Fall 2017 Schedule

09/8 – Jonathan Gemmell, PhD – DePaul University
Automatic Extraction of Informal Topics from Online Suicidal Ideation

Abstract: Suicide is an alarming public health problem accounting for a considerable number of deaths each year worldwide.  Many more individuals contemplate suicide.  Understanding the attributes, characteristics, and exposures correlated with suicide remains an urgent and significant problem.  As social networking sites have become more common, users have adopted these sites to talk about intensely personal topics, among them their thoughts about suicide.  Such data has previously been evaluated by analyzing the language features of social media posts and using factors derived by domain experts to identify at-risk users.  In this work, we automatically extract informal latent recurring topics of suicidal ideation found in social media posts.  Our evaluation demonstrates that we are able to automatically reproduce many of the expertly determined risk factors for suicide.  Moreover, we identify many informal latent topics related to suicide ideation such as concerns over health, work, self-image, and financial issues.

Bio: Jonathan F. Gemmell is an Assistant Professor in the School of Computing at DePaul University. He holds a BA in Classics, an MS in Computer Science and a PhD in Computer Science. His research focuses on the social web, data analysis and artificial intelligence. He is a faculty member at the Web Intelligence Laboratory. He has published dozens of articles in international peer-reviewed conferences and journals. His previous business experience includes trading foreign currency derivatives on the floor of the Chicago Mercantile Exchange and managing a trading and investment group.

09/15 (1 of 2) – Rami Ghannam – DePaul University
User-Targeted Denial of Service attacks

Abstract: Mobile networks are prevalent in today’s world, being used in a variety of applications ranging from personal use to the work environment and other. Ensuring security for users in a mobile network is therefore increasingly important. Denial-of-service attacks or DoS proved to be the biggest threat to mobile networks in recent years. A lot of work has been done in DoS targeting the infrastructure of the mobile network. User-targeted DoS attacks have been neglected in comparison. The fourth generation of cellular networks 4G LTE is the fastest growing mobile network in terms of subscriber numbers. The security aspect of mobile networks has improved throughout the generations, however, 4G proved to still have vulnerabilities in the signaling plane that allow a malicious attacker to target a specific user. In particular, the Attach Request procedure and the Tracking Area Update (TAU) procedure could be exploited. Deploying a rogue base station and forcing the targeted user to connect to it is therefore possible. The attacker could then deny selected services of the targeted user such as LTE data communication.

Bio: Rami Ghannam has a Bachelor’s and Master’s degree in Computer and Communications Engineering. He worked for two years as a network software engineer conducting the installation and quality control process for PBX platforms. He joined DePaul University in 2012 to pursue his PhD in Computer Science. His research interests include Computer Communications Networks, Software Defined Networks and Mobile Networks.

09/15 (2 of 2) – Himan Abdollahpouri – DePaul University
Effective Exploration Exploitation Trade-off in Sequential Music Recommendation

Abstract: Personalization is an essential part of the recommender systems. That is, tailoring the recommended items based on the tastes and interests of the end user. However, in many real world applications, there is a tremendous need to explore a broader range of items for a variety of reasons such as finding out more about what a user would like and what s/he wouldn’t, giving the opportunity to different items to be exposed to users and, lack of available items that match the user’s immediate preferences, to name just a few.

Music recommendation has become very popular in recent years due to its great performance in terms of helping users to find interesting songs in an easy-to-use manner. Similar to many other recommendation domains, explorations is very important in music recommender systems as it allows the system to learn more about the user and, at the same time, achieve more information about certain items that have not been rated enough. To the best of our knowledge, exploration in recommender systems has been done mostly at random. That is, there is no timing strategy to do exploration versus exploitation. In this project, we show that the previous sequence of the played content in a sequential music recommendation is important in deciding whether an explore item should be recommended or an exploit one.

Bio: Himan Abdollahpouri is a PhD candidate at the Web Intelligence lab at DePaul University. Himan holds an MSc in Artificial Intelligence and a BSc in Computer Engineering from Iran University of Science and Technology and Bu Ali Sina University, respectively. Prior to joining DePaul, Himan was a senior software engineer at TOSAN Inc., the most leading company in software engineering and payment/banking industry in Iran. While in the U.S., Himan did an internship as a data scientist in Summer 2017 at Pandora Media Inc. to help the company improve their user engagement via more efficient music recommendation strategies. Himan has several publications in the most prestigious venues in recommender systems such as ACM RecSys and ACM UMAP. Himan’s research interests primarily lie in machine learning, recommender systems, and data mining. In addition, he is also interested in psychology and sociology and how these two could be combined with machine learning to have better user modeling.

09/22 – Jonathan Gemmell – DePaul University
Minimum Constraint Removal Problem, Part 1

Abstract: Given a set of obstacles and two designated points in the plane, the Minimum Constraint Removal problem asks for a minimum number of obstacles that can be removed so that a collision-free path exists between the two designated points. In this work, we extend the study of Minimum Obstacle Removal. We show that the problem remains NP-hard in the two cases:  (1) when all the obstacles are axes-parallel rectangles, and  (2) when all the obstacles are line segments such that no three intersect at the same point. Our results improve the results of Erickson and LaValle and answer some of their open questions. As a byproduct of our NP-hardness reductions, we prove that, unless the Exponential-Time Hypothesis (ETH) fails, Minimum Constraint Removal cannot be solved in subexponential time 2^{o(n)}, where n is the number of obstacles in the instance. This shows that significant improvement on the brute-force 2^{O(n)}$-time algorithm is unlikely.

We then present a subexponential-time algorithm for instances of Minimum Constraint Removal in which the number of obstacles that overlap at any point is constant; the algorithm runs in time 2^{O(sqrt{N})}, where N is the number of the vertices in the auxiliary graph associated with the instance of the problem. We show that significant improvement on this algorithm is unlikely by showing that, unless ETH fails, Minimum Constraint Removal with bounded overlap number cannot be solved in time 2^{o(sqrt{N})}. We describe several exact algorithms and approximation algorithms that leverage heuristics and discuss their performance in an extensive empirical simulation.

Bio: Jonathan F. Gemmell is an Assistant Professor in the School of Computing at DePaul University. He holds a BA in Classics, an MS in Computer Science and a PhD in Computer Science. His research focuses on the social web, data analysis and artificial intelligence. He is a faculty member at the Web Intelligence Laboratory. He has published dozens of articles in international peer-reviewed conferences and journals. His previous business experience includes trading foreign currency derivatives on the floor of the Chicago Mercantile Exchange and managing a trading and investment group.

9/29 – Iyad Kanj – DePaul University
Minimum Constraint Removal Problem, Part 2
10/6 – Kim Brown- General Electric
Women in Technology Keynote Address,

Note the WiT event is scheduled for 12 – 6pm.  Please RSVP (https://www.eventbrite.com/e/women-in-technology-workshop-tickets-37075471734).

10/13 – Gabe Fils – DePaul University
Reusable Research Objects
10/20 – Elena Zheleva – University of Illinois at Chicago
Details forthcoming.
10/27 – Amor Montes De Oca, Director of Strategic Initiatives at 2112

Chicago’s first business incubator focused on the development of entrepreneurs in music, film/video and creative industry technology.

11/3 – Sugandha Malviya
Details forthcoming.
11/10 TBD