Member role: Master’s Student
| I am a Master’s student in Scientific Computing at Heidelberg University and a Research Intern at the Scientific AI Lab, working under Prof. Fred A. Hamprecht. My current research focuses on machine learning for stable and accurate orbital-free density functional theory (DFT), with an emphasis on improving the stability, efficiency, and accuracy of scientific simulations. I also work as a Student Assistant at Heidelberg University’s Scientific Software Center, where I develop Python-based methods for detecting deepfakes and AI-generated content, focusing on algorithmic design, model evaluation, and scientific software development. My research interests include machine learning systems, reinforcement learning, graph neural networks, natural language processing, recommender systems, and human-AI collaboration. |
| Umut Alperen Cengiz is a Master of Science student in Data and Computer Science at Heidelberg University. He holds a Bachelor’s degree in Computer Engineering from Ankara University, complemented by an Erasmus+ academic exchange at AGH University in Poland.
His core research interests center on Computer Vision, Large Language Models (LLMs), and foundational models such as CLIP. His AI expertise includes an undergraduate thesis on Video Anomaly Detection under the SAYZEK Ministry of Defence AI initiative, and his participation in the CARE-Liver challenge under DKFZ supervision, which resulted in a co-authored paper. Professionally, he worked as a Search Engine Quality Improvement Specialist at Yandex, where he curated tailored data and edited prompts to fine-tune the Yandex AI Engine. Additionally, he broadened his core engineering foundation through software and avionics internships at defense industry companies HAVELSAN and TUSAŞ/TAI. |
Emine Şevval Eş Uzunay is a Master’s student in Data and Computer Science at Heidelberg University. She holds a BSc in Computer Science and Engineering and a BA in International Studies from Sabancı University in Istanbul.
Before returning to academia, she worked for 4 years as a Full Stack Engineer in the Corporate Security unit at Garanti BBVA Technology, where she built and maintained large-scale production systems.
Her research interests lie in medical imaging and deep learning, computer vision, and natural language processing. As part of a research practical with the Medical Image Computing (MIC) group at DKFZ, she works on temporal deep learning for longitudinal biomedical data. More broadly, she is interested in building machine learning systems that are robust, efficient, and reliable enough to move from research into real-world use, drawing on her engineering background to connect solid software practice with impactful research.
| I am a Master’s in Mathematics student at TUM interested in topics lying at the intersection of deep learning and computational biology. I focus on using methods such as generative modeling, flow matching and optimal transport to solve problems in single-cell analysis, spatial transcriptomics and molecular dynamics. Previously, I was able to gain experience as a teaching assistant at TUM, a student researcher developing ML for Alzheimer’s hallmark detection at Helmholtz Munich, and a ML intern for anomaly detection on battery fleet data at BMW. |
I graduated from Sharif University of Technology in Computer Engineering in 2024, and I’m now pursuing my MSc in Computer Science (DSAI) at Saarland University. My current focus is LLM theory and AI safety, building on a background in Natural Language Processing, Machine Learning, and Human-Centered Computing.
Much of my published work sits in human-centered computing and NLP, from multimodal retrieval and accessibility to how people experience conversational systems. More recently, I’ve been moving toward understanding the foundations of large language models and how to make them safer and more reliable. Alongside research, I spent three years as a software engineer building products at scale.
| MSc Eshetu Moges Abuhay is a Senior Technical Assistant at the University of Gondar and an incoming ELIZA Master’s scholar at Technische Universität Darmstadt. He previously graduated with top honors from Samara University (BSc) and the University of Gondar (MSc), where he led various technical initiatives. His research focus centers on explainable machine learning and data-driven intelligent systems. Recently, he developed advanced software and predictive models aimed at improving healthcare workflows. His most influential research is his thesis, “Data-Driven Prediction of Emergency Severity Index in Ethiopia Using Explainable Machine Learning” (2026), currently under review by the Emergency Medicine Journal. This work addresses critical mistriage issues by identifying key clinical predictors and quantifying minimum data requirements for accurate emergency decisions. Alongside his academic contributions, he provides professional service in hardware and software maintenance and was awarded the prestigious ELIZA Master’s scholarship for academic excellence. |
| I am a Master’s student in Computer Science with a specialization in Artificial Intelligence at the University of Freiburg. My research interests span Computer Vision and Natural Language Processing, with a particular focus on synthetic data generation and personally identifiable information (PII) dataset creation. Previously, I conducted research at the Max Planck Institute for Biological Intelligence, where I completed my bachelor’s thesis on multi-camera object detection and tracking. I have also contributed to medical image analysis and processing as a Research Assistant in Hungary. Through these experiences, I have developed a strong foundation in applied machine learning, bridging theoretical AI research with real-world computer vision and data-centric challenges. I remain actively interested in synthetic data generation and its implications for privacy-aware AI development. |
| Youssef is an incoming master’s student in Machine Learning at Eberhard Karls University of Tübingen. He is currently completing his bachelor’s degree in Computer and Communications Engineering at Alexandria University. His research interests include computer vision, trustworthy AI, and generative models, with a particular focus on adversarial robustness. He has also gained research experience as an undergraduate research intern at Knights Lab, where he worked on video super-resolution. As part of the Konrad Zuse School ELIZA network and the Tübingen machine learning community, Youssef aims to deepen his work on reliable AI systems that are robust, privacy-preserving, and socially responsible. |
| Alexandros Ali Khan was recently admitted to the Machine Learning program at the University of Tübingen. He graduated Summa Cum Laude from the University of Miami on the President’s Scholarship, double majoring in Computer Science and Mathematics with Departmental Honors in both. As a graduating Senior, he was presented with the James McKnight Outstanding Mathematics Student Award. Following his studies, he worked as an Implementation Consultant for FAST Enterprises, LLC across the United States. His research interests lie at the intersection of the theoretical and mathematical foundations of Deep Learning, Generative Artificial Intelligence, and Language Models. He also aims to explore the cross-disciplinary links between Statistical Learning Theory, Cognitive Science, and the foundational mechanisms of intelligence. |