I am a machine learning researcher and engineer who specializes in the design and evaluation of deep learning models. As part of Sophos Data Science team, I currently focus on the development of deep learning applications for cybersecurity. I received my Master’s degree in Data Science at the Center for Data Science at New York University where I focused my research on adversarial learning applied to generative models. Prior to that I received my degree in Electrical Engineering from Universidad Catolica Argentina.
My main interests are in neural language models, and generative models more broadly, adversarial and reinforcement learning. Also, I’m currently exploring ways in which ML can be used to help improve the way we grow food.
Latest projects I have been working on:
Green-ML resources. [github-repo]
Felipe N Ducau, Ethan M Rudd, Tad M Heppner, Alex Long, Konstantin Berlin. SMART: Semantic Malware Attribute Relevance Tagging. 2019. [arxiv-preprint]
Ethan M Rudd, Felipe N Ducau, Cody Wild, Konstantin Berlin, Richard Harang. ALOHA: Auxiliary Loss Optimization for Hypothesis Augmentation. 2019. [arxiv-preprint]
Felipe Ducau, under the advice of Stanislas Lauly and Kyunghyun Cho. 2017. SEQ2SEQ Professor Forcing [pdf]
Recipient of the 2016 Capstone Project of the Year award - NYU Center for Data Science.
Some interesting courses I have taken:
Mathematics of Deep Learning
Natural Language Understanding with Deep Learning
Inference and Representation
Machine Learning and Computational Statistics