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Colloquia

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

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Yanjie Fu Missouri University of Science and Technology

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Mar 18

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David Luginbuhl Air Force Research Laboratory, Maxwell AFB, AL

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Mar 25

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Apr 1

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Apr 8

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Apr 15

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Apr 22

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Apr 29

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