Publications

Conference Proceedings, Journal Publications, and Preprints

All Publications

For all publications, please visit my Google Scholar.
(* indicates equal contribution; + indicates corresponding author)

Preprints

  • 🔥Pandey, A., Varghese, A.J., Patil, S., Xu, M. A Comparative Study on Dynamic Graph Embedding based on Mamba and Transformers. arXiv preprint arXiv:2412.11293, December, 2024.

    • Selected Publications

  • Candelori, L., Abanov, A.G., Berger, J., Hogan, C.J., Kirakosyan, V., Musaelian, K., Samson, R., Smith, J.E., Villani, D., Wells, M.T. and Xu, M. Robust estimation of the intrinsic dimension of data sets with quantum cognition machine learning. Scientific Reports, Feb, 2025.
  • 🔥Ramirez, H., Tabarelli, D., Brancaccio, A., Belardinelli, P., Marsh, E.B., Funke, M., Mosher, J.C., Maestu, F., Xu, M., Pantazis, D. Fully Hyperbolic Neural Networks - A Novel Approach to Studying Aging Trajectories. IEEE Journal of Biomedical and Health Informatics, Feb, 2025.(Corresponding author)
  • C Baker, G Smith, EB Marsh, M Funke, JC Mosher, F Maestu, Xu, M.+ D Pantazis, Hyperbolic graph embedding of MEG brain networks to study brain alterations in individuals with subjective cognitive decline, IEEE Journal of Biomedical and Health Informatics, June, 2024. (Corresponding author)
  • Yuhao Qiang*, Mengjia Xu, Mira Patel Pochron, Madhulika Jupelli, Ming Dao. A framework of computer vision-enhanced microfluidic approach for automated assessment of the transient sickling kinetics in sickle red blood cells. Frontiers in Physics, Feb, 2024.
  • Varghese, A.J., Bora, A., Xu, M.+ and Karniadakis, G.E.. TransformerG2G: Adaptive time-stepping for learning temporal graph embeddings using transformers. Neural Networks, Dec, 2023. (Corresponding author) (IF: 7.8)
  • Xu, M., Galanti, T., Rangamani, A., Rosasco, L. and Poggio, T., The Janus effects of SGD vs GD: high noise and low rank, CBMM Memo 144, 2023.
  • Galanti, T., Xu, M., Galanti, L., Poggio, T. Norm-based Generalization Bounds for Compositionally Sparse Neural Networks, In Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS), 2023. (Dec 10-16, New Orleans, LA)
  • 🔥Xu, M., Rangamani, A., Liao, Q., Galanti, T., Poggio, T. Dynamics in Deep Classifiers with Square Loss: Normalization, Low Rank and Neural Collapse. Research, vol.6, January 10, 2023. (IF:11.03) (Highlighted in MIT News)
  • Shukla, K., Xu, M., Trask, N. and Karniadakis, G. Scalable algorithms for physics-informed neural and graph networks. Data-Centric Engineering, 3, e24-1-e-24-26, 2022.
  • Xu, M, Singh, V. A. and Karniadakis, G. DynG2G: an efficient stochastic graph embedding method for temporal graphs. IEEE Transactions on Neural Networks and Learning Systems, June 10, 2022. (IF: 14.25)
  • 🔥Xu, M. Understanding graph embedding methods and their applications. SIAM Review, 63(4):825-853, 2021. (Invited paper)
  • Zhang, Q*, Sampani, K*, Xu, M*, Cai, S, Deng, Y, Li, H, Sun JK, Karniadakis, G. AOSLO-net: A deep learning-based method for automatic segmentation of retinal microaneurysms from adaptive optics scanning laser ophthalmoscope images. Translational Vision Science & Technology (TVST), 11 (8), 7-17, 2022. (Co-first author)
  • Xu, M., Sanz, D., Garces, P., Maestu, F., Li, Q. & Pantazis, D. A graph Gaussian embedding method for predicting Alzheimer’s disease progression with MEG brain networks. IEEE Transactions on Biomedical Engineering, 68(5):1579-1588, 2021. (IF: 4.75)
  • Xu, M., Wang, Z., Zhang, H., Pantazis, D., Wang, H., & Li, Q. A new Graph Gaussian embedding method for analyzing the effects of cognitive training. PLoS Computational Biology, 16 (9), e1008186, 2020. (IF: 4.78)
  • Poggio, T., Liao, Q. and Xu, M. Generalization in deep network classifiers trained with the square loss. CBMM Memo 112, 2020.
  • Qiu, W., Guo, J., Li, X., Xu, M., et al. Multi-label detection and classification of red blood cells in microscopic images, IEEE International Conference on Big Data, 4257-4263, 2020.
  • Zhang, M.* Li, X.*, Xu, M.*, et al. Automated Semantic Segmentation of Red Blood Cells for Sickle Cell Disease, IEEE Journal of Biomedical and Health Informatics, 2020, 24(11): 3095-3102. (Co-first author) (IF: 7.7)
  • Zhang, M*., Xu, M.*, Li, Q. RBC Semantic Segmentation for Sickle Cell Disease Based on Deformable U-Net, MICCAI 2018 (Granada, Spain). (Co-first author)
  • Xu, M., Papageorgiou, D., Abidi, S., Dao, M., Zhao, H. and Karniadakis, G.E. A deep convolutional neural network for classification of red blood cells in sickle cell anemia, PLoS Computational Biology, 13(10): e1005746, 2017. (Top 10% most cited PLOS Computational Biology papers published in 2017, and it has been highlighted in multiple popular media)
  • Xu, M., Yang, J., Zhao, H., A Multivariate Shape Quantification Approach for Sickle Red Blood Cell Using Patient-Specific Microscopy Image Data. The SPIE 9th International Conference on Digital Image Processing (ICDIP2017), 2017, vol. 10420. (Hong Kong, China).
  • Xu, M., Yang, J., Zhao, D. and Zhao, H. An image-enhancement method based on variable-order fractional differential operators. Bio-Medical Materials and Engineering, 2015, 26: 1325-1333.