Skip to content
Technical University of Munich

Dr. Andrés Camero

Tel:+49-(0) 8153 28-1033
Room: DLR-EOC 0058
E-mail:andres.camerounzueta <at> dlr.de
More information:Google Scholar
LinkedIn

Artificial Intelligence researcher, passionate about solving real-world problems, with 10+ years of academic
and professional experience. Possess a strong technical background, a proven ability to work in a multicultural environment, and experience in team leadership.

Curriculum Vitae

  • July 2020 to present, Helmholtz AI Consultant Team Leader @ German Aerospace Center (DLR), https://www.helmholtz.ai/
  • July 2020 to present, Visiting Senior Scientist, Data Science in Earth Observation, TUM
  • 2017 - 2020, Researcher, Lenguages y Ciencias de la Computación, Universidad de Málaga
  • 2019 - 2019, Visiting Scientist, LIACS, Leiden Univeristy
  • 2015 - 2016, Co-funder, AIS Networks
  • 2012 - 2014, IT Project Manager, LATAM Airlines Group
  • 2012 - 2012, Research Manager, Minera Los Pelambres
  • 2010 - 2012, Customer Intelligence Project Manager, LAN Airlines
  • 2009 - 2010, Customer Intelligence Analyst, LAN Airlines

Research Projects and Grants

  • SURF: (Semi/Un)supervised machine learning flood damage assessment (2022 - 2025). Funded by Helmholtz AI Project Call 2021. Role: PI. Amount: 200K € (from total amount 400K €).
  • Automatic detection of coastline change and causal linkage with natural and human drivers (AutoCoast) (2023 – 2025) Funded by Helmholtz Imaging Platform Project Call 2022. Role: Co-PI. Amount 155K € (from total amount 400K €).

Research Interests

  • AI4EO
  • Neuroevolution
  • Evolutionary Algorithms
  • Machine Learning
  • Smart City

Key Publications

  • Camero A., Wang H, Alba E, Bäck T (2021). Bayesian Neural Architecture Search using A Training-Free Performance Metric. Applied Soft Computing, 106, pp. 107356.
  • Camero A, Toutouh J, Stolfi DH, Alba E. Evolutionary Deep Learning for Car Park Occupancy Prediction in Smart Cities. In International Conference on Learning and Intelligent Optimization 2018 (pp. 386-401). Springer, Cham.
  • Camero A, Alba E. Smart City and information technology: A review. cities. 2019 Oct 1;93:84-94.
  • Camero A, Toutouh J, Alba E. Low-cost recurrent neural network expected performance evaluation. arXiv preprint arXiv:1805.07159. 2018 May 18.
  • Camero A, Toutouh J, Alba E. Random error sampling-based recurrent neural network architecture optimization. Engineering Applications of Artificial Intelligence. 2020 Nov 1;96:103946.

Awards

  • Doctoral Consortium CAEPIA 2018
    Doctoral thesis project: “Neuroevolución profunda: aplicaciones en ciudades inteligentes” (Deep neuroevolution: Smart City Applications), Second Prize. Conference of the Spanish Association for Artificial Intelligence (CAEPIA) 201.

Other Relevant Activities

  1. Co-organization of community building activities.
    1. DynamicEarthNet Challenge, DLR, TUM, Planet. EarthVision / CVPR, 2021.
    2. Enhanced Sentinel-2 Challenge, ESA, DLR, TUM. ESA, 2021.
    3. AI4FoodSecurity Challenge, Planet, DLR, TUM, Radiant Earth, hosted by ESA. ESA, 2021/22.
    4. Helmholtz AI CountMeIn Challenge, DLR, FZJ, KIT. Helmholtz Data Challenges, 2022.
    5. ML4Earth xAI Workshop and Hackathon, DLR, TUM. 2022.
  2. Reviewer.
    1. Dutch Research Council.
    2. Helmholtz Information & Data Science Academy (HIDA).
    3. Journals ASOC, EAAI, TEVC.
    4. WekEO Copernicus Jupyter Notebook Competition 2022
  3. Conference support.
    1. Program committee GECCO NEWK 2021, 2022.
    2. IT support CAEPIA 2020/21.
To top