Focus area: Foundations of ML
Frederik Nolte is a DPhil candidate in Engineering Science at the University of Oxford, specialising in applied machine learning, with a focus on object-centric and modular approaches for robotics, representation learning, and world models. He is supervised by Ingmar Posner and Bernhard Schölkopf and is a member of the ELLIS PhD Programme.
He previously completed an MSc in Artificial Intelligence at the University of Amsterdam and a BSc in Business Informatics at the University of Mannheim.
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 a Research Scientist at Prior Labs, where I develop the next generation of tabular foundation models. My work focuses on scalable pretraining, novel architectures, and scaling laws. I contribute to TabPFN-3, which scales tabular foundation models to datasets with up to one million training rows while substantially accelerating training and inference.
Previously, I was a Senior AI Researcher in Aleph Alpha Research’s Foundation Models team, working on efficient LLM pretraining, tokenizer-free architectures, and large-scale optimization. Before that, I was a Senior Expert at the Bosch Center for Artificial Intelligence, where my research centered on robust and reliable computer vision, neural architecture search, and synthetic data.
My broader interests span efficient and reliable machine learning, AutoML, and open-source software. I am an ELLIS member, an ELIZA Industrial Fellow, and an Area Chair for NeurIPS 2026. I have also contributed to scikit-learn, including its probability-calibration, kernel-ridge, and Gaussian-process modules.
I live in Böblingen and work in Freiburg. Outside research, I enjoy spending time with my wife and our three children. I am always happy to connect with people working on tabular foundation models, scalable pretraining, efficient or tokenizer-free architectures, and open-source machine learning.
| Ziheng Chen is an ELLIS PhD student in Information Engineering and Computer Science at the University of Trento, supervised by Prof. Nicu Sebe, and a visiting PhD student at the Max Planck Institute for Intelligent Systems, working with Prof. Bernhard Schölkopf. His research lies at the intersection of machine learning and differential geometry, with a focus on deep learning over non-Euclidean spaces such as Riemannian manifolds, hyperbolic spaces, matrix manifolds, and Lie groups. His work develops geometric counterparts of core neural-network components, including normalization, classification, attention, and hyperbolic layers. His research has appeared at ICLR, NeurIPS, CVPR, ICML, AAAI, and IEEE journals. He has received a DAAD Research Grant, ELSA and ELIAS Mobility Grants. |
I am a PhD candidate at the University of Freiburg working with Thomas Brox. I also hold a parallel position at Bosch Research in Renningen, Germany. My work deals with understanding representation learning in visual models through the lens of interpretability and explainability.
I did my Master’s at RIT, where I was an NSF AWARE-AI Fellow. I also worked at the RIT Center for Human Aware AI under the supervision of Prof. Andreas Savakis on unsupervised domain adaptation in large-scale foundation models.
I have had the pleasure of working alongside researchers and scientists from across the world. Prior to this, I have been associated with Microsoft Research, the Juno Mission at NASA JPL, IRIS-HEP at Princeton, the LIVIA Lab at ETS Montreal, ULR France, CamCann Smart Systems, and IIT Indore.
| Bhavyajeet Singh is a PhD student and doctoral researcher at the Ubiquitous Knowledge Processing Lab, Technical University of Darmstadt, advised by Prof. Iryna Gurevych. His research focuses on developing NLP methods for cybersecurity applications like penetration testing and secure code generation.
Previously, he was a Research Fellow at Microsoft Research India, where he worked on low-latency memory-augmented retrieval systems. This work has been published at ICML 2026 and was integrated into Bing Ads production pipelines. His broader research experience spans secure code generation, retrieval-augmented and memory-augmented NLP systems, and cross-lingual fact-to-text generation. One of his most influential earlier publications is XAlign: Cross-lingual Fact-to-Text Alignment and Generation for Low-Resource Languages, Tushar Abhishek, Shivprasad Sagare, Bhavyajeet Singh, Anubhav Sharma, Manish Gupta, and Vasudeva Varma, 2022. The work introduced a large-scale cross-lingual fact-to-text dataset with 0.45M pairs across eight low-resource languages, supporting research on grounded multilingual generation and cross-lingual alignment. The paper has gathered numerous citations, with the dataset being used my multiple downstream projects and models. |
I am a Professor of Computational Linguistics in the Department of Language Science and Technology at Saarland University. I am also a Professor by courtesy of Computer Science and Associate Faculty at the Max Planck Institute for Informatics.
I lead the Computational Linguistics group and am the speaker of the Research Training Group “Neuroexplicit Models of Language, Vision, and Action”. I am an ACL Fellow.
My PhD is in Computer Science from Saarland University; my advisors were Manfred Pinkal and Gert Smolka. Before coming back to Saarbrücken, I was a postdoc at Columbia University and the University of Edinburgh and a Professor of Theoretical Computational Linguistics at the University of Potsdam. I have spent sabbaticals at Meta AI in Paris and the Allen Institute for Artificial Intelligence (AI2) in Seattle.
I am a huge fan of Typst and try to do as much of my writing as possible in Typst rather than LaTeX. I maintain the tracl template for *ACL papers and the Pergamon bibliography package. I also maintain the Typstit tool for dragging Typst formulas into other apps. In my spare time, I am a certified coach for a cappella groups and was an internationally certified contest judge in the Musicality category from 2023-2026.
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.
I am a research scientist and team lead in AI and robotics at Google DeepMind in London where I work on creating artificial intelligence for the physical world.
Principal Scientist at Merck Healthcare, part of Merck KGaA, Darmstadt.
Dr. sc ETH in Pharmaceutical Sciences, ETH Zurich.
Bachelor & Master in Chemistry at ETH Zurich, Switzerland.
Interested in AI for Drug Discovery and Development.