Gustau Camps-Valls (IEEE Fellow'18, ELLIS Fellow, IEEE Distinguished lecturer, PhD in Physics) is a Full professor in Electrical Engineering and head of the Image and Signal Processing (ISP) group, http://isp.uv.es, at the Universitat de València. His research is related to statistical learning for modeling and understanding the Earth and climate systems.

Francisco is a Research Scientist working at DeepMind in the Data Efficient and Bayesian Learning Team. His research is focused on statistical machine learning, specially probabilistic modeling, approximate inference, generative models, and applications of machine learning to signal processing, mathematics, and computer science. Before joining DeepMind, Francisco was a Postdoctoral Research Scientist in the Department of Computer Science at Columbia University and in the Engineering Department at the University of Cambridge, where he held a Marie-Sklodowska Curie fellowship in the context of the E.U. Horizon 2020 program. He completed his Ph.D. and M.Sc. from the University Carlos III in Madrid.

I am a full Professor on Machine Learning at the Department of Computer Science of Saarland University in Saarbrücken (Germany), and Adjunct Faculty at MPI for Software Systems in Saarbrücken (Germany). I am a fellow of the European Laboratory for Learning and Intelligent Systems ( ELLIS), where I am part of the Robust Machine Learning Program and of the Saarbrücken Artificial Intelligence & Machine learning (Sam) Unit.

Prior to this, I was an independent group leader at the MPI for Intelligent Systems in Tübingen (Germany). I have held a German Humboldt Post-Doctoral Fellowship, and a “Minerva fast track” fellowship from the Max Planck Society. I obtained my PhD in 2014 and MSc degree in 2012 from the University Carlos III in Madrid (Spain), and worked as postdoctoral researcher at the MPI for Software Systems (Germany) and at the University of Cambridge (UK).

Matthias Bauer is a senior Research Scientist at DeepMind and interested in probabilistic machine learning and generative modelling. He completed his PhD at the University of Cambridge and the Max Planck Institute for Intelligent Systems, Tübingen, in 2019. Prior to this, he graduated with an M.Sc. in Physics from University of Munich and a Master of Advanced Studies in Physics from Cambridge University.

Miguel Ángel Fernández-Torres received the Audiovisual Systems Engineering degree, the Master degree in Multimedia and Communications and the PhD degree in Multimedia and Communications from Universidad Carlos III de Madrid, Spain, in 2013, 2014 and 2019, respectively. During the PhD period, his research was related to spatio-temporal visual attention modelling and understanding, applying both Bayesian networks and deep learning. After two years as a teaching assistant at Universidad Carlos III de Madrid, Spain, he works at present as a postdoctoral researcher in the ERC USMILE, H2020 XAIDA and ESA DeepExtremes projects, taking part in the Image and Signal Processing Group at the Universitat de València, Spain. His current research within the area of Machine Learning for Earth and Climate Sciences involves the design and understanding of explainable deep generative models and machine attention mechanisms to be deployed for anomaly and extreme event detection.

In addition to his work on visual attention, he has participated in other projects within the Computer Vision field, which includes image and video analysis, and medical image analysis and classification. He had also the opportunity to study at Technische Universität Wien, Vienna, Austria, during his Bachelor's degree, in 2013, and to do two research stays at …

I am a 3rd year PhD student from Oxford, supervised by Chris Holmes. As part of the StatML CDT, my doctoral studies are generously funded by ESPRC and Novartis. In general, I am interested in robust and safe use of Machine Learning tools to Health Care data. This is why I was super lucky to have interned with the Health Intelligence team at Microsoft Research Cambridge on robust representation learning.

A postdoctoral researcher that works with generative models applied to data assimilation problems in ocean settings.

I am a research scientist at Facebook, working on safe and robust experimentation and causal inference. I am also interested in interpretability and algorithmic fairness. I received my PhD in Statistics from Cornell University, where I was advised by Giles Hooker and Martin Wells, with Thorsten Joachims and Rich Caruana on my committee. I am on the board of the Women in Machine Learning organization. I co-founded the Trustworthy ML Initiative.

Pablo Samuel was born and raised in Quito, Ecuador, and moved to Montreal after high school to study at McGill, eventually obtaining his masters and PhD at McGill, focusing on Reinforcement Learning. He is currently a staff research Software Developer in Google Research (Brain team) in Montreal, focusing on fundamental Reinforcement Learning research, Machine Learning and Creativity, and being a regular advocate for increasing the LatinX representation in the research community. He is also an active musician.

born in Alhama de Almería, Spain, in 1963. He received the Ingeniero de Telecomunicación and Doctor Ingeniero de Telecomunicación degrees, both from the Universidad Politécnica de Madrid, Spain, in 1988 and 1992, respectively. He is a Professor at the Department of Signal Theory and Communications, Universidad Carlos III de Madrid, Spain. Prior to this he has occupied different teaching positions at Universidad de Vigo, Universidad Politécnica de Madrid, and Universidad de Alcalá, all of them in Spain. He has participated in more than 60 projects and contracts and he has co-authored more that 40 journal papers and more than 100 international conference papers. His research interests include signal processing, learning, and information theory methods, and its application to sensor networks, communications, and medical applications.

Jordi Vitrià is a Full Professor the University of Barcelona (UB), which he joined in 2007, and where he teaches an introductory course on Algorithms and advanced courses on Data Science and Deep Learning. He is also the director of the Master in Foundations of Data Science and co-director of the Big Data and Data Science Postgraduate course at UB.

His research, when personal computers had 128KB of memory, was originally oriented towards digital image analysis and how to extract quantitative information from them, but soon evolved towards computer vision problems. After a postdoctoral year at the University of California at Berkeley in 1993, he focused on Bayesian methods for computer vision methods. Now, he is leading a research group working in deep learning, computer vision and machine learning. He has authored more than 100 peer-reviewed papers and holds 8 international patents. He has directed 14 PhD theses in the area of machine learning and computer vision. He has been the leader of a large number of research projects at international and national level.

I am a grad student in my 2nd year, and my research interests lie in theoretical Machine Learning (NTKs, Bayesian DL, Generalization, etc.). Since I have made no research contributions to the community so far, I try to contribute to the community by volunteering and helping the volunteers at conferences (NeurIPS, ICML, and AIStats). In my free time, I like to play Cricket, wander in old Lahore city, read classical novels, fill my Firefox tabs with countless ICLR papers (which I never open again) and support human rights.