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Archives: Team

Alessio Santamaria

I am a highly motivated student currently pursuing a Master in Management at ESCP Business School, specialising in New Space Economy and Finance. I hold a Bachelor’s degree in Economics and Management from the European University of Rome, where I graduated with top honours after completing a thesis on the Space Economy and Public Finance.

Through my academic and professional experiences, I have developed a strong interest in innovation, commercialisation and public-private cooperation in the space sector, with a particular focus on Earth Observation and emerging New Space markets. I have also strengthened my communication and intercultural skills through academic and volunteering activities in international environments.

I am eager to contribute to Earth Observation innovation and to explore how data-driven solutions and business development can support a competitive European space ecosystem.

Marvin Burges

Marvin Burges is an Internal Research Fellow at ESA Phi-lab, where he works on machine learning and computer vision for Earth observation. He holds a Diplom in Computer Science (Dipl. Inf.) from TU Dresden and is finalizing his Dr. techn. at TU Wien, where his doctoral research focuses on interactive and active learning based object detection in remote sensing imagery, with applications ranging from bomb crater detection in WWII era aerial images to detecting greenhouses and waffle homes for damage assessment and population modeling. During his PhD, he completed an internship at Oak Ridge National Laboratory (USA), combining foundation models with active learning for efficient dataset annotation. His work emphasizes human in the loop approaches to enhance model reliability and accessibility, including the development of QGIS plugins for domain experts and user studies to evaluate model usability. He has published at venues such as WACV and ICCV.​​​​​​​​​​​​​​​​

Johannes Jakubik

I am a Research Scientist within the AI for Climate Impact team at IBM Research Europe. In my role, I am leading projects on multimodal generative modeling and am co-leading IBM Research’s activities on deep learning for planetary observations. I especially focus on pretraining and scaling multi-modal deep learning models in collaborations with NASA, ESA, and within the EU Horizon program. In addition, I’m co-leading work at the intersection of deep learning and quantum graph optimization.

My work on deep learning for planetary observations and weather modeling has been awarded with the NASA Agency Group Award, NASA Marshall Space Flight Center Honor Award, several IBM accomplishment awards, and was featured in international and national media. As part of my work, I am fortunate enough to co-supervise several exceptionally bright Ph.D. students at ETH Zurich and Trinity College.

I graduated from KIT and ETH, where my research spanned across all relevant subfields of deep learning-based systems: data-centricity, model-centricity, and human-centricity. My thesis on data-centric AI has been awarded with summa cum laude and a national-level dissertation award by the VDE – one of the largest technology organizations in Europe. I also received a best paper award, and a best paper award nomination for theoretical contributions to human-centric AI. In fall 2024, I was fortunate enough to be recommended as a top candidate for a tenure track professorship at a German university of excellence. Together with a range of amazing co-authors, my work has been published in highly recognized journals and conferences.

Ivar van der Spoel

Ivar van der Spoel is a Dutch Computer Science master’s student at Leiden University, where he completed his Bachelor’s degree in Computer Science in 2024. He has a special interest in Artificial Intelligence, particularly Neural Architecture Search, deep learning, and automated machine learning, alongside a solid foundation in Advanced Computing and Systems, spanning low-level topics such as computer architecture, operating systems, and CUDA programming, to high-level topics such as distributed and cloud systems.

Ivar is currently completing his master’s thesis as a visiting researcher in collaboration with ESA Φ-lab, where he investigates robustness in Neural Architecture Search for Earth observation. His research addresses the domain gap between simulated and real-world satellite data by incorporating realistic degradations and applying multi-objective optimization to develop robust and reliable models.

Clément Gilli

Clément Gilli is a final-year student at Télécom Paris and the MVA Master’s degree (ENS Paris-Saclay), specializing in Computer Vision and Signal Processing. With a strong interest in the space sector and Earth Observation, he focuses on applying his technical expertise to these fields, aiming to contribute to projects with a positive societal impact.

Damian Borth

Prof. Dr. Damian Borth is director of the Institute of Computer Science at the University of St.Gallen, where he holds a full professorship in Artificial Intelligence and Machine Learning (AIML). Previously, Damian was the founding director of the Deep Learning Competence Center at the German Research Center for Artificial Intelligence (DFKI) in Kaiserslautern, where he was also PI of the NVIDIA AI Lab at the DFKI.

He‘s research focuses on representation learning of neural network’s weight spaces and deep learning in domains such as computer vision or remote sensing. His work has been awarded with the ACM SIGMM Test of Time Award 2023, the HSG Impact Award 2022, the Google Research Scholar Award 2022, the Best Student Paper Award 2022 at CVPR Earth Vision Workshop, the NVIDIA AI Lab at GTC 2016, the Best Paper Award at ACM ICMR 2012, and the McKinsey Business Technology Award in 2011. Currently Damian serves as the member of the board of trustee at the International Computer Science Institute (ICSI) in Berkeley, California, the AI Advisory Board of Ringier Media Group, Zurich, the scientific advisory board of the Roman Herzog Institute, Munich. Previously, he was member of the board of the German Data Science Society (GDS), member of the review group at the AI program of the VolkswagenStiftung, and member of review committees at the Baden-Württemberg Stiftung, reviewer of the Helmholtz Association Data Science Schools, European Union ERC, DFG, US-DOE, and several other program committees of international conferences and workshops such as Nature, NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV, KDD, WACV, and ACM ICAIF.

Damian did his postdoctoral research at UC Berkeley and the International Computer Science Institute (ICSI) in Berkeley, where he was involved in big data projects at the Lawrence Livermore National Laboratory (LLNL). He received his PhD from the University of Kaiserslautern and the German Research Center for Artificial Intelligence (DFKI). During that time, Damian stayed as a visiting researcher at the Digital Video and Multimedia Lab at Columbia University, New York City, USA.

Tat-Jun Chin

I am Tat-Jun Chin, a Professor in the field of Computer Vision with a solid foundation in Machine Learning, mathematical optimization and computational geometry. My professional journey began at Monash University, where I earned my PhD degree in Computer Systems Engineering with a specialization in kernel learning methods for computer vison. With my skills, I have contributed as an academic staff member to the School of Computer Science at The University of Adelaide for 18 impactful years.

Silvia Ullo

I am Silvia Liberata Ullo, a professor and researcher in the field of Quantum Machine Learning applied to Remote Sensing with a solid foundation in Statistics and Signal Processing. My professional journey began at the University Federico II of Naples (Italy) in 1989, and then at the Massachusetts Institute of Technology (MIT) of Cambridge (U.S.A.), where I could earn my expertise both in engineering and management. With my skills, I have contributed first to ITALTEL S.p.A., a telecommunications company, for about 10 years, and to a public Municipality for 4 years. In 2004, I joined the Engineering Department of the University of Sannio.

Sébastien Lefèvre

Sébastien Lefèvre holds a Master of Science (1999), a Ph.D. (2002), and a Habilitation (2009) in Computer Science. He has been a Full Professor at Université Bretagne Sud since 2010, promoted to the exceptional class by the National Council of Universities in 2023. He is also Adjunct Professor at UiT – The Arctic University of Norway and Visiting Professor at ESA Phi-lab.

His work focuses on Artificial Intelligence for Earth and Environmental Observation. He founded the OBELIX team at IRISA and currently chairs the GeoData Science track of the EMJM Copernicus Master in Digital Earth. He co-founded the ECML-PKDD MACLEAN workshop series, chairs the AI4EO 2025 symposium, coordinates the UBS–JRC Doctoral Program on AI4EO, and holds the PANORAMIX chair within the SequoIA AI cluster (2025–2029).

He serves on the scientific committee of IGN – the French Mapping Agency, and is active in ELLIS, IEEE GRSS, ISPRS, and IAPR. His research interests include image analysis and deep learning for remote sensing. As Earth Observation requires more than standard AI solutions, Lefèvre aims to conduct cutting-edge research in Artificial Intelligence to develop novel, efficient, scalable, and responsible solutions to address high-impact tasks using complex remote sensing data.

Rossella Arcucci

Rossella Arcucci is a mathematician, machine learner, and societal engineer. She obtained her Master’s degree in Applied Mathematics in 2008 from the University of Naples Federico II and her PhD in Computational and Computer Science in 2012.

Professor Arcucci has been actively involved in operational research since her PhD, continuing through her first postdoctoral position at the Euro-Mediterranean Centre on Climate Change.

Her research addresses fundamental questions on how to effectively use big data, improve its reliability, and reduce uncertainty in predictive model development to extract meaningful features and actionable outcomes. She pioneered the integration of Data Assimilation with Machine Learning, creating the field of Data Learning. Her work spans diverse areas including weather prediction, wildfire modelling, flood nowcasting, healthcare, and epidemic control, all with a focus on saving human lives in crises.