Products

This page lists all the publications, posters and talks by our NRT students.

  1. J. D. Moorman, Thomas K. Tu, D. Molitor, and D. Needell, “Randomized Kacmarz with Averaging.” Proc. Information Theory and Applications Workshop, La Jolla, CA, Feb. 2019.
  2. Y.  Qiao,  C. Shi,  C. Wang, H. Li,  M. Haberland, X.  Luo, A. M. Stuart,  and A. L. Bertozzi, Uncertainty Quantification for Semi-Supervised Multi-Class Classification in Image Processing and Ego-Motion Analysis of Body-Worn Videos, to appear in Proc. Eletronic Imaging, Burlingame, 2019.
  3. J. Y. Jiang, M. Zhang, C. Li, M. Bendersky, N. Golbandi, and M. Najork. Semantic Text Matching for Long-Form Documents. In Proceedings of the 2019 World Wide Web Conference (WWW’19), ACM, 2019.
  4. Z. Li, J. Y, Jiang, Y. Sun, and W. Wang. Personalized Question Routing via Heterogeneous Network Embedding. In Proceedings of The 33rd AAAI Conference on Artificial Intelligence (AAAI’ 19), AAAI, 2019.
  5. J. D. Moorman, Thomas K. Tu, D. Molitor, and D. Needell, “Randomized Kacmarz with Averaging.” Proc. Information Theory and Applications Workshop, La Jolla, CA, Feb. 2019.
  6. N. Baker, H. Lu, G. Erlikhman, and P. J. Kellman. Local features and global shape information in object classification by deep convolutional neutral networks. Journal of Vision. 2019 (under review)
  7. J. D. Moorman, T. Tu, D. Molitor, and D. Needell. Randomized Kacmarz with Averaging, Information Theory and Applications. (2019)
  8. Chiu AM, Mitra M, Boymoushakian L, Coller HA. ”Integrative analysis of the inter-tumoral heterogeneity of triple-negative breast cancer” Scientific Reports. 8(1):11807, 2018
  9. N. Baker, H. Lu, G. Erlikhman, and P. J. Kellman. Deep convolutional networks do not classify based on global object shapes. PLoS computational biology, 14(12), e1006613. 2018.
  10. J. Y. Jiang, and W. Wang. RIN: Reformulation Inference Network for Context-Aware Query Suggestion. In Proceedings of The 27th ACM International Conference of Information and Knowledge Management (CIKM’ 18), ACM, 2018.
  11. G. Zhou, J. Y. Jiang, C. J.-T. Ju, and Wei Wang. Inferring Microbial Communities for City Scale Metagenomics Using Neural Networks. To appear in Proceedings of 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM ’18), IEEE, 2018.
  12. J. D. Moorman, Q. Chen, T. K. Tu, Z. M. Boyd, and A. L. Bertozzi, “Filtering Methods for Subgraph Matching on Multiplex Networks.” Proc. GTA3 2.0: The 2nd workshop on Graph Techniques for Adversarial Activity Analytics, IEEE International Conference on Big Data, Seattle, WA, Dec. 2018.
  14. B. Yuan, H. Li, A. L. Bertozzi, P.J. Brantingham, and M.A. Porter, Multivariate Saptiotemporal Hawkes Processes and Network Reconstruction, submitted to SIAM Journal on Mathematics of Data Science, 2018.
  15. N. LaPierre, S. Mangul, M. Alser, I. Mandric, N. Wu, C. Koslicki, and E. Eskin. MicoP: Microbial Community Profiling method for detecting viral and fungal organisms in metagenomic samples. bioRxiv, 243188. BMC Genomics, 2018.
  16. N. LaPierre, R. Egan, W. Wang, and Z. Wang. MiniScrub: De Novo Long read scrubbing using approximate alignment and deep learning. bioRxiv, 433573. 2018.
  17. T.R Meyer, D. Balagu, M. Camacho-Collados, H. Li, K. Khuu, P.J. Brantingham, and A. L. Bertozzi, A year in Madrid as described through the analysis of geotagged Twitter data, to appear in Environment and Planning B: Urban Analytics and City Science, 2018.
  18. J. D. Moorman, Q. Chen, T. Tu, M. B. Zachary, and A. L. Bertozzi. Filtering Methods for Subgraph Matching on Multiplex Networks. 3980-3985. (2018)
  20. H. Chen, H. Li, A. Song, M. Haberland, O. Akar, A. Dhillon, T. Zhou, A. L. Bertozzi, and J. P. Brantingham, Semi-supervised First-person Activity Recognition in Body-Worn Video, in preparation.


  1. J. D. Moorman, “Randomized Kacmarz with Averaging.” Information Theory and Application Workshop, La Jolla, CA, Feb. 2019. (poster)
  2. H. Li, Uncertainty Quantification for Semi-supervised Multi-Class Classification in Image Processing and Ego-Motion Analysis of Body-Worn Videos, IS&T Eletronic Imaging, Burlingame, 2019.
  3. N. LaPierre. “Miniscrub: De Novo Long read scrubbing using approximate alignment and deep learning. AWS-UCLA Symposium Presentation. February, 2019.
  4. Chiu AM, Pasaniuc B, Sankararaman S. ”Binomial probabilistic principal component analysis for genotype data” American Society for Human Genetics, 2018.
  5. Chiu AM, Pasaniuc B, Sankararaman S. “Binomial probabilistic principal component analysis for genotype data” 4th Annual QCBio Retreat, 2018.
  6. P. J. Kellman, and N. Baker. Visual perception of shape in humans and deep convolutional neural networks. Talk at the Society of Experimental Psychologists. Tuscon, Arizona. 2018.
  7. N. Baker, G. Erlikhman, H. Lu, and P. J. Kellman. Deep convolutional networks do not perceive illusory contours. The 40th Annual Meeting of the Cognitive Science Society. Madison, WI. 2018. (Poster)