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  • Paulo Maia
 Paulo Maia

Paulo Maia

Data Scientist

I'm a Biomedical Engineer who went from AI in Healthcare to multiple industries. Talk with me if you are interested in having data-driven projects in your organization, if you're stuck trying to improve a model's performance and need new ideas, and if you'd like an out-of-the-box approach to data science problems. Also, I'm an enthusiast of applications of data science for social good!

  • Knowledge
  • Business
  • Technical
  • Languages
  • English
  • Portuguese
  • Consultants

Industries

  • Automotive
  • Healthcare
  • Marketing & CRM
  • Real Estate
  • Telco

Areas of Expertise

  • Computer Vision
  • Design Thinking in AI
  • MLOps
  • NLP
  • Tabular Data

Education

  • Universidade do Porto MSc in Bioengineering (Biomedical Engineering) 2014-2019

Interests & Hobbies

  • Always eager to binge-watch the newest TV Show!
  • A curious mind for science.
  • Diving in water that is too cold for the typical human.

Articles by Paulo

Article
Achieving diverse product recommendations

In this blog post, you’ll learn about some examples of decision processes you can use in recommender systems: do you see any usage for recommending less popular products as a way to improve your business? You will see it now! The Use Case Let’s imagine a use case where you are building a MOOC platform […]

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Article
WDL – Solving Social Problems Using Data Science

This article describes the key points of my participation at the 2021 Edition of the World Data League. The Tech Moguls Team, composed of me, Tiago Gonçalves, Tomé Albuquerque and Joana Morgado, from INESC TEC, finished second place in this edition. World Data League (WDL) is a Data Science competition where groups of Data Scientists […]

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Article
Multiple Product Forecasting in the construction industry

In this article, we will cover a use case in the construction industry related to forecasting the needed materials for construction and the time in which they will be required. In the construction industry, there is a lot of uncertainty between the order time and the time in which it is actually executed, due to […]

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Article
ML System Design: Federated Learning

NILG.AI, together with Neu.ro decided to try a format similar to a Reading Club, where the topic is not a specific paper but an entire research area. After a short discussion, we had a System Design part where the team described a specific use case to apply the new approach. Ideally, the discussion would stick […]

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Article
An Introduction to Multiple Instance Learning

Multiple Instance Learning (MIL) is a form of weakly supervised learning where training instances are arranged in sets, called bags, and a label is provided for the entire bag, opposedly to the instances themselves. This allows to leverage weakly labeled data, which is present in many business problems as labeling data is often costly: Medical […]

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Article
Reducing Unemployment using AI

With COVID-19, many were affected by the economic crisis and lost their jobs. In Portugal alone, between February and September, there was a 30% increase in unemployment! AI can be a powerful tool in allocating scarce resources in a more efficient way. Inspired by DSSG Fellowship’s Project in Partnership with IEFP (Instituto de Emprego e […]

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Article
Difficult Targets to Optimize: the ROC AUC

In many binary classification problems, especially in domains with highly unbalanced problems (such as the medical domain and rare event detection), we need to make sure our model does not become too biased for the more predominant class.  Thus, you may have heard that accuracy is not a good metric to validate classifiers in unbalanced […]

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Article
Applying geospatial data for Machine Learning, with a focus on social good

In partnership with Data Science for Social Good Portugal, we are launching a series of webinars in AI topics related to social good. The first talk was by Paulo Maia, on the 28th of June, with the topic “Applying geospatial data for Machine Learning, with a focus on social good”. In case you weren’t able […]

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Article
Detecting Errors in Insurance Claims

Insurance codes are used by people’s health plan to make decisions about how much your doctor and other healthcare providers should be paid.  There is some variety of coding systems currently used [1]: Current Procedural Terminology (CPT) codes, used by physicians to describe the services they provide. Healthcare Common Procedure Coding System (HCPCS),  used by […]

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Article
Embedding Domain Knowledge for Estimating Customer Lifetime Value

As part of the rise of Deep Neural Networks in the ML community, we have observed an increasing fit-predict approach, where AI practitioners don’t take the time to think about the domain knowledge that is already available and how to embed that knowledge in the models. In this blogpost, we will cover how we created […]

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Article
Appendix: Embedding Domain Knowledge for Estimating Customer Lifetime Value

This is an appendix to the blog post Embedding Domain Knowledge for Estimating Customer Lifetime Value. We will describe some alternatives we considered for solving the proposed problem, but did not end up being implemented. First, let’s assume we have a pre-trained model for estimating the probability of the target and . Estimating Lifetime Value using […]

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Article
Objectively Estimating Data Quality

In Artificial Intelligence, it is important to measure the quality of the data we are trying to use. For instance, if we want to classify a cervix image according to the degree of cancer, how do we know if that image follows the acquisition protocol and can be used for diagnosing the patient [1] so […]

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