, we introduce and study a wide class of norms containing these, as well as many other norms. It will be proven that every norm in this class must satisfy a Cauchy-Schwarz-Gowers type inequality. I will show an application of this inequality to a conjecture of Sidorenko about subgraph densities.

Next, I disprove a conjecture of Dinur and Friedgut regarding the structure of the functions on the continuous cube with bounded total influence. Then I will prove a theorem which characterizes the structure of all such functions. Biography of Speaker:

After obtaining a B.Sc. from Iran’s Sharif University of Technology, Hamed Hatami then received a Master in Computer Science in 2005 from the University of Toronto, where he is presently pursuing his graduate studies and is expecting his Ph.D. Diploma this Summer.

2009/02/27 Software Engineering Place: McConnel 320
Time: 13:00 - 14:00
Speaker: Eric Bodden
Affiliation: McGill University
Title: Dependent Advice: A General Approach to Optimizing History-based Aspects
Abstract: Many aspects for runtime monitoring are history-based: they contain pieces of advice that execute conditionally, based on the observed execution history. History-based aspects are notorious for causing high runtime overhead. Compilers can apply powerful optimizations to history-based aspects using domain knowledge. Unfortunately, current aspect languages like AspectJ impede optimizations, as they provide no means to express this domain knowledge. In this paper we present dependent advice, a novel AspectJ language extension. A dependent advice contains dependency annotations that preserve crucial domain knowledge: a dependent advice needs to execute only when its dependencies are fulfilled. Optimizations can exploit this knowledge: we present a whole-program analysis that removes advice-dispatch code from program locations at which an advice’s dependencies cannot be fulfilled. Programmers often opt to have history-based aspects generated automatically, from formal specifications from model-driven development or runtime monitoring. As we show using code-generation tools for two runtime-monitoring approaches, tracematches and JavaMOP, such tools can use knowledge contained in the specification to automatically generate dependency annotations as well. Our extensive evaluation using the DaCapo benchmark suite shows that the use of dependent advice can significantly lower, sometimes even completely eliminate, the runtime overhead caused by history-based aspects, independently of the specification formalism.
2009/02/24 Faculty Candidate Talk Place: MC497
Time: 10:00 - 11:00
Speaker: Derek Ruths
Affiliation: Computer Science, Rice University
Area: Computational and mathematical modeling
Title: Deriving Executable Models of Biochemical Network Dynamics from Qualitative Data
Abstract: Progress in advancing our understanding of biological systems is limited by their sheer complexity, the cost of laboratory materials and equipment, and limitations of current laboratory technology.
Computational and mathematical modeling provides ways to address these limitations through hypothesis generation and testing without experimentation - allowing researchers to analyze system structure and dynamics in silico and, then, design lab experiments that yield desired information about phenomena of interest. These models, however, are only as accurate and complete as the data used to build them. Currently most models are constructed from quantitative experimental data. However, since accurate quantitative measurements are hard to obtain and difficult to adapt from literature and online databases, new sources of data for building models need to be explored. In my research, I design methods for building and executing computational models of cellular networks based on qualitative experimental data, which is more abundant, easier to obtain, and reliably reproducible. Such executable models allow for in-silico perturbation, simulation, and exploration of biological systems. In this talk, I will present two general strategies for building and executing Petri net-based models of biochemical networks. Both have been successfully used to model and predict the dynamics of signaling networks in normal and cancer cell lines, rivaling the accuracy of existing methods trained on quantitative data. Biography of Speaker:

Derek Ruths is studying in the Department of Computer Science at Rice University in Houston, Texas, where he received a B.Sc. in 2003, a Master in 2006, and is expecting his Ph.D. Diploma in the Spring of 2009.

2009/02/23 Faculty Candidate Talk Place: MC437
Time: 10:00 - 11:00
Speaker: Jérôme Waldispühl
Affiliation: Visiting appointment in the Department of Computer Science (CSAIL), Massachusetts Institute of Techn
Area: Computational Structural Biology
Title: Ensemble Predictions of RNA and Protein Structures
Abstract: In this talk, I will describe my Ph.D. and postdoctoral work in the area of computational structural biology. Throughout this time, my work has explored new ensemble modeling techniques which can analyze and predict an entire landscape of structural and evolutionary solutions, rather than simple single answer optimizations. This philosophy has a broad impact on our understanding of protein and RNA molecules -- both of which I have applied this approach to and which I will address in this talk.
First, I will introduce a new family of algorithms for investigating the folding landscape of transmembrane beta-barrel proteins based only on sequence information, broad investigator knowledge, and a statistical-mechanical approach using the Boltzmann partition function. This provides predictions of all possible structural conformations that might arise in-vivo, along with their relative likelihood of occurrence. Using a parameterizable grammatical model, these algorithms incorporate high-level information, such as membrane thickness, with an energy function based on stacked amino-acid pair statistical potentials to predict ensemble properties, such as the likelihood of two residues pairing in a beta-sheet, or the per-residue X-ray crystal structure B-value.
Then, I will present recent algorithmic advances we have made in the techniques of exploration and analysis of RNA sequence/structure maps, an abstract framework which allows us to bridge different aspects of the sequence/structure relationship. In particular, we have successfully applied these techniques to discover deleterious mutations that radically modify the structure in the Hepatitis C virus cis-acting replication element. At a higher level, we provided evidence that the complete sequence of the 3'UTR of the GB RNA virus C has been optimized to preserve the secondary structure of the evolutionarily conserved stem regions from the destabilizing effect of pointwise mutations. Biography of Speaker:

Jérôme Waldispühl is currently an Instructor in Applied Mathematics at MIT. He received a B.Sc. in Mathematics, with honors, from the University of Nice and Sophia-Antipolis (France), and began his graduate studies at the ENS Ulm & Cachan where he received a Master in Computer Science, again with honors. He then obtained a Ph.D. from the École Polytechnique (France).

2009/02/13 Faculty Candidate Talk Place: MC437
Time: 15:30 - 16:30
Speaker: Yury Makarychev
Affiliation: Princeton University
Area: Theoretical Computer Science Position
Title: Approximation Algorithms, Semi-definite Programming and Unique Games
Abstract: Best known approximation algorithms for many combinatorial optimization problems are based on semi-definite programming (SDP). Are these algorithms optimal? Are there any techniques better than SDP? We do not know the answer. However, the Unique Games Conjecture (UGC) implies that for a wide class of problems known SDP algorithms are indeed optimal. One of the central questions in the theory of approximation algorithms is whether this conjecture is true or false. To answer this question we need to better understand Unique Games and design good approximation algorithms for them.

In the first part of the talk, we will describe approximation algorithms for Unique Games that significantly improve previously known approximation guarantees and that are asymptotically optimal assuming UGC.

Then we will discuss other possible approaches to Unique Games. We will describe the Sherali-Adams (SA) hierarchy of relaxations for combinatorial optimization problems (the strongest LP hierarchy considered in the literature). We will prove negative results for MAX CUT, Vertex Cover, Max Acyclic Sub-graph and other problems. In particular, we will show that algorithms based on SA relaxations cannot disprove UGC.

Based on joint papers with Moses Charikar, Eden Chlamtac, and Konstantin Makarychev. Biography of Speaker:

Yury Makarychev began his graduate studies at Moscow State University where he received a Master in Mathematics and Applied Mathematics in 2001. He then applied to the Department of Computer Science at Princeton University where his Ph.D. Diploma was delivered in 2007.

2009/02/11 CQIL - Cryptography and Quantum Information Place: McConnell 103
Time: 10:30 - 11:30
Speaker: Jurg Wullschleger
Affiliation: University of Bristol
Title: Oblivious Transfer from Weak Noisy Channels

Various results show that oblivious transfer can be implemented using the assumption of noisy channels. Unfortunately, this assumption is not as weak as one might think, because in a cryptographic setting, these noisy channels must satisfy very strong security requirements. Unfair noisy channels, introduced by Damgard, Kilian and Salvail [Eurocrypt '99], reduce these limitations: They give the adversary an unfair advantage over the honest player, and therefore weaken the security requirements on the noisy channel.

In this talk I will show how the idea of unfair noisy channels can be generalized. I introduce two new models of cryptographic noisy channels (the weak erasure channel and the weak binary symmetric channel) and show how they can be used to implement oblivious transfer.

A preliminary version of this work is available at

2009/02/09 DMO - Discrete Mathematics and Optimization Place: Burnside 1205
Time: 16:30 - 17:30
Speaker: Dan Hefetz
Affiliation: ETH Zurich
Title: Weighted colorings and Alon-Tarsi choosability
Abstract: Alon and Tarsi have introduced an algebraic technique for proving upper bounds on the choice number of graphs; the upper bound on the choice number of G obtained via their method, was later coined the Alon-Tarsi number of G and was denoted by AT(G). In the talk I will relate this parameter to a certain weighted sum of the proper colorings of $G$. Other than the appealing notion of obtaining upper bounds on the choice number of a graph via its proper colorings (in some sense), this result has many applications. Some of them are known; for these we give unified, and often also simpler and shorter proofs; and some are new. In the first part of the talk I will introduce chromatic, choice, and Alon-Tarsi numbers of graphs. In the second part I will state the main result and some of its applications.
2009/02/04 CQIL - Cryptography and Quantum Information Place: McConnell 103
Time: 10:30 - 11:30
Speaker: Patrick Hayden
Affiliation: McGill University
Area: Quantum Information
Title: The Fidelity Alternative and Quantum Measurement Simulation
Abstract: Being able to correct errors in a quantum channel is equivalent to preventing any information leakage to the channel's environment. In this talk I'll present an analogous relationship between geometry preservation in a quantum channel and a type of weak encryption to the environment. This equivalence in turn connects equality testing for quantum states, known as quantum identification, with encryption and can be used to show that the (amortized) quantum identification capacity of a quantum channel is equal to its entanglement-assisted classical capacity. (Joint work with Andreas Winter.)
2009/01/28 CQIL - Cryptography and Quantum Information Place: MC103
Time: 10:30 - 11:30
Speaker: Frederic Dupuis
Affiliation: McGill University
Title: The capacity of quantum channels with side information at the transmitter
Abstract: We consider the problem of coding for quantum channels with side information that is available ahead of time at the transmitter but not at the receiver. We find a single-letter expression for the entanglement-assisted quantum capacity of such channels which closely parallels Gel'fand and Pinsker's solution to the classical version of the same problem.
2009/01/26 Vision, Graphics, and Robotics Place: ENGMC 437
Time: 15:00 - 16:00
Speaker: Chris Pal
Affiliation: University of Rochester & Ecole Polytechnique
Area: Computer Vision
Title: Learning Conditional Random Fields for Stereo with Sparse Message Passing
Abstract: As we construct richer models for stereo vision there is growing interest in learning model parameters. State of the art methods for dense correspondence stereo seek to find a globally optimal solution for per pixel depth estimates in an image. These methods frequently rely on recent advances in energy function minimization techniques based on graph cuts. In this talk I show how it is possible to re-formulate classical energy based stereo models as Conditional Random Fields (CRFs). CRFs are discriminative versions of traditional Markov Random Fields (MRFs). Given a formulation of the stereo problem as a CRF, I show how we can efficiently learn models using graph cuts or with a new technique for approximate inference called sparse message passing. I show how we can view this new technique as a variational method and perform rigorous, bound based optimizations. I show how sparse message passing leads to more accurate learning than graph cuts while reducing inference times over traditional belief propagation by over an order of magnitude. As such, we believe sparse message passing has great potential for accelerating learning and inference for other graphical models. Biography of Speaker:

Chris Pal is an assistant professor of Computer Science at the University of Rochester (currently on leave) and a Chercheur with the Département de génie informatique et génie logiciel of the École Polytechnique de Montréal. He has worked with the University of Toronto, the University of Massachusetts, Microsoft Research and Interval Research. He earned his doctorate from the University of Waterloo. He has three patents in the areas of computer vision, pattern recognition, machine learning and human computer interaction.

2009/01/15 Faculty Candidate Talk Place: McConnell 437
Time: 10:00 - 11:30
Speaker: Natasa Przulj
Affiliation: Computer Science Department, University of California (Irvine)
Area: Bioinformatics
Title: From Network Topology to Biological Function and Disease
Abstract: Historically, a new natural science proceeds through three stages of development: first, amassing observations about the world; second, developing simplistic models capable of approximately reproducing the observations; and finally, the development of accurate predictive theoretical models under which the observations and earlier models become evident. Our current understanding of biological networks can be likened to the state of physics before Newton: although Copernicus, Kepler, Galileo and others had amassed a huge corpus of observations, and even some simplistic, case-specific models describing various phenomena, there was no theoretical framework tying it all together to provide understanding. Systems biology is currently somewhere between the first and second stages: we can hardly even describe the observational data mathematically, much less understand it theoretically. Analysis and comparison of genetic sequences is well into the second stage mentioned above and making tentative steps into the third, but network analysis is just barely entering the second stage. In this talk I discuss new tools, developed in my lab, which are advancing network analysis into this second stage, and possibly giving hints towards the third stage -- a theoretical understanding of the structure of biological networks. Analogous to tools for analyzing and comparing genetic sequences, we are developing new tools that decipher large network data sets, with the goal of improving biological understanding and contributing to development of new therapeutics. Because nature is variable and the data are noisy, traditional graph isomorphism is of little use for graph comparison, and a more flexible, intentionally approximate approach is necessary. We introduce a systematic measure of a network's local structure that imposes a large number of local similarity constraints on networks being compared. In particular, we generalize the degree of a node to a degree vector describing the local topology around a node. We demonstrate that this local node similarity corresponds to similarity in biological function and involvement in disease. We also show how to use the degree vectors to design a network alignment algorithm that produces correct phylogenetic trees. Next, we demonstrate how statistics from large numbers of these local similarity measures can be combined to provide a global network similarity measure. Using this global similarity measure, we demonstrate that protein-protein interaction (PPI) networks are better modeled by geometric graphs than by any previous model. The geometric model is further corroborated by demonstrating that PPI networks can explicitly be embedded into a low-dimensional geometric space. Finally, we argue for a theoretical reason why PPI networks might be geometric. Biography of Speaker:

Nataša Pržulj received a B.Sc. in Computer Science in 1997 from Simon Fraser University, a Computer Science Master from the University of Toronto in 2000, where she continued her graduate studies and received a Ph.D. Diploma in Computer Science in the Spring of 2005.