David W. Romero

David W. Romero

Research Scientist, Generative AI



I am a Research Scientist in Efficient Generative AI at NVIDIA’s Deep Imagination Research Team, and a finishing PhD candidate at the Vrije Universiteit Amsterdam. I have spent time at Mitsubishi Electric Research Laboratories, Qualcomm AI Research and Google Research.

My research interests include all aspects of efficiency in Deep Learning, such as data efficiency, computational efficiency, and parameter efficiency. I am primarily interested in continuous parameterizations and relaxations of neural components and their use to improve efficiency aspects of Deep Learning. For example, Continuous Kernel Convolutions (CKConvs) can model long context with low parameter and time costs. Extensions of CKConvs have found application in modeling context that extends to millions of tokens [ 1, 2].

In my free time, I enjoy learning new things, such as coffee making and carpentry, and doing sports, e.g., fitness, basketball.


  • Efficient Deep Learning
  • Generative Modeling
  • Efficient Parameterizations
  • Group Equivariance


  • PhD., Efficient Deep Learning

    Vrije Universiteit Amsterdam

  • MSc., Computational Engineering, 2018

    Technische Universit├Ąt Berlin

  • BSc., Mechatronic Engineering, 2016

    Universidad Nacional de Colombia