Michael Herzog
EPFL SV BMI LPSY
SV 2807 (Bâtiment SV)
Station 19
1015 Lausanne
+41 21 693 96 46
+41 21 693 18 12
Office:
SV 2807
EPFL › SV › BMI › LPSY
Website: https://lpsy.epfl.ch/
+41 21 693 96 46
EPFL › SV › SV-SSV › SSV-ENS
Website: https://sv.epfl.ch/education
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Website: https://go.epfl.ch/edne
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Website: https://go.epfl.ch/phd-edne
Teaching & PhD
PhD Students
Melissa Mohamad Allouche, Melissa Faggella, Marie Isabelle Holdsworth, Sandali Liyanagoonawardena, Pierre Merret, Martina Morea, Elsa Scialom, Ece Yatikçi
Past EPFL PhD Students
Kristoffer Aberg (2010), Marco Boi (2010), Alban Bornet (2021), Gizay Ceylan (2024), Vitaly Chicherov (2015), Oh-Hyeon Choung (2021), Aline Françoise Cretenoud (2021), Adrien Doerig (2020), Leila Drissi Daoudi - Kleinbauer (2020), Ophélie Favrod (2019), Simona Adele Garobbio (2024), Dario Alejandro Gordillo Lopez (2024), Lukasz Grzeczkowski (2016), Maya Anna Jastrzebowska (2020), Marc Michael Lauffs (2017), Wei-Hsiang Lin (2024), Ben Lönnqvist (2025), Harshitha Machiraju (2024), Mauro Manassi (2014), Maëlan Menétrey (2024), Thomas Otto (2008), Ayberk Ozkirli (2025), Janir Nuno Ramos da Cruz (2019), Johannes Rüter (2010), Toni Saarela (2009), Bilge Sayim (2010), Frank Scharnowski (2007), Elisa Tartaglia (2009), Yvonne Evelina Thunell (2014), Lukas Vogelsang (2024), He Xu (2020)
Courses
Cognitive Landscapes
PENS-326
This course explores how brainwave dynamics can inform urban design through cross-frequency coupling as a model for adaptability, variability, and integration in urban systems.
Neuroscience: behavior and cognition
BIO-483
The goal is to guide students into the essential topics of Behavioral and Cognitive Neuroscience. The challenge for the student in this course is to integrate the diverse knowledge acquired from those levels of analysis into a more or less coherent understanding of brain structure and function.
Understanding statistics and experimental design
BIO-449
This course is neither an introduction to the mathematics of statistics nor an introduction to a statistics program such as R. The aim of the course is to understand statistics from its experimental design and to avoid common pitfalls of statistical reasoning. There is space to discuss ongoing work.