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The Multimodal Modelling and Machine Learning (M3L) Team

Tisch bei einer Besprechung von oben
Rolf K. Wegst

Below, you can find a list of current and past members of the research group Multimodal Modelling and Machine Learning. Clicking on a person's name gives access to a summary of their main activities and research interests, further information is available on the linked personal pages. 

Current Team Members

  • Prof. Dr. Ralph Ewerth

    Since April 2025, Prof. Dr. Ralph Ewerth been chair of "AI -- Multimodal Modelling and Machine Learning" at the Department of Mathematics and member of the Hessian Center for Artificial Intelligence (hessian.AI). He has been Head of the Visual Analytics Research Group at TIB -- Leibniz Information Centre for Science and Technology and is affiliated with the L3S Research Center at Leibniz University Hannover since 2015 and 2016, respectively. From 2015 to April 2025, he was Professor at Leibniz University Hannover. From 2012 to 2015, he was Professor for Image Processing and Media Technology at Jena University of Applied Sciences. He completed his PhD in 2008 at Marburg University with the thesis "Robust Video Content Analysis via Transductive Learning Methods." His research focuses on multimodal analysis, multimodal information retrieval, computer vision, and visual analytics with applications in education, psychology, social media analysis, and digital humanities.

  • Omkar Gavali

    Omkar is a Research Software Engineer and PhD candidate specializing in machine learning and multimodal information retrieval. He is part of the DFG-funded project Visual Analytics für Bilder aus kolonialen Kontexten (VaBiKo), where he develops research software and conducts studies on integrating visual and textual understanding to enhance information access and discovery. His doctoral research, supervised by Prof. Dr. Ralph Ewerth, focuses on multimodal information retrieval. He holds a Master’s degree in IT Business & Digitalization from HTW Berlin. His master’s thesis, "Information Retrieval on Large Corpus of Data Using LLM: RAG Approach,” explored the use of large language models for efficient data retrieval.

  • Dr. Anett Hoppe

    Dr. Anett Hoppe is a senior researcher who works on supporting human information consumption and learning through AI technologies. Her research focuses on understanding and enhancing how people learn from multimodal content and how AI can support both educational processes and scientific discovery workflows. She develops AI-assisted tools that range from accessibility systems for visually impaired students to automated journal recommendation systems that streamline scientific workflows. Anett is actively involved in organizing international workshops and has contributed to major conferences including AIED, ECIR, and TPDL.

  • Alex Krawczyk

    Alex is a Research Software Engineer and a PhD candidate at the Applied Computer Science Graduate School (PZAI) in Wiesbaden, supervised by Prof. Dr. Alexander Gepperth. His doctoral research explores applied machine learning, continual learning, and reinforcement learning. Previously, he taught programming and machine learning at the University of Applied Sciences Fulda. Currently, Alex contributes to the DFG-LIS project "SportVid", which is a portal that supports the search, analysis, and evaluation of videos in sports and training science. There, he develops the backend and implements advanced computer vision and machine learning algorithms. What fuels his work is a deep curiosity about the perceptual abilities of deep learning models. He is fascinated by how they can learn from their environments and is dedicated to creating intelligent methods that can effectively assist and guide users in various real-world scenarios. 

  • James Simpson

    James Simpson is a research in Computer Science at Philipps-Universität Marburg. His research focuses on Human–AI Interaction, with particular emphasis on conversational agents, large language models, and the development of validated frameworks for understanding how humans perceive and collaborate with AI systems. His PhD research in Psychology at Macquarie University examined whether conversational agents could effectively lead human teams engaged in a collaborative activity. This was achieved by way of a multi-phase research project which modelled how humans performed the activity, used Wizard of Oz prototyping to validate a communication scheme for an envisioned collaborative conversational agent, and ultimately evaluating competing agent approaches for leading human teams. His work has been published in leading venues including Frontiers in Psychology, ACM CSCW, and ICMI conferences. Prior to his research work in academia, he worked for the Canadian deep learning startup Maluuba, which developed technology to allow machines to understand and reason about natural language. Maluuba was acquired by Microsoft in 2017.