Location: Freiburg

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.

My keen interest lies in the explainability of tabular data and understanding of the components of black-box foundation and multi-modal models. Along with the strong belief in the potential of Causal Representation Learning in providing human-understandable and accessible foundation models.

Henry is currently a Master’s student in Computer Science at the University of Freiburg, where he is working on his Master’s Project at the Robot Learning Lab. His research focuses on perception and Parameter-Efficient Fine-Tuning (PEFT) with applications in robotics. Prior to his studies in Germany, Henry worked as a Research Assistant at the National Polytechnic University (Escuela Politécnica Nacional) in Ecuador, where he contributed to various projects related to robotics. A contribution that marked a formative step in his research journey is “Adaptive Sampling-Based Particle Filter for Visual-Inertial Gimbal in the Wild” (ICRA, 2023). In this project, he contributed to the application of image segmentation models and sensor fusion techniques for controlling gimbal platforms. Henry is passionate about advancing research in robotics, with a particular focus on perception and efficient learning methods in mobile and autonomous systems.

Frank Hutter is co-founder and CEO of PriorLabs, the world’s first company focussed on tabular foundation models. He is also a Hector-Endowed Fellow and PI at the ELLIS Institute Tübingen (part-time), as well as Full Professor for Machine Learning at the University of Freiburg (currently on leave). Frank holds a PhD from the University of British Columbia (UBC, 2009) and a Diplom (eq. MSc) from TU Darmstadt (2004). He received the 2010 CAIAC doctoral dissertation award for the best thesis in AI in Canada, and with his coauthors, several best paper awards and prizes in international competitions on machine learning, SAT solving, and AI planning. He is a Fellow of EurAI and ELLIS, the director of the ELLIS unit Freiburg and the recipient of 3 ERC grants. Frank is best known for shaping the field of automated machine learning (AutoML), including works on neural architecture search, efficient hyperparameter optimization, and meta-learning. He co-authored the first book on AutoML and the prominent AutoML tools Auto-WEKA, Auto-sklearn and Auto-PyTorch, won the first two AutoML challenges with his team, is co-teaching the first MOOC on AutoML, co-organized 15 AutoML-related workshops at ICML, NeurIPS and ICLR, and founded the AutoML conference as general chair in 2022 and 2023. In recent years, his focus has been on tabular foundation models like TabPFN.