With a solid background of 10+ years in the tech industry, I strive to improve businesses’ use of AI/ML, increasing the performance of AI/ML solutions in terms of accuracy and scalability while reducing their costs.
I have led and worked on diverse AI/ML projects including the use of LLMs for querying software documentation, analyzing application logs for anomaly detection, using weakly supervised learning for ontology matching, and customizing RL for the orchestration of distributed applications on the cloud and edge. Many of these projects have resulted in granted and registered patents in the US, Japan, and Europe with publications at top AI/ML and IoT conferences as well as production-ready systems.
If you’re currently working on AI/ML projects and need expert guidance, don’t hesitate to reach out.
I lead the development, implementation, and reporting of strategy and innovation initiatives for the tech directorate, managing teams that spread across all tech departments. My main topic is projects related to edge and cloud computing, in particular those including the use of AI/ML and AR applications.
Experience highlights:
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I’ve deployed an R&D edge node for Telefonica O2’s Innovation Area, leading a team of managers and engineers from the departments of tech (e.g., Cloud & Edge Platforms, Networks, Access & Transport) and external vendors, including the onboarding of AR/VR and AI applications.
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I’ve led the development of the sustainability strategy for Telefonica O2’s Tech ecosystem in a team including directors, managers, and experts from Tech and Corporate Responsibility while coordinating with Telefonica Global.
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I’ve contributed to identifying research funding calls and writing proposals, aligning them to Telefonica O2’s goals and organizational structure.
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I’ve advised executives on technology strategy and emerging trends including LLMs, edge AI, AR/VR and more, to contribute to Telefonica’s success.
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I led an internal project with a small team of researchers to design and implement a method and system to analyze applications’ logs using Neural Temporal Point Processes and NLP to detect errors and diagnose their root causes. This method is patent pending in Germany and the US (DE102022131127A1, US20240176692A1).
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I have developed an approach to monitor and profile the energy consumption of ML models, applying it mainly to optimize the efficiency of LLMs.
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I led the definition and implementation of a patenting process for GEC’s IP, contributing with tech managers and the company’s CTO.
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I’ve served as the Project Manager for the “Model Learning for Cloud-Edge Digital Twins” project funded by the EU and led NEC’s contribution to the Horizon EU project BigDataStack.
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I have developed a novel approach for using reinforcement learning to automate the configuration and adaptation of data-driven applications. This work has been recognized by the EU Innovation Radar and has resulted in three published papers and two granted patents in the US and EU (US11809977B2, US20210357767A1)
Previous roles at NEC: Research Associate (October 2018 - June 2020) and Research Intern (March 2018 - September 2018).
I designed and implementated a model to discover trends in Twitter, using Topic Modelling and visualising its evolution over time.
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I’ve developed new features and contributed to the improvement of different VoIP iOS apps of Media5 Corp.
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Testing software to identify and fix problems for Media5 Corp. projects.
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Documenting new features as well as maintaining the existing documentation.
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I designed, implementated, tested, and led user’s training and issue fixing for the Corrective Action Requests System.
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Implementation of SyncroDB System (to keep data consistency throughout different DBMS).
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Design, implementation and testing of the Personnel Attendance System.
I developed a novel algorithm capable of determining anchors in Tweets forautomatic link generation under the supervision of Prof. Gill Dobbie.
- Machine Learning
- Reinforcement Learning
- Probability & Statistics
- Software engineering / development
- Python
- Pytorch
- HuggingFace
- SQL
- NoSQL
- English [Fluent]
- Spanish [Native]
- German [Intermediate]
- Italian [Fluent]
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
How much energy do LLMs consume?
We use EnergyMeter, a Python tool, to measure the energy consumption of different LLMs including Llama, Dolly, and BLOOM.
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.
An introduction to Large Language Models and their biases (Guest Lecture)
Cloud-Edge Continuum (Guest Lecture)
The Energy Efficiency of our Code [ESP: La Eficiencia Energética de Nuestro Código] (Seminar Presentation)
Challenges and Opportunities in Machine Learning [ESP: Desafíos y Oportunidades en Machine Learning] (Presentation)
Applying Weak Supervision to Mobile Sensor Data: Experiences with Transport Mode Detection (Paper Presentation)
Reinforcement learning based orchestration for elastic services (Paper Presentation)