Institution name: IBM Research GmbH

Acronym: IBM

Partner coordinators: Mattia Rigotti and Thomas Brunschwiler

Website: https://research.ibm.com/labs/zurich

About

IBM Research Europe – Zurich is a leading research facility and the first European branch of IBM Research, head-quartered at the T.J. Watson Research Center in Yorktown Heights, NY, USA. Founded in Switzerland in 1956, the Zurich lab is part of a global network of 12 research centres and is home scientists from over 45 countries, making it a key player in the European research ecosystem. The lab is known for its innovative research across various fields, with outcomes that significantly contribute to the development of IBM products, including the IBM Environmental Intelligence Suite, the Maximo Suite, and watsonx.
As part of IBM, a global technology and innovation leader based in Armonk, New York, IBM Research Europe – Zurich benefits from the company's extensive resources and expertise. IBM is the largest technology and consulting employer worldwide, with over 350,000 employees serving clients in 175 countries. The company is at the forefront of cognitive computing and cloud-based solutions, helping clients navigate the complexities of a digitally interconnected world. IBM promotes open innovation and responsible technology through various initiatives, incl. the AI Alliance which it co-founded in 2023.

Role

Main Responsibilities: As the leader of Work Package 2 (WP2), IBM Research’s main role in the NATURE-DEMO project is to oversee the development of technological frameworks for the implementation of a risk assessment platform aimed at quantifying the benefit of Nature-based Solutions (NbS).

Key Activities: The organization will engage in creating a digital decision support platform, conducting workshops, and establishing pilot NbS interventions across the various biogeographic regions that are being analyzed in the project.

Expertise and Resources: IBM brings extensive expertise in AI, big data analytics, and geospatial data, supported by an outstanding research team of over 3,000 scientists worldwide.

Expected Contributions: IBM aims to enhance the scalability and effectiveness of NbS through innovative technological solutions, ultimately contributing to improved climate resilience and biodiversity.

Collaboration with Partners: IBM will collaborate closely with the diverse consortium of partners, including academic institutions and industry experts, and in particular with the partners who are involved in WP2.

The team

Thomas Brunschwiler

Current Position: Principal Research Scientist and Manager, AI for Climate Impact (ACT)
Educational Background: Advanced Studies in Computer Science from ETH Zurich and PhD in Electrical Engineering from TU Berlin.
Relevant work experience: Dr. Thomas Brunschwiler is a Principal Researcher and Research Manager leading the AI for Climate Impact team at IBM Research in Zurich. Under his leadership, his team pushes the frontiers of Earth Science Foundation Models, enabling faster insights into climate impacts and transition risks critical to both society and industry.
In addition, Dr. Brunschwiler oversees the development and application of scientific foundation models across the IBM Research laboratories globally. He is the principle investigator of international governmental projects, like FAST-EO (ESA funded) and Embed2Scale (Horizon Europe Project).
He currently serves on the Steering Committee for the Expert Group on AI in Climate & Sustainability at the Swiss Academy of Engineering Sciences – SATW, is recognized as an IEEE Senior Member, and contributes as a reviewer for InnoSuisse, Switzerland’s innovation funding agency.

Mattia Rigotti

Current Position: Senior Research Scientist, Data & AI Platforms
Educational Background: MSc Physics from ETH Zurich and PhD in Neuroscience from Columbia University
Relevant work experience: Dr. Mattia Rigotti is a Senior Research Scientist in the Data & AI Platforms Department at IBM Research. He is the technical lead of the Foundation Models for Visual Inspection Challenge where he’s in charge of Visual Prompting. His work focuses on fundamental machine learning and deep learning, interpretable and explainable AI, and developing multimodal models that combine vision foundation models and LLMs capable of reasoning on specific technical domains.