![Image of Graph Data Science With Python/NetworkX publication](http://assets.toptal.io/images?url=http%3A%2F%2Fbs-uploads.toptal.io%2Fblackfish-uploads%2Fcomponents%2Fblog_post_page%2Fcontent%2Fcover_image_file%2Fcover_image%2F1274794%2F0707-A_Python_Introduction-Waldek_Newsletter___blog-c6b0a0bace184749a9522dad0019446f-bfc6550ab26713c7ba4f71483a932bac.png&width=768)
![Federico Albanese, Developer in Buenos Aires, Argentina](http://assets.toptal.io/images?url=http%3A%2F%2Fbs-uploads.toptal.io%2Fblackfish-uploads%2Ftalent%2Fprofile%2Fpicture_file%2Fpicture%2F1073140%2Fhuge_e983dff0df2388dcceb961238f76a000-324a6630c8668733f1ad03d6e0428bfa.jpg&width=524)
Federico Albanese
Verified Expert in Engineering
Data Visualization Developer
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
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
http://github.com/YamilaBarrera/transparentar![Image of Graph Data Science With Python/NetworkX publication](http://assets.toptal.io/images?url=http%3A%2F%2Fbs-uploads.toptal.io%2Fblackfish-uploads%2Fcomponents%2Fblog_post_page%2Fcontent%2Fcover_image_file%2Fcover_image%2F1274794%2F0707-A_Python_Introduction-Waldek_Newsletter___blog-c6b0a0bace184749a9522dad0019446f-bfc6550ab26713c7ba4f71483a932bac.png&width=768)
![Image of A Deeper Meaning: Topic Modeling in Python publication](http://assets.toptal.io/images?url=http%3A%2F%2Fbs-uploads.toptal.io%2Fblackfish-uploads%2Fcomponents%2Fblog_post_page%2Fcontent%2Fcover_image_file%2Fcover_image%2F1309403%2FCOVER-7b6380175c9bf8678b8be9dcd21cff79.png&width=768)
A Deeper Meaning: Topic Modeling in Python
![Image of Strategic Listening: A Guide to Python Social Media Analysis publication](http://assets.toptal.io/images?url=http%3A%2F%2Fbs-uploads.toptal.io%2Fblackfish-uploads%2Fcomponents%2Fblog_post_page%2Fcontent%2Fcover_image_file%2Fcover_image%2F1309420%2F0920_Engineering_Social_Media_Analysis_in_Python_Milena_Blog-9717d47ac0b7c272324eadc01addf123.png&width=768)
Strategic Listening: A Guide to Python Social Media Analysis
Skills
Languages
Python 3, Python, JavaScript, R, SQL, C++
Libraries/APIs
Scikit-learn, PyTorch, Pandas, 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, Web Scraping, GPT, Generative Pre-trained Transformers (GPT), Generative Adversarial Networks (GANs), Machine Learning, Bayesian Statistics, Unsupervised Learning
Platforms
Linux, Windows
Education
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