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email: gota.morishita [at] gmail.com
Biography
Biography
I’m Gota Morishita, a PhD candidate in Decision, Risk, and Financial Sciences at the Centre for Brain, Mind and Markets (CBMM), University of Melbourne. I am fortunate to be supervised by Shinsuke Suzuki, Carsten Murawski, and Nitin Yadav.
My research focuses on how humans learn from others to guide their own decisions—that is, social or observational learning. I use computational modeling (e.g., reinforcement learning algorithms) and neuroimaging (fMRI) to understand the neural and computational mechanisms underlying social learning and decision making.
Before beginning my PhD, I received an M.S. in Science and Engineering from Keio University, Tokyo, Japan.
Prior to academia, I worked in the tech industry as a machine learning researcher and data scientist. At CyberAgent, I developed algorithms to personalize online advertising using causal inference techniques. Later, at note.inc., a blogging platform startup, I worked as a machine learning engineer and data analyst, developing deep-learning-based article recommendation algorithms and building an A/B testing pipeline to accurately estimate treatment effects.
Technical Skills
Acquried skills during Ph.D.
Computational Modeling: Modeling human decision-making using reinforcement learning frameworks in the context of social (observational) learning.
Data Analysis: Proficient in Python. I use R only when running generalized mixed-effects models.
Bayesian Modeling: Proficient in Stan; experience with Bayesian hierarchical modeling for cognitive and behavioral data.
Neuroimaging Analysis: fMRI preprocessing and model-based statistical analysis using SPM (MATLAB) and Nilearn (Python).
Experimental Programming: Proficient in PsychoPy and PsychoJS for designing and implementing behavioral experiments.
Acquried skills during work experience
Recommendation Algorithm Development: Fine-tuned deep learning models such as word2vec/sentence2vec and Graph Neural Networks to build high-quality recommendation systems (in the pre–ChatGPT era).
Machine Learning Pipeline Development: Built and maintained ML pipelines on AWS for one of Japan’s largest websites (ranked 11th nationally as of November 10, 2025).
A/B Testing: Designed, executed, and evaluated A/B tests at production scale.
Award
Riady Scholarship, 2025
A prestigious university-wide award granted to only one high-achieving research student per Faculty/Academic Division annually (selected from 50+ PhD candidates in the Faculty of Business and Economics).
Business and Economics Graduate Research Enhancement Grant, 2025
Competitive funding supporting advanced graduate research activities.
M. A. Bartlett Research Scholarship, 2025
Competitive award supporting graduate research fieldwork.
Business and Economics Graduate Research Abroad Travelling Scholarship, 2024
Competitive funding supporting overseas research and scholarly activities.
Business and Economics Graduate Research Enhancement Grant, 2024
Competitive funding supporting graduate research activities.
Business and Economics Graduate Research Abroad Travelling Scholarship, 2024
Competitive funding supporting international research engagement.
Business and Economics Doctoral Program Scholarship, 2021
Full scholarship supporting the duration of doctoral study and research.
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