Location: Munich
| 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 am a PhD researcher at Helmholtz Munich, affiliated with the Technical University of Munich and the ELLIS and ELIZA networks. I hold a bachelor’s degree in Physics and a master’s degree in Statistics and Data Science. My research focuses on generative AI for peptide and protein design, particularly machine learning methods for discovering antimicrobial and therapeutic peptides. I work with protein language models, geometric representation learning, generative modeling, and molecular optimization to design biologically active peptide candidates. Alongside these applications, I conduct research in machine learning theory, with a particular focus on neural collapse, feature learning, kernel methods, and the effects of data geometry and network architecture on learned representations. My work has been published at ICML, Bioinformatics Advances, TAG-DS, and TMLR. I am also a recipient of the Helmholtz AI Accelerator Award and the Deutschlandstipendium from LMU Munich, sponsored by Allianz.
| I am the Founder and Executive Lead of AI Safety Nepal (https://www.aisafetynepal.org/), where I work toward building a community of researchers, professionals, and students in Nepal who understand AI safety deeply, contribute to research, and coordinate on governance decisions. I am also currently researching quantifying failure modes in training-time AI alignment techniques. My research interests span AI safety, multilingual NLP, and low-resource language technologies. Previously, I worked as a machine learning engineer at REG-1, building multilingual legal AI systems and domain-specific NLP models for regulatory documents. I graduated in computer engineering from Tribhuvan University, Pulchowk Campus, where I also served as editor-in-chief of The Zerone, the annual technical publication of Pulchowk Campus. My past work includes research on an extractive Nepali question-answering system and building a multilingual legal search platform aimed at improving access to legal information in Nepal. |
| I am studying Informatics at TU Munich with a focus on computer vision and artificial intelligence. I completed my guided research at the Computer Vision Group at TUM lead by Prof. Daniel Cremers. My work deals with inpainting 3D Gaussian Splatting scenes after object removal and was accepted to the CVEU workshop at CVPR 2026. My master’s thesis will also be at the Computer Vision Group about 3D scenes and animations. |
| I am a Masters student in Informatics at TU Munich. My research interests are in theoretical deep learning and optimization theory. I am specifically interested in training dynamics, and emergence of phenomenon, and analyzing and interpreting them with theoretical frameworks. I have published papers at workshops at top conferences such as ICML and NeurIPs, as well as a first-author conference publication at AISTATS 2026. My work was around optimization algorithms for min-max problems in machine learning. I recently won the regional (Munich) CISPA AI Security Hackathon along with my teammates. |
| Neslihan Köse Cihangir joined Intel in Germany in 2018 as an AI/ML research scientist. She received her Ph.D. from Multimedia Communications, Télécom Paris, France (2014), and her M.Sc. from Electrical and Electronics Engineering, Middle East Technical University, Türkiye (2009).
Prior to joining Intel, Neslihan had worked as a post-doctoral researcher at EURECOM in France and Technical University of Munich in Germany. Neslihan’s research focus is mainly on computer vision, machine learning, and biometrics. Her projects at Intel focus diverse applications such as automated driving, smart manufacturing and trusted media that enable human-AI collaboration targeting efficient and reliable solutions. Her recent research is based on advancing AI technologies using vision LLMs, multimodal RAG, and their application in agentic framework. Her research in explainable AI and uncertainty estimation is pivotal in ensuring responsible AI deployment across various industries. She holds several patents and has authored numerous well-cited papers in top-tier journals and conferences. |
Veronika Eyring is Head of the Earth System Model Evaluation and Analysis Department at the German Aerospace Center (DLR) Institute of Atmospheric Physics and Professor of Climate Modelling at the University of Bremen. Veronika’s research focuses on improving climate models and projections with machine learning and spaceborne Earth observations for actionable climate science and technology assessments in aeronautics, space, transport, and energy research. She has authored many peer-reviewed journal articles and has contributed to the Intergovernmental Panel on Climate Change (IPCC) assessments since many years, including her role as Coordinating Lead Author for Chapter 3 “Human influence on the climate system” in the IPCC Sixth Assessment Report of Working Group I published in 2021. Veronika is involved in the World Climate Research Programme (WCRP) since many years, for example through her roles as Chair of the Coupled Model Intercomparison Project (CMIP) Panel (2014-2020) and member of scientific steering committees including the Working Group on Coupled Modeling (WGCM) from 2008-2018. She is corresponding PI of the European Research Council (ERC) Synergy Grant on „Understanding and Modelling the Earth System with Machine Learning (USMILE)“, Fellow of the European Lab for Learning & Intelligent Systems (ELLIS), and Member of the Scientific Advisory Board of Worldfund. Veronika received the German Research Foundation (DFG) Gottfried Wilhelm Leibniz Prize in 2021 for her significant contributions to improving the understanding and accuracy of climate projections through process-oriented modeling and model evaluation (see film portrait here). She urged world leaders to take action at the COP27 World Leaders Summit Opening Ceremony in 2022. She received the AGU Ambassador Award and was inducted in the group of AGU Fellows in 2024, recognizing her outstanding contributions to climate science, the research community and society (see 2024 Honors Ceremony here). In 2024, the Technical University of Munich (TUM) appointed her as TUM Distinguished Affiliated Professor. Veronika maintains a strong collaboration with the National Center for Atmospheric Research (NCAR, Boulder, CO, USA) as Affiliate Scientist.
Volker Tresp is a professor at Ludwig Maximilian University of Munich (LMU). He received his Diploma degree in physics from the University of Göttingen in 1984 and M.Sc., M.Phil. and Ph.D. degrees from Yale University in 1986 and 1989, respectively. During his Ph.D., he worked in Yale’s Image Processing and Analysis Group (IPAG). In 1990, he joined Siemens where he has been heading various research teams in machine learning. In 1997, he became Siemens Inventor of the Year for his innovations in neural networks research and in 2018 became the first Siemens Distinguished Research Scientist. He revolutionized steel processing by pioneering a novel Bayesian neural network approach that cleverly integrated real-world data with simulated data from a prior solution.*** In 1994 he was a visiting scientist at the Massachusetts Institute of Technology in the Center for Biological and Computational Learning, working with the teams of Tomaso Poggio and Michael I. Jordan. He was co-editor of Advances in Neural Information Processing Systems 13. In 2011, he was appointed professor in informatics at the LMU, where he teaches a course on machine learning and where he is leading a second research team. He is known for his work on Bayesian machine learning, in particular the Bayesian Committee Machine and his work on hierarchical learning with Gaussian processes. The IHRM, the SRM, SUNS, and RESCAL are milestones in representation learning for multi-relational graphs. His team has been doing pioneering work on machine learning with knowledge graphs, temporal knowledge graphs, and scene graph analysis. The work on the Tensor Brain reflects his interest in mathematical models for cognition and neuroscience. In 2020, he became a Fellow of the European Laboratory for Learning and Intelligent Systems (ELLIS). As co-director (with Kristian Kersting and Paolo Frasconi), he leads the ELLIS program “Semantic, Symbolic and Interpretable Machine Learning”.
I am full professor and chair for AI and Computational Linguistics at LMU Munich, Head of the Munich AI and NLP (MaiNLP) lab, and co-director of the Center for Information and Language Processing (CIS). I am also a visiting full professor at the IT University of Copenhagen.
Contact:eliza.muc@mcml.cit.tum.de