We are delighted to welcome Neelesh Babu as a Student Assistant in the Department for Digital Humanities and Social Studies.
Neelesh is currently pursuing his Master’s degree in Data Science at Friedrich-Alexander-Universität Erlangen-Nürnberg, with a specialisation in Artificial Intelligence and Machine Learning. His Master’s thesis, supervised by Prof. Mila Oiva from the Department of Digital Humanities and Social Studies and Prof. Marius Yamakou from Data Science, focuses on the multimodal analysis of affective meaning in Soviet newsreels. The project investigates how image, narration, speech, music, and historical context interact in the construction of emotional meaning, moving beyond single-modality approaches such as facial expression or text analysis alone.
He previously completed his Bachelor’s degree in Artificial Intelligence and Data Science in India, where he developed a strong foundation in programming, machine learning, data analysis, and software-oriented research projects. During his Bachelor’s studies, he received the Academic Excellence and Breaking the Barriers Award in recognition of his academic performance and achievements from 2020 to 2024.
Alongside his studies, Neelesh works as a Student Assistant in the AI Systems Project at FAU, where he supports students with coding tasks, technical issues, GitLab workflows, project submissions, reports, and presentations. This experience has strengthened his ability to communicate technical concepts clearly and to support students in a structured and accessible manner.
In his new role, Neelesh contributes to an open-source research software project dedicated to embedding space analysis and visualization. His work includes documentation, tutorial development, GitHub and Hugging Face resource support, website content, community-related tasks, workshop assistance, and the conversion of embedding models into ONNX format for integration into the project’s model zoo.
His research interests include machine learning, embedding models, data visualization, research software, digital collections, image and video data, and the application of AI methods to cultural and historical research. He has also contributed to academic and technical projects, including a published data-analysis study on Near-Earth orbit space debris risk assessment at IEEE ICECCC 2024.
