Thursday, July 6, 2017 - 11:00am - Gould-Simpson (GS) 906
Speaker: Tony Huang, Ph.D.
Title: "Role of Crossing Angle in Network Visualization"
Abstract: Many of the real world networks are visualized into node-link diagrams for a better understanding of them. However, the same network can be visualized in many different ways by simply changing the layout and different layouts affect human network comprehension differently. As a result, a number of quality metrics have been proposed in network visualization to ensure subsequent visualizations are easy to read and understand. Among those metrics, edge crossings have been identified as the most important factor with the greatest negative impact on humans. Subsequently, much effort has been devoted to crossing minimization in the design of network visualizations. However, crossing minimization is a difficult problem both in visualization design and implementation. A question that has long been asked is: is it possible to achieve a better or the same level of visualization quality if we do not minimize the number of crossings?
In this talk, Dr Huang will introduce his research that led to a positive answer to this question and a finding of a new metric: crossing angle. This talk will start with an exploratory eye tracking study of how people read networks. This study revealed a surprising finding that crossings may not as bad as we normally think. Then a second study is presented demonstrating that a visualization with large-angle crossings can be as good as a visualization with no crossings and that the negative impact of crossings can be reduced by increasing crossing angles.
Bio: Dr Huang is Director of Collaboration and Visual Analytics Research Lab at the University of Tasmania, Australia. He received his PhD in computer science from University of Sydney in 2008. His main research interests lie in human-centred computing in general, and Human-Computer Interaction and Visual Perception in particular. He is the author of over 100 publications in these areas including a book entitled "Handbook of human centred visualization."
Faculty Host: Dr. Stephen Kobourov
Tuesday, March 28, 2017 - 11:00am - Gould-Simpson (GS) 906
Speaker: Jian Huang, Ph.D. Candidate
Title: "Building Next-Generation Flash Storage Systems: From Data Centers to Wearables"
Abstract: Flash-based storage (e.g., SSD) has been widely adopted in almost every kind of platform spanning all the way from wearables, to mobiles, to data center servers. Since their arrival more than a decade ago, Flash has been improved significantly in terms of latency, throughput and capacity. While storage hardware has evolved, the corresponding software stack is the main bottleneck not only for Flash but also for imminent storage media deemed to be significantly faster than Flash.
In this talk, I will demonstrate new approaches to designing flash-based storage systems to unleash the power of hardware devices while preserving the simplicity and guarantees of system abstractions. Specifically, I will present three systems (FlashBlox, FlashMap, and WearDrive) that enable applications leverage the power of Flash with little software overheads by challenging conventional wisdom on storage system design. They improve performance and energy-efficiency significantly for applications in large-scale data centers as well as in small smart devices such as wearables.
Bio: Jian Huang is a Ph.D. candidate in the School of Computer Science at Georgia Institute of Technology. His research interests lie in the areas of computer systems, including operating systems, systems architecture, systems reliability and security, and distributed systems. He enjoys building practical, reliable, secure and high-performance systems. Most of his recent work focuses on building memory and storage systems with new and emerging memory technologies such as flash memory, battery-backed DRAM and phase-change memory. His research contributions have been published at ASPLOS, FAST, USENIX ATC, ISCA, VLDB, SoCC, and IPDPS. His work WearDrive won the Best Paper Award at USENIX ATC in 2015 and attracted popular press coverage in more than eight countries. His work FlashMap won the IEEE Micro Top Picks Honorable Mention in 2016. Most of the technologies he has developed have had an impact on industrial and real-world systems, some of them are being transferred into products including those at Microsoft data centers.
Faculty Host: Dr. Beichuan Zhang
Tuesday, March 21, 2017 - 11:00am - Gould-Simpson (GS) 906
Speaker: Yue Cheng, Ph.D. Candidate
Title: "Breaking the Monolith: Rethinking Storage System Design"
Abstract: Distributed object-based storage systems serve the growing and disparate needs of almost all of large-scale web services in use today. This is driving the proliferation of a wide variety of distributed object-based stores. However, extant, monolithic, scale-out storage system designs present unique challenges in adapting to the ever-changing storage requirements in both efficiency (for example, performance and resource efficiency), and flexibility (for example, ease-of-use and programmability).
My research takes two crucial steps on this difficult road to optimize and design better object-based stores. In this talk, I will first show that an approach to storage system design based on a simple core principle, resource partitioning, can yield systems with significantly improved performance and resource efficiency under dynamic, skewed, and multi-tenancy workloads. I will show how to effectively exploit fine- and coarse-grained resource partitioning in MBal, a distributed in-memory object caching system, that offers a holistic solution wherein the load balancing model tracks hotspots and applies different strategies based on the severity of the imbalance. Then, I will discuss a fundamental challenge faced by all practitioners and developers working on scalable storage: how to implement a fast and reliable scale-out storage system with minimal engineering effort? I will present how modular design rather than the extant monolithic approaches can ease the burden of designing new storage systems, especially by enabling an innovative decoupling of the control and data plane in distributed storage. I will conclude with a brief discussion of my vision for future storage and data-intensive systems.
Bio: Yue Cheng is a Ph.D. candidate in the Department of Computer Science at Virginia Tech. His research interests include distributed systems, cloud storage, and Internet of Things. His work has been published in premier venues in computer systems and high-performance computing, including USENIX ATC, ACM EuroSys, and ACM HPDC. He has worked and collaborated with leading storage researchers at IBM Research and Dell EMC. He received his B.Eng. in Computer Science from Beijing University of Posts and Telecommunications. Yue loves traveling and all kinds of outdoor activities.
Faculty Host: Dr. John Hartman
Tuesday, February 21, 2017 - 11:00am - Gould-Simpson (GS) 906
Speaker: Trinabh Gupta, Ph.D. Candidate
Title: "Toward practical and private online services"
Abstract: Today's common online services (social networks, media streaming, messaging, email, etc.) undermine privacy. Indeed, there have been numerous incidents (hacks, accidental disclosures, etc.) where private information has leaked. With a vision of reducing these risks, my research aims to build systems that provide strong privacy guarantees and are practical (i.e., have functionality and costs comparable to that of the status quo).
In this talk, I will describe the challenges faced in building such systems and and how I address them. As an example of one of the systems I have built, I will describe Popcorn, a Netflix-like media delivery system that provably hides, even from the content distributor (e.g., Netflix), which movie a user is watching; is otherwise consistent with prevailing commercial regime (copyrights, etc.); and achieves plausibly deployable performance (per-request dollar cost is 3.87 times that of a non-private system).
Bio: Trinabh Gupta is a PhD candidate at The University of Texas at Austin. He is also a visiting academic at NYU's systems group. His research interests are in systems, security, and privacy, and he has worked on privacy-preserving online services, and failure detection in distributed systems. His advisors are Lorenzo Alvisi and Michael Walfish. Prior to being a PhD student he was a computer science undergraduate student at Indian Institute of Technology Delhi (IITD).
Faculty Host: Dr. David Lowenthal
Thursday, February 16, 2017 - 11:00am - Gould-Simpson (GS) 906
Speaker: Hyunwoo J. Kim, Ph.D. Candidate
Title: "Statistical Machine Learning on Manifolds for Structured Data (without the pain)"
Abstract: Manifold-valued data naturally occur in many disciplines. For example, directional data can be represented as points on a unit sphere. Diffusion tensors in magnetic resonance images form a quotient manifold GL(n)/O(n), which is a space of symmetric positive definite (SPD) matrices. Also, the Hilbert unit sphere for the squareroot representation of orientation distribution functions (ODFs) or probability density functions (PDFs). Their data spaces are known, a priori, to have a nice mathematical structure with well-studied properties. It makes sense that if algorithms make use of this additional information, even more efficient inference procedures can be developed. Motivated by this intuition, in this talk we study the relationship between statistical learning algorithms and the geometric structures of data spaces encountered in machine learning, computer vision and neuroimaging using mathematical tools (e.g. Riemannian geometry). As a result, this framework will give new insights into statistical inference methods for image analysis and enable developing new models for manifoldvalued data and (potentially manifold-valued parameters) to improve statistical power.
Topics featured in this talk include: manifold statistics on Riemannian manifolds, manifold-valued multivariate general linear models, canonical correlation analysis on manifolds, Dirichlet Process for manifold-value variables, interpolation of k-GMMs, latent variable graphical model selection, and abundant inverse regression.
Bio: - PhD. student, Computer sciences, University of Wisconsin-Madison, advisor: Vikas Singh, 2011 -
present (PhD minor: Statistics)
- Internship at Amazon in Customer Service Machine Learning Analytics Team, 2013
- M.S., Computer sciences, University of Wisconsin-Madison, 2013
- Scientist, Music and Audio Research Group (MARG) in GSCST at SNU, 2010 - 2011
- M.S., Computer science and engineering, Seoul National University, 2010
- B.S., Computer science, Korea University, 2008
- Research Interests: Machine learning and computer vision, manifold statistics, medical imaging, and large scale numerical
optimization
Faculty Host: Dr. Joshua Levine
Tuesday, February 14, 2017 - 11:00am - Gould-Simpson (GS) 906
Speaker: Chien-Ju (âCJâ) Ho, Ph.D.
Title: "Learning and Incentives in Systems with Humans in the Loop"
Abstract: There is an increasing amount of human-generated data available on the internet -- including online reviews, user search histories, datasets labeled using crowdsourcing, and beyond. This has created an unprecedented opportunity for researchers in machine learning and data science to address a wide range of problems. On the other hand, human-generated data also creates unique challenges. Humans might be strategic or careless, possess diverse skills, or have behavioral biases. What is the right way to understand and utilize human-generated data? Furthermore, can we better design the systems with humans in the loop to generate more useful data in the first place?
In this talk, I will present my research which addresses the challenges in utilizing and eliciting data from humans. In particular, I will first introduce the problem of actively purchasing data from humans for solving machine learning tasks, and demonstrate how to convert a large class of machine learning algorithms into pricing and learning mechanisms. In the second part of the talk, I will discuss how to obtain high-quality data from humans using financial incentives and present our findings in a comprehensive set of behavioral experiments conducted on Amazon Mechanical Turk.
Bio: Chien-Ju is a postdoctoral associate at Cornell University. He obtained his Ph.D. in Computer Science from UCLA in 2015, advised by Jenn Wortman Vaughan. He also spent three years visiting the EconCS group at Harvard from 2012 to 2015, hosted by Yiling Chen. His research interests are in machine learning, algorithmic economics, online behavioral social science, crowdsourcing, and artificial intelligence. His dissertation was on the design and analysis of crowdsourcing mechanisms. He is the recipient of the Google Outstanding Graduate Research Award at UCLA in 2015. His work was nominated for Best Paper Award at WWW 2015.
Faculty Host: Dr. Carlos Scheidegger
Monday, February 13, 2017 - 11:00am - Gould-Simpson (GS) 906
Speaker: Faisal Nawab, PhD. Candidate
Title: "Efficient Coordination for Global-Scale Data Management"
Abstract: Replicating data across datacenters (geo-replication) provides higher levels of fault-tolerance and data availability. The Wide-Area Network (WAN) latency separating datacenters is orders of magnitude larger than traditional network latency within a datacenter. This makes it expensive to preserve the consistency of data copies. However, consistency and high-level access abstractions like database transactions are favored by developers because they hide the complexity of the underlying replica and concurrency control. This has led to the adoption of consistent transactions in large-scale geo-replicated systems.
In this talk, I will present the fundamental challenges in designing geo-replicated data management systems. Specifically, transaction latency is high due to the need to coordinate between datacenters spread across the world. Traditionally, coordination is performed by polling other datacenters for permissions to execute. This made a Round-Trip Time (RTT) latency inevitable. In geo-replication, this is an expensive cost and thus leads to the question: Is it possible to avoid the polling paradigm of coordination? Message Futures is a protocol that demonstrates a new paradigm of continuous, proactive coordination. In this paradigm, transactions can coordinate in sub-RTT latency. Breaking the RTT latency barrier invites the next part of the talk where I derive a lower bound for coordination latency. The proposed lower-bound model inspires a design of a coordination protocol called Helios that targets achieving the lower-bound latency. The talk will also discuss many of the practical aspects of building scalable large-scale data management and communication platforms for geo-replicated systems. I conclude the talk with future opportunities for global-scale data management in the context of edge computing, Internet of Things, and data science.
Bio: Faisal Nawab is a Ph.D. candidate at the University of California, Santa Barbara. His dissertation research lies at the intersection of Big Data management and distributed cloud computing systems. Specifically, he is interested in the challenges that arise in geographically-distributed data management systems. Faisal has also worked with HP Labs and Microsoft Research on data management systems over emerging memory technology such as Non-Volatile Memory. His research is published in leading database conferences, such as VLDB, SIGMOD, and ICDE.
Faculty Host: Dr. Michelle Strout
Thursday, February 9, 2017- 11:00am - Gould-Simpson (GS) 906
Speaker: Yan Shoshitaishvili, Ph.D Candidate
Title: "Making the World Safer through "Cyber"-Autonomy"
Abstract: Our world is driven by interconnected software. While this connectivity provides functionality and convenience, it is not without risks: vulnerabilities are still rampant in modern programs, and the exploitation of these vulnerabilities turns our connectivity into a liability. With the recent proliferation of "smart" devices, more vulnerable software than ever is connected to the internet, and open to attackers.
We must find and fix these vulnerabilities before they can be exploited. In this talk, I will describe my research into an analysis pipeline that is flexible and extensible enough to target the identification of different types of vulnerabilities in binary code. I will discuss angr, the analysis framework powering this pipeline, and detail how angr can be applied not only to vulnerability identification in binary code, but to vulnerability remediation as well. Finally, I will show the culmination of these techniques in the form of the Mechanical Phish, one of the world's first fully autonomous hacking systems. Last year, the Mechanical Phish won third place in the DARPA Cyber Grand Challenge by automatically finding, exploiting, and patching vulnerabilities in a live competition, at a scale that could not be achieved by human hackers.
I have open-sourced the Mechanical Phish and angr to give researchers a platform on which to build next-generation binary analysis techniques. The growing community around these projects, including research labs, companies, and enthusiasts around the world, are actively pushing forward the frontier of the field. With ever-improving vulnerability detection and remediation techniques, we hope to introduce automated binary analysis into the toolbox of the "good guys", making our world more secure in the process.
Bio: Yan Shoshitaishvili is a PhD candidate at UC Santa Barbara, with a primary research focus of program analysis and vulnerability identification. He is currently the captain of the Shellphish computer hacking group, and guides them through cybersecurity competitions worldwide. Over the last two years, Yan led Shellphish's participation in the DARPA Cyber Grand Challenge, applying his research to the creation of a fully autonomous hacking system that won third place in the competition. Underpinning this system is angr, an open-source binary analysis framework founded by Yan in the course of his research.
Faculty Host: Dr. Christian Collberg
Tuesday, February 7, 2017 - 11:00am - Gould-Simpson (GS) 906
Speaker: Bo Li, Ph.D.
Title: "Secure Learning in Adversarial Environments"
Abstract: Advances in machine learning have led to rapid and widespread deployment of software-based inference and decision making, resulting in various applications such as data analytics, autonomous systems, and security diagnostics. Current machine learning systems, however, assume that training and test data follow the same, or similar, distributions, and do not consider active adversaries manipulating either distribution. Recent work has demonstrated that motivated adversaries can circumvent anomaly detection or classification models at test time through evasion attacks, or can inject well-crafted malicious instances into training data to induce errors in classification through poisoning attacks. In addition, by undermining the integrity of learning systems, the privacy of users' data can also be compromised.
In this talk, I describe my recent research addressing evasion attacks, poisoning attacks, and privacy problems in adversarial environments. The key approach is to utilize game theoretic analysis and model the interactions between an intelligent adversary and a machine learning system as a Stackelberg game, allowing us to design robust learning strategies which explicitly account for an adversaryâs optimal response.
Bio: Dr. Bo Li is a postdoctoral research fellow in the department of Electrical Engineering and Computer Science at University of Michigan. She is a member of IEEE, AAAI, and ACM. She received the Symantec Research Labs Graduate Fellowship in 2015 as one of three recipients nationwide of the prestigious fellowship. Her research focuses on machine learning, security, privacy, game theory, social networks, and adversarial deep learning. She has designed several robust learning algorithms, a scalable framework for achieving robustness for a range of learning methods, and a privacy preserving data publishing system. Dr. Li interests in both theoretical analysis of general threat models and developing practical systems. She has evaluated the vulnerabilities of real-world machine learning models and developed resilient learning systems to not only preserve robustness, but also optimize resource allocation based on practical constraints. Another focus of her research is on developing scalable robust algorithms that can process massive amounts of data available for Internet-scale problems regarding specific cloud computing infrastructure to achieve secure learning for big data. She is also active in adversarial deep learning research for training generative adversarial networks (GAN) and designing robust deep neural networks against adversarial examples.
Faculty Host: Dr. Saumya Debray
Thursday, February 2, 2017 - 11:00am - Gould-Simpson (GS) 906
Speaker: Brad Reaves, Ph.D. Candidate
Title: "Fixing the Phone: Designing Strong Authentication for Telephony"
Abstract: Billions of users trust the telephone network with their most sensitive communications, including commerce, banking, industry, and personal communications. These users trust that the telephone network functions reliably despite the fact that telephones are the instrument of billions of dollars of fraud and abuse annually. This fraud is possible because telephony networks, including landline, cellular, and VoIP, have weak or non-existent abilities to identify and authenticate users.
In this talk, we will examine how phone networks are being misused for authentication and secure communications. We will then see how new systems leveraging techniques from networking, cryptography, and signal processing can provide strong authentication of phone calls. These techniques will pave the way for a more trustworthy telephone infrastructure.
Bio: Brad Reaves is a doctoral candidate at the University of Florida, where he serves as the lead graduate student for the Florida Institute for CyberSecurity (FICS) Research. His research is dedicated to measuring and improving the security and privacy of computer systems, with a particular emphasis on telephony networks and software for mobile platforms. This work has addressed detection and measurement of mobile malware in the wild, identified systemic risks in developing world mobile money systems, and provided new techniques to distinguish legitimate and fraudulent phone calls. He holds an MS in Computer Science from Georgia Tech as well as a BS and MS in Computer Engineering from Mississippi State University. His work has been recognized with two best paper awards, and he was named an NSF Graduate Research Fellow in 2010.
Faculty Host: Dr. Saumya Debray
Tuesday, January 24, 2017 - 11:00am - Gould-Simpson (GS) 906
Speaker: Philip Thomas, Ph.D.
Title: "Safe Machine Learning"
Abstract: Machine learning algorithms are everywhere, ranging from simple data analysis and pattern recognition tools used across the sciences to complex systems that achieve super-human performance on various tasks. Ensuring that they are safeâthat they do not, for example, cause harm to humans or act in a racist or sexist wayâis therefore not a hypothetical problem to be dealt with in the future, but a pressing one that we can and should address now.
In this talk I will discuss some of my recent efforts to develop safe machine learning algorithms, and particularly safe reinforcement learning algorithms, which can be responsibly applied to high-risk applications. I will focus on a specific research problem that is central to the design of safe reinforcement learning algorithms: accurately predicting how well a policy would perform if it were to be used, given data collected from the deployment of a different policy. Solutions to this problem provide a way to determine that a newly proposed policy would be dangerous to use without requiring the dangerous policy to ever actually be used.
Bio: Philip Thomas is a postdoctoral research fellow in the Computer Science Department at Carnegie Mellon University, advised by Emma Brunskill. He received his Ph.D. from the College of Information and Computer Sciences at the University of Massachusetts Amherst in 2015, where he was advised by Andrew Barto. Prior to that, Philip received his B.S. and M.S. in computer science from Case Western Reserve University in 2008 and 2009, respectively, where Michael Branicky was his adviser. Philip's research interests are in machine learning with emphases on reinforcement learning, safety, and designing algorithms that have practical theoretical guarantees.
Faculty Host: Dr. Mihai Surdeanu
Thursday, January 19, 2017 - 11:00am - Gould-Simpson (GS) 906
Speaker: Raymond Cheng, Ph.D. Candidate
Title: "Privacy as a Service"
Abstract: Current cloud services are vulnerable to hacks and surveillance programs that undermine user privacy and personal liberties. In this talk, I present how we can build practical systems for protecting user privacy from powerful persistent threats. I will discuss Talek, a private publish-subscribe protocol. With Talek, application developers can send and synchronize data through the cloud without revealing any information about data contents or communication patterns of application users. The protocol is designed with provable security guarantees and practical performance, 3-4 orders of magnitude better throughput than other systems with comparable security goals. I will also discuss Radiatus, a security-focused web framework to protect web apps against external intrusions, and uProxy, a Internet censorship circumvention tool in deployment today.
Bio: Jian Huang is a Ph.D. candidate in the School of Computer Science at Georgia Institute of Technology. His research interests lie in the areas of computer systems, including operating systems, systems architecture, systems reliability and security, and distributed systems. He enjoys building practical, reliable, secure and high-performance systems. Most of his recent work focuses on building memory and storage systems with new and emerging memory technologies such as flash memory, battery-backed DRAM and phase-change memory. His research contributions have been published at ASPLOS, FAST, USENIX ATC, ISCA, VLDB, SoCC, and IPDPS. His work WearDrive won the Best Paper Award at USENIX ATC in 2015 and attracted popular press coverage in more than eight countries. His work FlashMap won the IEEE Micro Top Picks Honorable Mention in 2016. Most of the technologies he has developed have had an impact on industrial and real-world systems, some of them are being transferred into products including those at Microsoft data centers.
Faculty Host: Dr. Beichuan Zhang
Thursday, January 19, 2017 - 3:30pm - Gould Simpson (GS) 906
Speaker: Ross Whitaker, Ph.D.
Title: "Summarizing and Understanding Ensembles of Complex, High-Dimensional Objects"
Abstract: A variety of technologies developed in diverse areas such as medical imaging, industrial inspection, and oil and gas present a common challenge-which is that the analysis of models and/or data present scientists with large collections, or ensembles, of complex data objects.
Often times, data analysts or application scientists benefit from a holistic view of the data. These views could be concise summarizes that allow people to develop intuitions about overall patterns or to confirm very general expectations, e.g. regarding data quality. Alternatively, these views might provide opportunities for exploratory investigations, where users or scientists might look for new patterns or trends. This talk describes some aspects of the general problem of summarizing ensembles of data and describes some recent work that spans the space of approaches from light-weight summarizes to more sophisticated analyses that allow for deeper investigations.
Bio: Ross Whitaker earned a B.S. degree in Electrical Engineering and Computer Science from Princeton University in 1986, and a Ph.D. in Computer Science from the University of North Carolina in 1994. Since 2000 he has been at the University of Utah, where he is the Director of the School of Computing and a Professor in the Scientific Computing and Imaging Institute. He is a recipient of the NSF Career Award and an IEEE and AIMBE Fellow. He teaches discrete math, scientific visualization, and image processing. He has leads a graduate-level research group in image analysis, geometry processing, and scientific computing, with a variety of projects supported by both federal agencies and industrial contracts.
Faculty Host: Dr. Joshua Levine
Thursday, November 17, 2016 - 11:00am - ENR2, S107
Stephen North Ph.D., Infovisible
Human-Centered Information Visualization in Practice
Thursday, November 3, 2016 - 11:00am - Gould-Simpson 906
Mathias Payer Ph.D., Purdue University
Memory Corruption: Why Protection is Hard
Thursday, October 27, 2016 - 11:00am - Gould-Simpson 906
Ming Li Ph.D., Dept. of Electrical and Computer Engineering - UA
Geometric Range Search over Encrypted Spatial Data
Tuesday, October 11, 2016 - 11:00am - Gould-Simpson 906
Nathan Dautenhahn Ph.D., Dept. of Computer and Information Science, University of Pennsylvania
Protection in Commodity Operating Systems
Thursday, September 29, 2016 - 11:00am - Gould-Simpson 906
Matt Berger Ph.D., Air Force Research Laboratory