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Jacob Si

PhD Student at Imperial College London

I am a PhD candidate at Imperial College London supervised by Prof. Yingzhen Li specializing in Generative AI. Before joining Imperial, I earned a Master of Engineering degree specializing in AI and ML from the University of California, Los Angeles and an Honours Bachelor of Science in Computer Science, Statistics and Economics at the University of Toronto. I am also fortunate to be advised by Prof. Rahul Krishnan at the Vector Institute and Prof. Jonathan Kao at UCLA where I research novel machine learning architectures through deep generative modeling.

Anyone interested in working on generative models feel free to contact me by email!

Email:

‘y.si23‘ @ ‘imperial.ac.uk‘

‘jacobyhsi‘ @ ‘ucla.edu‘

‘jacobyhsi‘ @ ‘cs.toronto.edu‘

Selected Publications [full list]

(*) denotes equal contribution

  1. NeurIPS
    TabUnite: An Efficient Encoding Framework for Tabular Data Generation
    Jacob Si, Zijing OuMike Qu, and Yingzhen Li
    In Submission. 38th Conference on Neural Information Processing Systems, 2024.
  2. ICMLSpotlight
    InterpreTabNet: Distilling Predictive Signals from Tabular Data by Salient Feature Interpretation
    Jacob Si, Wendy Yusi Cheng, Michael Cooper, and Rahul Krishnan
    In the 41st International Conference on Machine Learning, 2024.
    Spotlight Presentation [top 3.5%]
  3. Book Chapter
    Assessing Infant Mortality Rate: Problems stemming from Household Living Conditions, Women’s Education and Health
    Jacob Si, and Rohan Alexander
    In "Telling Stories with Data: With Applications in R" by Rohan Alexander