Member role: Master’s Student

I am currently pursuing my Master’s degree in Informatik at TU Darmstadt. Additionally, I work as a research assistant at the Intelligent Autonomous Systems Lab at TU Darmstadt, with a focus on humanoid locomotion. My past research experience has mainly focused on reinforcement learning, including an internship at Porsche Motorsport. In addition to robot learning, my current research interests include robotics in complex and uncertain environments.

I’m a Master’s student in Artificial Intelligence at the University of Freiburg and a researcher in the Machine Learning Lab. My work focuses on scaling laws for large language models. I also contribute to the NePS repository, a research framework for AutoML and hyperparameter optimization.

I am a Master’s student in Physics at the University of Heidelberg, where I also completed my Bachelor’s degree. In my Bachelor thesis, I worked in theoretical particle physics, applying Direct Diffusion to search for new physics beyond the Standard Model. Alongside my studies, I have gained nearly two years of industry experience at a German company specializing in AI-based speech technologies. My work focuses on designing RAG systems and integrating large language models to develop solutions such as chatbots and voicebots. In my further studies, I aim to deepen my expertise in machine learning and explore its applications in advancing modern particle physics research.

I am a Master’s student in Physics at the University of Heidelberg, specializing in machine learning and computational biology. My background in physics has given me an interdisciplinary perspective on machine learning and has driven my interest in both its theoretical foundations and practical applications. Alongside my studies, I gained industry experience at a software company focusing on tailored machine learning solutions in the medical sector. My work included automated detection of cancerous tissue and the implementation of voice-assisted control for a hand orthosis. I am currently working on analyzing perturbational effects in single-cell data using novel machine learning approaches based on Normalizing Flows, in a collaboration with the EMBL in Heidelberg. I am keen to investigate the potential of AI to better understand cellular activity and to advance data-driven biology.

Moritz Probst is a Master’s student in Mathematics at the Technical University of Munich (TUM), focusing on probability theory, statistics, and the theoretical foundations of machine learning, particularly at their intersection. His research interests center on the theoretical properties of machine learning systems and the development of mathematical guarantees for their reliable application in high-stakes domains such as medicine and pharmaceutical research, with a focus on uncertainty quantification and model reliability. Alongside his studies, he is involved in research with the Theis Lab at the Institute of Computational Biology at Helmholtz Munich and conducts student research with the Chair of Mathematical Foundations of Artificial Intelligence at LMU Munich, led by Prof. Dr. Gitta Kutyniok. Previously, he gained industry experience through internships at Deloitte and UniCredit.

Irmak Erkol is an AI Engineer and Data Scientist currently working at MSC Mediterranean Shipping Company while pursuing an M.Sc. in Data and Computer Science at Heidelberg University as an ELIZA Fellow. With over 5 years of experience spanning software engineering, machine learning, and data science, her work sits at the crossroads of rigorous research and real-world impact. At MSC, she has built and deployed 10+ production ML systems from a RAG chatbot serving 5,000+ employees to predictive maintenance pipelines for a 1,000+ vehicle fleet. Beyond industry, her research focus centres on medical AI and AI for science spanning ADHD diagnosis through eye-tracking data analysis, cognitive behaviour modelling, fairness in predictive justice systems, and complex network analysis. She holds a B.Sc. from Koç University and was awarded a Research-Oriented Master’s Scholarship at ELIZA.

Marc Saghir is a master’s student in Artificial Intelligence and Machine Learning at TU Darmstadt, after concluding his bachelor in Cognitive Science. He currently works as a student research assistant (HiWi) at the Remote Sensing Institute of TU Darmstadt, contributing to ML projects like landslide detection or sustainability evaluation of cities. Prior to his graduate studies, he gained industry experience working as a Data Scientist and Machine Learning Engineer, where he developed machine learning solutions for real-world applications. His research interests lie broadly in computer science and machine learning, with a particular focus on transdisciplinary applications. He is especially interested in exploring how AI methods can be applied to fields such as environmental science, where data-driven approaches can support the analysis of complex environmental systems. Through his graduate studies and research work, he aims to deepen his machine learning knowledge and contribute to applications of AI for scientific challenges.

I am an MSc student in Mathematics in Data Science at TUM, and active TUM.ai member. My research experience includes working as an AI Research Intern at Siemens AG to improve proprietary foundation models, and as a Research Collaborator at IBM Research, where I work on tool calling in Small Language Models. I am also involved with the Data Science Lab (Dlab) at EPFL, working with Prof. Robert West. I received my BSc in Mathematical Sciences for Artificial Intelligence from Sapienza University of Rome. In Rome, I worked in the GLADIA research group, advised by Emanuele Rodolà, with a thesis on tokenizing latent representations for efficient high-fidelity music generation. During my time there, I also contributed as an Autonomous Driving Engineer for the Sapienza Fast Charge team. In addition to my academic path, I enjoy hackathons, Caribbean dances, sports, connecting with people, trips, and stepping out of my comfort zone.