Computer Vision Developer2019 - PRESENTMindsLenses
Technologies: Amazon Web Services (AWS), Deep Learning, Python, AWS, PyTorch
- Created a pipeline that processes everyday movies collected from RPI cameras installed in a store. The processing includes several deep learning algorithms that detect human emotions in association with product interactions that are being handled in the store. I automated the process of daily running on the cloud, as well as the process of saving the results in a database also placed on the cloud.
- Trained deep learning models for object detection and classification. Utilized state-of-the-art frameworks to produce the best quality predictions.
Machine Learning Engineer2017 - 2019Sportradar US
- Created a neural network for the NFL to generate drive outcome predictions to further use from the betting companies.
- Built NBA shot probability neural network models using Pytorch for fan experience enhancement.
- Identified NBA events on games using tracking features with XGBoost.
- Implemented an NBA data pipeline from different data sources.
- Created an NBA similarity model for draft players for fan enhanced experience.
Data Scientist2016 - 2017Alem Research
Technologies: Java, Python
- Scraped 10 million company front pages from a social network with Java.
- Created a sentiment analysis neural network model using TensorFlow to label various English texts (including articles, comments, and more) as positive, negative, or neutral.