Lead Developer
2018 - PRESENTPinTecnologia- Built a system to process administrative documents over the Ethereum blockchain.
- Worked on the front-end and built the UI using React.
- Designed and developed the back-end using Django and the Django REST framework.
Technologies: Blockchain, Ethereum, React, DjangoProduct Owner
2017 - PRESENTBMD Software- Managed the PACScenter product which is an all-in-one medical imaging platform for patient studies (storage, visualization, and sharing)—enabling simple and efficient workflows.
- Developed core features for both the back-end and front-end.
- Defined the strategy for new version releases.
Technologies: MySQL, JavaScript, HTML, Hibernate, JPA, Scala, Java, Play FrameworkKaggle Competitions Expert
2018 - 2018TalkingData AdTracking Fraud Detection- Worked with a large dataset using big data tools like Apache Spark.
- Built an algorithm that predicts whether a user will download an app after clicking a mobile app ad which helps to combat click fraud.
Technologies: Feature-driven Development (FDD), Gradient Boosting, MLlib, Apache SparkKaggle Competitions Expert
2018 - 2018Toxic Comment Classification- Studied negative online behaviors e.g., toxic comments.
- Built a multi-label model that’s capable of detecting different types of toxicity like threats, obscenity, insults, and identity-based hate.
- Used a labeled dataset of comments from Wikipedia’s talk page edits.
Technologies: Support Vector Machines (SVM), Naive Bayes, Ensemble Methods, Gated Recurrent Unit (GRU), Embedded Development, GloVe, LSTM, Deep LearningKaggle Competitions Expert
2018 - 2018Recruit Restaurant Visitor Forecasting- Predicted how many customers to expect in each day in a restaurant to effectively purchase ingredients and schedule staff members.
- Developed a prediction model for this task; this was not easy to make because many unpredictable factors can affect restaurant attendance like the weather and local competition. It's even harder for newer restaurants with little historical data.
- Worked with heterogeneous datasets.
Technologies: Gradient Boosting, Time Series, ARIMA, LSTMKaggle Competitions Expert
2017 - 2017Sberbank Russian Housing Market- Created a prediction model capable of making predictions about realty prices so that renters, developers, and lenders are more confident when they sign a lease or purchase a building.
- Developed algorithms which use a broad spectrum of features to predict realty prices, using a rich dataset that includes housing data and macroeconomic patterns.
Technologies: Validation, Feature-driven Development (FDD), Ridge Regression, Gradient BoostingKaggle Competitions Expert
2017 - 2017Two Sigma Financial Modeling- Applied technology and systematic strategies to financial trading in order to forecast economic outcomes that can never be entirely predictable,.
- Back-tested to validate regression models that predict financial time series.
Technologies: Ridge Regression, ExtraTreesRegressor, TensorFlow, Reinforcement LearningKaggle Competitions Expert
2016 - 2016Santander Customer Satisfaction- Created a model that identifies dissatisfied customers.
- Worked with hundreds of anonymized features to predict if a customer is satisfied or dissatisfied with their banking experience.
Technologies: Principal Component Analysis (PCA), Neural Networks, Ensemble Methods, Decision Trees, Random ForestsKaggle Competitions Expert
2014 - 2014Loan Default Prediction- Determined whether a loan will default, as well as the loss incurred if it does default.
- Developed methods unlike traditional finance-based approaches to this problem, where one distinguishes between good or bad counter parties in a binary way, we sought to anticipate and incorporate both the default and the severity of the losses that result.
- Built, as a team, a bridge between traditional banking, where we are looking at reducing the consumption of economic capital, to an asset-management perspective, where we minimized the risk to the financial investor.
Technologies: Gradient Boosting, Random ForestsKaggle Competitions Expert
2013 - 2013Job Salary Prediction- Built a prediction engine for the salary of any UK job advertisement so they can make huge improvements in the experience of users searching for jobs, and help employers and job seekers figure out the market worth of different positions.
- Worked with a large dataset (hundreds of thousands of records) which was mostly unstructured text with few structured data fields. These were in a number of different formats because of the hundreds of different sources of records.
Technologies: Dimensionality Reduction, Tf-idf, Natural Language Processing (NLP), Ridge Regression, Random Forest Regression
Eriksson Monteiro
Natural Language Processing (NLP) Developer in Aveiro, Portugal
Member since June 4, 2018
Eriksson possesses a PhD in computer science and is an experienced full-stack developer and machine learning engineer. For the past five years, he's built web and mobile applications and has participated in several machine learning tasks in diverse data science fields. He's particularly good at working with application frameworks such as Django, Play Framework, and Node.js.
Eriksson is now available for hire
Portfolio
- PinTecnologiaBlockchain, Ethereum, React, Django
- BMD SoftwareMySQL, JavaScript, HTML, Hibernate, JPA, Scala, Java, Play Framework
- TalkingData AdTracking Fraud DetectionFeature-driven Development (FDD), Gradient Boosting, MLlib, Apache Spark
Experience
- Python 10 years
- Recommendation Systems 5 years
- Data Engineering 5 years
- Data Science 5 years
- Deep Neural Networks 5 years
- Natural Language Processing (NLP) 5 years
- Machine Learning 5 years
- TensorFlow 3 years
Availability
Part-time
Preferred Environment
Docker, Git, IntelliJ IDEA
The most amazing...
...thing I've built was a system that predicted whether a loan will default and the likelihood of loss incurred if it does.
Employment
Experience
- Dicom Anonymizerhttps://hub.docker.com/r/bioinformaticsua/us-image-anonymizer/
A tool that uses machine learning to automatically anonymize medical images' pixel data. The main objective of this project was to provide a suitable alternative to a manual process in medical image anonymization. It uses a neural network to find sensitive information in the images and removes regions containing this information.
- Scaleushttp://bioinformatics-ua.github.io/scaleus/
Scaleus is a semantic web data migration tool.
- Dicooglehttp://dicoogle.com
Dicoogle is an open-source PACS that's well fitted to small-medium sized institutions that want to install a PACS without a huge investment. It is based on a document-indexing model that can be shared over a LAN or a WAN, allowing the creation of federated views and extends the querying and retrieval over a large number of archives. It allows searching through free text queries as well as any DICOM tag. More information can be extracted from medical imaging repositories—offering increasing flexibility when compared to current query and retrieval DICOM services.
Skills
Languages
Java, SQL, Python, Scala, C#, JavaScript, HTMLFrameworks
Apache Spark, Play Framework, Django, JPA, HibernateLibraries/APIs
Matplotlib, Pandas, NumPy, Scikit-learn, Keras, TensorFlow, React, LSTM, MLlib, Node.js, SciPyTools
Jupyter, Docker Compose, PredictionIO, Git, IntelliJ IDEA, WekaParadigms
Data Science, Scrum, Clean Code, DevOps, Concurrent Programming, Object-oriented Programming (OOP), Distributed Computing, Continuous Integration (CI), Parallel Computing, Agile Software Development, Unit TestingPlatforms
Linux, Docker, MacOS, Windows, Ethereum, BlockchainOther
Deep Neural Networks, Data Mining, Machine Learning, Data Engineering, Predictive Analytics, RESTful Web Services, Regression Modeling, Classification Algorithms, Recommendation Systems, Recurrent Neural Networks, Convolutional Neural Networks, Random Forests, Gradient Boosted Trees, Statistics, Big Data, Natural Language Processing (NLP), Text Mining, GitFlow, Gradient Boosting, ARIMA, Time Series, Reinforcement Learning, ExtraTreesRegressor, Ridge Regression, Random Forest Regression, Tf-idf, Dimensionality Reduction, Feature-driven Development (FDD), Validation, Deep Learning, Embedded Development, Gated Recurrent Unit (GRU), Ensemble Methods, Support Vector Machines (SVM), Decision Trees, Neural Networks, Principal Component Analysis (PCA), Naive Bayes, GloVeStorage
Elasticsearch, MySQL, Memcached, Microsoft SQL Server, MongoDB, PostgreSQL
Education
- Doctor of Philosophy (PhD) Degree in Computer Science2012 - 2017MAP-i (University of Aveiro, Porto and Minho) - Portugal
- Master's Degree in Computer Science2007 - 2012University of Aveiro - Portugal
