I’m a principal research engineer with Ph.D. degree in Electrical and Computer Engineering. My interests include Machine Learning applied to Natural Language Processing, Automatic Speech Recognition and Computer Vision.
In addition to some theoretical analyses of existing Machine Learning models, such as bounds on the false
and truth positive rates based on a VC-style analysis, I invented Machine Learning algorithms that have been applied to problems in different areas by many other researchers, of which I highlight maximum margin training methods for neural networks, such as the Maximum-margin GDX (MMGDX) and Support Vector Neural Networks (SVNN). I'm also the author of the feature selector based on genetic algorithms and information theory, and the parallel seq2seq model, wich was applied to generative chatbots. You can find other contributions on my Google Scholar profile, and codes available on GitHub and Mathworks.
I’m currently working on long-term research on Machine Learning and its applications in automatic speech recognition (ASR), including audiovisual ASR (voice + lip reading) and low footprint neural language models at Cerence Inc.
