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Yahoo!-DAIS Seminar (CS591DAI) Orientation

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Presentation on theme: "Yahoo!-DAIS Seminar (CS591DAI) Orientation"— Presentation transcript:

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


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) Mailing list: (make sure you subscribe to it)

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

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

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

7 Overview of DAIS Faculty Research

8 Kevin C. Chang: Bridging Structured and Unstructured Data
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?

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

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

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

12 Project BigSocial: Social Data Analytics

13 Aditya Parameswaran Started August 2014
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 Data Science and Applied ML
Build Real Data Analytics Tools / Systems Data Systems Data Mining Theory Design Algorithms with Guarantees 14

15 Research Goal: Simplifying Data Analytics
“….(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? 15

16 Simplifying Data Analytics: Four Aspects
Unstructured Querying Visualizations Scale 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 Recent Research Focus on Data Mining
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, … Two ARL-funded projects in for Network Science Collaborative Technology Alliance (NSCTA) 20

21 Constructing Unified, Structured Knowledge Networks
Current State-of-the-Art Natural language processing Graph-based mining Theory of planned behavior Long Term Goal: Near perfect reliable network construction through progressive source processing and network refinement 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 Team: H. Ji (RPI) (lead), J. Han (UIUC), G. Cao (PSU), C. Voss (ARL), W. Wallace (RPI) 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. 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 21

22 Distributed, User-Oriented Multi-Scale Network Summarization and OLAP
Current State-of-the-Art Summarization on relational and text data Social and cognitive computing Online analytical processing on data cubes Long Term Goal: Support of distributed, multi-scale, multi-genre network summarization, OLAP and situation analysis for diverse user groups 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. Team: J. Han (UIUC) (lead), J. Hendler (RPI), T. Hanratty (ARL), H Ji (RPI), B. Welles (NEU) 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 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 22

23 From Data Mining to Mining Info. Networks
Han, Kamber and Pei, Data Mining, 3rd ed. 2011 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
Aspect Segmentation + Latent Rating Regression Reviews + overall ratings Aspect segments Term weights Aspect Rating Aspect Weight location:1 amazing:1 walk:1 anywhere:1 0.0 0.9 0.1 0.3 1.3 0.2 room:1 nicely:1 appointed:1 comfortable:1 0.1 0.7 0.9 1.8 0.2 nice:1 accommodating:1 smile:1 friendliness:1 attentiveness:1 0.6 0.8 0.7 0.9 3.8 0.6 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 = TAGCTATCACGACCGC S3 = TAGCTGACCGC S4 = TCACGACCGACA S1 = -AGGCTATCACCTGACCTCCA S2 = TAG-CTATCAC--GACCGC-- S3 = TAG-CT GACCGC-- S4 = TCAC--GACCGACA S1 S2 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! S4 S3

31 Avian Phylogenomics Project
Erich Jarvis, HHMI MTP Gilbert, Copenhagen G Zhang, BGI T. Warnow UT-Austin S. Mirarab Md. S. Bayzid UT-Austin UT-Austin Plus many many other people… Approx. 50 species, whole genomes 8000+ genes, UCEs 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
G. Ka-Shu Wong U Alberta J. Leebens-Mack U Georgia N. Wickett Northwestern N. Matasci iPlant T. Warnow, S. Mirarab, N. Nguyen, Md. S.Bayzid UT-Austin UT-Austin UT-Austin UT-Austin Plus many many other people… Plant Tree of Life based on transcriptomes of ~1200 species More than 13,000 gene families (most not single copy) Gene Tree Incongruence 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)

33 Current Projects 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 Biological data integration
Disease-related genes Functional homologs Hypothesis Experiments

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 ?

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
From cells … to data … to analysis … to hypotheses & experiments

43 Questions?

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