Glendale, CA, USA
Forward-thinking Software Engineer with background working effectively in dynamic environments. Fluent in Java and Python programming languages. Proud team player focused on achieving project objectives with speed and accuracy. Software Engineer skilled at technical leadership, communication and presentations. Experienced in full project life cycle from design to implementation to integration.
December 2020 - Present
June 2018 - November 2020
October 2016 - June 2018
June 2006 - October 2016
2016 - 2019
CohibaMate utilizes Google's Tensorflow to train a deep convolution neural network on an exclusive data-set. The pre-trained model with a 97% accuracy rate is then used by a Python/ Flask application which allows the user to upload an image of a Cohiba cigar band and have the model predict the image's label as being authentic or counterfeit.
Available at Google Play Store
Designed and implemented a data preprocessing pipeline to create the linkage network for the City of Los Angeles and to reasonably impute missing data for any time series. Trained and tested GRNN model to predict the traffic speeds of the streets in the Los Angeles Financial District, and verify the GRNN model and its resulting output using various visualizations.
Glendale, CA, USA
Forward-thinking Software Engineer with background working effectively in dynamic environments. Fluent in Java and Python programming languages. Proud team player focused on achieving project objectives with speed and accuracy. Software Engineer skilled at technical leadership, communication and presentations. Experienced in full project life cycle from design to implementation to integration.
December 2020 - Present
June 2018 - November 2020
October 2016 - June 2018
June 2006 - October 2016
2016 - 2019
CohibaMate utilizes Google's Tensorflow to train a deep convolution neural network on an exclusive data-set. The pre-trained model with a 97% accuracy rate is then used by a Python/ Flask application which allows the user to upload an image of a Cohiba cigar band and have the model predict the image's label as being authentic or counterfeit.
Available at Google Play Store
Designed and implemented a data preprocessing pipeline to create the linkage network for the City of Los Angeles and to reasonably impute missing data for any time series. Trained and tested GRNN model to predict the traffic speeds of the streets in the Los Angeles Financial District, and verify the GRNN model and its resulting output using various visualizations.