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.
Emanuelle Burton | University of Illinois at Chicago
This event will be held on Friday, 9/6/2019, from 4:00 - 5:00 p.m. in Stanley Thomas, Room 316. Please note the special weekday and venue for this event.
Abstract: Many engineering and science fields accreditation bodies require student instruction on ethics education. While typically this takes the form of case studies relating to research conduct, we contend that a meaningful education in ethics requires more than just transmitting information. An effective course enables students to begin (or continue) the work of self-transformation into more thoughtful and conscientious citizens of the profession and the world. How can we create classrooms and curricula that create the conditions for our students' transformation? And how can we as instructors induce students to undertake the messy and challenging work that this transformation entails? This session will include both a brief talk and an open discussion.
About the Speaker: Emanuelle Burton is a lecturer in ethics for the CS department at UIC. She holds a PhD in religion and literature form the University of Chicago. She is the co-author, with Judy Goldsmith, Nicholas Mattei, Sara-Jo Swiatek, and Corey Siler, of the forthcoming textbook "Understanding Technology Ethics Through Science Fiction," from MIT Press. Her courses have recently been featured in Wired Magazine (https://www.wired.com/story/how-we-learn-computer-science-ethics/) and the Communications of the ACM (https://cacm.acm.org/magazines/2018/8/229765-how-to-teach-computer-ethics-through-science-fiction/fulltext).
Victor Bankston and Avik Bhattacharya | Tulane University
Title: A Distributed Search for Graphs for Which Lovasz's Theta Is a Poor Approximation of the Independence Number
Speaker: Victor Bankston (Computer Science PhD Student, Tulane University)
Title: Improving Prediction of MHC Class II Antigen Presentation Using Conformational Stability Data
Speaker: Avik Bhattacharya (Computer Science PhD Student, Tulane University)
Dong Hye Ye | Marquette University
This event will be held on Tuesday, 10/29/2019, from 12:30 p.m. - 1:45 p.m. in Stanley Thomas, Room 302. Please note the special weekday and time for this event.
Abstract: Medical image processing is essential for clinical diagnosis by providing quantitative visualization and analysis of underlying anatomy. In recent years, it has become increasingly easy to gather large quantities of medical images. Processing these large image databases is key to unlocking a wealth of information with the potential to be used. However, both interpretation of that big data and connecting it to downstream medical image processing is still challenging. To tackle this challenge, I unlock the valuable prior knowledge from large image databases via machine learning techniques and use it to improve medical image processing. In this talk, I will present how machine learning can help medical image processing such as CT Metal Artifact Reduction, Organ Segmentation, and High-Throughput Microscopic Imaging.
About the Speaker: Dr. Dong Hye Ye is an Assistant Professor in Electrical and Computer Engineering at Marquette University. His research interests are in advancing image processing via machine learning. His publications have been awarded Best Paper at MICCAI-MedIA 2010, Best Paper Runner-Up at ICIP 2015, and Best Paper at EI-IMAWM 2018. During his PhD, Dong Hye conducted research at Section of Biomedical Image Analysis (SBIA) in Hospital of the University of Pennsylvania (HUP) and Microsoft Research Cambridge (MSRC). He received Bachelor’s degree from Seoul National University in 2007 and Master's degree from Georgia Institute of Technology in 2008.
Jörg Rothe | Heinrich-Heine-Universität Düsseldorf
This event will be held on Friday, 11/01/2019, from 4:00 p.m. - 5:00 p.m. in Stanley Thomas, Room 302. Please note the special weekday for this event.
Abstract: Borda Count is one of the earliest and most important voting rules. Going far beyond voting, we summarize recent advances related to Borda in computational social choice and, more generally, in collective decision-making. We first present a variety of well-known attacks modeling strategic behavior in voting—including manipulation, control, and bribery—and discuss how resistant Borda is to them in terms of computational complexity. We then describe how Borda can be used to maximize social welfare when indivisible goods are to be allocated to agents with ordinal preferences. Finally, we illustrate the use of Borda in forming coalitions of players in a certain type of hedonic game. All these approaches are central to applications in artificial intelligence.
About the Speaker: Jörg Rothe received his diploma in 1991, his PhD in 1995, and his habilitation degree in 1999, each from Friedrich-Schiller-Universität Jena, Germany. In 1993-1994 he was a visiting scholar and in 1997-1998 a visiting assistant professor at the CS department of University of Rochester, each with a DAAD research fellowship for working with Lane A. Hemaspaandra. Since 2000 he has been a professor of computer science at Heinrich-Heine-Universität Düsseldorf, Germany; in 2013 he was a visiting professor at the CS department of Stanford University, and since 2014 he has been the chair of the CS department at Heinrich-Heine-Universität Düsseldorf. After receiving a DFG Heisenberg Fellowship in 2000 he has been the principal investigator of six DFG projects, a principal investigator in a EUROCORES project of the European Science Foundation (ESF), and was involved in various other international collaborative research projects. His research interests are in computational social choice, algorithmic game theory, fair division, and argumentation theory, typically focusing on the algorithmic and complexity-theoretic properties of the related problems.
Speaker | Institution
Majid Mirzanezhad | Tulane University
Please join us for a PhD Prospectus presentation by Tulane computer science PhD student, Majid Mirzanezhad. This event will be held on Wednesday, 11/20/2019, at 3:00 p.m. in the Boggs Center for Energy and Biotechnology, Room 122. Please note the special weekday, time, and venue for this event.
Abstract: Because of its applications, Geographic Information Systems (GIS) interest researchers in various disciplines to develop methods and tools for a better understanding of the world and surrounding environment. In this prospectus, we consider several problems and future studies that involve applications in maps and networks in a geographic scene. We propose algorithmic methods for solving some important problems described in the following:
We first consider computing the Frechet distance for a special class of curve which is a very commonly used and popular metric for capturing the similarity between GPS trajectories, piecewise linear functions and generally any linear features on a map. The classical algorithm for computing this distance runs in quadratic time in terms of the total complexity of two piecewise linear curves. We propose several algorithms and a data structure that compute the Frechet distance substantially faster for the special case when the Frechet distance is relatively small. In particular, we give a linear-time greedy algorithm deciding and approximating the Frechet distance and a near-linear time algorithm computing the exact value of the distance between two curves in any constant dimension.
Next, we exploit the metric studied above for simplification purposes. We study how to simplify a polygonal curve on a map that may not need redundant details or high resolution. Simplification reduces the complexity of the input object and therefore speeds up any future computations under some approximation attained by itself in the preprocessing stage. We specifically consider the problem of computing an alternative polygonal curve with the minimum number of links whose distance to the input curve is at most some given real value. From the theoretical point of view, we prove that when the distance measure changes, the problem becomes either NP-hard or polynomially solvable. We also propose several exact and approximation algorithms when the placement of the output curve respects some degrees of freedom in the ambient space.
In our future studies, we intend to extend the simplification problem to geometric input trees and graphs since they represent a non-linear structure of features on a map such as rivers, watersheds, transportation networks and so forth. We obtain some preliminary results and end this prospectus with some interesting algorithmic problems in this context.
Speaker | Institution
Kristi Potter | National Renewable Energy Laboratory (NREL)