University of Glasgow - Schools - School of Computing Science - Research - Research sections - IDA-S

Time 2020-10-05 21:05:20

Web Name: University of Glasgow - Schools - School of Computing Science - Research - Research sections - IDA-S

WebSite: http://ir.dcs.gla.ac.uk

ID:71603

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Schools,School,Computing,

Description:

The Glasgow Information Retrieval Group within the School of Computing Science at the University of Glasgow was founded 32 years ago in 1986 by Professor C. J. ‘Keith’ van Rijsbergen, often considered one of the founders of modern Information Retrieval (IR). From its outset, the Glasgow IR group has focused on improving the effectiveness of IR systems, inventing new logic probabilistic retrieval models in the 90's and early 2000's, followed by the development of adaptive query expansion techniques, interactive multimedia models, the Divergence From Randomness framework, as well as leading research into quantum, expertise search and search result diversification models in the late 2000's.Since then, the Information Retrieval group embraced emerging machine learning and deep learning technologies for very large corpora and data streams, and have been at the forefront of research, development and application of those technologies for search and recommendation use-cases in a manner that ensures both effectiveness and efficiency.The Glasgow IR Group has a strong research track record. Indeed, the ACM Digital Library shows that the group isranked first by number of papers (429) at the SIGIR conference (the top CORE A* conference in the IR field). Meanwhile, a recent studyby Microsoft Research of the 40 years of SIGIR showed the University of Glasgow as the 5th most cited university at the conference and the 1st in Europe. The group is also renowned for developing the popular open source IR platform, Terrier.org, which has been downloaded over 60,000 since its first release in 2004 and is cited by over 3500 research papers. Furthermore, the group has a long history of engagement with the public and industry sectors from small SMEs to multinational corporations.The Informer magazine of BCS's Information Retrieval Specialist Groupcarried a recent profile on the Glasgow Information Retrieval Group. Asthe most active Information Retrieval group by publications in Europe and one of the longest running, our research covers the full-spectrum of topics that are relevant to the development of IR systems:IR Recommender Systems ModelsTheoretical modelling of IR systemsMachine learning and deep learning for information retrieval and recommender systemsInteractive information retrieval (personalised IR, emotion based search, user modelling for IR, gestural IR)User modelling and personal information accessTopic modeling; Entity search; Natural language processing for IRRecommender systems; Context-aware venue suggestionLarge-scale IR Efficient IRWeb information retrieval; Big data and information retrievalEfficient architecture for large-scale IR systems; Data stream processing architecturesData Streams IRReal-time information retrievalSearch in social and sensor networksArtificial Intelligence IRConversational information seeking and dialogue systemsInformation credibility, transparency, explainability and verification in IR systemsFairness in information retrieval recommender systemsNatural Language Processing IRInformation extraction includingentity and relation extractionAutomatic knowledge graph constructionMulti-task models, joint models and summarizationApplicationsMultimedia information retrievalDomain-specific information retrieval: smart cities; health; news; eDiscovery; sensitivity reviewEmergency management and crisis informaticsPolitics and MediaEvaluationTest collections and evaluation metricsEvaluation of IR systems and crowdsourcing for IROnline and Offline Evaluation of IR and Recommender SystemsEye-tracking and physiological approaches, such as fMRI Academic Staff:Current Research Assistants and Research Students:Ting SuXi WangXiao WangSiwei LuJingMin HuangYashon WuXin XinCarlos GemmelFederico RossettoSarawoot KongyoungAlexander HepburnIan MackieJun Choi HyunHitarth NarvalaRecent GraduatesJarana Manotumruksa (2019), University College London, ResearcherAnjie Fang (2019), Amazon, Applied ScientistJorge David Gonzalez Paule (2019), Jobandtalent Espana, Data ScientistColin Wilkie (2019), Siemens, Data EngineerDavid Maxwell (2019), University of Deft, Data EngineerGraham McDonald (2019), University of Glasgow, LecturerJames McMinn (2018), ScoopAnalytics, Co-FounderStuart Mackie (2018), BiP Solutions/Strathclyde Uni, Data ScientistHoratiu Bota (2018), Prodsight, Data ScientistJesus Alberto Rodriquez Perez (2018), University of Glasgow, Postdoctoral ResearcherFajie Yuan (2018), Tencent, Senior ResearcherNotable AlumniRyen White (Research Manager, Microsoft Research AI)Mark Sanderson (Professor, Royal Melbourne Institute of Technology)Mounia Lalmas (Head of Tech Research, Spotify)Ian Ruthven (Professor, Strathclyde University)Fabio Crestani (Professor, University of Lugano)Vassilis Plachouras (Software Engineering, Facebook)Leif Azzopardi (Chancellor's Fellow, Strathclyde University)Rodrygo Santos (Assistant Professor, Federal University of Minas Gerais)Eugene Kharitonov (Research Engineer, Facebook)Saul Vargas (Senior Machine Learning Scientist, ASOS)Dyaa Albakour (Lead Data Scientist, Signal Media)Nut Limsopatham (Senior Researcher, Microsoft AI)Amir Jadidinejad (AI Engineer, Glaxo Smith Kline)Zaiqiao Meng (Researcher, Cambridge University) Terrier is a highly flexible, efficient, and effective open source search engine, readily deployable on large-scale collections of documents developed by the IR group. Terrier implements state-of-the-art indexing and retrieval functionalities, and provides an ideal platform for the rapid development and evaluation of large-scale retrieval applications. Indeed, Terrier is used internationally, with over 60,000 downloads since its first release in 2004. Terrier is is used widely by the research community, with over3700 citations in research papers according to Google Scholar.Visit the website at http://terrier.org to learn more and download Terrier for free. For those new to the Information Retrieval field, the group maintains a useful set of common resources for researchers and practitioners:Information Retrieval Test Collections: On this page are a list of publically available IR test collections. Some are held locally and some are pointers to remote sites.Collections of text and corpora: What's the difference between a test collection and a text collection? Well a test collection has to have associated queries and relevance judgements. The things in here are simply document collections.Language reference works: This page contains links to online language reference works, such as dictionaries, thesauri etc.IR systems: A list of links to some sites that have information about IR systems.Linguistic utilities: Bits of IR language related utilities like stemmers, stop words lists, morphological taggers, etc.IR Journals: Various table of contents and abstracts of the papers in a number of well known IR journals.IR Organisations: Various IR groups and more formal organisations.Books: Supplements of books or whole books online. Upcoming events A Brief History of Deep Learning applied to Information Retrieval: A Personal PerspectiveGroup: Information Retrieval (IR)Speaker: Rodrigo Nogueira, University of Waterloo (Canada)Date: 19 October, 2020Time: 15:00 - 16:00Location: https://uofglasgow.zoom.us/j/99538824270?pwd=bG9nU2ozTmwvczFEMlN3eHFDMXZPZz09Abstract: In the past two years, we have seen remarkable progress in the development of information retrieval systems. Behind this ongoing revolution are pre-trained deep learning models, whose initial success in natural language processing promptly sparked interest in the information retrieval community.In this talk, I will discuss my journey ofapplying deep learning to information retrieval, from a naive start of developing navigational methods, passing by spectacular failures in using reinforcement learning in query reformulation, to finally succeed with pre-trained language models applied to multi-stage ranking and document expansion.Short Bio:Rodrigo Nogueira is a post-doctoral researcher at the University of Waterloo (Canada), an adjunct professor at UNICAMP (Brazil), and a senior research scientist at NeuralMind (Brazil).He holds a Ph.D. from New York University (NYU), where he worked on at the intersection of Deep Learning, Natural Language Processing, and Information Retrieval under the supervision of prof. Kyunghyun Cho. He has an Ms.C. degree from UNICAMP, where he developed with prof. Roberto Alencar Lotufo an award-winning algorithm for detecting fake fingerprints.zoom passcode:420597 #1 Information Retrieval group in Europe (ACM SIGIR publications)Creator of world-famous Terrier.org IR platformLeader in next generation Big Data processing technologiesLeading international data challenges (TREC CARS, TREC Incident Streams)Driving innovate intelligent systems for the home, public and commercial sectors The University of Glasgow is a registered Scottish charity: Registration Number SC004401

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