# JP van Paridon, PhD
<i class="fa-solid fa-envelope"></i>&ensp;[jvparidon@gmail.com](mailto:jvparidon@gmail.com)  
<i class="fa-brands fa-github"></i>&ensp;[github.com/jvparidon](https://github.com/jvparidon)  
<i class="fa-brands fa-linkedin"></i>&ensp;[linkedin.com/in/jp-van-paridon](https://linkedin.com/in/jp-van-paridon)  
<i class="fa-solid fa-file"></i>&ensp;[jvparidon.io/resume](https://jvparidon.io/resume)

## Profile
I am a computational cognitive scientist and data scientist with a background in language sciences. I use online and in-person experiments and large public datasets to inform statistical and computational models of human behavior and cognition.

## Employment
### Postdoctoral Researcher, University of Wisconsin-Madison, 2020-present
- Investigated how people learn about word meaning from other people's language use
- Designed and analyzed **online experiments** using statistical and ML techniques including **Bayesian hierarchical regression** models, dimensionality reduction, and clustering algorithms
- Used **NLP** models like word embeddings (e.g. word2vec) and transformer models (e.g. **BERT**, ALBERT) to model language use and learning mechanisms
### PhD Candidate, Max Planck Institute for Psycholinguistics, the Netherlands, 2015-2019
- Built computational and statistical models in **Python** to model the temporal dynamics of speech
- Developed and taught _Introduction to Python Programming_ course for graduate students in 2018 and 2019
- Organized 2018 Conference of the International Max Planck Research School for Language Sciences
- Engaged in international research collaborations as statistical and technical consultant
### Athletics Commissioner, Leiden University Rowing Club Asopos de Vliet, 2013-2014
- Fixed-term, full-time volunteer position supported financially by the University
- Coordinated coaching staff and regatta participation as well as fleet transports, maintenance, and purchasing
- During my tenure, the club won a Freshmen Eights National Championship for the first time in its forty-year history

## Education
- PhD, Cognitive Science, 2021, Radboud University & Max Planck Institute, the Netherlands
- MSc (with honors), Cognitive Neuroscience, 2015, Leiden University, the Netherlands
- BSc (honors thesis), Psychology, 2012, Leiden University, the Netherlands

## Open source software
- Lead developer and maintainer of [lmerMultiMember](https://jvparidon.github.io/lmerMultiMember/), an **R package** for modeling multiple membership random effects in (generalized) linear mixed effects models
- Developed [subs2vec](https://github.com/jvparidon/subs2vec), a **Python package** with **word embeddings** from subtitles in 55 languages

## Skills
- Statistical modeling in both **frequentist** and **Bayesian** frameworks
- **Machine learning**, including **NLP, feature selection, clustering,** and **dimension reduction**
- Data extraction and cleaning using e.g. pandas, dplyr, and **SQL**
- **Data visualization** in ggplot2, matplotlib and seaborn
- **Python** programming, including NumPy, scikit-learn, Jupyter notebooks, and PyMC
- **R** programming, including lme4, brms, rmarkdown, and various tidyverse packages
- **Version control** and **continuous integration** using git & Github Actions
- Designing and programming behavioral and **online experiments** using HTML/CSS/JavaScript
- Presenting technical concepts to non-technical audiences
- Coordinating international and multidisciplinary research collaborations

## Published papers
1. **J. van Paridon** & G. Lupyan (2022). Implicit communication of word meaning through co-occurrence. _Proceedings of the Joint Conference on Language Evolution._ [[pdf](pdfs/vanparidon2022implicit.pdf)]
2. G. Montero-Melis, **J. van Paridon,** M. Ostarek, & E. Bylund (2022). No evidence for embodiment: The motor system is not needed to keep action verbs in working memory. _Cortex._ [[pdf](pdfs/monteromelis2022evidence.pdf)][[doi](https://doi.org/10.1016/j.cortex.2022.02.006)]  
> For this project, we developed a **sequential testing** framework using **Bayesian multilevel regression** models to efficiently sample participants without violating statistical assumptions. I also integrated a Python interface for a MIDI drumkit into our behavioral experiment protocol as a cheap and effective way to track motor activity in participants' hands and feet.
3. M. Arunkumar, **J. van Paridon,** M. Ostarek, & F. Huettig (2021). Do illiterates have illusions? A conceptual (non) replication of Luria (1976). _Journal of Cultural Cognitive Science._ [[pdf](pdfs/arunkumar2021illiterates.pdf)][[doi](https://doi.org/10.1007/s41809-021-00080-x)]
4. **J. van Paridon,** Q. Liu, & G. Lupyan (2021). How do blind people know that blue is cold? Distributional semantics encode color-adjective associations. _Proceedings of the Annual Meeting of the Cognitive Science Society._ [[pdf](pdfs/vanparidon2021blind.pdf)][[doi](https://escholarship.org/uc/item/6sq7h506)]  
> For this project, I used a variety of **NLP and statistical techniques** to demonstrate that color knowledge in both blind and sighted people can be predicted from word embeddings. I also reworked the original **word2vec** algorithm to gain access to word embeddings during model training and track how specific sentences in the training corpus affect the final state of the embedding model.
5. **J. van Paridon,** M. Ostarek, M. Arunkumar, & F. Huettig (2021). Does neuronal recycling result in destructive competition? The influence of learning to read on the recognition of faces. _Psychological Science._ [[pdf](pdfs/vanparidon2021neuronal.pdf)][[doi](https://doi.org/10.1177/0956797620971652)]  
> In this project, I used **Bayesian multilevel regression** models to analyze behavioral data collected from illiterate participants to show that acquiring literacy does not degrade performance on other visual tasks (a claim that had been made repeatedly in the literature in prior years).
6. **J. van Paridon** & B. Thompson (2021). subs2vec: Word embeddings from subtitles in 55 languages. _Behavior Research Methods._ [[pdf](pdfs/vanparidon2021subs2vec.pdf)][[doi](https://doi.org/10.3758/s13428-020-01406-3)]  
> In this project, we produced a novel set of **word embeddings** in 55 languages from a large archive of film and television subtitles, using the fastText algorithm. Using several classic evaluation metrics (predicting similarity ratings using cosine distances; solving lexical analogies) and a novel lexical norm prediction task (implemented using **ridge regression**), we demonstrate that embeddings trained on subtitles contain information not well-represented in embeddings trained on e.g. Wikipedia, for instance about how offensive a given word is. The **subs2vec package** I developed alongside this project also provides a lightweight framework for working with word embeddings in Python.
7. G. Montero-Melis, P. Isaksson, **J. van Paridon,** & M. Ostarek (2020). Does using a foreign language reduce mental imagery? _Cognition._ [[pdf](pdfs/monteromelis2020foreign.pdf)][[doi](https://doi.org/10.1016/j.cognition.2019.104134)]
8. M. Ostarek, **J. van Paridon,** & G. Montero-Melis (2019). Sighted people’s language is not helpful for blind individuals’ acquisition of typical animal colors. _Proceedings of the National Academy of Sciences._ [[pdf](pdfs/ostarek2019sighted.pdf)][[doi](https://doi.org/10.1073/pnas.1912302116)]
9. **J. van Paridon,** A. Roelofs, & A. Meyer (2019). A lexical bottleneck in shadowing and translating of narratives. _Language, Cognition and Neuroscience._ [[pdf](pdfs/vanparidon2019lexical.pdf)][[doi](https://doi.org/10.1080/23273798.2019.1591470)]  
> In this project, I built a **computational model** to **simulate** the temporal coordination of speaking and listening processes during simultaneous interpreting, demonstrating that one significant limit on the rate of speech during this task is imposed by the demand to access lexical networks for both speech production and speech comprehension processes.
10. C. M. Warren, K. D. Tona, L. Ouwerkerk, **J. van Paridon,** F. Poletiek, J. A. Bosch, & S. Nieuwenhuis (2019). The neuromodulatory and hormonal effects of transcutaneous vagus nerve stimulation as evidenced by salivary alpha amylase, salivary cortisol, pupil diameter, and the P3 event-related potential. _Brain Stimulation._ [[pdf](pdfs/warren2019neuromodulatory.pdf)][[doi](https://doi.org/10.1016/j.brs.2018.12.224)]
11. Z. Shao, **J. van Paridon,**, F. Poletiek, & A. Meyer (2019). Effects of phrase and word frequencies in noun phrase production. _Journal of Experimental Psychology: Learning, Memory, and Cognition._ [[pdf](pdfs/shao2019effects.pdf)][[doi](https://psycnet.apa.org/doi/10.1037/xlm0000570)]

## Technical whitepapers and preprints
1. J. Sulik, **J. van Paridon,** & G. Lupyan (2021). Explanations in the Wild. _Preprint._ [[pdf](pdfs/sulik2021explanations.pdf)][[doi](https://doi.org/10.31234/osf.io/djaex)]
2. P. M. Alday & **J. van Paridon** (2020). Away from arbitrary thresholds: using robust statistics to improve artifact rejection in ERP. _Technical whitepaper._ [[pdf](pdfs/alday2020away.pdf)][[doi](https://doi.org/10.31234/osf.io/wqrb5)]  
> In this paper, we make recommendations for improving EEG analyses by using **robust models** which can account for outliers without having to reject data after setting arbitrary thresholds. We provide examples of analyses using robust models in both **frequentist** (using a robust estimator) and **Bayesian** (using a heavy-tailed likelihood) frameworks.
3. **J. van Paridon** & P. M. Alday (2020). A note on co-occurrence, transitional probability, and causal inference. _Technical whitepaper._ [[pdf](pdfs/vanparidon2020note.pdf)][[doi](https://doi.org/10.31234/osf.io/92zbx)]  
> In this paper, we explain that the high degree of **multicollinearity** between various measures of word frequency and transitional probability in linear regression models used in psycholinguistic analyses means that coefficients are often misinterpreted. It is hard to make blanket recommendations for analytical decisions, but we argue that in many cases, a theoretically motivated choice for one specific predictor leads to the most interpretable outcome.
4. M. Ostarek, **J. van Paridon,** & F. Huettig (2018). Cross-decoding reveals shared brain activity patterns between saccadic eye-movements and semantic processing of implicitly spatial words. _Preprint._ [[pdf](pdfs/ostarek2018crossdecoding.pdf)][[doi](https://doi.org/10.1101/415596)]  
> In this project, my role was to set up an **analysis pipeline** using **(cross-validated) SVM classifiers** trained on fMRI images from an eye-saccade task to predict the spatial orientation of words processed by the same participants in a different task from their corresponding fMRI images.

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