Co-Adaptive & Evolutionary AI
Evolutionary and coevolutionary methods for optimizing interacting learning systems, including multi-objective, distributed and adaptive learning.
Artificial Intelligence · Evolutionary Computation · Machine Learning
Jamal Toutouh, PhDTenured Associate Professor
Building adaptive AI systems that learn, evolve, and collaborate.
University of Málaga — Tenured Professor
MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) — Research collaboration
My research lies at the intersection of evolutionary computation and machine learning. I investigate how intelligent systems can adapt, compete, cooperate and co-evolve, and how those mechanisms can support efficient, trustworthy and societally useful AI.
Opportunity creates responsibility.
What moves me
“I see academic success not merely as something to accumulate, but as something to circulate.”
I have benefited from education, international collaboration, and people and institutions that opened doors throughout my career. I therefore see academic opportunity as carrying a responsibility: to create knowledge, to make it useful, to open opportunities for others, and to return part of what academia has given me to the communities that make it possible.
Research vision
From evolutionary computation to co-adaptive artificial intelligence
A central question guides much of my current work: what happens when AI systems do not learn in isolation, but learn through interaction with other intelligent systems?
Generative & Agentic AI
Generative models and interacting AI agents capable of learning through competition, cooperation, feedback and structured adaptation.
Trustworthy & Distributed AI
Federated, privacy-preserving and resource-aware learning for environments in which data, computation and decision-making are distributed.
AI for Sustainability
Machine learning and optimization for sustainable cities, mobility, environmental monitoring, climate intelligence and resilient infrastructure.
Current directions
Research that connects methods with meaningful problems
Co-Adaptive Learning
How multiple machine-learning models can jointly evolve and adapt, from coevolutionary generative models to semi-supervised, federated and agentic systems.
Climate & Environmental Intelligence
AI methods that connect environmental observations, machine learning and optimization to improve our understanding of atmospheric and climate-related phenomena.
Sustainable & Intelligent Cities
Machine learning and evolutionary optimization for urban mobility, transportation, resource management, infrastructure and environmental monitoring.
Knowledge in circulation
Academic success should create value beyond the individual
The same principle that shapes my research also shapes how I think about academic life: knowledge becomes more valuable when it moves—between researchers, students, institutions, industry and society.
Mentoring
Creating opportunities for students and early-career researchers to grow through ambitious, supported research.
Scientific Community
Contributing leadership, reviewing, organization and service to the communities that make research possible.
Innovation & Transfer
Helping useful ideas move from academic research toward practical technologies and real-world applications.
Public Engagement
Making AI and scientific knowledge accessible beyond academia through outreach, talks, media and public dialogue.
Teaching & mentoring
Research grows when opportunity is shared
Teaching and mentoring are integral parts of my academic work. I teach across artificial intelligence, machine learning, deep learning, cloud computing and computer science, and supervise research ranging from generative AI and federated learning to climate intelligence and sustainable cities.
PhDSupervision across emerging AI topics
MScResearch-led Master's projects
AIFrom foundations to advanced topics
ExcellentLatest teaching evaluation
International development & public interest
Using data and technology where they can improve public decisions
Since 2016, I have worked as an external consultant on international development-cooperation projects funded by the World Bank, the Inter-American Development Bank (IDB/BID), and AECID. Through initiatives including SIASAR and SANIHUB, I have contributed to information systems, data-analysis models, indicators, service monitoring and planning-support tools. My broader public-interest work also includes collaboration with NGOs such as the Red Cross and Engineers Without Borders.
Collaboration
Let’s work together
I am interested in research collaborations, doctoral supervision, visiting opportunities, invited talks, academic initiatives, technology transfer and responsible applications of AI.