Spring 2019 Colloquia

Check back soon for more information on the computer science seminar series. Unless otherwise noted, the seminars meet on Mondays at 4pm in Stanley Thomas 302. If you would like to receive notices about upcoming seminars, you can subscribe to the announcement listserv.

Jan 14

Approximation Algorithms for Optimal Packing in Two and Three Dimensions

Helmut Alt Visiting Professor, Tulane University

Abstract: Space efficient packing of geometric objects in two or three dimensions is a very natural problem which has interested mathematicians for centuries. Unfortunately, the computational complexity of finding space optimal packings seems to be very high. Even simple variants are NP-hard so that efficient approximation algorithms are called for. In the lecture, it will be shown how to approximate optimal packing for convex polygons in two and convex polyhedra in three dimensions if rigid motions are allowed for moving the objects. If only translations are allowed, we still can approximate the optimal packing of convex polygons. In three dimensions however, this problem seems to be much harder and we could only find algorithms for very special kinds of objects. This is joint work with Nadja Scharf.

About the Speaker: Helmut Alt studied mathematics since 1968 at Universitaet des Saarlandes, Germany. He graduated with a PhD which focused on complexity theory in 1976. He was a research associate at Universitaet des Saarlandes and an assistant professor at Pennsylvania State University. Since 1986 he was a Professor of Computer Science at Freie Universitaet Berlin, Germany. The focus of his work is on algorithms and complexity, in particular computational geometry.
Jan 23

Using Interactive Learning Activities to Address Challenges of Peer Feedback Systems

Amy Cook Carnegie Mellon University

This event will be held on Wednesday, 1/23/2019, from 4:00 - 5:00 p.m. in Stanley Thomas, Room 302. Please note the special weekday for this event.

Abstract: Project-based learning helps prepare students for jobs by providing not only the technical experience of completing a project, but also the soft skills such as teamwork and communication that employers desire. Peer feedback, where students critique each other’s work, is an essential aspect of project-based learning. However, students often struggle to engage in peer feedback, to improve the quality of feedback they provide, and to reflect on the feedback they receive. My research explores how digital systems and interactive learning activities can improve the peer feedback process.

About the Speaker: Amy Cook is a PhD candidate in the Human-Computer Interaction Institute at Carnegie Mellon University. Her research lies at the intersection of human-computer interaction and STEM education. Her work involves designing both digital systems and learning activities to facilitate effective classroom interaction.
Jan 28

What Happens If You Postpone Your Decision?

Christer Karlsson South Dakota School of Mines & Technology

Abstract: Many of our algorithms when it comes to decision making are constructed using a greedy approach. The algorithms often conduct an evaluation, make an early decision on what path to take upon what seems to be most profitable at that moment. What happens if we first walk down both the path less traveled and the most promising one, and at the next intersection evaluate the progress and sees what looks most promising? I will describe work done on trying to arrange heterogeneous processes within a message passing interface, as well as work conducted using a delayed greedy approach on decision tree classifiers. I will try to answer questions like: What befits are there? What are the costs? Is it worth the effort?

About the Speaker:
Dr. Christer Karlsson has had a long and winding road. He started his career as an officer in the Swedish army, where he among other things, spent almost nine months with the peace keeping forces in the Balkans, followed by almost 3 years as a teacher at the War Academy, where he was responsible for teaching computer science, electronics and ballistics. He came to the United States in 1999, and became a citizen in 2006. Dr. Karlsson earned his Ph.D. in computer science at Colorado School of Mines in 2012 while working mostly on optimization problems for message passing on systems with heterogeneous nodes. He has since January 2013 worked as an Assistant Professor at South Dakota School of Mines and Technology.
Feb 11

Deep Adversarial Learning

Jihun Hamm Ohio State University

Abstract: Adversarial machine learning in a broad sense is the study of machine learning theory and algorithms in environments with multiple agents that have different goals. This broad definition of adversarial machine learning includes narrow-sense adversarial machine learning which studies the vulnerability of learning algorithms in the presence of adversarial data perturbation during the training or the testing phases. However, there are many learning problems that involve multiple agents and objectives in different contexts. For example, generative adversarial nets, domain adaptation, data sanitization, attacking/defending deep neural net, and hyperparameter optimization can all be formulated as adversarial learning problems. In this talk, I will introduce several applications of adversarial learning focusing on privacy preservation, and then discuss the challenges of adversarial optimization and propose new solutions. I will also present ongoing and future research in this direction.

About the Speaker: Dr. Hamm is a Research Scientist at the Department of CSE, the Ohio State University. He received his Ph.D. from the University of Pennsylvania in 2008 with a focus on nonlinear dimensionality reduction and kernel methods, and was a post-doctoral researcher at the Penn medical school working on machine learning applied to medical data analysis. Dr. Hamm's recent research is focused on machine learning theory and algorithms in adversarial settings and in the field of security and privacy. He has received the best paper award from MedIA-MICCAI (2010), was a finalist for MICCAI Young Scientist Publication Impact Award (2013), and is a recipient of the Google Faculty Research Award (2015). He has served as a reviewer for JMLR, IEEE TPAMI/TNN/TIP/TIFS, NN, PR, IJPR, and others, and also as a program committee member for NIPS, ICML, AAAI, and AISTATS.
Feb 20

Large-Scale Analysis of Online Social Networks for Social Understanding, Crisis Informatics, and Information Security

Cody Buntain New York University

This event will be held on Wednesday, 2/20/2019, from 4:00 - 5:00 p.m. in Stanley Thomas, Room 302. Please note the special weekday for this event.

Abstract:The volume of data now available in online social networks has accelerated research into social good and advances in understanding both the real and virtual world, but this volume also makes finding useful information difficult and facilitates the spread of malicious content. In this talk, I discuss these intersectional issues, wherein online social networks simultaneously enable and corrupt the flow of information in society, and I present my research into potential solutions.

First, I describe two computational social science efforts, demonstrating how these online spaces enable understanding but also lead to polarization and conflict. In this description, I show how politicized topics evolve and how malicious actors have tried to leverage online platforms to increase polarization. Second, I present research on information retrieval and real-time summarization for making the information contained in these online systems more accessible to those individuals who most need it. This work includes real-time stream processing of social networking data to extract timely and concise information and a large-scale machine learning effort for identifying information needs and priorities for crisis responders. Third, to combat exploitation in the online ecosystem, I discuss a related machine learning effort to automate credibility assessment in online discussions.

I conclude with an overview of my research agenda for advancing my work into the larger, multi-platform information ecosystem. My research in this context includes applications of multi-view learning and social contagion for tracking topical and community evolution across platforms and time; unifying these cross-platform interactions into consistent models of information security, reputation, and resiliency; and integrating multi-modal content across these platforms to enhance crisis informatics.

About the Speaker: Cody Buntain received his PhD from the Computer Science Department at the University of Maryland and is a postdoctoral researcher with New York University's Social Media and Political Participation Lab. His primary research areas apply large-scale computational methods to social media and other online content, specifically studying how individuals engage socially and politically and respond to crises and disaster in online spaces. Current problems he is studying include cross-platform information flows, network structures, temporal evolution/politicization of topics, misinformation, polarization, and information quality. Recent publications include papers on influencing credibility assessment in social media, consistencies in social media's response to crises, the disability community's use of social networks for political participation, and characterizing gender and direction in online harassment.
Feb 22

3D Flood Extent Detection From UAV Imagery in Support of Flood Management

Leila Hashemi-Beni North Carolina AT&T State University

This event will be held on Friday, 2/22/2019, from 4:00 - 5:00 p.m. in Stanley Thomas, Room 302. Please note the special weekday for this event.

Abstract: Unmanned aerial vehicles (UAVs) offer a great potential alternative to conventional platforms for acquiring high-resolution remote sensing data at lower cost and increased operational flexibility for flood modeling and management. UAV data analytic is a key step in the development of UAV remote sensing to correctly predict the extent of the flood, supporting emergency-response planning, and providing damage assessment in both spatial and temporal measurements. In spite of recent developments in UAV data collection technologies that have made them “lighter, smaller and simpler,” more sophisticated processing is required to compensate for the necessarily limited performance of these platforms and sensors. This talk will focus on the data processing on UAVs imagery to construct the inundation areas in 3D using Structure from Motion as well as Deep learning methods.

About the Speaker: Leila Hashemi-Beni is an assistant professor of Geomatics at the Department of Built Environment at College of Science and Technology, North Carolina A&T State University. She holds a BSc. and a MSc. in Civil-Surveying Engineering (Geomatics) and a PhD in Geomatics Science. Her research experience and interests span the areas of 3D data modeling, UAV and satellite remote sensing and data analytics, automatic matching and change detection between various datasets and developing GIS and remote sensing methodologies for different applications. She is currently working as a PI/Co-PI on 5 externally-funded projects totaling $1.7M, including three from the National Science Foundation, and two from the Energy Industry. She has participated to the 3D Nation Elevation Requirements and Benefits Study by NOAA and USGS, North Carolina Chapter.
Feb 25

Learning to Cache: Accuracy or Speed?

Jian Li University of Massachusetts, Amherst

Abstract: Caching is fundamental to and has been used in many applications, ranging from computing systems on chip-level to content distribution. It becomes particularly important due to the rise of video streaming, which is the dominant application in today's Internet. The content accessed by the user is usually delivered by a large-scale networked system called a content distribution network (CDN). CDNs usually use caching as a mean to reduce access latency as well as bandwidth requirement at a central content repository.

In the talk, I will discuss an online learning approach to study the fundamental performance limit of caching algorithms. Typical analysis of caching algorithms using the metric of steady state hit probability under a stationary request process does not account for performance loss under a variable request arrival process. I instead conceptualize caching algorithms as online learning algorithms, and use this vantage point to study their adaptability from two perspectives: (a) the accuracy of learning a fixed popularity distribution; and (b) the speed of learning items' popularity. I propose a learning error metric representing both how quickly and how accurately an algorithm learns the optimum, and use this to determine the tradeoff between these two objectives of many popular caching algorithms. Informed by the analytical results, I propose a novel hybrid algorithm, Adaptive-LRU, that learns both faster and better the changes in the popularity. I show numerically that it also outperforms all other candidate algorithms when confronted with either a dynamically changing synthetic request process or using real world traces. I will conclude with thoughts on using learning-based approach to build better models, algorithms, and systems to support big data applications.

About the Speaker: Jian Li is a postdoctoral research associate at University of Massachusetts Amherst. He received his Ph.D. in Computer Engineering from Texas A&M University in December, 2016, and B.E. in Electrical Engineering from Shanghai Jiao Tong University in June, 2012. His current research interests lie broadly in the interplay of large scale networked systems and big data analytics focusing on advancing machine learning, data analysis, game theory, and signal processing technologies in big data applications.

Mar 11

Learning from Spatial-Temporal-Networked Data: Dynamics Modeling, Representation Learning, and Applications

Yanjie Fu Missouri University of Science and Technology

Abstract: The pervasiveness of mobile, IoT, and sensing technologies have connected humans, physical worlds, and cyber worlds into a grand human-social-technological system. This system consists of users and systems that interact with each other in real time and at different locations. Therefore, big spatial-temporal-networked behavioral data have been accumulated from mobile devices and App services. In this talk, I will first introduce what are spatial-temporal-networked data and why it is difficult to make sense of spatial-temporal-networked data.

Then, I will focus on the dynamics, patterns, and applications of spatial-temporal-networked data, including (1) modeling dynamics and annotating semantics of spatial-temporal-networked behaviors; (2) learning deep representations of spatial-temporal-networked behaviors; (3) their applications to smart transportation systems and adaptive human-technology interaction. Finally, I will conclude the talk and present the big picture on developing close-looped intelligent and trustworthy data science systems.

About the Speaker: Dr. Yanjie Fu received his Ph.D. degree from Rutgers University in 2016, the B.E. degree in Computer Science from University of Science and Technology of China in 2008, and the M.E. degree in Computer Engineering from Chinese Academy of Sciences in 2011. He is currently an Assistant Professor at Missouri S&T (University of Missouri-Rolla).

His general interests include data mining and big data analytics. His recent research focuses are collective, dynamic, and structured machine learning, spatial-temporal-networked data mining, automated data science systems, with applications to big data problems, including intelligent transportations, user and system behavior analysis, power grids, recommender systems, disaster and emergency management. He has research experience in industry research labs, such as IBM Thomas J. Watson Research Center and Microsoft Research Asia. He has published prolifically in refereed journals and conference proceedings, such as IEEE Transactions on KDE, ACM Transactions on KDD, IEEE Transactions on MC, ACM Transactions on IST, SIGKDD Conference, AAAI Conference, IJCAI Conference.

Mar 18

The Human Factor in Computing

David Luginbuhl Air Force Research Laboratory, Maxwell AFB, AL

Abstract: It is easy for us as computer scientists to be so focused on what machines can do that we lose sight of human involvement in the process of computing. To be sure, there has been plenty written about and studied on human-centered computing/user-centered design, and human-machine teaming continues to grow as an interest area as we anticipate autonomous or semi-autonomous systems finding their way into society at large. But these are primarily concerned with the human as user. Humans are really a much larger part of the computing landscape.

In this talk, I will examine several roles that humans play as a part of the computing process. I’ll discuss why those roles are important to consider, and I’ll survey research that addresses each of these roles. I’ll also look at implications for us as computer scientists in comprehending these different roles.

My intent is to demonstrate the need for computer scientists to understand and appreciate the intersection of our field with the human sciences (e.g., psychology, physiology, cognitive science, sociology). Only by taking a comprehensive, multidisciplinary approach can we hope to design machines that will interact effectively with us on an individual basis and that will be beneficial to society.

About the Speaker: Dr. David Luginbuhl’s thirty-five years of professional experience have included posts in higher education, as well as management and leadership in government research and development organizations, both as an Air Force officer and a civilian. He has taught at the Air Force Institute of Technology, Western Carolina University, and the Air War College. He has worked for the Air Force Research Laboratory in a number of posts, including program manager at the Air Force Office of Scientific Research, Assistant Chief Scientist at the 711th Human Performance Wing, and AFRL Chair at Air University. Dr. Luginbuhl received his Doctor of Philosophy degree in computer science from the University of Illinois at Urbana-Champaign and his master’s and bachelor’s degrees in math and computer science from Florida State University.
Mar 29

Code Obfuscation: Why Is This Still a Thing?

Christian Collberg University of Arizona

This event will be held on Friday, 3/29/2019, from 4:00 - 5:00 p.m. in Stanley Thomas, Room 302. Please note the special weekday for this event.

Abstract: Early developments in code obfuscation were chiefly motivated by the needs of Digital Rights Management (DRM). Other suggested applications included intellectual property protection of software and code diversification to combat the monoculture problem of operating systems.

Code obfuscation is typically employed in security scenarios where an adversary is in complete control over a device and the software it contains and can tamper with it at will. We call such situations the Man-At-The-End (MATE) scenario. MATE scenarios are the best of all worlds for attackers and, consequently, the worst of all worlds for defenders: Not only do attackers have physical access to a device and can reverse engineer and tamper with it at their leisure, they often have unbounded resources (time, computational power, etc.) to do so. Defenders, on the other hand, are often severely constrained in the types of protective techniques available to them and the amount of overhead they can tolerate. In other words, there is an asymmetry between the constraints of attackers and defenders. Moreover, DRM is becoming less prevalent (songs for sale on the Apple iTunes Store are no longer protected by DRM, for example);there are new cryptographically-based obfuscation techniques that promise provably secure obfuscation; secure enclaves are making it into commodity hardware, providing a safe haven for security sensitive code; and recent advances in program analysis and generic de-obfuscation provide algorithms that render current code obfuscation techniques impotent.

Thus, one may reasonably ask the question: "Is Code Obfuscation Still a Thing?"

One of the reasons for this resurgence of code obfuscation as a protective technology is that, more and more, we are faced with applications where security-sensitive code needs to run on unsecured endpoints. In this talk we will show MATE attacks that appear in many novel and unlikely scenarios, including smart cars, smart meters, mobile applications such as Snapchat and smartphone games, Internet of Things applications, and ad blockers in web browsers. We will furthermore show novel code obfuscation techniques that increase the workload of attackers and which, at least for a time, purport to restore the symmetry between attackers and defenders.

About the Speaker: Christian Collberg is a Professor in the Department of Computer Science at the University of Arizona. Prior to arriving in Tucson he worked at the University of Auckland, New Zealand, and before that got his Ph.D. from Lund University, Sweden. He has also held a visiting position a the Chinese Academy of Sciences in Beijing, China, and taught courses at universities in Russia and Belarus.

Dr. Collberg's main research interest is the so-called Man-At-The-End Attack which occurs in settings where an adversary has physical access to a device and compromises it by tampering with its hardware or software. He is the co-author of Surreptitious Software: Obfuscation, Watermarking, and Tamperproofing for Software Protection, published in Addison-Wesley's computer security series. It has also been translated into Portuguese and Chinese.

In addition to his security research, Dr. Collberg is an advocate for Reproducibility, Repeatability, and Sharing in Computer Science. He maintains the site which aims to be the most authoritative and complete catalog of research artifacts (e.g., code and data) related to Computer Science publications.
Apr 1

Protein Structure Analysis and Comparison: Identifying Regions of Similarity Using a Graph Analysis of Underlying Structural Information

Aaron Maus University of New Orleans

Abstract:The study and analysis of proteins and their structures is arguably one of the most important endeavors in modern biology with major applications for both the basic understanding of biology and the development and design of the next generation of medicines. In the pursuit of understanding proteins, it is integral to be able to compare and analyze their structures. Whether for the analysis of the protein structure prediction effort, to study conformational changes of the same protein, or to study similar conformations of evolutionarily related proteins, the comparison and analysis of the complex three-dimensional shapes of protein structures is a difficult yet fundamental task. All existing protein structure comparison methods return a score for similarity, but few give an underlying look at the parts of the structures which match. By converting the underlying geometric information of two structures into a graph, a maximum clique analysis can be used to identify the largest non-overlapping regions of similarity between structures. These regions can easily be visualized, and they lend themselves to a deep analysis of the underlying similarities between structures, complementing existing methods of comparison by providing additional information that is not readily available. Applications of this technique will be presented, and it will also be shown that even though this method relies on solutions to an NP-complete problem, these problems are feasible in this context.

About the Speaker: Aaron Maus is a doctoral candidate working with Dr. Christopher Summa in the Engineering and Applied Science program at the University of New Orleans. His research interests include proteins structure prediction, the design of energy functions for structural refinement, and the comparison and analysis of protein structures.
May 7

Discourse Models for Multimodal Communication

Malihe Alikhani Rutgers University

This event will be held on Tuesday, 5/7/2019, from 10:30 a.m. - 11:30 a.m. in Stanley Thomas, Room 316. Please note the special weekday and time for this event.

Abstract: The integration of textual and visual information is fundamental to the way people communicate. My hypothesis is that despite the differences of the visual and linguistic communication, the two have similar intentional, inferential and contextual properties, which can be modeled with similar representations and algorithms. I present three successful case studies where natural language techniques provide a useful foundation for supporting user engagement with visual communication. Finally, I propose using these findings for designing interactive systems that can communicate with people using a broad range of appropriate modalities.

About the Speaker: Malihe Alikhani is a 4th year Ph.D. student in the department of computer science at Rutgers University, advised by Prof. Matthew Stone. She is pursuing a Certificate in Cognitive Science through the Rutgers Center for Cognitive Science and holds a BA and MA in mathematics. Her research aims at teaching machines to understand and generate multimodal communication. She is the recipient of the fellowship award for excellence in computation and data sciences from Rutgers Discovery Informatics Institute in 2018.

303 Stanley Thomas Hall, New Orleans, LA 70118 504-865-5785