Location: Heidelberg

I am an ELLIS PhD student in the Medical Image Computing department at the German Cancer Research Center (DKFZ). Before starting my PhD, I worked as a Junior Data Scientist at AstraZeneca in Gothenburg (Sweden) and as a Research Assistant at Uppsala University.

My current research focuses on improving model generalizability across clinical settings, with a particular emphasis on brain imaging. I am developing a stroke identification algorithm designed to perform robustly in multi-centric environments. I am also collaborating with the University of Amsterdam on analyzing temporal brain image data to extract patterns that enhance model generalization.

I have received several travel grants for international research stays during my PhD, as well as excellence scholarships during my Bachelor’s and Master’s studies.

I am a Master’s student in Data and Computer Science at Heidelberg University, specializing in Computer Vision. With a Bachelor’s thesis in this field and nearly five years of industry experience, I have worked extensively on 3D reconstruction and SLAM. Currently, as a student researcher (HiWi), I continue to explore SLAM and related topics while expanding my knowledge in other areas of machine learning.

I am currently a PhD student in the group of Fred Hamprecht at the Interdisciplinary Centre for Scientific Computing (IWR) of Heidelberg University. My background is physics and I am primarily interested in how we may use our intuition and tools from theoretical physics to understand and improve machine learning methods. I first became interested in this during my masters thesis where (among other things) I worked on a grand canonical MCMC sampling algorithm for inference problems. [1] In my PhD, I am working on inference problems on manifolds particularly the simplex (ie inference problems where the parameters are distributions themselves). The physical methods we use mostly stem from differential geometry (as in general relativity and information geometry) and our applications are also in physics, namely cosmology.

My name is Dorina Ismaili. I am a Master’s Student in Scientific Computing at University of Heidelberg. Previously, I completed my Bachelor studies in Mathematics at Bilkent University, and I hold a Master’s degree in Mathematics from Technical University of Munich, where I completed my master’s thesis in the field of Optimal Control. I have worked for two years as a scientific assistant at TU Munich at the Chair of Theoretical Information Technology. I had the opportunity to work on quantum networks, and quantum channels. Now, I want to continue my work in a more applicable direction, and I intend to focus on AI tools in medicine.

Tim Hudelmaier is currently pursuing a Master of Science (M.Sc.) in Molecular Biotechnology at the Heidelberg University, where he is specializing in the field of Bioinformatics. His research is centered on employing advanced AI models, trained on vast and diverse biological datasets, to both understand and engineer biological systems. Going beyond the mere training of these sophisticated models, Tim actively investigates their internal mechanisms using cutting-edge techniques from mechanistic interpretability. This deeper analysis seeks to unravel how these AI systems represent complex biological phenomena. Ultimately, his work aims to illuminate fundamental aspects of biology by thoroughly understanding the artificial intelligence models capable of capturing its intricacies.

I am currently part of the Anders Lab at BioQuant in Heidelberg. Biology has become a data-rich science, ripe for machine learning. Yet it is still waiting for its ChatGPT moment. Just as convolutional networks enabled computer vision and transformers revolutionized NLP, there must be a framework that unlocks biology and thus possibly transforming personalized medicine. Good algorithms working on good data could lead to breakthroughs just as profound, if not even more so.

I am a master’s student at Heidelberg University. I specialize in computational physics, with a focus on statistics and scientific machine learning. A foray into psychology sparked my interest in scientific modeling and Bayesian statistics. Advancing modern Bayesian methods has since been my primary research focus. The BayesFlow team introduced me to amortized Bayesian inference (ABI) and the ways it can benefit from machine learning. I have since become a contributor and a co-maintainer of the project. I have contributed to various paper in this research area, and regularly blog about ongoing development and research. Our methods are not specific to a certain field, but many practitioners are new to machine learning methods. Therefore supporting users and writing documentation are important to me as well, to make the results of our research as accessible as possible.

I am currently pursuing a Master’s degree in Systems Biology at the University of Heidelberg, with a strong interest in computational biology. Throughout my academic journey, I have worked on various research and internship projects, collaborating with experienced professionals in institutions such as the German Cancer Research Center (DKFZ), BioMedX GmbH, and the Institute for Biotechnology at the Vietnam Academy of Science and Technology (VAST).

My work has involved developing automation solutions and analyzing multi-omics data—including single-cell and spatial transcriptomics, proteomics, and metagenomics. My current focus lies in developing machine learning approaches for preclinical drug development, particularly in the context of proteins and small molecules. I am especially interested in leveraging AI to understand molecular mechanisms of disease and contribute to solving complex biological problems.

I am a Master’s student in Molecular Biotechnology at the University of Heidelberg, where I also completed my Bachelor’s degree. My focus lies in bioinformatics and machine learning, with a particular interest in applying AI to the life sciences. I am especially interested in modeling biological processes—such as gene regulation—and in developing interpretable deep learning models to uncover patterns in complex biological data. In addition to research, I am passionate about mentoring Bachelor’s students and supporting their development in computational biology.

I am a Master’s student in Physics at Heidelberg University, specializing in the intersection of physics and artificial intelligence. My research journey began at the German Cancer Research Center, where I worked on my Bachelor’s thesis applying deep neural networks to extract interventional tools from very low dose X-ray projections, leveraging temporal information to demonstrate the potential of AI in healthcare.

A subsequent internship at Bosch introduced me to computer vision, which is now the focus of my Master’s thesis. My current research centers on diffusion models and distillation methods, aiming to improve the efficiency of generative models while preserving image quality. I am particularly motivated by translating theoretical insights into practical AI solutions—whether by optimizing neural networks or building new computer vision pipelines.

I value interdisciplinary collaboration and sharing ideas. ELIZA provides a unique opportunity to connect with like-minded peers, take part in engaging workshops, and gain fresh perspectives on cutting-edge AI research.