Research intern
2017 - PRESENTUniversity of Buenos Aires- Analyzed the texts of news using natural language processing techniques. In particular, recursive deep models for semantic compositionality over sentiment treebank in order to detect the sentiment of a sentence, dimensional reduction algorithms and topic detection methods were used with the intention of characterizing the mass media bias during presidential elections.
- Focused my study on developing better machine learning techniques which efficiently uses the information of a node and its neighbours. In addition, This new semi supervised methodology will be validated using graphs of biological and social origin.
Technologies: CatBoost, XGBoost, Keras, TensorFlow, Scikit-learn, MATLAB, R, PythonPhD. Software Engineer Intern
2021 - 2021Facebook- I proposed a lookalike model to generate expanded audiences for Facebook and Instagram ads, using user embeddings and graph clustering.
- I implemented a lookalike model to generate expanded audiences in Python.
- I evaluated my proposed machine learning method, and the results showed an improvement in the precision, recall, and conversion score on synthetic and real data.
Technologies: Python, Machine Learning, SQL, C++, Unsupervised Learning, StatisticsPhD. Software Engineer Intern
2020 - 2020Facebook- I experimented with and benchmarked uncertainty estimation methods for ad ranking models of Facebook.
- Implementing Bayesian Deep Learning Models and analyzing more than 20 terabytes of data.
- I improved the normalized entropy score for the ads matching task by 1.6%.
Technologies: Python, Machine Learning, Bayesian Statistics, SQLMachine Learning Team Leader
2018 - 2019Mototech- Developed a forecasting algorithm that predicts the performance of NFL players.
- I managed a group of 4 software engineers and data scientists.
- The results outperform the state-of-the-art techniques by reducing the MSE from 8.541 to 5.576.
Technologies: Keras, TensorFlow, Scikit-learn, R, PythonInvited Professor
2018 - 2019Digital House- Dictated theoretical and applied lectures with code examples.
- I used TensorFlow, Python, and Keras during the lessons.
- The syllabus included the following topics: topic detection, dimensional reduction, embeddings, time series analysis, sentiment analysis, and text analysis/text mining.
Technologies: Keras, Seaborn, Matplotlib, Bokeh, TensorFlow, Scikit-learn, Python, StatisticsData Scientist
2016 - 2017Hexagon Consulting- Implemented different predictive models in order to describe the future financial behavior of bank clients using Python.
- Designed and implemented a recommendation system that uses text reviews in order to recommend a movie using text analysis (topic detection, sentiment analysis, and word embedding).
- Created and develop a software which statistically calculates a personal index of inflation based on a big economic and financial database.
Technologies: Bokeh, Seaborn, D3.js, JavaScript, Predictive Modeling, Scikit-learn, Python
Data Visualization Developer in Buenos Aires, Argentina
Member since January 9, 2019
Federico is a developer and data scientist who has worked at Facebook, where he made machine learning model predictions. He is a Python expert and a university lecturer. His Ph.D. research pertains to machine learning. He can continuously learn and implement state-of-the-art algorithms during this process and become a better data scientist each day.
Federico is now available for hire
Portfolio
- University of Buenos AiresCatBoost, XGBoost, Keras, TensorFlow, Scikit-learn, MATLAB, R, Python
- FacebookPython, Machine Learning, SQL, C++, Unsupervised Learning, Statistics
- FacebookPython, Machine Learning, Bayesian Statistics, SQL
Experience
- Data Visualization 5 years
- Predictive Modeling 5 years
- Python 3 5 years
Availability
Part-time
Preferred Environment
Git, Spyder, Jupyter, Windows, Linux
The most amazing...
...prediction model I've coded outperformed the state-of-the-art models in that area by 15%. This model was also fast and easy to interpret.
Employment
Experience
- Transparentarhttps://github.com/YamilaBarrera/transparentar
Created and developed software which uses statistics in order to analyze a complex network of fishing data.
- A Deeper Meaning: Topic Modeling in Python (Publication)Colloquial language doesn’t lend itself to computation. That’s where natural language processing steps in. Learn how topic modeling helps computers understand human speech.
- Graph Data Science With Python/NetworkX (Publication)Data inundates us like never before—how can we hope to analyze it? Graphs (networks, not bar graphs) provide an elegant approach. Find out how to start with the Python NetworkX library to describe, visualize, and analyze "graph theory" datasets.
Skills
Languages
Python 3, Python, JavaScript, R, SQL, C++Libraries/APIs
Scikit-learn, Keras, Matplotlib, XGBoost, TensorFlowTools
MATLAB, Jupyter, Spyder, GitParadigms
Data ScienceOther
Neural Networks, Text Mining, Natural Language Processing (NLP), Embedded Software, Word2Vec, Regression Modeling, Predictive Modeling, Data Visualization, Statistics, Data Analysis, Generative Adversarial Networks (GANs), Machine Learning, Bayesian Statistics, Unsupervised LearningPlatforms
Linux, Windows
Education
- Ph.D. Candidate in Computer Science and Machine Learning2018 - 2022Buenos Aires University - Buenos Aires, Argentina
- Licenciatura (Equivalent to a Bachelor + Master Degree) in Physics2011 - 2017Buenos Aires University - Buenos Aires, Argentina
