HIDA CIFAR Summer School on Data Science & Climate

Speaker information

Ann Gregory

Ann Gregory’s research explores the human virome, the vast collection of viruses residing within and on our bodies. Using both computational and experimental approaches, Ann’s team aims to unravel how these viruses interact with our immune system and other microbes, and how they influence our health. Computationally, her lab is leveraging advanced technology to identify new viruses based on their 3D structures and map viral interactions across the human body to understand their distribution and impact. Experimentally, her lab is isolating gut viruses to develop personalized phage therapies for gut-related diseases. Her lab aims to advance virus discovery and pave the way for novel therapeutic strategies using viruses.

Ariel L. Furst

Ariel L. Furst is the Cook Career Development Professor of Chemical Engineering at MIT. Her lab combines biological, chemical, and materials engineering to solve challenges in human health and environmental sustainability. They develop technologies for implementation in low-resource settings to ensure equitable access to technology. She completed her Ph.D. in the lab of Prof. Jacqueline K. Barton at the California Institute of Technology developing new cancer diagnostic strategies based on DNA charge transport. She was an A. O. Beckman Postdoctoral Fellow in the lab of Prof. Matthew Francis at UC, Berkeley developing sensors to monitor environmental pollutants. She is the recipient of the NIH New Innovator Award, the NSF CAREER Award, the Dreyfus Teacher-Scholar Award, and the Sloan Fellowship. She is the cofounder of three startups: Seia Bio, Helix Carbon, and Ouroloop. She is passionate about STEM outreach and increasing participation of underrepresented groups in engineering.

Dagmar Kainmueller

Dagmar Kainmueller heads the Biomedical Image Analysis Lab at the Max-Delbrueck-Center for Molecular Medicine Berlin. Her team pursues theoretical advances in Machine Learning to solve challenging image analysis problems in biology, with a focus on cell segmentation, classification, and tracking.

Maximilian Gelbrecht

Researcher at the interface of machine learning, dynamical system theory and climate science.

2009-2013: B.Sc. Physics / Mathematics, Humboldt-Universität zu Berlin
2013-2016: M.Sc Physics, Humboldt-Universität zu Berlin 2017: Scientific Assistant, Potsdam Institute for Climate Impact Research
2017-2021: Research Fellow/Ph.D Student, Department of Physics, International Research Training Group (IRTG) 1740, Humboldt-Universität zu Berlin with PhD Thesis on “Physics-based machine learning approaches to complex systems and climate”, supervisor Prof. Jürgen Kurths
2017-2024: Guest Scientist, Potsdam Institute for Climate Impact Research
2018: Guest Scientist, Institute for Astronomy, Geophysics and Atmospheric Sciences, Universidade de São Paulo
2021: Postdoctoral Researcher, Institute of Mathematics, Freie Universität Berlin
2022-2025: Postdoctoral Researcher, Earth System Modelling Group, Technische Universität München

Selected Publications:

M. Gelbrecht, A. White, S. Bathiany, N. Boers: Differentiable Programming for Earth System Modeling, Geoscientific Model Development, 2023
A. White, N. Kilbertus, M. Gelbrecht, N. Boers: Stabilized Neural Differential Equations for Scientific Machine Learning with Conservation Laws, NeurIPS, 2023
M. Gelbrecht, N. Boers, J. Kurths: Neural Partial Differential Equations for Chaotic Processes, New Journal of Physics, 2021
M. Gelbrecht, N. Boers, J. Kurths: Phase coherence between precipitation in South America and Rossby waves, 2018

Mike Sips

Mike Sips is a senior researcher with a professional background in Computer Science. His research focuses on supporting domain scientists in solving data-intensive problems through data science, algorithmic, and Explainable AI methods. Mike is responsible for the Big Data Analytics and Explainable AI group at GFZ, where he collaborates with a fantastic interdisciplinary team on diverse projects. Mike’s research is grounded in the belief that collaboration between computer scientists and domain scientists drives innovation. He actively promotes Open Science, and his team releases systems as open-source software, including ClarifAI – an interactive XAI system, and PyRQA – a fast toolkit for recurrence quantification analysis.

More information about Mike and his research interests can be found here.

Peer Nowack

Peer Nowack leads the Chair for Artificial Intelligence (AI) in Climate and Environmental Sciences at the Karlsruhe Institute of Technology (KIT), Germany. His group works on a diverse range of scientific challenges in climate science, combining machine learning techniques, numerical Earth system models, and Earth observations (e.g., satellite data). Before taking up his position at KIT, he worked for almost 10 years in the UK. Most recently, he held a permanent position as Lecturer in Atmospheric Chemistry and Data Science at the University of East Anglia (UEA), which he took up in 2020 after leading a junior research group at Imperial College London. Peer holds a PhD from the University of Cambridge, which he completed following his undergraduate studies in Interdisciplinary Sciences at ETH Zurich (Switzerland). During summer 2022, he was a guest researcher at the National Center for Atmospheric Research (USA).

Sigrid Roessner

Sigrid Roessneris a Senior Scientist at GFZ Potsdam and based in Section 1.4 – Remote Sensing and Geoinformatics. Her background is in Geography and her research focus on Applied Remote Sensing and GIS in the fields of natural hazards, geomorphology, physical geography and urban ecology. She has been involved in interdisciplinary remote sensing research using a variety of remote sensing data including optical and radar systems. One of her research foci is the spatiotemporal analysis of landslide processes by remote sensing based time series analysis for improved hazard assessment.

Stefan Bauer

Current AI systems are limited in their ability to understand the world around us, as shown in a limited ability to transfer to new problems or lack of skill in applying known tools in an unknown scenario. This is a shared problem for many established approaches, which mostly focus on predictability when inferring patterns and structure from data. Stefan Bauer’s key research goal is to design machines that can extrapolate experience across environments and tasks by learning independent mechanisms that can flexibly be used, composed and re-purposed.

Wolfgang Graf zu Castell - Rüdenhausen

Wolfgang zu Castell holds a professorship for Geodata Science at the Ludwig-Maximilians Universität München. He has a PhD in mathematics from Friedrich-Alexander University Erlangen-Nürnberg and has long taught at the Technical University of Munich. Since November 2021, he serves as Director of the Department Geoinformation and CIO at the GFZ German Research Centre for Geosciences. He chairs the Open Science Working Group of the Helmholtz Association and contributes to several strategic initiatives such as the Helmholtz Metadata Collaboration, Helmholtz Imaging, the National Research Data Infrastucture consortium NFDI4Earth, as well as the DataHub of Helmholtz Earth & Environment. His research focuses on data-driven analysis of complex systems, kernel methods, mathematical imaging, and applied data science, alongside interests in open science and digital transformation.