Research Group

Our group is performing research in unsupervised machine learning and information processing.

Group members

PhD students @ UT Austin

• Negin Raoof

• Romy Luo (with Kristen Grauman)

• Yating Wu (with Jessy Li)

• Matt Jordan

• Sriram Ravula

• Georgios Smyrnis

• Giannis Daras

• I also collaborate with Danny Diaz our IFML computational protein engineer.

Alumni

• Jay Whang (Phd UT Austin)→Google

• Ajil Jalal (Phd UT Austin)→UC Berkeley Postdoc

• Qi Lei(雷琦) (Phd UT Austin)→Princeton Postdoc→ Assistant Professor, NYU

• Shanshan Wu (Phd UT Austin)→Google Research

• Erik M. Lindgren (Phd UT Austin)→Google Research NY

• Murat Kocaoglu, (Phd UT Austin)→ IBM Research→ Assistant Professor, Purdue University

• Ethan Elenberg, (Phd UT Austin)→ ASAPP Research Scientist

• Rashish Tandon (Phd UT Austin)→ Apple

• Ashish Bora (MS UT Austin)→ Google Health

• Anastasios Kyrillidis, (Postdoc), Assistant Professor, Rice University

• Karthikeyan Shanmugam, (PhD UT Austin), IBM Research

• Megasthenis Asteris (PhD UT Austin)→ Google

• Michael Borokhovich (Postdoc)→ Amazon

• Dimitris Papailiopoulos (PhD UT Austin)→UC Berkeley PostDoc→ UW-Madison, Assistant Professor.

• Maheswaran Sathiamoorthy (PhD USC)→Google
  

• MS

• Dave Van Veen (MS UT Austin)→ Stanford

• Samer Chucri (MS UT Austin), Google

• Arash Saber Tehrani (joint with Giuseppe Caire )

• Negin Golrezaei (USC MS), Joined Business School Phd program

• Yi-Hsuan (Griffey) Kao (USC MS), Phd program at USC.

Older Projects (please see Google Scholar for an updated publication list)

Matrix Factorizations and Sparse PCA

Sparse PCA with provable approximation gurantees (pdf) ICML 2013 paper and (video)

Multiple components for SPCA using bipartite matchings (pdf) NIPS 2015

Orthogonal Nonnegative Matrix Factorization through Subspace Exploration (pdf) NIPS 2015

Stay on path: PCA along graph paths (pdf)

Learning sparse polynomials and causality

Sparse polynomial learning and Graph sketching (pdf) NIPS 2014

Learning Causal graphs with Small Interventions (pdf) NIPS 2015

Big graph analytics

Estimating profiles and motifs of large graphs (pdf) KDD 2015.

Distributed estimation of Graph 4-profiles (pdf) WWW 2016.

New erasure codes for big data storage.

Xorbas HDFS is a module for Hadoop Mapreduce that implements Locally repairable codes. These new codes can perform rebuilds with significantly smaller disk IO and network traffic compared to Reed-Solomon and other other classic codes. Our prototype is open source and available on Github and was tested on Amazon and Facebook clusters. It is a modification of the HDFS RAID module used by Facebook for big data storage protection.

Project homepage

Papers

• M. Sathiamoorthy, M. Asteris, D.S. Papailiopoulos, A.G. Dimakis, R. Vadali, S. Chen, and D. Borthakur,
  XORing Elephants: Novel Erasure Codes for Big Data
  Proceedings of the VLDB Endowment 2013.

• D.S. Papailiopoulos and A.G. Dimakis
  Locally Repairable Codes,
  IEEE Int. Symposium on Information Theory, 2012.

• A.G. Dimakis, K. Ramchandran, Y. Wu, and C. Suh
  A Survey on Network Codes for Distributed Storage
  Proceedings of the IEEE | Vol. 99, No. 3, March 2011.

• A.G. Dimakis, P. B. Godfrey, Y. Wu, M. J. Wainwright and K. Ramchandran
  Network Coding for Distributed Storage Systems
  IEEE Transactions on Information Theory, Vol. 56(9), 4539-4551, Sept. 2010.

Femtocaching

The main idea of this project is to add caching capabilities to mobile femtocell stations. Several algorithmic and coding questions arise.

Papers

• N. Golrezaei, A.F. Molisch, A.G. Dimakis and G. Caire,
  Device-to-Device Collaboration: A New Architecture for Wireless Video Distribution
  IEEE Communications Magazine, (to appear).

• N. Golrezaei, K. Shanmugam, A.G. Dimakis, A.F. Molisch and G. Caire,
  FemtoCaching: Wireless Video Content Delivery through Distributed Caching Helpers
  International Conference on Computer Communications (INFOCOM) 2012.

• N. Golrezaei, A.G. Dimakis and A.F. Molisch,
  Wireless Device-to-Device Communications with Distributed Caching
  IEEE International Symposium on Information Theory (ISIT) 2012.

Connecting coding theory and compressed sensing through LP relaxations

We show that the standard LP relaxations for binary channel coding and compressed sensing have a non-trivial connection. This allows us to leverage results from LDPC codes to establish performance guarantees for basis pursuit in compressed sensing.

Papers

• A. Khajehnejad, A.S. Tehrani, A.G. Dimakis and B. Hassibi
  Explicit Matrices for Sparse Approximation
  IEEE International Symposium on Information Theory (ISIT) 2011.

• A.G. Dimakis, R. Smarandache, P. O. Vontobel,
  LDPC Codes for Compressed Sensing
  IEEE Transactions on Information Theory, 58(5), 3093-3114, May 2012.

Index Coding

• K. Shanmugam, A.G. Dimakis and M. Langberg Local Graph Coloring and Index Coding (submitted for publication)