With over 7 years of experience in both research and software engineering, I have strong expertise in the application of ML to optimize resource utilization and enhance the performance of Cloud and Edge Computing applications. I have actively participated in several EU-funded research projects as well as industrial projects, publishing their results in highly-esteemed AI and data events such as VLDB and AAAI Symposia.
I am currently developing an approach to monitor and optimize the energy consumption of ML-based applications to improve the sustainability of software on cloud and edge infrastructure.
I have led the research and development of an approach for anomaly detection on applications’ logs using Neural Temporal Point Processes and NLP to detect errors and diagnose their root causes.
Furthermore, I have played a key role in defining a patenting process to protect GEC’s intellectual property.
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Developed a novel approach for the use of RL for system’s configuration and applications adaptation, which has led to the application for an international patent, two publications and its implementation in a Horizon 2020 EU project.
Collaborated in the development of multiple publications and patents related to the application of data programming to IoT data and the development of self-adaptive applications for edge computing.
Participated in the development of other EU project proposals as well as internal research proposals.
Previous roles at NEC: Research Associate (October 2018 - June 2020) and Research Intern (March 2018 - September 2018).
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Design and implementation of a model to discover trends in Twitter, using Topic Modelling and visualising its evolution over time.
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Developing new features and contributing to the improvement of different VoIP iOS apps of Media5 Corp.
Testing software to identify and fix problems for Media5 Corp. projects.
Documenting new features as well as maintaining the existing documentation.
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Design, implementation, testing, user’s training and issue fixing for the Corrective Action Requests System.
Implementation of SyncroDB System (to keep data consistency throughout different DBMS).
Design, implementation and testing of the Personnel Attendance System.
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Developed a novel algorithm capable of determining anchors in Tweets forautomatic link generation under the supervision of Prof. Gill Dobbie.
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- Machine Learning
- Reinforcement Learning
- Probability & Statistics
- Software engineering / development
- Python
- Pytorch
- HuggingFace
- SQL
- NoSQL
VersaMatch: ontology matching with weak supervision
Tutor4RL: Guiding Reinforcement Learning with External Knowledge
Towards Knowledge Infusion for Robust and Transferable Machine Learning in IoT
Applying Weak Supervision to Mobile Sensor Data: Experiences with Transport Mode Detection
Reinforcement Learning Based Orchestration for Elastic Services
Elastic Services for Edge Computing
Towards adaptive actors for scalable iot applications at the edge
Some personal programming projects I have published on Github and Apple’s iOS App Store.
Adaptive Applications Simulator - Github repository
The Adaptive Applications Simulator provides a fast way to implement and test applications that use an adaptive logic to adapt to the current execution context in order to deliver high performance while complying with their requirements’.
SIR on Gnutella - Github repository
Simulation of a Susceptible-Infected-Recovered Epidemic process on a Gnutella p2p network.
A photo editing application to create your own photo filters. No longer available on App Store.
Fixture 2014 - iOS Application
A World Cup Scorer to access live results from matches and the matches agenda. It reached #1 spot in Sports category of the App Store in Argentina and Uruguay. No longer available on App Store.
I often write on Medium about AI and in particular, Reinforcement Learning.
Transfer Learning in Reinforcement Learning
A quick review of how transfer learning improves the performance of RL on new, unseen tasks by exploiting learnings from past tasks.
5 Websites to Download Pre-trained Machine Learning Models
No need to train that machine learning model, just download a pre-trained one and let others do the heavy lifting!
How to Explain Decision Trees’ Predictions
We develop an approach to explain why a learned tree model chooses a certain class for a given sample, providing examples in Python.
Reinforcement Learning with TensorFlow Agents — Tutorial
Try TF-Agents for RL with this simple tutorial, published as a Google colab notebook so you can run it directly from your browser.
10+ Free Resources to Download Datasets for Machine Learning
A list of online resources to search and download datasets for your Machine Learning and AI projects
How to Access Stocks Market Data for Machine Learning on Python
If you want to use Machine Learning for trading stocks, you will need to create a dataset of stock markets data. Find out how to easily do it, with ready-to-use code.
Tutoring Reinforcement Learning
Reinforcement Learning agents start from scratch, knowing nothing and learning by experience, which is effective but slow. Could we give them some hints to get them started?
5 Frameworks for Reinforcement Learning on Python
Programming your own Reinforcement Learning implementation from scratch can be a lot of work, but you don’t need to do that. There are lots of great, easy and free frameworks to get you started in few minutes.
How is Reinforcement Learning used in Business?
Reinforcement Learning has proved it can achieve better results than humans in different games in recent years. But can RL also be used in businesses in the real world?
Entropy Regularization in Reinforcement Learning
In our everyday language, we commonly use the term “entropy” to refer to the lack of order or predictability of a system (for example, the universe.) In Reinforcement Learning (RL), the term is used in a similar fashion: in RL, entropy refers to the predictability of the actions of an agent.
Reinforcement Learning for everyone
RL has become popular in the AI community, but most people still don’t know what it is about. Come and read, no matter your background!
Make smarter agents with Hierarchical Reinforcement Learning
An introduction to Hierarchical Reinforcement Learning and an overview of different hierarchical approaches.
When to use Reinforcement Learning (and when not to)
What to consider to decide if Reinforcement Learning is the right approach to solve your problem.
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- German [Intermediate]
- Italian [Fluent]