I am a final-year PhD Student at the Vrije Universiteit Amsterdam supervised by Erik Bekkers (UvA), Jakub Tomczak and Mark Hoogendoorn. 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. My specific focus is on continuous relaxations and parameterizations of Deep Learning methods, such as Continuous Kernel Convolutions. Continuous Kernel Convolutions are a new family of neural parameterizations with interesting efficiency properties for which I received the Qualcomm Innovation Fellowship.
In my free time, I enjoy learning new things, such as coffee making and carpentry, and doing sports like fitness and basketball.
PhD. in Efficient Deep Learning
Vrije Universiteit Amsterdam
MSc. Computational Engineering, 2018
Technische Universität Berlin
BSc. Mechatronic Engineering, 2016
Universidad Nacional de Colombia
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