معرفی
Dr. Reny Baykova is a Lecturer in Psychological Methods (Reproducibility) at the University of Sussex, School of Psychology, appointed in January 2024. Previously, she served as a Business Systems Analyst (2023) and Research Fellow (2021-2023) within the same university. Her academic foundation includes a PhD in Cognitive Neuroscience from Sussex (2016-2020) and degrees from the University of Glasgow in Psychological Science.
Her educational background:
- PhD, Cognitive Neuroscience, University of Sussex (2016-2020)
- MSc, Research Methods in Psychological Science, University of Glasgow (2015-2016)
- MA (SocSci), Psychology, University of Glasgow (2011-2015)
Dr. Baykova's research bridges computational reproducibility and time perception neuroscience. She pioneers pre-submission reproducibility verification systems while investigating how Bayesian inference shapes duration estimation. Her work on the Perception Census (2023-2025) demonstrates large-scale application of these principles, examining perceptual phenomena through an open science lens. This dual focus creates unique synergies between methodological rigor and cognitive theory.
Her publication record shows evolution from traditional cognitive neuroscience (2015-2019 electrophysiology studies on duration perception) toward reproducibility infrastructure development (2021-present). Recent outputs emphasize practical open science frameworks rather than conventional papers, reflecting her institutional role in research reform.
Recognition for her contributions includes:
- Shortlisting for the 2025 Sussex Awards in Open Research
- Shortlisting for the 2024 Openness in Research Award
- UK Reproducibility Network Data Management Badge (2025)
As an educator, she teaches core methodology courses including Discovering Statistics and Research Reform and Open Science, while supervising undergraduate/master's projects. Her external reproducibility audit work and public engagement (e.g., Big Bang Fair 2022) demonstrate commitment to translating research infrastructure into practical impact. Though not leading a named lab, her OSF-hosted reproducibility project (https://osf.io/dr35v/) serves as a focal point for collaborative methodological development.
