Federico Albanese, Data Visualization Developer in Buenos Aires, Argentina
Federico Albanese

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 Aires
    CatBoost, XGBoost, Keras, TensorFlow, Scikit-learn, MATLAB, R, Python
  • Facebook
    Python, Machine Learning, SQL, C++, Unsupervised Learning, Statistics
  • Facebook
    Python, Machine Learning, Bayesian Statistics, SQL

Experience

Location

Buenos Aires, Argentina

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

  • Research intern

    2017 - PRESENT
    University 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, Python
  • PhD. Software Engineer Intern

    2021 - 2021
    Facebook
    • 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, Statistics
  • PhD. Software Engineer Intern

    2020 - 2020
    Facebook
    • 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, SQL
  • Machine Learning Team Leader

    2018 - 2019
    Mototech
    • 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, Python
  • Invited Professor

    2018 - 2019
    Digital 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, Statistics
  • Data Scientist

    2016 - 2017
    Hexagon 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

Experience

Skills

  • Languages

    Python 3, Python, JavaScript, R, SQL, C++
  • Libraries/APIs

    Scikit-learn, Keras, Matplotlib, XGBoost, TensorFlow
  • Tools

    MATLAB, Jupyter, Spyder, Git
  • Paradigms

    Data Science
  • Other

    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 Learning
  • Platforms

    Linux, Windows

Education

  • Ph.D. Candidate in Computer Science and Machine Learning
    2018 - 2022
    Buenos Aires University - Buenos Aires, Argentina
  • Licenciatura (Equivalent to a Bachelor + Master Degree) in Physics
    2011 - 2017
    Buenos Aires University - Buenos Aires, Argentina

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