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

Data Visualization Developer in Buenos Aires, Argentina

Member since October 6, 2018
Federico is currently pursuing a PhD in computer science, studying and designing new machine learning techniques. During this process, he's able to continuously learn and implement state-of-the-art algorithms and become a better data scientist each day. In the last few years, Federico has also worked at a financial consulting company, analyzing transactional data and making model predictions.
Federico is now available for hire

Portfolio

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

  • Machine Learning Team Leader

    2018 - PRESENT
    Mototech
    • Developed a forecasting algorithm that predicts the performance of NFL players. The results outperform state of the arte techniques by reducing the MSE from 8.541 to 5.576.
    Technologies: Keras, TensorFlow, Scikit-learn, R, Python
  • Invited Professor

    2018 - PRESENT
    Digital House
    • Dictated theoretical and applied lectures on the following topics: topic detection, conditionality reduction, embedding, time series analysis, embeddings, sentiment analysis, and text analysis/text mining. I used Tensorflow, Python, and keras during the lessons.
    Technologies: Keras, Seaborn, Matplotlib, Bokeh, TensorFlow, Scikit-learn, Python
  • 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
  • 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
  • Libraries/APIs

    Scikit-learn, Keras, D3.js, Matplotlib, XGBoost, CatBoost, TensorFlow
  • Tools

    MATLAB, Jupyter, Spyder, Git, Seaborn
  • Other

    Neural Networks, Text Mining, Natural Language Processing (NLP), Embedded Software, Word2Vec, Regression Modeling, Predictive Modeling, Data Visualization, Generative Adversarial Networks (GANs), Bokeh
  • Platforms

    Linux, Windows

Education

  • Ph.D. Candidate in Computer Science and Machine Learning
    2018 - 2021
    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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