Publication
Strategic Listening: A Guide to Python Social Media Analysis
https://www.toptal.com/developers/python/python-social-media-analysis
Federico is currently unavailable
Federico Albanese
Verified Expert in Engineering
Artificial Intelligence (AI) Developer
Buenos Aires, Argentina
Toptal member since January 9, 2019
Bio
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.
Portfolio
University of Buenos Aires
XGBoost, Keras, TensorFlow, Scikit-learn, MATLAB, R, Python...
Meta
Python, Machine Learning, SQL, C++, Unsupervised Learning, Statistics...
Facebook
Python, Machine Learning, Bayesian Statistics, SQL, Data Visualization, Git...
Experience
- Artificial Intelligence (AI) - 10 years
- Python 3 - 5 years
- Predictive Modeling - 5 years
- Data Visualization - 5 years
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.
Work Experience
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: XGBoost, Keras, TensorFlow, Scikit-learn, MATLAB, R, Python, Artificial Intelligence (AI)
PhD Software Engineer
2021 - 2021
Meta
- Proposed a lookalike model to generate expanded audiences for Facebook and Instagram ads, using user embeddings and graph clustering.
- Implemented a lookalike model to generate expanded audiences in Python.
- 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, Predictive Modeling, Data Visualization, Git, Python 3, Pandas, Artificial Intelligence (AI)
PhD Software Engineer
2020 - 2020
- Experimented with and benchmarked uncertainty estimation methods for ad ranking models of Facebook.
- Implemented deep learning models and analyzed more than 20 terabytes of data.
- Improved the normalized entropy score for the ads matching task by 1.6%.
- Designed and developed Bayesian deep learning models.
Technologies: Python, Machine Learning, Bayesian Statistics, SQL, Data Visualization, Git, Python 3, Pandas, Artificial Intelligence (AI)
Machine Learning Team Leader
2018 - 2019
Mototech
- Developed a forecasting algorithm that predicts the performance of NFL players.
- Managed a group of four software engineers and data scientists.
- Used web scrapping tools to automatically download data from different types of websites.
- Outperformed the results of the state-of-the-art techniques by reducing the MSE from 8.541 to 5.576.
Technologies: Keras, TensorFlow, Scikit-learn, R, Python, Data Visualization, Python 3, Pandas, Artificial Intelligence (AI)
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, Matplotlib, TensorFlow, Scikit-learn, Python, Statistics, Data Visualization, Pandas
Data Scientist
2016 - 2017
Hexagon Consulting
- Implemented different machine learning forecasting models using financial datasets.
- Designed and implemented a recommendation system that used text analysis (natural language processing, topic detection, sentiment analysis, and word embedding).
- Created and developed software that statistically calculates a personal index of inflation based on a big economic and financial database.
Technologies: JavaScript, Predictive Modeling, Scikit-learn, Python, Data Science, Data Visualization, Python 3, Artificial Intelligence (AI)
Experience
Transparentar
https://github.com/YamilaBarrera/transparentarCreated and developed software which uses statistics in order to analyze a complex network of fishing data.
Education
2018 - 2022
Ph.D. Candidate in Computer Science and Machine Learning
Buenos Aires University - Buenos Aires, Argentina
2011 - 2017
Licenciatura (Equivalent to a Bachelor + Master Degree) in Physics
Buenos Aires University - Buenos Aires, Argentina
Skills
Libraries/APIs
Scikit-learn, PyTorch, Pandas, OpenAI API, Keras, Matplotlib, XGBoost, TensorFlow
Tools
MATLAB, Jupyter, Spyder, Git
Languages
Python 3, Python, JavaScript, R, SQL, C++
Platforms
Amazon Web Services (AWS), Linux, Windows
Other
Neural Networks, Text Mining, Natural Language Processing (NLP), Embedded Software, Word2Vec, Regression Modeling, Predictive Modeling, Data Visualization, Data Science, Statistics, Data Analysis, Web Scraping, Generative Pre-trained Transformers (GPT), Large Language Models (LLMs), Generative Artificial Intelligence (GenAI), Artificial Intelligence (AI), Agentic AI, Generative Adversarial Networks (GANs), Machine Learning, Bayesian Statistics, Unsupervised Learning
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