Jakob Macke | Principal Investigator |

Jakob has been Professor for “Machine Learning in Science” since May 2020. The W3 professorship has been set up as part of the Cluster of Excellence “Machine Learning: New Perspectives for the Sciences”. He is also an Adjunct Research Scientist at the Max Planck Institute for Intelligent Systems, Director of the Bernstein Center for Computational Neuroscience, and an ELLIS Fellow and member of the ELLIS Unit Tübingen.

Jakob studied mathematics at the Oxford University, worked as a PhD student at the Max Planck Institute for Biological Cybernetics in Tübingen, as a postdoc at the Gatsby Unit at University College London, and as a Bernstein Fellow in Tübingen. He was a Max Planck Group Leader at the Caesar Research Centre in Bonn, a Professor at the Centre for Cognitive Science at TU Darmstadt, and from 2018 to 2020, Professor of Computational Neuroengineering at TU Munich. He was a member of the Young Academy at the German Academy of Sciences Leopoldina (2013-2018), and a FENS Kavli Scholar of Excellence (2018-2023).

Franziska Weiler | Administrative Assistant |

Franzi studied multilingual management and started to work at the University of Tübingen in March 2017. Her first two jobs were at the Central Administration. She has been working part time for the Hennig lab since July 2018 and joined the MackeLab in July 2020. She is responsible for all kinds of things here, among them Human Resources, Purchasing and Financing.

Eszter Stuber | Project coordinator ClinbrAIn |

Eszter joined the lab as project coordinator for the project ClinbrAIn in November 2022. She is working part time for the Mackelab and part time the AI Center. She is responsible for the Administration and Financing the ClinbrAIn Project.

Cornelius Schröder | Postdoc |

Cornelius joined the lab as a postdoc in July 2021. He currently works on different aspects of simulation based inference but has a broad interest in applying ML to different scientific problems. Cornelius studied Mathematics in Freiburg and did his PhD in Tübingen with Philipp Berens where he worked on different levels of modeling retinal computations.

Richard Gao | Postdoc |

Richard joined the lab as a postdoc in the middle of a pandemic (Jan 2021). He currently works with simulation-based inference tools and mechanistic models of neural circuits to better connect neurobiology with brain dynamics and cognition. More broadly, he is interested in model-discovery for dynamical systems using both parametric and non-parametric approaches. He studied Engineering Science in Toronto and did his PhD in Cognitive Science at UC San Diego with Bradley Voytek.

Auguste Schulz | PhD Student |

Auguste studied physics in Heidelberg/London and for her masters neuroengineering in Munich before joining the lab as a PhD student in fall 2019. She is currently working on state-space models for neuroscience, in particular on sequential variational autoencoders for linking neural population activity and behaviour.

Guy Moss | PhD Student |

Guy joined the lab in October ‘21. He obtained his Masters in Mathematics and Physics from the University of Warwick. His interests lie in simulation-based inference, particularly in its application in analysing the history of ice sheets. He is co-advised by Prof. Reinhard Drews.

Jaivardhan Kapoor | PhD Student |

Jai joined the lab as a PhD student in September ‘21. He has a Bachelors and Masters in Electrical Engineering from Indian Institute of Technology Kanpur. Jai develops diffusion-based generative models for medical imaging and neuroscientific data. He is co-advised by Dr. Christian Baumgartner.

Janne Lappalainen | PhD Student |

Janne studied physics in Göttingen and neuroengineering in Munich. He uses deep learning to model neural circuits to understand circuit structure, stimulus-driven neural computation, and behavior in intelligent systems. In particular, in collaboration with researchers from the Janelia Research Campus, through models of the visual system of Drosophila trained to recognize motion in naturalistic movie sequences.

Julius Vetter | PhD Student |

Julius studied Cognitive Science and Computer Science in Tübingen. He joined the lab in September 2021 to work on machine learning methods for physiological time series data.

Linda Ulmer | PhD Student |

Linda joined the lab in April 2023 for a research internship investigating the dimensionality of a connectome-constrained network of the fruit fly visual system. She continued in the lab for her master’s thesis, working on integrating knowledge of neuronal tuning into connectome-constrained deep mechanistic models, and subsequently started her PhD in the group.

Lisa Haxel | PhD Student |

Lisa joined the lab in July 2023 as a PhD student, in collaboration with Prof. Ulf Ziemann’s Brain Networks and Plasticity Group. With an academic background in Biomedical Sciences and Psychology, her current research is focused on advancing EEG decoding techniques for personalized closed-loop brain stimulation.

Lucas-Raphael Müller | PhD Student |

Lucas studied Physics in Heidelberg. He joined the lab in May 2022 and works on Machine Learning algorithms for Microscopy. His office is at the European Molecular Biology Laboratory (EMBL), Heidelberg.

Manuel Glöckler | PhD Student |

Manuel joined the lab in April 2021 as a Master’s student. He worked on variational methods for simulation based inference together with Michael (arxiv). He continued as PhD student in April 2022, working on adversarial robustness and uncertainty calibration for amortized inference methods.

Matthijs Pals | PhD Student |

Matthijs studied Artificial Intelligence and Biomedical Engineering at the University of Groningen followed by Neuroengineering at the Technical University of Munich. He started his PhD in November 2021, with a focus on using deep learning (e.g. recurrent neural networks) to study representations and dynamics in the medial temporal lobe, in collaboration with Prof. Florian Mormann

Michael Deistler | PhD Student |

Michael joined the lab as a master’s student in March 2019 and then continued in the lab as a PhD student. He develops machine learning tools for neuroscience reseach and works on algorithms for simulation-based inference.

Sebastian Bischoff | PhD Student |

Sebastian joined the lab as a PhD student in late August ‘21. He studied IT-Systems Engineering at the Hasso Plattner Institute and Mathematics in Data Science at the Technical University of Munich (with stays at Georgia Tech and EPFL). He is working on dynamical models for single-cell data and is co-advised by Prof. Manfred Claassen.

Zinovia Stefanidi | PhD Student |

Zina joined the lab as a PhD student in February ‘22. She obtained her Bachelors and Masters in Computer Science from the University or Crete. She is working on how connectivity constrains the activity and dynamics of mechanistic models in neuroscience, and in particular how it influences efficient temporal processing.

Alana Darcher | Associate PhD Student |

Alana is a joint PhD student in the Macke group and Florian Mormann’s group at the University of Bonn. Prior to her PhD, Alana studied Neuroscience/Philosophy at Emory University in Atlanta, USA and Systemic Neurosciences at Ludwig Maximilian University of Munich. She joined the lab in 2019 as a Master’s student to study how human medial temporal neural populations track naturalistic stimuli over time, and has continued working on the DeepHumanVision project as a PhD student.

Felix Pei | Intern |

Felix joined the lab as an intern in November 2023. He is working with connectome-constrained models of the fruit fly visual system.

Franziska Gerken | Associate PhD Student |

Franziska joined the lab in 2020 as a joint PhD student in the Macke lab and the Dynamic Vision and Learning Group of Laura Leal-Taixé at the Technical University of Munich. She obtained her Bachelor’s and Master’s degree in Mathematics from University of Utrecht and University of Muenster. Franziska is working on the DeepHumanVision project and applies deep learning methods to understand how populations of neurons in the human brain process dynamic visual information.


Pedro Gonçalves | Postdoc |

Pedro worked in the lab as a postdoc and now leads a group in computational neuroscience and machine learning at KU Leuven/NERF. He is broadly interested in building biologically constrained theoretical models (combining methods from dynamical systems and machine learning) to guide new experiments and ultimately refine the models to further our understanding of neural systems.

Before joining the lab, Pedro was a postdoctoral research fellow working with Maneesh Sahani at the Gatsby Computational Neuroscience Unit, UCL. His PhD was supervised by Christian Machens at École normale supérieure in Paris (2012).

Álvaro Tejero-Cantero | Postdoc |

Álvaro worked in the lab as a postdoc. He developed software tools that make simulation-based inference accessible to scientist from diverse areas, in particular through the sbi toolbox. After his time in the lab, he set up the ML-Science-Collaboratory at the University of Tübingen, as special unit which seeks to increase the impact of machine learning (ML) on the sciences and the humanities.

David Greenberg | Postdoc |

David worked in the lab as a postdoc, developing algorithms for simulation-based inference and applying them to problems in the neurosciences. He then started the Model-driven Machine Learning Group at Helmholtz-Centre Hereon, which develops ML/physics hybrid models for simulating and forecasting climate and weather systems.

Jan Boelts | PhD Student |

Jan studied Cognitive Science in Osnabrueck and Computational Neuroscience in Berlin before joining as a PhD student in 2018. He worked on improving simulation-based inference algorithms to apply them to problems in neuroscience, e.g., to synapse formation rules in the rat cortex, or to models of decision-making.

Poornima Ramesh | PhD Student |

Poornima joined the lab as an intern/master’s student in 2016, while pursuing an MSc in Physics at IISER Kolkata in India. She started her PhD in 2018, and worked on applying statistical methods and generative adversarial networks (GANs) to neural data and simulation based inference.

Artur Speiser | PhD Student |

Artur studied physics in Karlsruhe and cognitive science in Osnabrück. In his PhD, he developed deep learning based inference algorithms for biophysical models (Read the article in the ML for Science Blog on DECODE). Throughout his PhD he closely collaborated with Dr. Srinivas Turaga from the Janelia Research Campus.

Giacomo Bassetto | PhD Student |

Giacomo got his Master’s in Bioengineering at the University of Padova in 2013. He joined the group as a PhD student in February 2014. His main project was the the development of statistical tools for the characterization of tuning properties and variability of neural population activity in primary visual cortex.

Jan-Matthis Lückmann | PhD Student |

Jan-Matthis studied Neuroscience in Amsterdam, Bordeaux, and Berlin for his Masters, and joined the lab in October 2014. In his PhD, he worked on simulation-based inference: Developing and benchmarking algorithms as well as applying them to mechanistic models in Neuroscience.

Marcel Nonnenmacher | PhD Student |

Marcel did his PhD in the lab. He worked on tools for analysing (sparse) neural recordings (see here and here) and on inference algorithms for mechanistic simulators (see here and here). After his PhD, he joined the Model-driven Machine Learning group at the Helmholtz-Zentrum Hereon for a postdoc, and subsequently the Gatsby Unit at UCL.

Margareta Schlüter | Master's student |

Margareta studied Neural Information Processing and joined the lab in November 2021 for a laboratory rotation and continued for her master thesis under the supervision of Matthijs and Stefanie. She worked on Recurrent Neural Network models using spiking activity that was recorded from humans during a working memory task.

Philipp von Bachmann | Master's student |

Philipp joined the lab in November 2022 for his Master’s thesis supervised by Janne, in which he investigates how ensembles can be used to perform probabilistic inference for neural circuit simulations.

Mila Gorecki | Master's Student |

Mila joined the lab in May 2021 for a research internship on simulation-based inference for settings where one is not only interested in finding the posterior over model parameters given a single observation, but instead aims to obtain the posterior given a range of permissible observation values. She continued in the lab to work her master’s thesis on loss-calibrated simulation-based inference.

Ingeborg Wenger | Master's student |

Ingeborg is pursuing a MSc in computer science with a focus on machine learning at Eberhard Karls University Tübingen. She joined the lab in April 2021 for an internship on simulation-based inference applied to drift-diffusion models, and continued in the lab to work on her Master’s thesis, supervised by Jan.

Hanna Dohmen | Master's Student |

Hanna joined the Lab in October 2021 for her Master’s thesis as part of her Master’s in Neural & Behavioral Sciences. Her project, supervised by Pedro and Julius, aimed to detect and predict freezing of gait in Parkinson’s disease patients.

Katharina Anderer | Master's student |

Katharina joined the lab in November 2021 for her master thesis, supervised by Cornelius, in which she investigated how one can infer micro-scale parameters like cell morphologies or conductances from a macro-scale brain signal like an event-related potential.

Paul Fischer | Master's Student |

Paul joined the lab in April 2021 as a Master’s student to study the rewiring of the adult brain in fruit flies. He developed probabilistic machine learning techniques to model fly walking behaviour, supervised by Auguste.

Florian Schönleitner | Master's Student |

Florian received a bachelor’s degree in Engineering Sciences (TUM) in 2018 after which he joined the elite master’s program in Neuroengineering. His research centers on machine learning algorithms for decoding neuronal signals in brain–computer interfaces. For his my master thesis he switched gears and developed a model-based diagnosis tool for middle-ear pathologies. Specifically, he works on a method which combines patient specific finite element models of the middle- and inner ear with simulation-based inference to enable quantitative, objective, and interpretable diagnosis of pathologies.