Full name: Felipe Ferreira de Freitas
Position: Postdoctoral researcher (FCT funded)
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Degrees:
-Catholic University of Pernambuco: B.Sc (Bacharelado) in Medical Physics, 2010
-Federal University of Paraíba : M.Sc in Theoretical Particle Physics, 2013
-Federal University of Paraíba / University of Sussex: Ph.D. in Theoretical Particle Physics, 2017
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Previous positions:
-Institute of Theoretical Physics-CAS, 2017- 2019, Postdoctoral Researcher
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Research:
My research interests are in Particle Physics models beyond the Standard Model and on its phenomenological and cosmological implications. I am mostly interested in the use of Deep learning and Machine Learning techniques to help the identification of beyond Standard Model signal at the Large Hadron Collider (LHC). Since 2016 I have been involved in the development of a set of analysis frameworks for Deep learning and computer vision that make use of the information from Monte Carlo simulations applied in different data formats to extract patterns hidden from conventional analysis. Recently, I have been applied computer vision and time series predictions to develop analysis framework for detection and classification of Gravitational Waves events. I am also interesting on the usage of Deep Learning in medical applications (https://github.com/FFFreitas/X-ray-and-ML), natural language processing (NLP) and social networks patterns predictions.
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Degrees:
-Catholic University of Pernambuco: B.Sc (Bacharelado) in Medical Physics, 2010
-Federal University of Paraíba : M.Sc in Theoretical Particle Physics, 2013
-Federal University of Paraíba / University of Sussex: Ph.D. in Theoretical Particle Physics, 2017
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Previous positions:
-Institute of Theoretical Physics-CAS, 2017- 2019, Postdoctoral Researcher
----------------------------------------------------------------------
Research:
My research interests are in Particle Physics models beyond the Standard Model and on its phenomenological and cosmological implications. I am mostly interested in the use of Deep learning and Machine Learning techniques to help the identification of beyond Standard Model signal at the Large Hadron Collider (LHC). Since 2016 I have been involved in the development of a set of analysis frameworks for Deep learning and computer vision that make use of the information from Monte Carlo simulations applied in different data formats to extract patterns hidden from conventional analysis. Recently, I have been applied computer vision and time series predictions to develop analysis framework for detection and classification of Gravitational Waves events. I am also interesting on the usage of Deep Learning in medical applications (https://github.com/FFFreitas/X-ray-and-ML), natural language processing (NLP) and social networks patterns predictions.