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About Me

Email: bcyuan@fb.com

Mailing Address: Facebook Headquarters 1 Hacker Way Menlo Park, CA 94025

I started as a Research Scientist in Facebook after receiving my PhD from UCLA in March 2020. Before that I graduated with a B.S. in Math from Zhejiang University. During my Ph.D. study, I also did internships at FB, Alibaba DAMO academy and Los Alamos National Laboratory, working on various areas such as graph-based product recommendation, deep generative models for spatio-temporal data from social science and time series analysis in medical imaging and seismology. My PhD research was supported by a National Institute of Justice Graduate Research Fellowship (GRF-STEM).

I am currently interested in large-scale recommendation systems (Ads ranking) using approaches from graph learning, representation learning and optimization.

Research

Conference Papers & Others

  1. Yan Li, Dhruv Choudhary, Xiaohan Wei, Baichuan Yuan, Bhargav Bhushanam, Tuo Zhao, Guanghui Lan, Frequency-aware SGD for Efficient Embedding Learning with Provable Benefits, International Conference on Learning Representations (ICLR) 2022.
  2. Baichuan Yuan, Xiaowei Wang, Jianxin Ma, Chang Zhou, Andrea Bertozzi, Hongxia Yang, Variational Autoencoders for Highly Multivariate Spatial Point Processes Intensities, International Conference on Learning Representations (ICLR) 2020.
  3. B. Wang, X. Luo, F. Zhang, B. Yuan, A. L. Bertozzi, and P. J. Brantingham, Graph-Based Deep Modeling and Real Time Forecasting of Sparse Spatio-Temporal Data, accepted in 4th Workshop on Mining and Learning from Time Series (MileTS), at KDD London, August 2018.
  4. Yonatan Dukler, Yurun Ge, Yizhou Qian, Shintaro Yamamoto, Baichuan Yuan, Long Zhao, Andrea L. Bertozzi, Blake Hunter, Rafael Llerena, and Jesse T. Yen, Automatic decomposition and mitral valve segmentation of cardiac ultrasound time series data, Proc. SPIE conference on medical imaging, 2018.
  5. Baichuan Yuan, Sathya R Chitturi, Geoffrey Iyer, Nuoyu Li, Xiaochuan Xu, Ruohan Zhan, Rafael Llerena, Jesse T Yen, Andrea L Bertozzi, Machine Learning for Cardiac Ultrasound Time Series Data, Proc. SPIE conference on medical imaging, 2017, preprint version. Code

Journal Papers

  1. H Dröge, B Yuan, R Llerena, JT Yen, M Moeller, AL Bertozzi Mitral Valve Segmentation Using Robust Nonnegative Matrix Factorization, Journal of Imaging 2021.
  2. Yuan, B., Schoenberg, F.P., and Bertozzi, A.L. Fast estimation of multivariate spatiotemporal Hawkes processes and network reconstruction, AISM 2021.
  3. P. Jeffrey Brantingham, Baichuan Yuan and Denise C. Herz, Is Gang Violent Crime More Contagious than Non-gang Violent Crime? Journal of Quantitative Criminology. 2021.
  4. Baichuan Yuan, Hao Li, Andrea Bertozzi, P. Jeffrey Brantingham, and Mason Porter, Multivariate Spatiotemporal Hawkes Processes and Network Reconstruction, SIAM J. Mathematics of Data Science, 1(2), pp. 356-382, 2019; ArXiv preprint version. Code
  5. B Yuan, YJ Tan, MK Mudunuru, OE Marcillo, AA Delorey, PM Roberts, JD Webster, CNL Gammans, S Karra, GD Guthrie, PA Johnson, Using Machine Learning to Discern Eruption in Noisy Environments: A Case Study using CO2-driven Cold-Water Geyser in Chimayo, New Mexico., Seismological Research Letters, 2019.
  6. E. Lai*, D. Moyer*, B. Yuan*, E. Fox, B. Hunter, A. L. Bertozzi, and P. J. Brantingham (*Equal Contribution), Topic Time Series Analysis of Microblogs, IMA Journal of Applied Mathematics, 81(3) pp. 409-431, 2016.

Packages

  • Multivariate (network) Spatio-temporal Hawkes process inference and simulation: GitHub
  • NMF methods for Cardiac Ultrasound: GitHub

Service

I am glad to serve as a reviewer for journal or conference papers, as well as a TPC member for conference or workshop, in the area of Point Process, Deep Learning, Machine Learning, Graph Analytics and Social Networks, etc. Please feel free to contact me through email
  • Journal Reviewers of The Journal of Artificial Intelligence Research (JAIR), Seismological Research Letters, Bernoulli
  • Conference Reviewers of ACC 2020, KDD 2022
  • Program Committee of WWW 2022 Social Network Analysis and Graph Algorithms Track
  • Program Committee of IEEE Big Data 2020 workshop