Tuesday, November 7, 2017.
Title: Uncovering and Addressing Security Assumptions About Hardware
Abstract: Due to manufacturing error, reliability failure modes, or just complex feature design, hardware occasionally exhibits surprising behaviors. Unknowingly, software security can rest on incorrect assumptions about hardware minutiae. In my research I expose how previously unknown or under-appreciated hardware behaviors can result in side-channels that have high-level privacy and security impact in software like web browsers. Motivated by these attacks, I also work to build architectures for both software and hardware that are inherently resistant to side-channels for mitigation against both known and unknown attacks.
In this talk I highlight attacks we have developed using details of hardware behavior, as well as a defensive browser scheme to mitigate such attacks. I first describe how we use floating-point timing side-channels to break web privacy in all major desktop web browsers. I then use these attacks and others as a motivation for our defensive browser proposal: Fermata. I discuss both the complete vision of Fermata as well as diving into the details of our prototype implementation: Fuzzyfox. Fuzzyfox is an incomplete Fermata implementation designed to field-test the ideas of Fermata and their impact on security and usability.
David is a PhD candidate in Computer Science at UC San Diego working in security, systems, and hardware. His research interests focus on the collision between software security theory and hardware reality. Previously, David received his B.S. in Computer Science from Carnegie Mellon University in 2011 and co-founded the San Diego-based security company Somerset Recon in 2012. He expects to defend his thesis in 2018.
Faculty Host: Dr. Christian Collberg
Thursday, October 26, 2017.
Title: Machine Learning By the People, for the People
Machine learning is concerned with the design and analysis of algorithms that compute general facts about an underlying data-generating process by observing limited amounts of that data. Classically, the outcome of a learning algorithm is considered in isolation from the effects that it may have on the process that generates the data or computes the outcome. With data science and the applications of machine learning revolutionizing day-to-day life, however, people and organizations increasingly interact with learning systems. It is essential to account for the wide variety of social and economical limitations, aspirations, and behaviors demonstrated by these people and organizations, which fundamentally change the nature of learning tasks and the challenges involved. I will describe three examples from my work on the theoretical aspects of machine learning and economics that account for these interactions: learning optimal policies in game-theoretic settings, without an accurate behavioral model, by interacting with people; learning the parameters of an optimal economic mechanism when the behavior and preferences of people can change over time and as the result of their interactions with the learning system; and collaborative learning in a setting where multiple learners attempt to discover the same underlying concept.
Nika Haghtalab is a Ph.D. candidate at the Computer Science department of Carnegie Mellon University, co-advised by Avrim Blum and Ariel Procaccia. She is a recipient of the IBM and Microsoft Research Ph.D. fellowships, and the Siebel Scholarship.
Faculty Host: Dr. John Kececioglu
Tuesday, October 10, 2017.
Title: Visual Analytics of Stance in Social Media
This talk will give an overview of the StaViCTA framework project that aims to tackle the challenge of investigating stance (such as attitudes, feelings, perspectives, or judgements) in written human communication. After introducing our definition of stance and providing visualization showcases on how stance analysis might be used to better understand social media, I will discuss several visual analytics tools that were especially designed to support the development of stance classification. They reach from approaches providing fundamental insights into text data that are necessary for building an appropriate linguistic stance theory to approaches for text data annotation and visualization that facilitate the entire process of training a stance classifier.
Andreas Kerren received the B.S. and M.S. degrees as well as his PhD degree in Computer Science from Saarland University, Saarbrücken (Germany). In 2008, he achieved his habilitation (docent competence) from Växjö University (Sweden). Dr. Kerren is currently a Full Professor in Computer Science at the Department of Computer Science, Linnaeus University (Sweden), where he is heading the research group for Information and Software Visualization, called ISOVIS. His main research interests include the areas of Information Visualization, Visual Analytics, and Human-Computer Interaction. He is, among others, editorial board member of the Information Visualization journal, has served as organizer/program chair at various conferences, such as IEEE VISSOFT 2013/2018, IVAPP 2013-15/2018 or GD 2018, and has edited a number of successful books on human-centered visualization.
Faculty Host: Dr. Helen Purchase
Tuesday, October 2, 2017.
Title: Depth Based Visualizations for Ensemble Data and Graphs
Ensemble datasets are being increasingly seen in a range of domains. Such datasets often appear as a result of a collection of solutions recorded from simulation runs with different parameters/initial conditions, as well as precision uncertainty associated with repeated measurements of a natural phenomenon. Studying ensembles in terms of the variability between members can provide valuable insight into the generating process; particularly when mathematically modeling the process is complex or infeasible. Ensemble visualization can be a powerful way to study the generating process by analyzing ensembles of solutions or possible outcomes. In ensemble visualization, key interests include understanding the typical/atypical members as well as variability in the ensemble. In absence of any information about the underlying generative model, a family of nonparametric methods known as data depth is able to quantify the notion of centrality and provide center-outward order statistics for ensembles. In this talk I will explore novel applications of existing depth based methods, and describe my research on new advantageous visualizations—and associated methods to compute depth—for ensembles of various data types—namely, 3D isocontours, paths on a graph, nodes on a graph, graphs, and data in inner product spaces.
Mukund Raj graduated with a B.S. degree in Electronics and Telecommunications Engineering from the University of Pune in 2008. From 2008 to 2011 he worked as a software engineer at Infosys Labs, where he worked on developing web based accessibility tools. From 2011 to 2013 he was a member of the Visual Perception and Spatial Cognition lab at the University of Utah. In 2013 he graduated with an M.S. degree in computing from the University of Utah, where is also currently working toward a PhD degree in computing.
Faculty Host: Dr. Alon Efrat
Thursday, September 28, 2017.
Title: Designing Secure Systems for Censorship Resistance
Tools to circumvent censorship aim to hide the websites that users access from a government censor. Some even disguise traffic patterns by mimicking allowed protocols or using services such as Skype to tunnel censored content. These systems have evolved as a result of a cat-and-mouse game between nation-state censors and censorship resistors: as new techniques for evading censorship arise, censors tweak their filtering systems to identify the weaknesses in existing tools that signal their usage. In this talk, I will describe key events in the censorship arms race and how to design and implement censorship circumvention tools that tilt the arms race in the favour of the censorship resistor.
Faculty Host: Dr. David Lowenthal
Thursday, Septemeber 14, 2017.
Title: Visual Analytics Methods for Spatiotemporal Analysis
From smart phones to fitness trackers to sensor enabled buildings, data is currently being collected at an unprecedented rate. Now, more than ever, data exists that can be used to gain insight into how policy decisions can impact our daily lives. For example, one can imagine using data to help predict where crime may occur next or inform decisions on police resource allocations or diet and activity patterns could be used to provide recommendations for improving an individual's overall health and well-being. Underlying all of this data are measurements with respect to space and time. However, finding relationships within datasets and accurately representing these relationships to inform policy changes is a challenging problem. This research talk will address fundamental questions of how we can effectively explore such space-time data in order to enhance knowledge discovery and dissemination. Examples in this talk will focus on my lab group's recent research efforts in criminal analysis looking at methods of extending kernel density estimation, a theoretical analysis of cluster projections in choropleth maps, and novel visualization methods for tracking geographical hotspots with an emphasis on disease surveillance.
Ross Maciejewski is an Associate Professor of Computer Science at Arizona State University whose primary research interests are in the areas of geographical visualization and visual analytics focusing on public health, social media, sustainability, criminal incident reports and dietary analysis. He has served on the organizing committee for the IEEE Conference on Visual Analytics Science and Technology and the IEEE/VGTC EuroVis Conference and is serving as the Vice Chair for IEEE VIS 2017 in Phoenix, AZ. His work has been recognized through award winning submissions to the IEEE Visual Analytics Contest (2010, 2013 and 2015), and a best paper award in EuroVis (2017). He is a Fellow of the Global Security Initiative at ASU and the recipient of an NSF CAREER Award (2014).
Tuesday, September 12, 2017.
Speaker: Kyle Fox, Ph.D.
Title: Maps Between Geometric Data Sets
We will discuss two variants of the problem of computing maps between data sets. First, we will describe a near-linear time approximation algorithm for computing dynamic time warping maps between point sequences, a central problem in the analysis of trajectories and other curves. Next, we will describe fast approximation algorithms for computing transportation maps, a widely used method for comparing and relating two distributions. In both cases our goal is to develop simple, fast, hopefully near-linear-time approximation algorithms.
Kyle Fox recently joined the University of Texas at Dallas as an Assistant Professor after completing a postdoc at Duke University. He obtained his Ph.D. from the University of Illinois at Urbana-Champaign in 2013. His research interests lie primarily in algorithms, including geometric algorithms, computational topology, combinatorial optimization, and their applications to data analysis and graph algorithms. He was a recipient of the Department of Energy Office of Science Graduate Fellowship and a winner of the C. W. Gear Outstanding Graduate Student award while at the University of Illinois.
Thursday, August 24, 2017.
Title: Graph Drawings: as created by users (or 'Doing the Future Work')
Much effort has been spent on designing algorithms for the automatic layout of graphs. Typically, the worth of these algorithms has been determined by their computational efficiency and by the extent to which the graph drawings they produce conform to pre-defined "aesthetics" (for example, minimising the number of edge crosses and edge bends, or maximising symmetry).
Prior experimental work has focussed on the extent to which the layout of a graph drawing assists with the comprehension of the embodied relational information. This seminar presents an alternate approach to determining the relative worth of graph layout aesthetics, based on how users create their own graph drawings. The seminar will present the results of both the published research experiments, as well as two follow-up studies.
Dr Helen Purchase is Senior Lecturer in the School of Computing Science at the University of Glasgow. She has worked in the area of empirical studies of graph layout for several years, and also has research interests in visual aesthetics, task-based empirical design, collaborative learning in higher education, and sketch tools for design. She has recently written a book on Empirical methods for HCI research.