I’m a final year PhD student in the Statistics Department, Columbia University. My advisor is Liam Paninski. I recently interned at Google Brain, I was hosted by Jasper Snoek. The picture above is from my hometown: Santiago, Chile. There I obtained by B.S. in Mathematical Engineering at Universidad de Chile.
Brief Research philosophy
As many, I believe we have entered a golden era of statistics. We are living fascinating times where vast biological/medical datasets have been made available. ML has flourished as a prominent discipline to learn and extract structure from these data, providing principles for the development of complex statistical models and efficient scalable inference algorithms.
I am thrilled by researching ML algorithms that I can apply to real-world problems in the sciences. These problems require the interaction and contribution from people of different backgrounds. My work intends to contribute to the mutual nourishment imposed by collaboration in multidisciplinary teams where problems are represented in terms of statistical models, whose structures are researched. The results of this pursue are algorithms that efficiently probe the desired features in data, as well as novel machine learning methodology.
I am actively seeking research positions. In the future I intend to conduct fundamental research in ML, and develop algorithms to address complex problems in Life Sciences. See research for an overview of my current and past research projects.
- Dec 22nd, 2017. Our paper Reparameterizing the Birkhoff Polytope for Variational Permutation Inference (joint with S. Linderman, H. Cooper, J. Cunningham and L. Paninski) was accepted to AISTATS, 2018. Nos vemos en Lanzarote!
- Dec 1st, 2017. I will be presenting two workshop papers at NIPS: Our work on Sinkhorn Networks will be presented in the Optimal Transport and Machine Learning Workshop. Also, our work on permutation inference for C. elegans data will be presented in the Worms Information Processing Workshop.
- Nov 13rd, 2017. Our methods paper Electrical stimulus artifact cancellation and neural spike detection on large multi-electrode arrays is now in press at PLOS Computational Biology. We think this is a significant step towards enabling closed-loop experiments featuring high-resolution neural electrical stimulation.