Federico Albanese
Verified Expert in Engineering
Predictive Modeling Developer
Buenos Aires, Argentina
Toptal 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.
Portfolio
Experience
- Python 3 - 5 years
- Predictive Modeling - 5 years
- Data Visualization - 5 years
Availability
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
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.
PhD. Software Engineer Intern
- 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.
PhD. Software Engineer Intern
- 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 develop Bayesian deep learning models.
Machine Learning Team Leader
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.
Invited Professor
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.
Data Scientist
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.
Experience
Transparentar
https://github.com/YamilaBarrera/transparentarEducation
Ph.D. Candidate in Computer Science and Machine Learning
Buenos Aires University - Buenos Aires, Argentina
Licenciatura (Equivalent to a Bachelor + Master Degree) in Physics
Buenos Aires University - Buenos Aires, Argentina
Skills
Libraries/APIs
Scikit-learn, PyTorch, Pandas, Keras, Matplotlib, XGBoost, TensorFlow
Tools
MATLAB, Jupyter, Spyder, Git
Languages
Python 3, Python, JavaScript, R, SQL, C++
Platforms
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), Generative Adversarial Networks (GANs), Machine Learning, Bayesian Statistics, Unsupervised Learning
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