Georgetown University Computer Science Department

Georgetown University Computer Science Department Georgetown's Department of Computer Science consists of eighteen full-time faculty working with students through independent study and in faculty research.

Georgetown's Department of Computer Science consists of eighteen full-time faculty working in the areas of algorithms, artificial intelligence, bioinformatics, computer and network security, cryptography, database systems, data mining, distributed algorithms, distributed systems, human-computer interaction, information assurance, information retrieval, machine learning, networking, non-standard pa

rallel computing, parallel algorithms, theory, and visual analytics. We are a small department, which provides both undergraduate and graduate students considerable opportunities for interaction with the faculty through independent study and in faculty research. We take great pride in our students, teaching, and research. We also have an active CS&E club, programming team, and CS Women's group.

11/13/2017

CS COLLOQUIUM: LAURA DIETZ (UNH)

Tuesday November 14 | STM 326 | 11 AM

Retrieving Complex Answers through Knowledge Graph and Text

We all turn towards Wikipedia with questions we want to know more about, but eventually find ourselves on the limit of its coverage. Instead of providing "ten blue links" as common in Web search, why not answer any web query with something that looks and feels like Wikipedia? This talk is about algorithms that automatically retrieve and identify relevant entities and relevant relations and can identify text to explain this relevance to the user. The trick is to model the duality between structured knowledge and unstructured text. This leads to supervised retrieval models can jointly identify relevant Web documents, Wikipedia entities, and extract support passages to populate knowledge articles.

Bio: Laura Dietz is an Assistant Professor at the University of New Hampshire, where she teaches "Information Retrieval" and "Data Science for Knowledge Graphs and Text". She coordinates the TREC Complex Answer Retrieval Track and runs a tutorial/workshop series on Utilizing Knowledge Graphs in Text-centric Retrieval. Previously, she was a research scientist in the Data and Web Science group at Mannheim University, and a research scientist with Bruce Croft and Andrew McCallum at the Center for Intelligent Information Retrieval (CIIR) at UMass Amherst. She obtained her doctoral degree with a thesis on topic models for networked data from Max Planck Institute for Informatics, supervised by Tobias Scheffer and Gerhard Weikum.

Laura Dietz, Department of Computer Science, University of New Hampshire -- [email protected]

GU Women Coders Week Is HERE! JOIN US! Daily Event! You can check us at Red Square until 1 pm today! Women Love Co...
11/13/2017

GU Women Coders Week Is HERE! JOIN US! Daily Event!



You can check us at Red Square until 1 pm today! Women Love Coding Photo Montage! Get your Complimentary Doughnut!!!!

GU Women Coders - GU WeCode

11/09/2017

JOIN US!!!!

CS COLLOQUIUM: BENJAMIN CARTERETTE (U. OF DELAWARE)

Friday, November 10 at 11:00 am | STM 326

Offline Evaluation of Search Systems Using Online Data

Evaluation of search effectiveness is very important for being able to iteratively develop improved algorithms, but it is not always easy to do. Batch experimentation using test collections--the traditional approach dating back to the 1950s--is fast but has high start-up costs and requires strong assumptions about users and their information needs. User studies are slow and have high variance, making them difficult to generalize and certainly not possible to apply during iterative development. Online experimentation using A/B tests, pioneered and refined by companies such as Google and Microsoft, can be fast but is limited in other ways.

In this talk I present work we have done and work in progress on using logged online user data to do evaluation offline. I will discuss some of the user simulation work I have done with my students in the context of evaluating system effectiveness over user search sessions (in the context of the TREC Session track), based on training models on logged data for use offline. I will also discuss work on using historical logged data to re-weight search outputs for evaluation, focusing on how to collect that data to arrive at unbiased conclusions. The latter is work I am doing while on sabbatical at Spotify, which provides many motivating examples.

Bio: Ben Carterette is an Associate Professor in the Department of Computer and Information Sciences at the University of Delaware, and currently on sabbatical as a Research Scientist at Spotify in New York City. He primarily researches search evaluation, including everything from designing search experiments to building test collections to obtaining relevance judgments to using them in evaluation measures to statistical testing of results. He completed his PhD with James Allan at the University of Massachusetts Amherst on low-cost methods for acquiring relevance judgments for IR evaluation. He has published over 80 papers, won 4 Best Paper Awards, and co-organized two ACM SIGIR-sponsored conferences--WSDM 2014 and ICTIR 2016--in addition to nearly a decade's worth of TREC tracks and several workshops on topics related to new test collections and evaluation. He was also elected SIGIR Treasurer in 2016.

6TH ANNUAL UNDERGRADUATE SCIENCE RESEARCH OPPORTUNITIES FAIRDo you want to become a student researcher?Do you want to en...
11/09/2017

6TH ANNUAL UNDERGRADUATE SCIENCE RESEARCH OPPORTUNITIES FAIR

Do you want to become a student researcher?
Do you want to enhance your research skills?
Prepare for grad school or med school?
Then don't miss the 6th Annual Georgetown Undergraduate Science Research Opportunities Fair hosted by Georgetown University's Chapter of Psi Chi!

Over 25 labs, representing diverse GU science departments: including biology, chemistry, computer science, economics, linguistics, mathematics, physics, and psychology will be providing information on getting involved.

Free food will also be served.

Nov 13 to Nov 17
11/06/2017


Nov 13 to Nov 17

10/30/2017

CS COLLOQUIUM: MATT MARGE (ARL)
FRIDAY, NOVEMBER 3 AT 1:00PM | STM 326

Towards Natural Dialogue with Robots

Robots can be more effective teammates with people if they can engage in natural language dialogue. In this talk, I will address one fundamental research problem to achieving this goal: understanding how people will talk to robots in collaborative tasks, and how robots could respond in natural language to maintain an effective dialogue that stays on track....

Bio: Matthew Marge is a Research Scientist at the Army Research Lab (ARL). His research focuses on improving how robots and other artificial agents can build common ground with people via natural language. His current interests lie at the intersection of computational linguistics and human-robot interaction, specializing in dialogue systems. He received the Ph.D. and M.S. degrees in Language and Information Technologies from the School of Computer Science at Carnegie Mellon University, and the M.S. degree in Artificial Intelligence from the University of Edinburgh.

10/24/2017

CS COLLOQUIUM: TIM FININ (UMBC)
FRIDAY, OCTOBER 27 AT 11:00AM

STM 326

From Strings to Things: Populating Knowledge Graphs from Text

The Web is the greatest source of general knowledge available today but its current form suffers from two limitations. The first is that text and multimedia objects on the Web are easy for people to understand but difficult for machines to interpret and use. The second is the Web's access paradigm, which remains dominated by information retrieval, where keyword queries produce a ranked list of documents that must be read to find the desired information. I'll discuss research in natural language understanding and semantic web technologies that addresses both problems by extracting information from text to produce and populate Web-compatible knowledge graphs. The resulting knowledge bases have multiple uses, including (1) moving the Web's access paradigm from retrieving documents to answering questions, (2) embedding semi-structured knowledge in Web pages in formats designed for computer to understand, (3) providing intelligent computer systems with information they need to perform their tasks, (4) allowing the extracted data and knowledge to be more easily integrated, enabling inference and advanced analytics and (5) serving as background knowledge to improve text and speech understanding systems. I will also cover current work on applying the techniques to extract and use cybersecurity-related information from documents, the Web and social media.

Biosketch: Tim Finin is the Willard and Lillian Hackerman Chair in Engineering and a Professor of Computer Science and Electrical Engineering at the University of Maryland, Baltimore County (UMBC). He has over 35 years of experience in applications of artificial intelligence to problems in information systems and language understanding. His current research is focused on the Semantic Web, analyzing and extracting information from text, and on enhancing security and privacy in computing systems. He is a fellow of the Association for the Advancement of Artificial Intelligence, an IEEE technical achievement award recipient and was selected as the UMBC Presidential Research Professor in 2012. He received an S.B. degree from MIT and a Ph.D. from the University of Illinois at Urbana-Champaign. He has held full-time positions at UMBC, Unisys, the University of Pennsylvania and the MIT AI Laboratory. He served as an editor-in-chief of the Journal of Web Semantics and is a co-editor of the Viewpoints section of the Communications of the ACM.

10/13/2017

10/13/2017

Over 75+ students came out to network with recruiters who also happen to be Georgetown University Alumni!
10/13/2017

Over 75+ students came out to network with recruiters who also happen to be Georgetown University Alumni!

Today | 2:00 pm to 3:00 pm | Leavey Program Room
10/13/2017


Today | 2:00 pm to 3:00 pm | Leavey Program Room

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