*equal contribution, #corresponding author; full list of Dr. Martin Zhang’s publications

Preprints

  1. Tiwari, M., *Kang, R., *Lee, J.-Y., Lee, L., Piech, C., Thrun, S., #Shomorony, I., & #Zhang, M. J. (2022). Faster Maximum Inner Product Search in High Dimensions. ArXiv Preprint ArXiv:2212.07551.
  2. Jiang, X., *Zhang, M. J., *Zhang, Y., *Durvasula, A., Inouye, M., Holmes, C., #Price, A. L., & #McVean, G. (2022). Age-dependent topic modelling of comorbidities in UK Biobank identifies disease subtypes with differential genetic risk. MedRxiv, 2022–2010.

Publications

2022

  1. *#Zhang, M. J., *#Hou, K., Dey, K. K., Sakaue, S., Jagadeesh, K. A., Weinand, K., Taychameekiatchai, A., Rao, P., Pisco, A. O., Zou, J., Wang, B., Gandal, M., Raychaudhuri, S., #Pasaniuc, B., & #Price, A. (2022). Polygenic enrichment distinguishes disease associations of individual cells in single-cell RNA-seq data. Nature Genetics, 54(10), 1572–1580. ASHG 2021 Charles J. Epstein Trainee Awards Postdoctoral Semifinalist.
  2. Ginart, T., Zhang, M. J., & Zou, J. (2022). Mldemon: Deployment monitoring for machine learning systems. International Conference on Artificial Intelligence and Statistics, 3962–3997.
  3. Li, X., Yung, G., Zhou, H., Sun, R., Li, Z., Hou, K., Zhang, M. J., Liu, Y., Arapoglou, T., Wang, C., & others. (2022). A multi-dimensional integrative scoring framework for predicting functional variants in the human genome. The American Journal of Human Genetics, 109(3), 446–456.
  4. Tiwari, M., *Kang, R., *Lee, J., Piech, C., Thrun, S., #Shomorony, I., & #Zhang, M. J. (2022). MABSplit: Faster Forest Training Using Multi-Armed Bandits. Advances in Neural Information Processing Systems, 35, 1223–1237.

2021

  1. *#Zhang, M. J., Pisco, #A. O., Darmanis, S., & #Zou, J. (2021). Mouse aging cell atlas analysis reveals global and cell type-specific aging signatures. Elife, 10, e62293.

2020

  1. Gao, L., Kang, M., Zhang, M. J., Reza Sailani, M., Kuraji, R., Martinez, A., Ye, C., Kamarajan, P., Le, C., Zhan, L., & others. (2020). Polymicrobial periodontal disease triggers a wide radius of effect and unique virome. Npj Biofilms and Microbiomes, 6(1), 10.
  2. *Zhang, M. J., *Ntranos, V., & Tse, D. (2020). Determining sequencing depth in a single-cell RNA-seq experiment. Nature Communications, 11(1), 774. 2020 Top 50 Life and Biological Sciences Articles.
  3. A single-cell transcriptomic atlas characterizes ageing tissues in the mouse. (2020). Nature, 583(7817), 590–595.
  4. Tiwari, M., Zhang, M. J., Mayclin, J., Thrun, S., Piech, C., & Shomorony, I. (2020). Banditpam: Almost linear time k-medoids clustering via multi-armed bandits. Advances in Neural Information Processing Systems, 33, 10211–10222.
  5. Sailani, M. R., Metwally, A. A., Zhou, W., Rose, S. M. S.-F., Ahadi, S., Contrepois, K., Mishra, T., Zhang, M. J., Kidziński, Ł., Chu, T. J., & others. (2020). Deep longitudinal multiomics profiling reveals two biological seasonal patterns in California. Nature Communications, 11(1), 4933.

2019

  1. Zhang, M. J., Zou, J., & Tse, D. (2019). Adaptive monte carlo multiple testing via multi-armed bandits. International Conference on Machine Learning, 7512–7522.
  2. Zhang, M. J., Xia, F., & Zou, J. (2019). Fast and covariate-adaptive method amplifies detection power in large-scale multiple hypothesis testing. Nature Communications, 10(1), 3433. RECOMB 2019 Best Paper Award.
  3. Zhou, W., Sailani, M. R., Contrepois, K., Zhou, Y., Ahadi, S., Leopold, S. R., Zhang, M. J., Rao, V., Avina, M., Mishra, T., & others. (2019). Longitudinal multi-omics of host–microbe dynamics in prediabetes. Nature, 569(7758), 663–671.

2018

  1. *Bagaria, V., *Kamath, G., *Ntranos, V., *Zhang, M. J., & Tse, D. (2018). Medoids in almost-linear time via multi-armed bandits. International Conference on Artificial Intelligence and Statistics, 500–509.
  2. *Abid, A., *Zhang, M. J., Bagaria, V. K., & Zou, J. (2018). Exploring patterns enriched in a dataset with contrastive principal component analysis. Nature Communications, 9(1), 2134.

2017

  1. *Xia, F., *Zhang, M. J., #Zou, J., & #Tse, D. (2017). NeuralFDR: learning decision threshold from hypothesis features. Neural Information Processing.

2016

  1. Zhang, M. J., & Ou, Z. (2016). Block-wise map inference for determinantal point processes with application to change-point detection. 2016 IEEE Statistical Signal Processing Workshop (SSP), 1–5.

2014

  1. Zhang, M. J., Chen, L., Boufounos, P. T., & Gu, Y. (2014). On the theoretical analysis of cross validation in compressive sensing. 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 3370–3374.