Member role: Master’s Student

Collaborated on cross-site research in autonomous driving systems.

I am a Master’s student in Computer Science at the University of Freiburg specializing in Artificial Intelligence. My research focus lies at the intersection of deep learning and biology, in particular in RNA structure and interaction prediction. Together with Frederic Runge, Jörg Franke, and Lars Gerne, we have published a workshop paper “RNA-Protein Interaction Prediction via Sequence Embeddings” at the 12th International Conference on Learning Representations in 2024. This work explores the benefit of using embeddings learned from foundation models to predict interactions between RNA and proteins and proposes homology-based splits for the data for a more realistic evaluation of model generalizability. My past work experience includes a Student Assistant position at the Machine Learning Lab at the University of Freiburg and an internship at IBM in Poland. I am currently writing my Master’s thesis on dimensionality reduction for tabular data with a focus on biomedical applications.

I am a master’s student in Machine Learning at the University of Tübingen, supported by the ELIZA Research-Oriented Master’s Scholarship. I am broadly interested in (3D) Computer Vision and Machine Learning, and Graphics, with experience in human pose estimation and domain adaptation.

I am a Master’s student in Data and Computer Science at Heidelberg University, specializing in Computer Vision. With a Bachelor’s thesis in this field and nearly five years of industry experience, I have worked extensively on 3D reconstruction and SLAM. Currently, as a student researcher (HiWi), I continue to explore SLAM and related topics while expanding my knowledge in other areas of machine learning.

İrem Karaca is a Master’s student in Machine Learning at Eberhard Karls University of Tübingen. She earned her Bachelor’s degree in Computer Engineering from Koç University, Türkiye, graduating Summa Cum Laude. İrem is currently a research assistant at the Robust Machine Learning Research Group at the ELLIS Institute in Tübingen, where she is working on the AI Tutor project. Her role focuses on enhancing the tutor’s teaching style and didactical abilities while developing a robust benchmarking framework. Her research interests include Natural Language Processing (NLP), Large Language Models (LLMs), and AI in education. Beyond her academic and research roles, İrem is actively involved in community initiatives. She has coordinated mentorship programs at inzva, connecting young computer science students with industry professionals. Additionally, she has volunteered as a tutor in several organizations, helping students develop foundational programming skills.

I am a Robot Learning enthusiast, currently pursuing a Master’s degree in Computer Science with a specialization in Artificial Intelligence at the University of Freiburg. In my Bachelor’s thesis, I developed an innovative transformer-based approach for lane graph prediction using solely aerial imagery, enabling precise mapping without reliance on additional sensor inputs like LiDAR. This work was presented at the RSS 2024 workshop.

I’m a master’s student in Machine Learning at the University of Tübingen, currently working as a student assistant on the Scholar Inbox. Before this, I interned at Cisco in Bengaluru, focusing on improving deployment pipelines and automating error analysis. My main interests are in Deep Learning, Natural Language Processing, and building practical ML systems—like my project on generating lecture notes by fine-tuning BERT models and creating knowledge graphs. I’ve also worked on small projects ranging from depth-aware neural style transfer to chatbots for visually impaired students. I was a finalist at the JPMC Code for Good Hackathon and received the ELIZA stipend for 2024–25. I enjoy tackling real-world problems with technology to make a difference.