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Yahoo!-DAIS Seminar (CS591DAI) Orientation ChengXiang (“Cheng”) Zhai Department of Computer Science University of Illinois at Urbana-Champaign.

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Presentation on theme: "Yahoo!-DAIS Seminar (CS591DAI) Orientation ChengXiang (“Cheng”) Zhai Department of Computer Science University of Illinois at Urbana-Champaign."— Presentation transcript:

1 Yahoo!-DAIS Seminar (CS591DAI) Orientation ChengXiang (“Cheng”) Zhai Department of Computer Science University of Illinois at Urbana-Champaign

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3 Basic Information about CS591DAI (=Yahoo!-DAIS seminar) Meets at 4-5pm, Tuesdays, 0216 SC 1 Credit Hour  Miss at most two talks At each meeting, you can expect  An interesting research talk  Opportunity for research discussion  An attendance sheet for collecting signatures  And snacks (thanks to Yahoo!) Seminar coordinators: Yucheng Chen, Hao Luo Website: (under construction)http://dais.cs.uiuc.edu/seminars.html Mailing list: (make sure you subscribe to

4 Prof. Emerita Bioinformatics The DAIS Group: Data & Information Systems Kevin Chang Jiawei HanSaurabh SinhaMarianne WinslettCheng Zhai Tandy Warnow (+ Bioenginering) Hari Sundaram (+Advertising) Aditya Parameswaran Jian Peng + many grad/undergrad students + few postdocs/visitors

5 Landscape of DAIS Research Scalability Intelligence Application impact Health/Medical/Biology Education Productivity (Web, , …) Decision making (government, business, personal) Search Data/Information Access Information analysis & Data mining Browsing Recommendation Decision/Task support Intelligent information agent GigabytesTerabytesPetabytes Storage 5

6 DAIS & Related Areas in CS Scalability Intelligence Application impact Health/Medical/Biology Education Productivity (Web, , …) Decision making (government, business, personal) Search Data/Information Access Information analysis & Data mining Browsing Recommendation Decision/Task support Intelligent information agent GigabytesTerabytesPetabytes Storage Artificial Intelligence Statistics Human-Computer Interaction, Graphics Systems & Networking Parallel Computing Theory & Algorithms 6

7 Overview of DAIS Faculty Research

8 How to bring structured/semantic-rich access to the myriad and massive unstructured data which accounts for most of the world's information?  How to search the Web that Google does not see?  How to reach into and across pages that Google only takes you to?  How to listen to buzz of the world and make sense? Kevin C. Chang: Bridging Structured and Unstructured Data

9 Project 1. MetaQuerier: Exploring and Integrating the Deep Web MetaExplorer source discovery source modeling source indexing MetaIntegrator source selection schema integration query mediation FIND sources QUERY sources db of dbs unified query interface

10 10 Data Aware Search Project 2. WISDM : Data-aware Search over the Web

11 Demo: Entity Search-- “university of california #location”

12 Project BigSocial : Social Data Analytics Social Data Analytics

13 Aditya Parameswaran Started August 2014 i.stanford.edu/~adityagp Interests: Data Management, Mining and Algorithms Specific topics of interest  Interactive Data Analytics  Crowd-powered Analytics  Approximate Analytics  Visual Analytics and Data Mining

14 Research Style 14 Data Systems Data Mining Theory Data Science and Applied ML Build Real Data Analytics Tools / Systems Design Algorithms with Guarantees

15 Research Goal: Simplifying Data Analytics 15 “….(in the next few years) we project a need for 1.5 million additional analysts in the United States who can analyze data effectively…“, -- McKinsey Big Data Study, 2012 How do we make it easier for novice data analysts to get insights from data?

16 Simplifying Data Analytics: Four Aspects UnstructuredQueryingScale Visualizations Crowd Powered Analytics Interactive Analytics Approximate Analytics Visual Analytics

17 Example Projects Crowd-powered search Crowd-powered data extraction & cleaning Interactive query synthesis Speculative querying and caching Recommending visualizations automatically Approximate visualizations with guarantees Fundamental principles: cost, latency, error Browsing-based query processing Crowd Query Visual Approx

18 Hari Sundaram Prof. Hari Sundaram used a separate file for his presentation, which is available at;

19 Jiawei Han: Data Mining & Information Networks How to perform data mining effectively in massive data and in heterogeneous information networks? How to mine structures and construct networks from unstructured real-world data? Specific research subareas:  Effective methods for mining heterogeneous networks  Construction of heterogeneous networks from unstructured data  Multi-dimensional unstructured data summarization & OLAP  Truth discovery and outlier mining in networked data  Spatiotemporal and cyber-physical data mining (e.g., mobile objects, sensor/mobile data mining)

20 20 Recent Research Focus on Data Mining Network Construction, Search and Mining on Real datasets Network Construction, Search and Mining on Real datasets  News Network: 10M news articles + news of last 70 years  Computer Science Research Network: DBLP + citations + abstracts + Web pages of researcher + other related web pages  Tweet network and other social media network  Bio-Medical Research Network: PubMed and other medical sources  Networkfication of Knowledge-Bases: Wikipedia, DBPedia, Freebase  Cyber-Physical Networks (internet of things) Other frontiers: trajectory mining, truth finding, anomaly, … Other frontiers: trajectory mining, truth finding, anomaly, … Two ARL-funded projects in for Network Science Collaborative Technology Alliance (NSCTA) Two ARL-funded projects in for Network Science Collaborative Technology Alliance (NSCTA)

21 21 Constructing Unified, Structured Knowledge Networks Team: H. Ji (RPI) (lead), J. Han (UIUC), G. Cao (PSU), C. Voss (ARL), W. Wallace (RPI) Current State-of-the-Art  Natural language processing  Graph-based mining  Theory of planned behavior Army Needs and Benefits  Exploitation of unstructured data for improved situational analysis  Predictive tools to understand adversarial intent Construction of latent social and information networks require understanding connecting the dots in documents by linking entities and understanding human behavior Rotations: Ji: post-doc (Hong): 3 mo. at ARL; Han: student (Liu/Brova): 3 mo. at ARL; Wallace: student (Yulia): 3 mo. at ARL; each PI: visits to ARL totaling 1 wk or more. RPI students visit UIUC for one week each. Research Topics/Technical Approach Automated Construction of adaptable knowledge networks  Preliminary network construction via NLP and social network techniques  Exploitation of links for network refinement  Streaming updates Multi-dimension truth analysis  Credibility analysis by processing the interactions of the physical and social/cognitive states of the social network and its interactions with the information network Information processing & social/cognitive modeling  Build socio-cognitive models to predict human behavior  User-oriented and constraint-aware information processing

22 22 Distributed, User-Oriented Multi-Scale Network Summarization and OLAP Team: J. Han (UIUC) (lead), J. Hendler (RPI), T. Hanratty (ARL), H Ji (RPI), B. Welles (NEU) Current State-of-the-Art  Summarization on relational and text data  Social and cognitive computing  Online analytical processing on data cubes Army Needs and Benefits  Summarization/visualization matched to the user cognitive abilities for improved situational awareness  Flexible and efficient situation analysis for diverse groups of users Distributed, user-oriented, multi-scale summarization of networks to support online information processing and situation analysis for diverse groups of users Rotations: Han: student (Tao/Song): 3 mo. at ARL; Ji: student (Zhang): 3 mo. at ARL; each PI: visits to ARL, total 1 wk; 3 univ. mutual-visits: PIs + students Research Topics/Technical Approach Multi-scale network summarization & aggregation  Creation and enrichment of multi-dimension information from text and unstructured data  Network cube construction: conflict resolution, topical hierarchy generation, multidimensional indexing & selective, partial cube materialization User-oriented adaptation of network cube views  User- or social network-oriented OLAP  Accommodate dynamic updates of underlying social and/or communication networks Cost- and constraint-aware network cube  Distributed, user- or community-oriented cube views  Cost-, constraint- and availability- aware drilling, search and analysis Cognitive modeling, visualization, and supporting human decision making  Experimental platform: news, blogs, tweets, etc.

23 From Data Mining to Mining Info. Networks 23 Han, Kamber and Pei, Data Mining, 3 rd ed Yu, Han and Faloutsos (eds.), Link Mining, 2010 Sun and Han, Mining Heterogeneous Information Networks, 2012

24 ChengXiang Zhai: Intelligent text information management & analysis How can we develop intelligent algorithms and systems to help people manage and exploit large amounts of text data (e.g., Web pages, blog articles, news, , literature…)? Two subtopics:  Information retrieval: how can we connect the right information with the right users at the right time with minimum or no user effort?  Text mining: How can we automatically discover useful knowledge from text? How can we mine text data together with non-textual data in an integrative manner? Applications: Web, biomedical, health, education, … Research methodology:  Emphasize general & principled solutions without manual effort  Mainly use statistical models, machine learning, and natural language processing techniques

25 Sample Project: Latent Aspect Rating Analysis How to infer aspect ratings ? Value Location Service ….. How to infer aspect weights? Value Location Service

26 Solution: Latent Rating Regression Model Reviews + overall ratingsAspect segments location:1 amazing:1 walk:1 anywhere: nice:1 accommodating:1 smile:1 friendliness:1 attentiveness:1 Term weightsAspect Rating room:1 nicely:1 appointed:1 comfortable: Aspect SegmentationLatent Rating Regression Aspect Weight Topic model for aspect discovery +

27 Aspect-Based Opinion Summarization

28 Reviewer Behavior Analysis & Personalized Ranking of Entities People like cheap hotels because of good value People like expensive hotels because of good service Query: 0.9 value 0.1 others Non-Personalized Personalized

29 Tandy Warnow

30 Gene Tree Estimation: first align, then construct the tree S1 = -AGGCTATCACCTGACCTCCA S2 = TAG-CTATCAC--GACCGC-- S3 = TAG-CT GACCGC-- S4 = TCAC--GACCGACA S1 = AGGCTATCACCTGACCTCCA S2 = TAGCTATCACGACCGC S3 = TAGCTGACCGC S4 = TCACGACCGACA S1 S4 S2 S3 Sounds easy, but every good approach is NP-hard, and statistical methods (based on stochastic models of evolution) are very slow. Accuracy is essential, datasets are big, and they are also messy. Species tree estimation is even harder, because gene trees can be different from the species tree!

31 Avian Phylogenomics Project G Zhang, BGI Approx. 50 species, whole genomes genes, UCEs MTP Gilbert, Copenhagen S. Mirarab Md. S. Bayzid UT-Austin UT-Austin T. Warnow UT-Austin Plus many many other people… Erich Jarvis, HHMI Challenges: Maximum likelihood tree estimation on multi-million-site sequence alignments Massive gene tree incongruence My students and I developed a new technique (“Statistical Binning”) to enable a statistical estimation of the avian species tree, taking gene tree incongruence into account (both papers under review in Science)

32 1kp: Thousand Transcriptome Project Plant Tree of Life based on transcriptomes of ~1200 species More than 13,000 gene families (most not single copy) Gene Tree Incongruence G. Ka-Shu Wong U Alberta N. Wickett Northwestern J. Leebens-Mack U Georgia N. Matasci iPlant T. Warnow, S. Mirarab, N. Nguyen, Md. S.Bayzid UT-Austin UT-Austin Challenges: Multiple sequence alignment of datasets with > 100,000 sequences Gene tree incongruence My students and I developed ASTRAL – a technique to estimate species trees on large datasets (ECCB), and used it to analyze this dataset (under review in PNAS) UPP – new multiple sequence alignment method that can analyze up to 1,000,000 sequences (in preparation) Plus many many other people…

33 Computer science and mathematics issues: Heuristics for NP-hard optimization problems Graph algorithms Statistical estimation on messy data Mining sets of trees/alignments High Performance Computing Mathematical modelling Probabilistic analysis of algorithms Bioinformatics Problems Multiple Sequence Alignment Gene Tree Estimation Species tree estimation (when gene trees conflict) Genome rearrangement phylogeny Phylogenetic network estimation Metagenomic data analysis Also: Computational Historical Linguistics

34 Jian Peng: Machine learning for computational biology Biological data Machine learning Knowledge

35 Disease-related genes Functional homologs Experiments Biological data integration Hypothesis

36 Biological data integration: network biology  Modeling information diffusion on biological networks  Integrating networks from multiple species  Inference of gene function from network data

37 Other computational biology projects Protein science  Structure prediction  Protein folding  Viral proteins Translational bioinformatics  Drug discovery and optimization  Drug repositioning Genomics  Large-scale read mapping  Algorithms for genome assembly

38 Machine learning: modeling complex data Graphical models  Latent variable models  Efficient learning and inference algorithms  Causal/correlation structures  Applications to protein folding, gene expression analysis and biological network construction Learning representations for heterogeneous data  Low-dimensional embedding for network, text and molecular data sets Learning structured prediction with complex loss functions  Applications to biology, computer vision, speech recognition and natural language processing

39 Saurabh Sinha: Bioinformatics How is information about us encoded in our DNA ? How does this information evolve, giving rise to what Darwin called “endless forms most beautiful”? Research questions:  Gene regulation: How are genes turned on and off in precisely orchestrated ways?  Comparative genomics: What can we learn by comparing genomes of tens of different species?  Regulatory evolution: Can we build a mathematical model of evolution?  Genomics of behavior: How does DNA encode animal behavior ? 39

40 Genomics of behavior: honeybee What causes older bees to be more aggressive than younger ones? What causes Africanized bees to be more aggressive than European ones? What causes a bee to become aggressive if you annoy them? DNA sequence analysis shows that origins of aggression are the same !

41 Genomics of aging Find genes associated with aging, by searching the DNA sequence for certain patterns. Knock down one such gene; old cells became young !

42 A complete bioinformatics pipeline Slide 42 From cells … to data … to analysis … to hypotheses & experiments

43 QUESTIONS? 43


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