ECE 6111 Graduate Seminar on Big Data for Mobile Social Network

Time: Friday. 11:00am-12:00pm

Place: E220, Engineering Building 2

Zhu Han

Topic: Big data, related courses

1.      http://research.microsoft.com/apps/video/default.aspx?id=193507&l=i

2.      http://www.kdd.org/kdd2013/accepted-tutorials

3.      http://kdd2012.sigkdd.org/summer_school.shtml#liu

4.     https://class.coursera.org/bigdata-2012-001/lecture/index

5.      http://www.stanford.edu/class/cs246/

6.      http://www.ee.ucla.edu/ee236a/

7.      http://www.dtc.umn.edu/seminars/events.php?eventdesc=690&menu=program

8.      http://simons.berkeley.edu/programs/bigdata2013

 

2016 Schedule

1.      2/5, Hung Nguyen, pdf

2.      2/12, Xunsheng Du, pdf

3.      2/19, Yunan Gu, pdf

4.      2/26, Yanru Zhang, pdf

5.      3/4, Radwa Sultan, pdf

6.      3/11, Jingyi Wang, pdf

7.      3/25, Errapotu,Saimounika, pdf

8.      4/1, Jahanipour,Jahandar, pdf

9.      4/8, Li, Xiaoyang, pdf

10.   4/8, Li, Lianyang, pdf

11.   4/15, Lu Hengyang, pdf

12.   4/15, Kevin Nathan, pdf

13.   4/22, Qiuyang Shen, pdf

14.   4/22, Hao Wu, pdf

15.   4/29, Lifeng Yan, pdf

16.   4/29, Xiong Zhou, pdf

2013 Schedule

1.      8/28, Sparse optimization 3: Alternating direction method of multipliers and split Bregman

2.      9/4, Sparse optimization 2: Sparse dual optimization

3.      9/11 Sparse optimization 1: Classic solvers and shrinkage operation, Sublinear algorithm (video)

4.      9/18 Sparse optimization 4 and 5: Prox-linear, coordinate minimization and gradient descent, homtopy, non-convex, greedy, algorithm for low rank matrices, and how to choose an algorithm

5.      9/25, QD-Learning: A Collaborative Distributed Strategy for Multi-Agent Reinforcement Learning Through Consensus + Innovations

6.      9/25 Asymptotically Efficient Distributed Estimation With Exponential Family Statistics

7.      10/2, Distributed covariance estimation in Gaussian graphical models

8.      10/2 Distributed Linear Precoder Optimization and Base Station Selection for an Uplink Heterogeneous Network

9.      10/9, The pathologies of big data. A Jacobs, etc, 2009. Communications of the ACM.

10.   10/9, A Scalable Data Store for Transactional Multi key Access in the Cloud. S Das, etc, 2010.

11.   10/16, Dryad: distributed data-parallel programs from sequential building blocks. M Isard, etc, 2007. Proceedings of the 2nd ACM SIGOPS/EuroSys European Conference on Computer Systems

12.   10/16, Interpreting the data: Parallel analysis with Sawzall. R Pike, etc, 2005. Scientific Programming.

13.   10/23, A comparison of approaches to large-scale data analysis. A Pavlo, etc, 2009. Proceedings of the 2009 ACM SIGMOD International Conference on Management of data

14.   10/23, A density-based algorithm for discovering clusters in large spatial databases with noise. M Ester, etc.1996, Proceedings of 2nd International Conference on Knowledge Discovery and Data Mining

15.   10/30, Efficient clustering of high-dimensional data sets with application to reference matchingA McCallum, etc. 2000, Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining

16.   10/30, Efficient Algorithms for Mining Outliers from Large Data Sets. S Ramaswamy, etc. 2000, Proceedings of the 2000 ACM SIGMOD international conference on Management of data

17.   11/6, MAFIA: Efficient and Scalable Subspace Clustering for Very Large Data Sets. S Goil, etc. 1999, Technical report

18.   11/6, Automatic subspace clustering of high dimensional data for data mining applications. R Agrawal, etc, 1998. Proceedings of the 1998 ACM SIGMOD international conference on Management of data

19.   11/13, Learning to classify short and sparse text & web with hidden topics from large-scale data collections. XH Phan, etc, 2008. Proceedings of the 17th international conference on World Wide Web.

20.   11/13, Social influence analysis in large-scale networks.  J Tang, etc. 2009. Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining

21.   11/20, SRDA: An efficient algorithm for large-scale discriminant analysis. D Cai, etc, 2008. IEEE Transactions on Knowledge and Data Engineering

22.   11/20, Predictive discrete latent factor models for large scale dyadic data. D Agarwal, etc. 2007

23.   11/27, Computational solutions to large-scale data management and analysis. E Schadt, etc. 2010. Nature Reviews Genetics

24.   11/27, MAD skills: new analysis practices for big data.  J Cohen, etc. 2009, Proceedings of the VLDB Endowment,

 

2012 Schedule

1.      9/5/12, Professor Qing Ling, Decentralized Jointly Sparse Optimization by Reweighted Lq Minimization

2.      9/12/12, 9/19/12, Yi Huang and Lanchao,  Mining Heterogeneous Information Networks

3.      9/26/12,  10/3/12, Thanh and Wenhao,  Large Graph Mining - Patterns, Tools and Cascade Analysis

4.      10/10/12, 10/17/12, Yitong and Linsen, Modeling Opinions and Beyond in Social Media

5.      10/24/12, Mahammad, Methods for Mining Social Media and Networks  part 1

6.      10/31/12, Najmeh, Methods for Mining Social Media and Networks  part 2

7.      11/7/12, Yanru, Two Computational Paradigms for Big Data

8.      11/14/12, 11/21/12, Preetham and Ali, Managing and Mining Billion-Node Graphs