Alexander MathisAlexander studied pure mathematics with a minor in logic and theory of science at the Ludwig Maximilians University in Munich. For his PhD also at LMU, he worked on optimal coding approaches to elucidate the properties of grid cells. As a postdoctoral fellow with Prof. Venkatesh N. Murthy at Harvard University and Prof. Matthias Bethge at Tuebingen AI, he decided to study olfactory behaviors such as odor-guided navigation, social behaviors and the cocktail party problem in mice. During this time, he increasingly got interested sensorimotor behaviors beyond olfaction and started working on proprioception, motor adaption, as well as computer vision tools for measuring animal behavior.
In his group, he is interested in elucidating how the brain gives rise to adaptive behavior. One of the major goals is to synthesize large datasets into computationally useful information. For those purposes, he develops algorithms and systems to analyze animal behavior (e.g. DeepLabCut), neural data, as well as creates experimentally testable computational models.
Martin SchrimpfExpertise
NeuroAI, computational neuroscience, computer vision, natural language processing, human alignment, startups [CV]
Mission
Modeling the brain: Martin's research focuses on a computational understanding of the neural mechanisms underlying natural intelligence in vision and language. To achieve this goal, he bridges Deep Learning, Neuroscience, and Cognitive Science, building artificial neural network models that match the brain’s neural representations in their internal processing and are aligned to
Mackenzie MathisCenter for NeuroprostheticsEPFL ELLIS Unit Faculty MemberCenter for Intelligent Systems
Mats Julius StensrudTeaching & PhD PhD Students Ignacio Gonzalez Perez, Gellért Géza Perényi, Lorenzo Gasparollo, Julien David Laurendeau Past EPFL PhD Students Matias Janvin, Amit Sawant Courses Causal thinking MATH-352 This course will give a unified presentation of modern methods for causal inference. We focus on concepts, and we will present examples and ideas from various scientific disciplines, including medicine, computer science, engineering, economics and epidemiology. Probability and statistics (
Virginia LangVirginia started in the medical research field as a Coordinator of Research at the Clinical Research Unit Kennedy Institute of Johns Hopkins Medical Center. She was responsible for the initiation, coordination, and management and/or analysis of research projects in the Clinical Research Unit. These projects were funded by various Federal Government Agencies. Subsequent to Johns Hopkins Medical Center, Virginia entered the field of Human Factors through the doctoral program at SUNY Binghamton. Virginia held a fellowship at the Aeromedical Research Laboratory of the United States Air Force at Wright-Patterson AFB, Dayton, Ohio and a fellowship at NASA. Her research dealt with cognitive factors that differentially affect the characteristics of visual displays. Applications of this research are directly related to various medical devices and software applications providing Medical Device Human Factors by HirLan clients with 30 years of Human Factors experience. In addition, Virginia has extensive experience in the areas of product design, Customer Centered Participatory Design processes, and product lifecycle management. She is also known in the Human Computer Interaction and Usability Communities for her innovative techniques for collecting and incorporating user/customer feedback throughout the product lifecycle. In July 2014, Virginia founded and established the HirLan Institute of Human Factors with labs in Carlsbad, California and a Consortium arrangement in London, UK. HirLan supports Top Ten pharma/medical device companies, as well as, start-ups with consulting and testing services. These services are in conjunction with their clients’ FDA 510(k) and CE mark applications. Virginia holds a PhD in Cognitive-Experimental Psychology from SUNY Binghamton, and a Master of Science (ABT) in Applied Technology, Systems Science from SUNY Binghamton.
Valerio ZerbiDr. Valerio Zerbi is a pioneer of preclinical magnetic resonance functional neuroimaging. At his lab at EPFL, Valerio and his team use functional MRI recordings in rodents in combination with neural modulation and computational approaches to study the mechanisms that link cellular activity to large-scale network (dys)function. His long-term ambition is to advance the development of personalised strategies based on imaging and artificial intelligence, for the diagnosis and prognosis of developmental brain disorders. Valerio is the recipient of the ETH Postdoctoral (2014), the Swiss National Science Foundation AMBIZIONE
(2017) and ECCELLENZA (2021) fellowships.
Angela Ingrid RentonExpertise
Visual perceptionAttentionPredictionDecision-makingM/EEGClosed-loop neuroimagingOpen & reproducible science
Mission
Angela Renton completed her PhD in 2022 at under the supervision of Prof. Jason Mattingley, at The University of Queensland, Australia. Her PhD investigated the development of novel neurofeedback and closed-loop research methods to causally investigate the neural mechanisms of visual perception. As a postdoctoral research fellow, she has expanded on this work, working on real-time ne
Olga FinkTeaching & PhD PhD Students Han Sun, Leandro Von Krannichfeldt, Keivan Faghih Niresi, Zepeng Zhang, Vinay Sharma, Chenghao Xu, Raffael Pascal Theiler, Kevin Antonio Steiner, Sergei Garmaev, Amaury Wei Past EPFL PhD Students Ismail Nejjar, Mengjie Zhao Courses Data Science for infrastructure condition monitoring CIVIL-332 The course will cover the relevant steps of data-driven infrastructure condition monitoring, starting from data acquisition, going through the steps pre-processing of real
Gioele La MannoTeaching & PhD PhD Students Albert Dominguez Mantes, Xinyi Huang, Laura Lapeña Gil, Alessandro Valente, Luca Fusar Bassini, Hao-An Hsiung, Alireza Gargoorimotlagh, Morgane Alice Storey, Rosan Martine Hans, María Cristina Loureiro Casalderrey, Lada Polyakova Past EPFL PhD Students Alex Russell Lederer, Irina Khven Past EPFL PhD Students as codirector Alexandre Coudray, Alex Russell Lederer, Halima Hannah Emilie Schede, Irina Khven Courses Biological data science I: statistical learning BIOENG-210
Steffen Alexander HerffSteffen graduated in Psychology at the Heinrich-Heine University Duesseldorf in Germany before going on to complete his PhD at the MARCS Institute for Brain, Behaviour, and Development focusing on 'Memory for Melody' in 2017, under the supervision of Prof. Roger Dean and Dr. Kirk Olsen.After his PhD, Steffen secured a DAAD-UA grant to conduct the "Noisy Ear" project in Germany, exploring context effects of background noise on memory before accepting a position at the Agency for Science, Technology and Research in Singapore, where he worked on Statistical Learning and memory paradigms in the auditory domain.Steffen's research is situated at the intersection between Memory, Music, and Neuroscience. His diverse interests and background have made him an integral part of many inter-disciplinary projects. These projects range from fundamental memory, learning, and perception research, over social negotiation between musical co-performers and the creation of new compositional tools, to non-invasive and invasive brain imaging in musical rhythm perception as well as brain stimulation to modulate memory encoding. Steffen strongly believes that inter-disciplinary research is the key to tackle complex, multi-facetted research questions.
Georg Michael Joachim StarkeDr. Georg Starke is a postdoctoral researcher at the College of Humanities at EPFL, working in the
Intelligent Systems Ethics Group
. As member of the
Hybrid Minds
project, Georg’s research examines the ethics of using artificial intelligence in medicine, with particular regard to neurological and psychiatric applications. Beyond AI ethics, Georg is also interested in history and philosophy of science, philosophy of psychiatry, medical ethics, and questions at the intersection of philosophy of mind and comparative psychology.
Georg has been trained in medicine and in philosophy, concluding both studies with a respective doctoral degree. He studied medicine at the Technical University of Munich and was visiting student at the University of Buenos Aires, the Hebrew University Jerusalem, and the University of Oxford. His medical doctoral thesis investigated subcortical correlates of social fear, using model-based fMRI. In parallel to his medical studies, Georg obtained a BA in philosophy from the Munich School of Philosophy and an MPhil in History, Philosophy and Sociology of Science, Technology and Medicine from the University of Cambridge. His PhD in bioethics at the University of Basel, defended in 2022, shed light on the notion of trust in medical AI and scrutinized the trustworthiness of clinical AI appliances with particular view to fairness and explainability.
