Location: Freiburg
Jun.-Prof. Dr. Maria Kalweit holds the Tenure-Track CRIION Professorship for Bioinformatics: AI for Oncology Research at the Department of Computer Science, University of Freiburg. She earned her PhD in Computer Science from the University of Freiburg in 2022 under Prof. Joschka Boedecker. She also serves as Chief Scientific Officer at the Collaborative Research Institute Intelligent Oncology (CRIION) of the Mertelsmann Foundation gGmbH. Her research centers on robust, efficient, and explainable machine learning for oncology, with a focus on limited-data settings, biological variability, and technical heterogeneity. Her work has resulted in multiple patents and a digital biomarker. Her paper “Early Seizure Detection with an Energy-Efficient Convolutional Neural Network on an Implantable Microcontroller” received the Best Paper Award at IJCNN 2018. She has further been recognized with the Wolfgang-Gentner-Nachwuchsförderpreis (2023) and the Gips-Schüle-Nachwuchspreis in Technikwissenschaften (2024).
Tim Welschehol is a research group leader in the Robot Learning Lab at the university of Freiburg. He is a member of the Department of Computer Science and the BrainLinks-BrainTools center.
He received his PhD in Robotics working with Prof. Wolfram Burgard at the Autonomous Intelligent Systems group in 2020. After his PhD, he worked as a postdoctoral researcher (2020-2022) and subsequently as commissary lead (2022-2023) of the Autonomous Intelligent Systems group.
His research work is in the field of robot learning with focus on mobile manipulation and long horizon reasoning. The overarching goal of his research is to enable robots to autonomously learn, reason and act in human centered environments. His most acknowledged work, which was awarded with the Ewald Marquardt Zukunftspreis 2023 and a best paper award, is “Learning Navigation for Arbitrary Mobile Manipulation Motions in Unseen and Dynamic 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 Computer Science at the University of Freiburg specializing in Artificial Intelligence. My research focus lies at the intersection of deep learning and biology, in particular in RNA structure and interaction prediction. Together with Frederic Runge, Jörg Franke, and Lars Gerne, we have published a workshop paper “RNA-Protein Interaction Prediction via Sequence Embeddings” at the 12th International Conference on Learning Representations in 2024. This work explores the benefit of using embeddings learned from foundation models to predict interactions between RNA and proteins and proposes homology-based splits for the data for a more realistic evaluation of model generalizability. My past work experience includes a Student Assistant position at the Machine Learning Lab at the University of Freiburg and an internship at IBM in Poland. I am currently writing my Master’s thesis on dimensionality reduction for tabular data with a focus on biomedical applications.
I am a Robot Learning enthusiast, currently pursuing a Master’s degree in Computer Science with a specialization in Artificial Intelligence at the University of Freiburg. In my Bachelor’s thesis, I developed an innovative transformer-based approach for lane graph prediction using solely aerial imagery, enabling precise mapping without reliance on additional sensor inputs like LiDAR. This work was presented at the RSS 2024 workshop.
Nick Heppert is a Doctoral Researcher at the University of Freiburg, part of ELLIS and an ELIZA Fellow since March 2024. His research focuses on robot learning, with an emphasis on perception for robotic manipulation. He holds an M.Sc. in Autonomous Systems from TU Darmstadt and has conducted research at Stanford University and the Toyota Research Institute. His most influential publication, “CARTO: Category and Joint Agnostic Reconstruction of ARTiculated Objects” (CVPR 2023), addresses generalizable 3D perception of articulated objects for robotics. He has published at top robotics and machine learning conferences, including IROS, ICRA, CVPR, and CoRL. Nick actively contributes to the research community as a reviewer for RA-L, ICRA, and IROS, and through organizing academic workshops. He has also mentored over 10 Master’s students at the University of Freiburg. His work aims to bridge perception and manipulation to enable more capable and adaptable robotic systems. He is also part of the interdisciplinary ReScaLe project, which focuses on developing responsible and scalable learning for robots assisting humans, with particular attention to social impact and regulatory dimensions.
I am a master’s student in Environmental Sciences at the University of Freiburg, where I focus on data science and environmental modelling—always on the lookout for ways to make machine learning more meaningful in the context of climate and Earth system science. Through my work, I aim to build bridges between disciplines, helping ML models learn not just from data but also from the physical laws and expert knowledge that govern our planet.
PhD student, AutoML Freiburg
Sai Prasanna Raman is a doctoral candidate at the University of Tuebingen and IMPRS-IS, specializing in reinforcement learning for robot learning with an expertise in world models. His research aims to build generally capable agents, particularly focusing on agents that can adapt to diverse environments and tasks.
Abhinav Valada is a Full Professor (W3) at the University of Freiburg, where he directs the Robot Learning Lab. He is a member of the Department of Computer Science, the BrainLinks-BrainTools center, and a founding faculty of the ELLIS unit Freiburg. Abhinav is a DFG Emmy Noether AI Fellow, Scholar of the ELLIS Society, IEEE Senior Member, and Chair of the IEEE Robotics and Automation Society Technical Committee on Robot Learning.
He received his PhD (summa cum laude) working with Prof. Wolfram Burgard at the University of Freiburg in 2019, his MS in Robotics from Carnegie Mellon University in 2013, and his BTech. in Electronics and Instrumentation Engineering from VIT University in 2010. After his PhD, he worked as a Postdoctoral researcher and subsequently an Assistant Professor (W1) from 2020 to 2023. He co-founded and served as the Director of Operations of Platypus LLC from 2013 to 2015, a company developing autonomous robotic boats in Pittsburgh, and has previously worked at the National Robotics Engineering Center and the Field Robotics Center of Carnegie Mellon University from 2011 to 2014.
Abhinav’s research lies at the intersection of robotics, machine learning, and computer vision with a focus on tackling fundamental robot perception, state estimation, and planning problems to enable robots to operate reliably in complex and diverse domains. The overall goal of his research is to develop scalable lifelong robot learning systems that continuously learn multiple tasks from what they perceive and experience by interacting with the real world. For his research, he received the IEEE RAS Early Career Award in Robotics and Automation, IROS Toshio Fukuda Young Professional Award, NVIDIA Research Award, AutoSens Most Novel Research Award, among others. Many aspects of his research have been prominently featured in wider media such as the Discovery Channel, NBC News, Business Times, and The Economic Times.