1. Multimodal Information Access and Synthesis A DHS Institute of Discrete Science Center @ UIUC Dan Roth Department of Computer Science University of Illinois at Urbana/Champaign

2. M I A S Multi-Modal Information Access & Synthesis 2 Most of the data today is unstructured books, newspaper articles, journal publications, field reports, images, and audio and video streams. How to deal with the huge amount of unstructured data as if it was organized in a database with a known schema. how to locate, access, organize, analyze and synthesize unstructured data. MIAS Mission: develop the theories, algorithms, and tools for analysts to access a variety of data formats and models integrate them with existing resources transform raw data into useful and understandable information. MIAS Mission

3. M I A S Multi-Modal Information Access & Synthesis 3 Task Perspective In the next decade, intelligence analysts will need to monitor a huge number of interesting events and entities formulate and evaluate hypotheses with respect to them. Analysts must interact, at the appropriate level of semantic abstraction, with a system that can synthesize, summarize and interpret vast amounts of multimodal information, integrate observed data with domain models and background information in multiple formats, propose hypotheses, and help verify them.

4. M I A S Multi-Modal Information Access & Synthesis 4 Scenario Consider an intelligence analyst researching a problem Iranian nuclear program – generate a list of Iranian nuclear scientists, affiliations, specialties, biographies, photos, and notable recent activities. Medical treatment – what is known about it; who are the experts; what do users say about it; what side effects have been reported Current technologies have solved the problem of collecting and storing huge amounts of information; it would be reasonable to assume that the information she is after does exist; However, multiple barriers exist on the way to a successful completion of the analysis, each posing a significant research challenge.

5. M I A S Multi-Modal Information Access & Synthesis 5 Multimodal Information Access & Synthesis Focused Multimodal Data Retrieval News Articles Field reports Specific Web sites Text Repositories Relational databases Surveillance Videos... News Articles Field reports Specific Web sites Text Repositories Relational databases Surveillance Videos... Online Data Sources Web pages Text documents * * * * * * * * * U. of Tehran Elkhan Factory * * * * * * visited Saeed Zakeri Relational data Images Saeed Zakeri attended Infer Metadata: Semantic entities Discover Relations Between Semantic Entities Support Information Analysis, Knowledge Discovery, Monitoring Discover unusual events, entities, and associations. Continuous monitoring of events, entities & associations Rapid retrieval of all info. about a particular entity Efficient search, querying, question answering, browsing, mining, loc = Northern Iran name = Elkhan Factory topics = fertilizer, enrichment Semantic categories Temporal categories Subjectivity/Opinions Semantic Disambiguation & Integration across multiple sources and modalities

6. M I A S Multi-Modal Information Access & Synthesis 6 MIAS Processes Focused data retrieval and integration Identify and collect relevant data from multiple sources Semantic data enrichment Infer semantics from unstructured data and images; Allow navigation and search across disparate data modalities; augment KBs Entity identification and relations discovery Identify real-world entities and relations among them Relate them to existing institutional resources for information integration Knowledge discovery and hypotheses generation and verification Construct the rich semantic structure and hidden networks of entity linkages Foundations Machine learning, database and data mining, natural language processing, inference and optimization and computer vision techniques Required for and driven by the aforementioned problems.

7. M I A S Multi-Modal Information Access & Synthesis 7 Develop diverse human resources to enhance the scientific research, educational, and governmental workforce in MIAS Educational and Outreach Initiatives: Target students from small research programs & minority-serving Expose them to the national labs Open opportunities for bigger impact A comprehensive education program designed to increase participation in the study and practice of MIAS topics: Provide substantive training for a new generation of experts in the field, Serve as a tool for recruiting an experienced group of undergraduates into graduate study in one of the broad fields of information science Be an intellectual community center, where participants at all levels of expertise come together in an enriched environment of collaboration. Integrated Mission: Research & Education

8. M I A S Multi-Modal Information Access & Synthesis 8 Intensive Course in The Math of Data Sciences 1.Probability and Statistics 2.Linear Algebra 3.Data Structures and Algorithms 4.Optimization 5.Learning & Clustering Data Science Summer Institute at UIUC Research Projects Led by co-PIs and Grad Students Topics: 1.Virtual Web: Focused Crawling 2.Relations and Entities 3.Text and Images Advanced MIAS Related Tutorials 1st DSSI: May 2007 Huge Success Speaker Series 8 weeks course 25 students Faculty from UIUC, Kansas State, UTSA, UTEP

9. M I A S Multi-Modal Information Access & Synthesis 9 Machine Learning & Data Mining Information Retrieval & Web Information Access Computer Vision Natural Language Processing & Information Extraction Databases & Information Integration Data Science Summer Institute at UIUC Advanced MIAS Related Tutorials

10. M I A S Multi-Modal Information Access & Synthesis 10 Data Science Summer Institute at UIUC

11. M I A S Multi-Modal Information Access & Synthesis 11 Our Team Leading researchers in intelligent information access & analysis and its foundations A large number of affiliates/consultants covering all areas of interest to the MIAS center.

12. M I A S Multi-Modal Information Access & Synthesis 12 Kevin C. Chang: Data Integration and Retrieval Deep Web MetaQuerier: Large-scale integration over deep Web pioneered the “holistic integration” paradigm Widely published at SIGMOD, VLDB, ICDE System building and demo at CIDR, SIGMOD, VLDB, … PC/editor/organizers of SIGMOD, ICDE, WWW, SIGKDD special issue, WIRI06, IIWeb06 workshops Awards: NSF CAREER, IBM Faculty, NCSA Faculty VLDB00 Best Paper Selection

13. M I A S Multi-Modal Information Access & Synthesis 13 AnHai Doan: Data Integration ACM Doctoral Dissertation Award 2004 Sloan Fellowship 2006 Edited special issues on Data Integration Co-chaired workshops on data integration, Web technologies, machine learning Co-writing a book titled “Data Integration” Monitoring People and Events Data integration Matching Schemas, Ontologies, Entities Integrating databases and text

14. M I A S Multi-Modal Information Access & Synthesis 14 David Forsyth: Computer Vision and Learning Linking Text and Images Labeling images via (a lot of) caption text Leading Computer Vision researcher, Over 110 papers on vision, graphics, learning applications Program Chair, CVPR 2000, General chair CVPR 2006, regular member of PC in all major vision conferences IEEE Technical Achievement Award, 2006 Lead author of main textbook, widely adopted

15. M I A S Multi-Modal Information Access & Synthesis 15 Jiawei Han: Data Mining Mining patterns and knowledge discovery from massive data Research focus: Mining data streams, frequent patterns, sequential patterns, graph patterns, and their applications Privacy preserving Data Mining Developed many popular data mining algorithms, e.g., FPgrowth, PrefixSpan, gSpan, StarCubing, CrossMine, RankingCube, and CrossClus Over 300 research papers published in conferences and journals Editor-in-Chief, ACM Transactions on Knowledge Discovery from Data Textbook, “Data mining: Concepts and Techniques,” adopted worldwide

16. M I A S Multi-Modal Information Access & Synthesis 16 UIUC CS Department Director of Diversity Programs Lecturer for Discrete Math, Data Structures courses at UIUC One of the leading teachers and educational leaders at UIUC. Research in Data Mining and Data bases Speaker and regular presenter at conferences for young women, including: GAMES and WYSE summer camps Expanding Your Horizons careers conference Grace Hopper Celebration of Women in Computing 2004 - panel on best practices for recruiting women into undergraduate programs in CS. SIGCSE 2006 - workshop, “How to host your own Small Regional Celebration of Women in Computing.” Cinda Heeren: Data Mining, Education Summer School MIAS Summer School Director Mathematical Foundation of Data Science Discrete

17. M I A S Multi-Modal Information Access & Synthesis 17 ChengXiang Zhai: Information Retrieval and Text Mining Probabilistic Paradigms for Information Retrieval Personalized/Context Dependent Search Relation Identification and Data Integration Leading expert in information retrieval and search technologies Recipient of the 2004 Presidential Early Career Award for Scientists and Engineers (PECASE), Main architect and key contributor of the Lemur Toolkit (being used by many research groups and IR companies around the world) ACM SIGIR’04 best paper award Selected services include Program Chair of ACM CIKM 2004; HLT/NAACL 06; SIGIR’09

18. M I A S Multi-Modal Information Access & Synthesis 18 Leading Researcher in Machine Learning, NLP, AI Developed Popular Machine Learning system and machine learning based NLP tools used in industry and NLP classes. Program Chair, ACL’03, CoNLL’02; Regular senior PC member in all major Machine Learning, NLP and AI conferences Associate Editor: Journal of Artificial Intelligence Research; Machine Learning Multiple papers awards Dan Roth: Machine Learning, NLP Semantic Analysis and Data enrichment Entity and Relation Identification and Integration Textual Entailment Machine Learning Methods for NLP and IE

19. M I A S Multi-Modal Information Access & Synthesis 19 Information Access & Synthesis Processes Focused data retrieval and integration, Identify and collect relevant data from multiple sources Semantic data enrichment, Infer semantics from unstructured data and images; Allow navigation and search across disparate data modalities; augment KBs Entity identification and relations discovery, Identify real-world entities and relations among them Relate them to existing institutional resources for information integration Knowledge discovery and hypotheses generation and verification, Construct the rich semantic structure and hidden networks of entity linkages Foundations Machine learning, database and data mining, natural language processing, inference and optimization and computer vision techniques Called for and driven by the aforementioned problems. Tools Text Processing& Analysis Semantic Analysis & Information Extraction Information Integration Machine Learning & Data Mining Integrating Text & Images