Data science and machine learning for climate research
While we are collecting and storing more and more data, understanding and interpreting the underlying processes and relationships between different observations remains a challenging problem. The Future Earth Research School on “Data Science and Machine Learning for Climate Research” aims to provide a comprehensive overview of the mathematical foundations of machine learning, with a particular focus on methods for the analysis of complex dynamical systems and time-series data. The course will cover the following topics:
- Reduced order modeling and system identification,
- Koopman operator theory,
- kernels and Gaussian processes,
- deep learning,
- quantum computing for classical dynamical systems,
- stochastic differential equations and Markov state models,
- agent-based models,
- applications in climate science and sustainability.
The Summer School is mainly geared towards PhD students and postdocs in Mathematics or Climate Science with a strong interest in data science and machine learning. The course requires basic programming experience in Python (e.g., numpy, scipy, matplotlib).
The course will include lectures given by leading researchers in the fields of machine learning, dynamical systems theory, statistics, and climate science as well as seminars, hands-on sessions, and group projects. The lectures will take place in the morning, while the afternoons are reserved for tutorials, programming exercises, discussions, and group work. The course requires basic programming experience in Python (e.g., numpy, scipy, matplotlib).
One of the main objectives is to learn about different modeling frameworks and data-driven methods for the analysis of large data sets. Applications include model reduction (e.g., identifying dynamically relevant variables), forecasting, control, and change-point detection. The goal of the course is to first learn about state-of-the-art machine learning methods, to implement these methods in Python, and to then apply them to real-world problems. Furthermore, we will discuss novel applications of machine learning approaches, but also limitations and open problems.
Stefan Klus – Director of the Course
Stefan Klus is an associate professor in mathematics at the Heriot-Watt University, Edinburgh. He has several years of experience developing data-driven methods for the analysis of high-dimensional dynamical systems, with applications in molecular dynamics, fluid dynamics, quantum mechanics, agent-based modeling, and climate science. Before he joined the Heriot-Watt University, Dr. Klus worked at the University of Surrey, the Freie Universität Berlin, and the United Technologies Research Center, where he provided expertise in the areas of data science, machine learning, parallel computing, spectral and algebraic graph theory, and control of multi-agent systems, focusing on the analysis and optimization of numerical algorithms.
Houman Owhadi
Houman is professor of applied and computational mathematics and control and dynamical systems at the California Institute of Technology. His interests include uncertainty quantification, numerical approximation, statistical inference/learning, data assimilation, stochastic and multiscale analysis. His research is focused on solving numerical approximation problems as learning problems, learning problems as numerical approximation problems, and uncertainty quantification problems as adversarial games. He was a plenary speaker at SIAM CSE 2015, a tutorial speaker at SIAM UQ 2016, the recipient of the 2019 Germund Dahlquist Prize (SIAM), and a SIAM Fellow (class of 2022). His research is supported by DARPA, the Department of Energy, NASA/JPL, AFOSR, ONR, National Nuclear Security Administration, Los Alamos National Laboratory, Beyond Limits, and the National Science Foundation.
Feliks Nüske
Feliks Nüske received his Ph.D. in applied mathematics from Freie Universität Berlin in 2017. He then joined the Center for Theoretical Biological Physics at Rice University, U.S., for a postdoc. After a second postdoc in the Department of Mathematics at Paderborn University, Germany, he joined the Max-Planck-Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany, as a research group leader. His research is on data-driven methods for modelling and simulation of molecular systems.
Jürgen Kurths
Jürgen Kurths is a mathematician and a physicist. He received the Ph.D.degree from the GDR Academy of Sciences and his Dr. habil. from the university of Rostock.. He was a Full Professor with the University of Potsdam, from 1994 to 2008. He has been a Professor of Nonlinear Dynamics at the Humboldt University, Berlin, and the Chair of the Research Domain Complexity Science of the Potsdam Institute for Climate Impact Research, since 2008.
He is a Fellow of the American Physical Society, of the Royal Society of Edinburgh and of the Network Science Society and a member of the Academia Europaea. He received an Alexander von Humboldt Research Award in 2005 and 2021, the Richardson award from the European Geoscience Union in 2013 and the Lagrange Award in 2022.
The primary research interests of Jürgen Kurths include complex systems science, in particular synchronization, complex networks, and time series analysis and its applications in Earth Sciences, Physiology, engineering and others. Main recent studies are on inferring networks from data, improved predictions of extreme climate events, generalized stability concepts for and design of modern power grids, influence of Le´vy noise on complex systems and hypernetworks.
Sarah Wolf
Sarah Wolf leads a MATH+ junior research group on „mathematics for sustainability transitions” at Freie Universität Berlin, where she works on agent-based modelling of complex socio-economic, -ecologic, and -technical systems and embeds these models and their simulations in stakeholder dialogues in the Decision Theatre format. Her research topics include sustainable mobility, as well as mechanisms behind a possibility of transitioning to Green Growth. While her background is in mathematics, she has worked in interdisciplinary research teams ever since her PhD studies, first at the Potsdam Institute for Climate Impact Research and later at the Global Climate Forum, now at FU Berlin.
Cristiano De Nobili
Cristiano is a Theoretical Particle Physicist with a PhD in Quantum Information Theory at SISSA. Currently is the Lead AI Scientist at Pi School where he helps startups and companies to integrate AI in their process. He is a Deep Learning lecturer at the Master in High-performance Computing (Trieste) and at Bicocca University (Milan). At Pi School, he is mainly working in NLP and Deep Learning applied to environmental challenges. Last September, he took part in a scientific collaboration at the SIOS Remote Sensing Center in Svalbard to teach AI techniques to Arctic scientists. Currently he is part of a collaboration with the National Observatory of Athens and Pi School, working on Explainable AI for Wildfire Forecasting. He is highly interested in Quantum Technologies, especially in the Climate Tech sector. He just launched a newsletter on AI and Emergent Technologies: Turning bits into dreams.
Location
The course will be held in The University Residential Center of Bertinoro
Bertinoro is situated halfway between the cities of Forlì and Cesena, 6 km away from SS9 (Via Emilia). Forlì is the town of reference for transport by train and bus to and from Bertinoro.
Accomodation
The accomodation will be at the University Residential Center of Bertinoro. Please remember that participation in presence is mandatory.
The school is offering a limited financial assistance to cover the accomodation costs at the Center of Bertinoro over the entirety of the two weeks stay. If you are interested, please check the section below.
For those who won’t get the financial assistance, all the costs will be comunicated during the selection process.
Lunches and dinners will be provided and offered by the school for all the students. Students are free to organize themselves at their own expense upon notice. The school will not cover any extra costs.
Transport
Nearest airport: Bologna’s airport “Guglielmo Marconi”
Nearest train station: Forlì Station (20 min. far from Bertinoro by car)
On how to reach the Centre, please consult this link
Given that most of the students will arrive in Bologna – especially from abroad – the school will organize a shuttle who will bring students from Bologna to Bertinoro. More details will be given to the selected candidate.
The school offer a limited financial assistance, based on the applicant’s needs. It will cover accommodation expenses in Bertinoro. If interested, please specify it in the application form in the dedicated section. You can also leave a statement (max 200 words) explaining why you should benefit from this assistance.
The assistance WILL NOT cover travel costs.
Since there is a limited number of places, you are encouraged to apply early to avoid disappointment.
If accepted, you will receive an email confirming your financial assistance by secretariat@fersschool.it