[Eecs-news-links] FW: CIS Colloquia - Faculty Candidates, TWO DATES: Tuesday, Feb 23 AND Thursday, Feb 25, 2016 @ 3:30pm in 22 Deschutes Hall

Batten, Tina tina.batten at oregonstate.edu
Mon Feb 22 09:18:40 PST 2016



-----Original Message-----
From: Adriane Bolliger [mailto:adriane at cs.uoregon.edu] 
Sent: Monday, February 22, 2016 9:18 AM
To: colloquia at cs.uoregon.edu; dept at cs.uoregon.edu; grads-mail at cs.uoregon.edu
Subject: CIS Colloquia - Faculty Candidates, TWO DATES: Tuesday, Feb 23 AND Thursday, Feb 25, 2016 @ 3:30pm in 22 Deschutes Hall

*Please Notes: there will be two CIS colloquia this week featuring faculty candidates for the CIS department.

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Talk #1: Tuesday, Feb 23, 2016
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Cornelia Caragea
University of North Texas


Abstract

Keyphrase extraction is defined as the problem of automatically extracting descriptive phrases or concepts from documents. Keyphrases for a document act as a concise summary of the document and have been successfully used in many applications such as query formulation, document clustering, classification, recommendation, indexing, and summa- rization. Previous approaches to keyphrase extraction generally use the textual content of a target document or a local neighborhood that consists of textually-similar documents.

We posit that, in a scholarly domain, in addition to a document's textual content and textually-similar neighbors, other informative neighborhoods exist that have the potential to improve keyphrase extraction. In particular, research papers are not isolated. Rather, they are highly inter-connected in giant citation networks, in which papers cite or are cited by other papers in appropriate citation contexts, i.e., short text segments surrounding a citation's mention. These contexts are not arbitrary, but they serve as brief summaries of a cited paper. We effectively exploit citation context information for keyphrase extraction and show remarkable improvements in performance over strong baselines in both supervised and unsupervised settings.

Biography

Cornelia Caragea is an Assistant Professor at the University of North Texas in the Computer Science and Engineering department, where she directs the Machine Learning group. Her research interests lie at the intersection of artificial intelligence, machine learning, data mining, information retrieval, and natural language processing, with appli- cations to text and image analysis, scientific data analysis, bioinformatics, and social media.

She has published research papers in prestigious venues such as AAAI, IJCAI, WWW, EMNLP, ICDM, and ACM Transactions on the Web. Cornelia reviewed for many journals including Nature, ACM TIST, JAIR, and IEEE TKDE, served on several NSF panels, and was a program committee member for top conferences such as AAAI, IJCAI, ACL, NAACL, EMNLP, Coling, and CIKM. She also helped organize several workshops on scholarly big data in conferences such as IJCAI, AAAI, and IEEE BigData.

Cornelia earned a Bachelor of Science degree in Computer Science and Mathematics from the University of Bucharest, and a Ph.D. in Computer Science from the Iowa State University. Prior to joining the University of North Texas in Fall 2012, she was a post-doctoral researcher at the Pennsylvania State University.


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Talk #2: Thursday, Feb 25, 2016
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Bin Li
University of Illinois at Urbana-Champaign

Abstract

Efficient utilization of network resources plays an important role in the complex network systems penetrating our real life, such as communication networks, cyber-physical systems, cloud computing, and data centers. In this talk, I will first talk about load balancing in large cloud storage systems, which is used to exploit the redundancy in file storage (through replication or coding) to reduce mean file access delay. We use mean-field analysis to show that, for a given storage capacity per file, coding strictly outperforms replication at all traffic loads.

Then, I will talk about efficient resource allocation algorithm designs in cyber-physical systems, whose performance is heavily dependent on the accurate, prompt, and consistent feedback from the physical systems. This motivates us to develop resource allocation algorithms that not only achieve high throughput but also provide timely and consistent service. 

Biography

Bin Li received his B.S. degree in Electronic and Information Engineering in 2005, M.S. degree in Communication and Information Engineering in 2008, both from Xiamen University, and Ph.D. degree in Electrical and Computer Engineering under the supervision of Prof. Atilla Eryilmaz from The Ohio State University (OSU) in May 2014.

Since June 2014, he has been a Postdoctoral Researcher working with Prof. R. Srikant at the University of Illinois at Urbana-Champaign (UIUC). His research spans communication networks, data centers, cloud computing, and cyber-physical systems. He received the Presidential Fellowship from The Ohio State University and Chinese Government Award for Outstanding Ph.D. Students Abroad.

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DATE:	Tuesday, Feb 23 AND Thursday, Feb 25, 2015 
TIME:	3:30 p.m. talk, refreshments following talk
PLACE:	220 Deschutes Hall (Colloquium Room), University of Oregon

For all CIS public talks, go to:
http://www.cs.uoregon.edu/Activities/Public_Talks/







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