Welcome to Youngser Park's Home Page

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Youngser Park (박영서)
Associate Research Scientist
Center for Imaging Science (CIS)
The Institute for Computational Medicine (ICM)
The Johns Hopkins University

Short bio
Full CV (as of 01/06/2017)

Recent Grant Awarded:

  • "Universally Useful Primitives for Aligning Networks Across Time and Space,’’ DARPA MAA, Co-PI with Vince Lyzinski (PI), C.E. Priebe, and Daniel Sussman, 2017.

  • “What Would Tukey Do? <enter>,” DARPA D\(^3\)M, 2/1/2017–1/31/2021, Co-PI with C.E. Priebe (PI), M. Tang, A. Athreya, V. Lyzinski, and J. Vogelstein.

Recent Publication:

  • K. Eichler, F. Li, A. L. Kumar, Y. Park, I. Andrade, C. Schneider-Mizell, T. Saumweber, A. Huser, D. Bonnery, B. Gerber, R. D. Fetter, J. W. Truman, C.E Priebe, L. F. Abbott, A. Thum, M. Zlatic, and A. Cardona, “The complete wiring diagram of a high-order learning and memory center, the insect mushroom body,” accepted for publication, Nature, 2017.

  • V. Lyzinski, Y. Park, C. E. Priebe, M. W. Trosset, “Fast Embedding for JOFC Using the Raw Stress Criterion,” , Journal of Computational and Graphical Statistics, accepted for publication, 2016.

  • V. Lyzinski, M. Tang, A. Athreya, Y. Park, C. E. Priebe, “Community Detection and Classification in Hierarchical Stochastic Blockmodels,” , IEEE Transactions on Network Science and Engineering, vol. 4, no. 1, pp 13-26, 2017.

  • M. Tang, A. Athreya, D.L. Sussman, V. Lyzinski, Y. Park, C.E. Priebe, “A semiparametric two-sample hypothesis testing problem for random dot product graphs,” Journal of Computational and Graphical Statistics, vol 26, no 2, pp 344-354, 2017

  • D. Zheng, D. Mhembere, Y. Park, J. T. Vogelstein, C. E. Priebe, R. Burns. “Spectral Clustering For Billion-Node Graphs,” SIAM Workshop on Network Science (NS2016), Boston, MA, July, 2016.

  • Y. Park, H. Wang, T. Nobauer, A. Vaziri, C. E. Priebe, “Anomaly Detection on Whole-Brain Functional Imaging of Neuronal Activity using Graph Scan Statistics” ACM Conference on Knowledge Discovery and Data Mining (KDD), Workshop on Outlier Definition, Detection, and Description (ODDx3), August 10, 2015.

  • H. Wang, M. Tang, Y. Park, and C.E. Priebe, “Locality Statistics for Anomaly Detection in Time Series of Graphs,” IEEE Transactions on Signal Processing, Vol. 62, No. 3, pp. 703-717, February, 2014.

Submitted to journals:

  • C.E. Priebe, Y. Park, M. Tang, A, Athreya, V. Lyzinski, J. Vogelstein, Y. Qin, B. Cocanougher, K. Eichler, M. Zlatic, A. Cardona, “Semiparametric spectral modeling of the Drosophila connectome,” submitted, 2017.

  • H. G. Patsolic, Y. Park, V. Lyzinski, C.E. Priebe, “Vertex Nomination Via Local Neighborhood Matching,” submitted, 2017.

  • A. Athreya, M. Tang, V. Lyzinski, Y. Park, B. Lewis, M. Kane, C.E. Priebe, “Numerical tolerance for spectral decompositions of random dot product graphs,” submitted, 2016.

  • N. H. Lee, I. J. Wang, Y. Park, C. E. Priebe, M. Rosen, “Techniques for clustering interaction data as a collection of graphs,” 2014.

  • M. Tang, Y. Park, and C. E. Priebe, “Out-of-sample Extension for Latent Position Graphs,” 2013.

  • V. Lyzinski, S. Adali, J. T. Vogelstein, Y. Park, C. E. Priebe, “Seeded Graph Matching Via Joint Optimization of Fidelity and Commensurability,” 2013.

Recent / Future Talk:

  • “Vertex Nomination via Seed Graph Matching,” BK21+ Seminar, Seoul National University, Seoul, Korea, January 11, 2017.

  • “Repeated Motif Hierarchical Stochastic Blockmodels,” Theoretical Foundations for Statistical Network Analysis, Isaac Newton Institute for Mathematical Sciences, Cambridge, England, July 15, 2016.

  • “Anomaly Detection in Time-Series of Graphs,” BK21+ Seminar, Seoul National University, Seoul, Korea, July 21, 2015.

  • “Discovery of Brainwide Neural-Behavioral Maps via Multiscale Unsupervised Structure Learning,” BK21+ Seminar, Seoul National University, Seoul, Korea, June 27, 2014.

Contact

Suite 301, Clark Hall,
3400 N. Charles St.,
Johns Hopkins University
Baltimore, MD 21218
(E) youngser at jhu dot edu
(W) http://www.cis.jhu.edu/~parky/

Academic Interests

  • clustering algorithms

  • pattern classification

  • data mining for high-dimensional data

  • statistical inference on graph data