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Lab:
63-149 ENG IV UCLA Campus

Postal Address:
UCLA
EE Department
420 Westwood Plaza
Los Angeles, CA 90095-1594
ATTN: 63-149, ENG IV
UCLA Main Campus

Email:
songlinqi@ucla.edu

Linqi Song

Biography

Linqi Song received the Ph.D. degree in Electrical Engineering from UCLA under the supervision of Prof. Christina Fragouli. He received the B.S. and M.S. degrees in Electronic Engineering, Tsinghua University, China. He is currently a Postdoctoral Scholar at ARNI in the Electrical Engineering Department, UCLA.

Research Interests


  • Network Coding, Index Coding, Content-type Coding
  • Wireless Communications
  • Algorithm Design
  • Machine Learning and Big Data
  • Recommender System and Prediction
  • Medical and Healthcare Informatics
  • Smart Grids

Education


  • Ph.D. in Electrical Engineering, University of California, Los Angeles
  • M.S. in Electronic Engineering, Tsinghua University, China
  • B.S. in Electronic Engineering, Tsinghua University, China

Awards


  • Fellowship, UCLA, 2012-2013
  • Excellent Graduate Award, Tsinghua University, China
  • Scholarship, Tsinghua University, China

Publications


Preprints

  • L. Song, and C. Fragouli, “A pliable index coding approach to data shuffling.” arXiv preprint, arXiv:1701.05540, 2017.
  • L. Song, and C. Fragouli, “Making recommendations bandwidth aware,” submitted to IEEE Transactions on Information Theory. arXiv preprint, arXiv:1607.03948, 2016.
  • L. Song, and J. Xu, “A contextual bandit approach for stream-based active learning.” arXiv preprint, arXiv:1701.06725, 2017.

Journal Papers

  • L. Song, and C. Fragouli, “A polynomial-time algorithm for pliable index coding,” accepted and to appear in IEEE Transactions on Information Theory, 2017. arXiv preprint, arXiv:1610.06845.
  • J. Xu, L. Song, J. Y. Xu, G. J. Pottie and M. van der Schaar, “Personalized active learning for activity classification ssing wireless wearable sensors,” in IEEE Journal of Selected Topics in Signal Processing, vol. 10, no. 5, pp. 865-876, Aug. 2016.
  • L. Song, C. Tekin and M. van der Schaar, “Online learning in large-scale contextual recommender systems,” in IEEE Transactions on Services Computing, vol. 9, no. 3, pp. 433-445, May-June, 2016.
  • L. Song, W. Hsu, J. Xu and M. van der Schaar, “Using contextual learning to improve diagnostic accuracy: application in breast cancer screening,” in IEEE Journal of Biomedical and Health Informatics, vol. 20, no. 3, pp. 902-914, May 2016.
  • L. Song, Y. Xiao, and M. van der Schaar, “Demand side management in smart grids using a repeated game framework,” IEEE J. Sel. Areas Commun., vol. 32, no. 7, 2014.
  • L. Zhang, L. Song, Q. Xie, and J. Wang, “Design of soft-in-soft-out decoder of NR dode in DTMB national standard system,” Video Engineering, vol. 33, no. 3, 2009.
  • L. Song, and J. Wang, “Demapping method in DTMB system,” Video Engineering, vol. 32, no. 6, 2008.
  • L. Song, J. Wang, and C. Pan, “Design and implementation of convolutional de-interleaver in DTMB system,” Video Engineering, vol. 32, no. 1, 2008.

Conference Papers

  • L. Song, S. Rajan, and C. Fragouli, “The benefit of being flexible in distributed computation,” accepted and to appear in ITW 2017.
  • M. Karmoose, L. Song, M. Cardone, and C. Fragouli, “Preserving privacy while broadcasting: k - limited-access schemes,” accepted and to appear in ITW 2017.
  • L. Song, and C. Fragouli, “Making recommendations bandwidth aware,” IEEE International Symposium on Information Theory (ISIT), 2243-2247, Aachen, Germany, 2017.
  • L. Song, and C. Fragouli, “A pliable index coding approach to data shuffling,” IEEE International Symposium on Information Theory (ISIT), 2558-2562, Aachen, Germany, 2017.
  • M. Karmoose, L. Song, M. Cardone, and C. Fragouli, “Private broadcasting: an index coding approach,” IEEE International Symposium on Information Theory (ISIT), 2543-2547, Aachen, Germany, 2017.
  • L. Song, and C. Fragouli, “A polynomial-time algorithm for pliable index coding,” IEEE International Symposium on Information Theory (ISIT), 120-124, Barcelona, Spain, 2016.
  • L. Song, and C. Fragouli, “Content-type coding”, in Network Coding (NetCod), IEEE International Symposium on, Sydney, Australia, 2015.
  • L. Song, Y. Xiao, and M. van der Schaar, “Non-stationary demand side management method for smart grids,” IEEE ICASSP 2014.
  • L. Song, C. Tekin, and M. van der Schaar, “Clustering based online learning in recommender systems: a bandit approach,” IEEE ICASSP 2014.
  • J. Xu, J. Y. Xu, L. Song, G. Pottie, and M. van der Schaar, “Context-driven online learning for activity classification in wireless health,” Global Communications Conference (GLOBECOM), 2014 IEEE, pp. 2423-2428, 2014.
  • L. Song, J. Wang, C. Pan, and J. Fu, “A normalized LLR soft information demapping method in DTMB system,” IEEE ICCS, 2008.

Miscellaneous

Hungarian algorithm or Munkres algorithm The following 6-step algorithm is a modified form of the original Munkres' Assignment Algorithm (sometimes referred to as the Hungarian Algorithm). This algorithm describes to the manual manipulation of a two-dimensional matrix by starring and priming zeros and by covering and uncovering rows and columns. This is because, at the time of publication (1957), few people had access to a computer and the algorithm was exercised by hand.

Step 0: Create an nxm matrix called the cost matrix in which each element represents the cost of assigning one of n workers to one of m jobs. Rotate the matrix so that there are at least as many columns as rows and let k=min(n,m). Step 1: For each row of the matrix, find the smallest element and subtract it from every element in its row. Go to Step 2.

Step 2: Find a zero (Z) in the resulting matrix. If there is no starred zero in its row or column, star Z. Repeat for each element in the matrix. Go to Step 3.

Step 3: Cover each column containing a starred zero. If K columns are covered, the starred zeros describe a complete set of unique assignments. In this case, Go to DONE, otherwise, Go to Step 4.

Step 4: Find a noncovered zero and prime it. If there is no starred zero in the row containing this primed zero, Go to Step 5. Otherwise, cover this row and uncover the column containing the starred zero. Continue in this manner until there are no uncovered zeros left. Save the smallest uncovered value and Go to Step 6.

Step 5: Construct a series of alternating primed and starred zeros as follows. Let Z0 represent the uncovered primed zero found in Step 4. Let Z1 denote the starred zero in the column of Z0 (if any). Let Z2 denote the primed zero in the row of Z1 (there will always be one). Continue until the series terminates at a primed zero that has no starred zero in its column. Unstar each starred zero of the series, star each primed zero of the series, erase all primes and uncover every line in the matrix. Return to Step 3.

Step 6: Add the value found in Step 4 to every element of each covered row, and subtract it from every element of each uncovered column. Return to Step 4 without altering any stars, primes, or covered lines.

DONE: Assignment pairs are indicated by the positions of the starred zeros in the cost matrix. If C(i,j) is a starred zero, then the element associated with row i is assigned to the element associated with column j.

Some of these descriptions require careful interpretation. In Step 4, for example, the possible situations are, that there is a noncovered zero which get primed and if there is no starred zero in its row the program goes onto Step 5. The other possible way out of Step 4 is that there are no noncovered zeros at all, in which case the program goes to Step 6.


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