Fredy Barrientos-Espillco

Research Scientist & AI Engineer | Deep Learning · Computer Vision · Generative Models

Email: fredybar[at]ucm[dot]es
Phone: (+34) 91 394 4375
Room: Physics - 237

Bio

Hi! I’m Fredy Barrientos-Espillco, a Research Scientist and AI Engineer specialized in Deep Learning, Computer Vision, and Generative Models.

Ph.D. in Computer Science from the Complutense University of Madrid (2020–2024), awarded Summa Cum Laude, advised by Professor Gonzalo Pajares and Eva Besada. My research focuses on the development of deep learning algorithms for object detection, semantic segmentation, and generative models, with direct applications in autonomous navigation and environmental monitoring.

I have led lines of research within the framework of high-impact R&D projects, such as SMART-BLOOMS, AI-GES BLOOM-CM, and AMPBAS, funded by the Spanish Government and the European Union. My main contributions include:

  • Designing multi-task CNN architectures that integrate detection and segmentation for complex aquatic environments.
  • The customization of text-to-image diffusion models for synthetic data generation, combined with LLM-assisted prompt engineering, improves training diversity and quality.
  • Developing real-time visual perception algorithms for Autonomous Surface Vehicles (ASVs) in challenging conditions.
I have published in top-tier journals such as Expert Systems with Applications, Applied Soft Computing, and Neural Networks, with contributions on generative modeling, efficient network architectures, and robust scene understanding.

I am passionate about translating cutting-edge AI research into scalable solutions, and my current interests expand towards the synergy of Computer Vision and Large Language Models (LLMs) to tackle next-generation multimodal challenges.

I’m open to collaborations and opportunities where I can contribute to advancing AI research while building impactful, real-world applications.

Publications

A collection of my published research papers.

Customization of the Text-to-Image Diffusion Model by Fine-Tuning for the generation of synthetic images of cyanobacterial blooms in lentic water bodies

F. Barrientos-Espillco, G. Pajares, J.A. López-Orozco, E. Besada-Portas

Expert Systems with Applications, August 2025

This paper presents a novel approach that combines DreamBooth-based fine-tuning of the Stable Diffusion XL model with LLM-driven prompts generation (LLaMa 2) to synthesize realistic images of cyanobacterial blooms and navigational obstacles in lentic water bodies.[paper]

Integration of object detection and semantic segmentation based on Convolutional Neural Networks for navigation and monitoring of cyanobacterial blooms in lentic water scenes

F. Barrientos-Espillco, M.J. Gómez-Silva, E. Besada-Portas, G. Pajares

Applied Soft Computing, September 2024

In this paper we present an architecture based on Convolutional Neural Networks (CNNs) designed to simultaneously address object detection and semantic segmentation in lentic aquatic environments.[paper]

Filter Pruning for Convolutional Neural Networks in Semantic Image Segmentation

C. I. López-González, E. Gascó, F. Barrientos-Espillco, E. Besada-Portas, G. Pajares

Neural Networks, January 2024

This paper presents innovative filter pruning and fine-tuning strategies for CNNs for use in small, resource-constrained devices, such as machine vision systems embedded in Autonomous Surface Vehicles (ASVs), where real-time computation is critical for safe navigation and contaminant detection.[paper]

Semantic segmentation based on Deep learning for the detection of Cyanobacterial Harmful Algal Blooms (CyanoHABs) using synthetic images

F. Barrientos-Espillco, E. Gascó, C.I. López-González, M.J. Gómez-Silva, G. Pajares

Applied Soft Computing, July 2023

This AI Paper proposes the detection of Cyanobacterial Harmful Algal Blooms (CyanoHABs) from the perspective of an Autonomous Surface Vehicle (ASV) using deep learning-based semantic segmentation methods with synthetic images.[paper]

Research Projects

A showcase of my key research and engineering projects.

  • Inspection and maintenance in harsh environments by multi-robot cooperation (INSERTION)

    2022-2025

    Funding: Knowledge Generation Projects. Ministry of Science, Innovation and Universities (PID2021-127648OB-C33).

    The objectives of this competitive project (where researchers from Universidad Pablo Olavide, Universidad Politéctica de Madrid and UCM colaborate) are the development of new techniques for perception, localisation and mapping in difficult environments; robust navigation and precise control of robots in complex environments; and robot-to-robot cooperation for complex scenarios.

  • Beyond the use of digital technologies in cyanobacterial blooms: intelligent management of cyanobacteria using digital twins and edge computing (SMART-BLOOMS)

    2022-2024

    Funding: Ecological Transition and Digital Transition Projects. Ministry of Science, Innovation and Universities (TED2021-130123B-I00).

    This Ecological and Digital Transition competitive project (where researchers from the UCM and the UAM cooperte) aims to improve the digitization of the management process of cyanobacteria appearing in waterbodies y creating a digital twin of the whole system and using an IoT infrastructure with edge computing.

  • Towards a comprehensive system for cyanobacterial BLOOM alert and management in inland waters (IA-GES BLOOM-CM)

    2021-2024

    Funding: Community of Madrid (Y2020/TCS- 6420).

    Development of an IoT system for early warning and efficient characterization of cyanobacteria blooms in dammed waters, using sensors embedded in ASVs guided by AI and Modeling & Simulation techniques.

  • Automatic Monitoring of Pollutants in Dammed Waters using Biosensors and Autonamous Surface Vehicle (AMPBAS)

    2019-2021

    Funding: Challenges - Research. Ministry of Science, Innovation and Universities (RTI2018-098962-B-C21).

    Development of an automatic multi-boat system, equipped with biosensors to detect and alert on contamination by microorganisms in dammed water.

Skills & Expertise

My technical skills and areas of research focus.

Research Areas

  • Computer Vision
  • Deep Learning
  • Generative Models

Programming Languages

  • Python
  • Java
  • C++
  • MATLAB
  • JavaScript

Frameworks & Libraries

  • PyTorch
  • TensorFlow
  • Keras
  • scikit-learn
  • OpenCV
  • NumPy
  • Pandas

Others

I regularly collaborate as a reviewer for several journals.