SELECTED CONFERENCE PUBLICATIONS
(Selected journal publications can be found here.)
(Selected submitted papers can be found here.)


  1. Aranyak Acharyya, Joshua Agterberg, Michael Trosset, Michael Trosset, Youngser Park, Carey E. Priebe, "Convergence guarantees for response prediction in latent structure networks on unknown one-dimensional manifolds", 16th International Conference of the ERCIM WG on Computational and Methodological Statistics (CMStatistics 2023), 16-18 December 2023, HTW Berlin, University of Applied Sciences, Berlin, Germany.


  2. Cencheng Shen, Youngser Park, Carey E. Priebe, "Graph Encoder Ensemble for Simultaneous Vertex Embedding and Community Detection", ADMIT 2023: Proceedings of the 2023 2nd International Conference on Algorithms, Data Mining, and Information Technology, September 15-17, 2023, Chengdu, China. (changed to online)


  3. A. Athreya, Z. Lubberts, C. Priebe, Y. Park, "Discovering underlying dynamics in time series of networks", 6th International Conference on Econometrics and Statistics 1-3 August 2023, Waseda University, Tokyo, Japan.


  4. Z. Lubberts, A. Athreya, C. Priebe, Y. Park, "Beyond the adjacency matrix: Random line graphs and inference for networks with edge attributes", 6th International Conference on Econometrics and Statistics 1-3 August 2023, Waseda University, Tokyo, Japan.


  5. Ashwin De Silva, Rahul Ramesh, Carey E. Priebe, Pratik Chaudhari, Joshua T. Vogelstein, "The Value of Out-of-Distribution Data," International Conference on Machine Learning, 2023


  6. Avanti Athreya, Zachary Lubberts, Youngser Park, Carey E. Priebe, "Discovering underlying dynamics in time series of networks,", 15th International Conference of the ERCIM WG on Computational and Methodological Statistics (CMStatistics 2022), 17-19 December 2022, King's College, London, UK.


  7. Ningyuan Huang, Soledad Villar, Carey E. Priebe, Da Zheng, Chengyue Huang, Lin Yang, Vladimir Braverman "From Local to Global: Spectral-Inspired Graph Neural Networks", NeurIPS 2022 GLFrontiers Workshop, New Orleans, November 28 - December 9, 2022.


  8. Ashwin De Silva, Rahul Ramesh, Carey E. Priebe, Pratik Chaudhari, Joshua T. Vogelstein, "The Value of Out-of-distribution Data", NeurIPS 2022 DistShift Workshop, New Orleans, November 28 - December 9, 2022. won the best short paper.


  9. Rajeev Yasarla, Carey Priebe, Vishal Patel, "ART-SS: An Adaptive Rejection Technique for Semi-Supervised restoration for adverse weather-affected images", European Conference on Computer Vision, Tel Aviv, October 23-27, 2022. publisher site


  10. Kelly Marchisio, Ali Saad-Eldin, Kevin Duh, Carey Priebe, Philipp Koehn, "Bilingual Lexicon Induction for Low-Resource Languages using Graph Matching via Optimal Transport", EMNLP 2022, Abu Dhabi, December 7-11, 2022.


  11. Ashwin De Silva, Rahul Ramesh, Carey E. Priebe, Pratik Chaudhari, Joshua T. Vogelstein, "The Value of Out-of-Distribution Data", Out-of-Distribution Generalization in Computer Vision (OOD-CV) workshop, ECCV 2022, Tel Aviv, October 23-27, 2022. won the best short paper.


  12. Kelly Marchisio, Youngser Park, Ali Saad-Eldin, Anton Alyakin, Kevin Duh, Carey Priebe, Philipp Koehn, "An Analysis of Euclidean vs. Graph-Based Framing for Bilingual Lexicon Induction from Word Embedding Spaces", EMNLP 2021, Punta Cana, November 7-11, 2021.


  13. Konstantinos Pantazis, Daniel L. Sussman, Youngser Park, Carey E. Priebe, Vince Lyzinski,"Multiplex graph matching matched filters", GTA³ 2019 Workshop on Graph Techniques for Adversarial Activity Analytics, in conjuction with 12th ACM International Conference on Web Search and Data Mining, Los Angeles, CA, December 9, 2019.


  14. Dan Sussman, Vince Lyzinski, Youngser Park, C.E. Priebe,"Matched Filters for Noisy Indued Subgraph Detection", GTA³ 2018: Workshop on Graph Techniques for Adversarial Activity Analytics, in conjuction with 11th ACM International Conference on Web Search and Data Mining, Marina Del Rey, CA, Feb 9, 2018. (won the best paper award)


  15. Da Zheng, Disa Mhembere, Joshua T. Vogelstein, C.E. Priebe, and Randal Burns,"FlashR: Parallelize and Scale R for Machine Learning using SSDs,", accepted for The 23rd ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming (PPoPP 2018), Vienna, Austria, Feb 24 - 28, 2018.


  16. Keith Levin, Avanti Athreya, Minh Tang, Vince Lyzinski, C.E. Priebe,"A central limit theorem for an omnibus embedding of multiple random dot product graphs," Data Driven Discovery Workshop, IEEE International Conference on Data Mining (ICDM), New Orleans, Louisiana, USA, 18 November 2017


  17. W.M. Campbell, Lin Li, C. Dagli, J. Acevedo-Aviles, K. Greyer, J.P. Campbell, C.E. Priebe. "Cross-Domain Entity Resolution in Social Media," The 4th International Workshop on Natural Language Processing for Social Media", July 11, New York, 2016.


  18. D. Zheng, R. Burns, J. Vogelstein, C. E. Priebe, and A. S. Szalay. "An SSD-based eigensolver for spectral analysis on billion-node graph,". CoRR, abs/1602.01421, 2016.


  19. Joshua T. Vogelstein and John Bogovic and Aaron Carass and William R. Gray and Jerry L. Prince and Bennett L and Luigi Ferrucci and Susan M. Resnick and Carey E. Priebe and R. Jacob Vogelstein, "Graph-Theoretical Methods for Statistical Inference on MR Connectome Data," OHBM, 2015.


  20. Da Zheng, Disa Mhembere, Randal Burns, Joshua Vogelstein, Carey E. Priebe, Alexander S. Szalay, "FlashGraph: Processing Billion-Node Graphs on an Array of Commodity SSDs," In 13th USENIX Conference on File and Storage Technologies (FAST 15), 2015.


  21. Youngser Park, Heng Wang, Tobias Nöbauer, Alipasha Vaziri, Carey E. Priebe, "Anomaly Detection on Whole-Brain Functional Imaging of Neuronal Activity using Graph Scan Statistics," 21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD-2015), Workshop on Outlier Definition, Detection, and Description (ODDx3), August 10, Sydney, Australia

  22. Heng Wang, Da Zheng, Randal Burns, Carey E. Priebe, "Active Community Detection in Massive Graphs," SDM-Networks 2015: The Second SDM Workshop on Mining Networks and Graphs: A Big Data Analytic Challenge, April 30 - May 02, 2015, Vancouver, BC, Canada

  23. J. Feng, X. Tang, M. Tang, C.E. Priebe, M.I. Miller, "Metric space structures for computational anatomy," 4th International Workshop of Machine Learning in Medical Imaging, Nagoya, Japan, September 22, Springer, pp 123–130, 2013.

  24. H. Wang, M. Tang, C.E. Priebe, Y. Park, Inference in Time Series of Graphs using Locality Statistics , IEEE Global Conference on Signal and Information Processing, Austin, Texas, Dec 3-5, 2013. poster.


  25. D. Mhembere, W.G. Roncal, D. Sussman, C.E. Priebe, R. Jung, S. Ryman, R.J. Vogelstein, J.T. Vogelstein, R. Burns, Computing Scalable Multivariate Glocal Invariants of Large (Brain-) Graphs IEEE Global Conference on Signal and Information Processing, Austin, Texas, Dec 3-5, 2013. poster.


  26. Z. Lubberts, C.E. Priebe, Doug Comer, Daniel Sussman, Li Chen, A Statistical Approach to Archaeological Surveys Using Remotely Sensed Data , The 3rd annual Hopkins Imaging Conference, Nov. 21, 2013. Zack's photo.


  27. D.L Sussman, D. Mhembere, S. Ryman, R. Jung, R.J. Vogelstein, R. Burns, J.Vogelstein, and C.E. Priebe, Massive Diffusion MRI Graph Structure Preserves Spatial Information , Organization for Human Brain Mapping, Seatle, WA, 2013.


  28. B. Castle, M.W. Trosset, and C.E. Priebe, A Nonmetric Embedding Approach to Testing for Matched Pairs , Technical Report 11-04, Department of Statistics, Indiana University, Bloomington, IN, 2011.


  29. L. Chen, D.C. Comer, C.E. Priebe, D.L. Sussman, and J.C. Tilton, "Refinement of a Method for Identifying Probable Archaeological Sites from Remotely Sensed Data," A book chapter in Mapping Archaeological Landscapes from Space, SpringerBriefs in Archaeological Heritage Management, 2013. Also appeared in Johns Hopkins Engineering.

  30. M. Sun, M. Tang, C.E. Priebe, A Comparison of Graph Embedding Methods for Vertex Nomination , 2012 11th Internaltional Conference on Machine Learning and Applications (ICMLA), Boca Raton, FL, 2012.


  31. J.C. Tilton, D.C. Comer, C.E. Priebe, D.L. Sussman, L. Chen, Refinement of a Method for Identifying Probable Archaeological Sites from Remotely Sensed Data, SPIE Defense, Security, and Sensing 2012, April, 23-27, Baltimore, 2012.


  32. N.H. Lee, C.E. Priebe, M. Tang, An implied latent position process for doubly stochastic messaging activities, Annual International Conference on Computational Mathematics, Computational Geometry & Statistics (CMCGS 2012). January, 30-31, Singapore, 2012.


  33. D.S. Lee and C.E. Priebe, "Bayesian vertex nomination," 11th World Meeting of the International Society for Bayesian Analysis (ISBA 2012), 25-29 June 2012, Kyoto, Japan.


  34. C.E. Priebe, A. Rukhin, Invariant Theory for Hypothesis Testing on Graphs , 58th Session of the International Statistical Institute: August 21-26, 2011, Dublin, Ireland.


  35. D. Marchette, C.E. Priebe, G. Coppersmith, Vertex nomination via attributed random dot product graphs , 58th Session of the International Statistical Institute: August 21-26, 2011, Dublin, Ireland.


  36. N.H. Lee, T.S.T. Leung, C.E. Priebe, Random Graphs Based on Self-Exciting Messaging Activities, 2011 International Conference on Business Intelligence and Financial Engineering, December, 12-13, Hong Kong, 2011.


  37. C.E. Priebe, N.H. Lee, Y. Park, M. Tang, Attribute Fusion in a Latent Process Model for Time Series of Graphs, IEEE International Workshop on Statistical Signal Processing 2011 (SSP'11), pp 513-516, Nice, France, June 28-30, 2011.


  38. C.E. Priebe, G.A. Coppersmith, and A. Rukhin, You say "graph invariant," I say "test statistic", ASA Sections on Statistical Computing Statistical Graphics, SCGN Newsletter, 21, 2010.


  39. Samuel Carliles, Tamás Budavári, Sebastien Sebastien, Carey E. Priebe, Alexander Szalay, Photometric Redshift Estimation on SDSS Data Using Random Forests, Astronomical Data Analysis Software and Systems XVII P5.10 ASP Conference Series, Vol. XXX, 2008.


  40. H. Zhou, D. Karakos, S. Khudanpur, A.G. Andreou and C.E. Priebe, On Projections of Gaussian Distributions using Maximum Likelihood Criteria, in Proceedings of the 2009 Workshop on Information Theory and its Applications (Invited Talk), UCSD Campus, La Jolla, CA, February 8-13, 2009.


  41. D. Karakos, S. Khudanpur and C.E. Priebe, Computation of Csiszar's Mutual Information of Order alpha, in Proceedings of the 2008 IEEE International Symposium on Information Theory (ISIT-08), Toronto, Canada, July 6-11, 2008.


  42. D. Karakos, S. Khudanpur, D.J. Marchette, A. Papamarcou and C.E. Priebe, On the Minimization of Concave Information Functionals for Unsupervised Classification via Decision Trees, Statistics and Probability Letters, Vol. 78, No. 8, pp. 975-984, June 2008.


  43. D. Karakos, S. Khudanpur, J. Eisner and C.E. Priebe, Iterative Denoising using Jensen-Renyi Divergences with an Application to Unsupervised Document Categorization, in Proceedings of the 2007 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP-07), Honolulu, Hawaii, April 15-20, 2007.


  44. D. Karakos, J. Eisner, S. Khudanpur and C.E. Priebe, Cross-Instance Tuning of Unsupervised Document Clustering Algorithms, in Proceedings of the 2007 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-HLT 2007), Rochester, NY, April 22-27, 2007.


  45. D.J. Marchette, C.E. Priebe, Y. Park, D. Karakos, Iterative Denoising for Adaptive Sensors, in Proceedings of the 2006 Hawaii International Conference on Statistics, Mathematics and Related Fields, Honolulu, Hawaii, January 16-18, 2006.


  46. D. Karakos, S. Khudanpur, J. Eisner and C.E. Priebe, Unsupervised Classification via Decision Trees: An Information-Theoretic Perspective, in Proceedings of the 2005 IEEE International Conference on Acoustics, Speech and Signal Processing (Invited Talk), Philadelphia, PA, March 18-23, 2005.


  47. C.E. Priebe, D.J. Marchette, Y. Park, E. Wegman, J. Solka, D. Socolinsky, D. Karakos, K. Church, R. Guglielmi, R. Coifman, D. Lin, D. Healy, M. Jacobs, A. Tsao, Iterative Denoising for Cross-corpus Discovery, in Proceedings of the 2004 Symposium on Computational Statistics (Invited Talk), Prague, August 23-27, 2004.


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