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 parallel 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.



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! #AllAreWelcome  #GUEvents #GURedSquareYou can check us at Red Square...

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

#AllAreWelcome #GUEvents #GURedSquare

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




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...


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.

#GetInvolved #CSDeptGU

#GuWomenCodersWeek   Nov 13 to Nov 17#JoinUs #GUWeCode

Nov 13 to Nov 17
#JoinUs #GUWeCode



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.

#CSEvents #CSColloquium #GUCS



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.

#GetInvolved #ComputerScienceDept #WeeklyEvents #UpcomingEvents #GeorgetownUniversityCS

#GetInvolved #ComputerScienceDept #WeeklyEvents #UpcomingEvents #GeorgetownUniversityCS



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

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

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

#TechnologyCareerPanel #CakePops #Coffee #GUWomenCoders Today | 2:00 pm to 3:00 pm | Leavey Program Room

#TechnologyCareerPanel #CakePops #Coffee #GUWomenCoders
Today | 2:00 pm to 3:00 pm | Leavey Program Room


This Friday is a busy day for the CS Dept


STM 326

Allowing Bounded Leakage in Secure Computation: A New Application of Differential Privacy

Secure computation allows two or more parties to perform arbitrary computations on encrypted data. While this was purely of theoretical interest 10 years ago, today the techniques are quite practical, and the application space of secure computation is rapidly growing. As researchers and users attempt to apply these techniques to larger data sets, a new set of challenges arise. In our work, we explore a new trade-off between efficiency and privacy, allowing some bounded amount of leakage to be observed by the computing servers, in the form of access patterns to memory. However, unlike much of the prior work that has made a similar tradeoff, we give provable guarantees about what is revealed, demonstrating that what is leaked in the process of computing preserves the differential privacy of the users that have contributed their data. In this talk we will give some background on both secure computation and differential privacy, before presenting our new results that combine the techniques from these two fields.

Bio: Dov Gordon is currently an assistant professor at George Mason University. In his research, he explores techniques for computing on encrypted data, investigating what is feasible from a theoretical standpoint, and advancing what is practical today. Dov received his PhD with Jonathan Katz at the University of Maryland in 2010, and then spent two years as a postdoc at Columbia University, as a recipient of the CRA computing innovations fellowship. He joined George Mason after three years as a research scientist at Vencore Research labs. Dov has lived near 6 different green line Metro stations since 2004




STM 326

Algorithmic Stability for Adaptive Data Analysis

Adaptivity is an important feature of data analysis - the choice of questions to ask about a dataset often depends on previous interactions with the same dataset. However, statistical validity is typically studied in a nonadaptive model, where all questions are specified before the dataset is drawn. Recent work by Dwork et al. (STOC, 2015) initiated the formal study of this problem, and gave the first upper bounds on the achievable generalization error for adaptive data analysis.

The results of Dwork et al. are based on a connection with algorithmic stability in the form of differential privacy. We extend their work by giving a quantitatively optimal, more general, and simpler proof of their main theorem that stable algorithms of the kind guaranteed by differential privacy imply low generalization error. We also show that weaker stability guarantees such as bounded KL divergence and total variation distance lead to correspondingly weaker generalization guarantees.

Joint work with Raef Bassily, Kobbi Nissim, Adam Smith, Thomas Steinke, and Jonathan Ullman.

Bio: I am a post-doctoral fellow at the Center for Research on Computation and Society (CRCS), Harvard University. I completed my Ph.D. in computer science at Ben-Gurion University, where I was lucky to have Prof. Amos Beimel and Prof. Kobbi Nissim as my advisors. My research is focused on connections between learning theory and differential privacy, a line of work aimed at enabling rich analyses on sensitive individual data, while providing strong privacy guarantees for the individuals.

Job Fair Reception

Job Fair Reception

Job Fair Reception

Tavish Vaidya - First place: Hidden Voice CommandsCyber Security Awareness Week 2016APPLIED RESEARCH

Tavish Vaidya - First place: Hidden Voice Commands
Cyber Security Awareness Week 2016

Georgetown University Computer Science Department's cover photo

Georgetown University Computer Science Department's cover photo


Join the Department of Computer Science for our CS Colloquium. Friday, October 14, 2016 in St. Mary's Hall room 326 at 11:00am
Prof. Hal Daume III of University of Maryland, College Park will be speaking on "Learning Language through Interaction". See you there!

Distinguished GentlemanProfessor Mark Maloof

Distinguished Gentleman
Professor Mark Maloof


Join The Department of Computer Science in our Mid-Semester Celebration!!

Meet us in St. Mary's Hall Room 326 TODAY at 12:30PM for good food, dessert and soft drinks.

See you there!!

Georgetown University Computer Science Department's cover photo

Georgetown University Computer Science Department's cover photo

Georgetown University Computer Science Department

Georgetown University Computer Science Department


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Paid Student Fellowship Opportunity: Hello, my name is Daniel and I am a sophomore at American University. The nonprofit I work for is actively looking to fill the position of Web Development Fellow. Please check out this job posting for details, requirements, and time commitment, and message me with specific questions. Thank you!
Hello world! The Department of Computer Science and Engineering, IIT (BHU) Varanasi, India presents to you a brand new edition of its international online coding festival, Codefest, which will be held from 22nd to 24th September 2017. Covering the areas of Algorithmic Programming, Application Development, Mathematics, Open Source Development, Machine Learning, Cyber Security, Natural Language Processing and Constrained Programming, Codefest provides the perfect platform for fresh enthusiasts and the experienced ones to code together and compete for ultimate glory. With over 10,000 participants from 2000+ institutes in 92 countries, competing for a total prize money of INR 450,000, the rebirth of Codefest in 2016 was phenomenal! Along with the online competitive events, Codefest '16 also witnessed a plethora of informals, thus having the perfect blend of learning and fun. Motivated and enthralled, we have come back this year with loads of surprises in store for you. So put on your coding caps, waste no time and visit our website to get yourself registered for the coding extravaganza! Follow us on Facebook at to stay updated about Codefest '17.