Dr. David Camacho is currently Full Professor at Technical University of Madrid (Computer Science Department), and leads the Applied Intelligence & Data Analysis (AIDA: https://aida.ii.uam.es) group. AIDA is a specialized group in the application and new development on both, artificial intelligence (machine learning) and data mining techniques. He has published more than 350 research works, international journals (100 mostly JCR-SCI indexed), books, and conference papers.
His expertise comprises: Big Data; Machine Learning: Clustering, Hidden Markov Models, Classification and Deep Learning; Computational Intelligence: Evolutionary computation, Swarm Intelligence; Pattern and Process modeling and mining; Graph Computing and Social Mining, and Data Analysis for complex industrial applications for companies, such as: Airbus Defence & Space, Codice Technologies, ImpactWare, or Jobssy S.L among others.
Selected projects are: “tracking tool based on social media for risk assessment on radicalisation” (RiskTrack: http://www.risk-track.eu/en/), “Stardust Reloaded (Stardust-R) EU (813644-H2020-MSCA-ITN-2018), ” Strengthening European Youngsters Resilience through Serious Games (YoungRes) EU ISFP-2017-AG-RAD – Radicalisation, “CYBERSECURITY: data, information and risks” (CIBERDINE:http://aida.ii.uam.es/projects/ciberdine), and “New Bio-inspired computational models for Massively Complex Environments (DeepBio)” (https://deepbio.wordpress.com/). Related to Erasmus+ projects, Dr. Camacho has participated in two different proposals as UAM-PI: “Improving Sociocultural Outcomes for Students in the Higher Education through Participation on Virtual Mobility” (UbiCamp: http://ubicamp.uniovi.es/) and “Saving the dream of a grassroots sport based on values” (SaveIT: http://saveitproject.eu/saveit/). 
He has participated in the organization of many conferences in the field of data mining, computational intelligence, and machine learning such as: IEEE CEC, GECCO, EVO*, IEEE SSCI, IDEAL, IDC, DEXA, etc. He has participated as Guest Editor of high quality journals as Information Fusion, Soft Computing, Future Generation of Computer Systems, or Ambient Intelligence and Humanized Computing among others,  and has participated as Associate Editor at eight different Journals (IEEE Transactions on Education, Applied Intelligence, International Journal of Bioinspired Computation, etc.).

Areas of expertise

  • Unsupervised Machine Learning: Clustering, Hidden Markov models,
  • Suervised Machine Learning: classification (ensembles), Deep learning
  • Evolutionary computation
  • Swarm intelligence: Ant Colony Optimization
  • Graph computing
  • Social Network Analysis, Social mining
  • AI-based applications (Space, IoT, Health, Fake news)
  • Big Data