In order to improve the customer retention rate for a telecommunications company, I took charge of developing a proactive model to identify customers who were at risk of leaving. By utilizing various machine learning techniques such as Logistic Regression, RandomForestClassifier, KNeighborsClassifier, and CatBoostClassifier, we were able to create an effective solution.Assigned with the responsibility of enhancing the review filtering system for an online film forum, I spearheaded the development of a sophisticated model that could accurately classify movie reviews as either positive or negative. Through the implementation of diverse machine learning models and techniques, including normalization, lemmatization, TF-IDF feature creation, and extensive testing with Logistic Regression, LGBMClassifier, and BERT, we successfully crafted a highly accurate solution.

Literature Review: Conducting in-depth literature reviews to identify current trends, methodologies, and advancements in deep learning research. This involves searching academic papers, journals, conference proceedings, and online resources to gather relevant information.Data Collection and Preprocessing: Collecting and preprocessing datasets for deep learning experiments. This may involve sourcing publicly available datasets, cleaning and formatting data, and preparing it for analysis.Model Development: Assisting in the development of deep learning models for various tasks such as image classification, object detection, natural language processing, or speech recognition. This includes implementing algorithms, designing neural network architectures, and fine-tuning model parameters.Training and Evaluation: Training deep learning models using frameworks like TensorFlow, PyTorch, or Keras. This involves setting up training pipelines, optimizing hyperparameters, monitoring training progress, and evaluating model performance using metrics such as accuracy, precision, recall, and F1 score.Data Analysis: Analyzing experimental results and interpreting findings to draw meaningful conclusions. This may involve visualizing data, comparing model performance, identifying patterns or trends, and troubleshooting issues that arise during analysis.Algorithm Optimization: Optimizing deep learning algorithms and techniques to improve model efficiency, scalability, and performance. This may include exploring new architectures, incorporating regularization techniques, or experimenting with different optimization algorithms.Research Collaboration: Collaborating with senior researchers, faculty members, and fellow research assistants to contribute to ongoing research projects. This involves participating in research meetings, sharing insights and ideas, and contributing to the development of research proposals or publications.

Intern, Artificial Intelligence using Generative Adversarial Networks (GAN) algorithms application togenerate new features with Keras and Tensorflow. https://github.com/summerinternship2019

Assigned Survey Guyana and Indonesia. Implemented the QC procedure base project plan to be sure theseismic processing testing parameter achieves each process expectation. Schedule and track the phaseprocess in order to deliver on time. Manage survey evaluate and advice for seismic processing usingOmega 2017.1. and Matlab. full reporting and communication with the team involving in the project.

Geophysics project leader seismic processing [Tripoli 2007-2009]. Project leader, field processing usingOmega 2 [Kuwait, Chad, Argelia 2005-2006. Seismic processing [Poza Rica-Mexico 2002-2005]. Technicalsupport and advisor multiples seismic survey [Mexico 1997-2002].

Geophysics, Manage survey, processing, and interpretation seismic data assign in Peru and the USA.

Intern Seismic Interpretation, well evaluation by well log data. [Lima-Peru]