Research Application Evaluation Clara Bersch

to my


PhD Student

technology & psychology

Working at the Max Planck Insitute in Berlin
| Center for Humans and Machines

Research Focus

With expertise in computer science and psychology, I explore applications for efficient and collaborative knowledge engineering, real-time information extraction and processing to facilitate human-machine and human-human interaction, reasoning and decision making.


  • Concept Learning in Humans and Machines
  • Natural Language Processing
  • Ontology Learning
  • Knowledge Graphs
  • AI for Civic Engagement
  • Climate Change Applications
  • Blockchain
  • Complex Systems

Tools I use

  • Machine Learning
  • Natural Language Processing
  • Information Extraction
  • Multilevel Analysis



About me


passion & profession




  • Clara N. Bersch,
    PhD Student


  • Studies:
    Psychology B.Sc. in Düsseldorf and Psychology M.Sc. in Cologne with studies abroad in England and the United States.
    Finishing my Computer Science B.Sc. alongside my PhD in Computer Science.

  • Skills:
    Python, R, Java, JavaScript, TensorFlow, PyTorch, Flutter, Dart, Node.js, ReactJS, PHP, Blender, Adobe Photoshop, Illustrator and Premiere Pro

  • Passion:
    AI Explainability think tank Berlin



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Knowledge Engineering

There are three dominant criteria for addressing grand challenges: Knowledge, Multi-stakeholder Collaboration, and Citizen Co-creation. In my research, I investigate methods for collaborative ontology and knowledge engineering, information extraction at different levels of granularity, and application design for intuitive human-machine interaction and effective facilitation of human reasoning and decision making.

AI & Smallholder Farming

Within a GIZ and SDG funded project called "AgriPath" we utilize smart technologies to bring personalized, context-relevant solutions for sustainable agriculture directly to farmers’ smartphones. We develop and test tools together with local farmers and developers to serve the conditions met in rural areas as model predictions and recommendations need to translate into results that farmers of all literacy levels can use to make better decisions about their crops. A special focus lies on how to introduce bottom-up knowledge generation and sharing through digital pathways.
AgriPath Website Watch Video