Location: Tübingen

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.

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’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.

I am a master’s student in Machine Learning at the University of Tübingen and an ELIZA scholarship recipient. My research lies at the intersection of computer vision, graphics, and machine learning, with a focus on 3D reconstruction and representation learning. At the Real Virtual Humans group led by Prof. Gerard Pons-Moll, I have worked on controllable facial avatars, 3D reconstruction of humans in clothing, and human-object interaction. I am broadly interested in how we can capture, model, and understand the 3D world from visual data, with applications in digital humans, AR, and VR. I plan to pursue a PhD to deepen my research and contribute to this field.

I am an ELLIS PhD student supervised by Dieter Büchler (University of Alberta, Max Planck Institute for Intelligent Systems), Ingmar Posner (University of Oxford), and Bernhard Schölkopf (Max Planck Institute for Intelligent Systems). My research interests generally lie in reinforcement learning and robotics. More concretely, I am interested in the role of action representations in reinforcement learning, discovering and exploiting structure in the learning process, and applying reinforcement learning to muscular robots for solving dynamic tasks.

Matthias Hein is Bosch endowed Professor of Machine Learning and  the coordinator of the international master program in machine learning at the University of Tübingen. He is member of the Excellence Cluster “Machine Learning: New Perspectives for Science” and the Tübingen AI Center. His main research interests are to make machine learning systems robust, safe and explainable and to provide theoretical foundations for machine learning, in particular deep learning. He serves regularly as area chair for ICML, NeurIPS or AISTATS and has been action editor for Journal of Machine Learning Research (JMLR) from 2013 to 2018. He is an ELLIS Fellow and has been awarded the German Pattern recognition award, an ERC Starting grant and several best paper awards (NeurIPS, COLT, ALT).

Contact: ml-sekretariat@inf.uni-tuebingen.de

I am a Masters in Machine Learning student at the University of Tübingen. Prior to this I’ve worked as a software engineer at various startups for 3 years. I am currently interested in Computer Vision especially 3D Computer Vision and have recently worked on Human Motion Modeling and Layered Clothing. The most recent project I’ve contributed to is a generative text to motion model that effectively generates realistic lifelike motions using an auto-regressive diffusion model in the latent space.

Dóra Molnár is an ELIZA Master’s Scholarship holder currently studying Machine Learning at the University of Tübingen. She holds a Bachelor’s degree in Mathematics and is passionate about combining computational tools with real-world scientific questions. Her research focuses on developing and applying state-space models to infer neural dynamics from voltage imaging data. She completed an individual research project on this topic in Prof. Jakob Macke’s lab and is currently writing her Master’s thesis in the same group, exploring new methodological directions and extensions.
Beyond neuroscience, Dóra has also contributed to TFpredict, a supervised machine learning-based tool for the identification and structural characterization of transcription factors. This work was presented at the COMBINE 2024 conference, highlighting her interest in interdisciplinary applications of AI across biology. She is particularly driven by projects at the intersection of mathematics, machine learning, neuroscience, and computational biology, and is eager to continue working on impactful, research-oriented challenges..