About me

<<This website serves mainly as a summary of my academic projects>>
Before joining EY, I was a postdoctoral researcher (Cai Lab, UTokyo) with a wide range of interests. My work is interdisciplinary and combines artificial learning systems, computational modeling and neural data. My academic work has focused on understanding how training experiences and the representational format of knowledge interact to produce learning and intelligent behavior. Enthusiastic about leveraging my expertise in analyzing complex datasets for practical problem-solving and data-driven decision-making as I make the transition to data science.
Previously at Summerfield lab, University of Oxford.

Selected publications

Experience

Cross-task fMRI decoding of spontaneous thought

Charted the dynamics of spontaneous thought content, and how this is affected in depression, using functional magnetic imaging, cross-task shared response modeling and large language models (CLIP and GPT)

[2023 CCN paper (currently still under embargo)]

PhD Project 1: Internal noise as a determinant of curriculum efficacy

Using neural network simulations, behavior and EEG, we establish that boundary-proximal training is sensitive to internal late noise, which forestalls learning

[2019 SBDM conference poster] [Thesis Chapter 2]

PhD Project 2: Limitations in human probabilistic evidence integration

Standard models predict that choice becomes more deterministic the more cues are available. We demonstrate across 7 experiments that this is not the case for human learners, and explain this using models involving heuristic decision rules and constraints on information capacity

[Thesis Chapter 3]

PhD Project 3: novel task learning as recombination of past knowledge

Primarily theoretical work positing that few-shot learning and forward transfer may be subserved by treating new tasks not as de novo problems, but as problems that can be solved by compositional recombination of past solutions

[internal lab presentation]

PhD Project 4: compositional rule learning in humans and machines

Our behavioral work shows that humans, but not standard learning algorithms, succeed at generalization in a compositional rule learning task. We propose a comprehensive model based on recursive computation and specialized task modules to bridge these differences

[paper] [Thesis Chapter 4]

Tesla: Consultancy project for healthcare innovation company Active Cues

Conducted market research for 50 psychopathological groups. Brought together researchers, developers and clinicians and developed a serious game tackling substance abuse using interactive light projections, bringing a scientific framework into practice

[poster] [brochure] [report] [prototype]

Amsterdam Brain and Cognition (ABC journal)

As part of the ABC journal's editorial board, I compiled research from the local research community, reviewed and selected articles, conducted interviews and assembled this into a publishable format using Adobe InDesign.
(password of the attached example is ABCjournal4)

[example issue]

Saccadic adaptation and mislocalization (Free University Amsterdam)

Designed and conducted an eye-tracking experiment, supervised by Daan van Es & Tomas Knapen

[report]

Education

  • University of Oxford
    DPhil in Experimental Psychology | 2017 - 2021
  • University of Amsterdam
    MSc in Brain & Cognitive Sciences (cum laude) | GPA: 9.0/10 | 2015 - 2017
    BSc in Psychobiology (with honours) | 2012-2015
    BSc in Interdisciplinary Sciences (B├Ęta-gamma) | 2010-2015