Teaching
Taught bachelor's-level research methodology to undergraduate students during the Winter 2025/26 semester at Aix-Marseille University.
PhD candidate in cognitive neuroscience with an interdisciplinary background spanning linguistics, cognitive science, computational methods, and neural time-series analysis. My work focuses on spoken communication and naturalistic conversation, with a particular interest in the methodological and theoretical challenges of studying interactive behaviour and brain activity together.
Aix-en-Provence / Marseille, France
+33 (0)7 88 80 78 57
hiroyoshi.yamasaki@etu.univ-amu.fr
Python, R, SQL, Java, C/C++
NumPy, pandas, SciPy, scikit-learn, PyTorch, matplotlib, MNE-Python
Praat, Jupyter, Git, Linux, Slurm, LaTeX
Japanese (native), English (C1/C2), German (C1), French (C1), Russian (C1), Czech (C1), Ukrainian (B2), Yiddish (B1, reading only)
Taught bachelor's-level research methodology to undergraduate students during the Winter 2025/26 semester at Aix-Marseille University.
Research focus on conversation, communication, and computational approaches to naturalistic speech.
Conduct interdisciplinary research on spoken communication and naturalistic conversation using neural time-series methods, integrating linguistics, neuroscience, and computational modeling.
Developed AI-based methods for automatic extraction of conversational features from speech data, contributed to corpus generation and curation, and co-authored two conference papers.
Master's thesis on machine-learning analysis of MEG data acquired during speech production. Overall grade 16.7/20; Year 2 grade 17.0/20.
Conducted machine-learning analysis of MEG data acquired during speech production as part of my master's thesis.
Theoretical research on relationships between neuroscience, artificial intelligence, and psychology.
Coursework in machine learning and natural language processing as a non-degree student.
Digitised and organised Yiddish materials at the Heidelberg Institute for Slavic Studies.
Focused on Russian, Ukrainian, and Czech linguistics. Grade: 1.1 (excellent).
Coursework in mathematical analysis, linear algebra, theoretical physics, and computer science as a non-degree student.