Function and Responsibilities:

• Scientific leader of the research group "Big-Data Analytics and Explainable AI"
• Research Scientist in the 3-D ABC Foundational Model Project of the Helmholtz Association
• Research Scientist in the subproject "Efficient Execution of DAW for forest disturbances" within the DFG priority program FONDA – Foundations of Workflows for Large-Scale Scientific Data Analysis (DFG Collaborative Research Center 1404 at Humboldt-Universität zu Berlin)
  

Research Interests:

I collaborate with a fantastic team on diverse data-intensive science projects. Our expertise spans adopting data mining methods and machine learning models to fit specific data-intensive scenarios, developing explainable artificial intelligence (XAI) methods, and developing computational concepts for big data.

• I collaborate with Yulia Grushetskaya and Qian Song on pioneering new methods in Explainable Artificial Intelligence (XAI). Our work focuses on developing model-agnostic XAI techniques and specialized XAI methods for specific models, such as transformers. We aim to improve ML models' interpretability and transparency and make them more accessible and understandable for everyone. You can assess our methods through our XAI system called ClarifAI
• I contribute with Qiang Song to the Helmholtz Foundational Model Project 3D-ABC
• I collaborate with Magdalena Stefanova Vassileva and Mahdi Motagh to build the interactive Visual Analytics System "MultiScale4Slow" for better understanding the underlying processes of landslide events
  

In our projects, we develop innovative systems that assist scientists in key analytical steps of data-intensive science:

1. Understanding the properties and characteristics of scientific data.
2. Extracting structures and patterns from data using machine learning and artificial intelligence.
3. Explaining results to users through effective visual encodings of models, residuals, and data.

My experience has shown that the true power of our work lies in the collaboration between computer scientists and geoscientists. The synergy of collaborations leads to the development of novel methods and results, expanding scientific methodologies in both disciplines through the mutual influence of diverse scientific perspectives.

We follow the concept of Open Science and publish our research systems as open-source software and support the use and dissemination of our systems

• Fast recurrence analysis with PyRQA
• XAI-System ClarifAI
  

• February 2025: Together with Yulia Grushetskaya (GFZ), Thomas Kosch (Humboldt Universität zu Berlin), and Panagiotis Papapetrou (Stockholm University), I am organizing the workshop "User-Centered Explanations in XAI (UCEX-XAI)" at the 28th European Conference on Artificial Intelligence in Bologna from October 25-October 30.This is a great opportunity to bring together researchers and practitioners to advance user-centered explainability in AI, fostering insightful discussions and new collaborations (Link).
• January 2025: Our Hydrological Informatics Conference (HIC) "Development of a Digital Workflow for Layer Reduction Strategies and Visual-Analytical Outcome Analysis to Enhance Geo-Hydraulic Modeling Efficiency" is accepted as regular paper in the journal Digital Water: Knowledge Application & Hydroinformatics (Link)
  
• December 2024: Member of international program committee of EuroVis 2025
• June 2024: Presentation and demonstration of the ClarifAI system to the Helmholtz AI community at HAICU 2024
  
• May 2024: Our scientific paper "HPExplorer: XAI method to explore the relationship between hyperparameters and model performance" was accepted for the applied data science track at the European flagship conference on machine learning and data mining ECML PKDD  (Link)
• May 2024: Our method for reducing complexity of geo-hydraulic models are out: "Development of a Digital Workflow for Layer Reduction Strategies and Visual-Analytical Outcome Analysis to Enhance Geo-Hydraulic Modeling Efficiency" at Hydrological Informatics Conference (HIC)
  
• April 2024: Presentation and demonstration of the ClarifAI system to the geo-scientific community at EGU 2024
  
  

Career:

• 2010-present: Scientific leader (tenured) of the research group "Big Data Analytics"

Education:

• Master (2001 Martin-Luther-Universität Halle) und PhD (2005 Universität Konstanz) in computer science. 
• Stanford University (2006-2008) and Max-Planck-Institut Informatik (2008-2010)

Projects:

• GeoMultiSens: Scalable multisensor analysis of remote sensing data"
•  SEVA: Scalable exploratory change detection for big Sentinel-2 data"

Awards:

• 2006-2010: Research stipend of the Max-Planck-Centers für Visual Computing with a two year research visit of Stanford University (Palo Alto, USA) und Max-Planck Institut Informatik (Saarbrücken)
• 2006: EADS Dornier Research Award for outstanding PhD thesis.
• 2006: Winner of the IEEE  InfoVis Contest
  
• 2002: Summer Internship mit AT&T Shannon Research Laboratory, Florham Park, NJ, USA.