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A group of researcher from the Swiss Center of Affective Science investigated the neuronal networks underlying fundamental processes of taste perception.

I processed all the MRI data of 97 individuals to analyze and summarize the results in support of the paper intitled 3D-Printed Mouthpiece for fMRI-Compatible Gustometers.

My work allowed to visualize the parameter distribution of the BOLD signal in 3D.

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You can read the scientific paper on eNeuro. The source code of the project are of course on Github.

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Building and comparing brain maps between several models is a common task for neuroscientist.

Here we performed a Bayesian model selection (BMS) to select the best model given the data for group-level analysis. This procedure computes the probability of the data given the model (model evidence) for a set of candidate models. It also allows to compare characterize maps"heat zones" and to visualize them.

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You can read the scientific paper on BiorXiv. The source code of the project are on Github.

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I collaborated on the Hierarchical Bayesian modeling of Decision-Making tasks library hBayesDM. hBayesDM is a user-friendly package that offers hierarchical Bayesian analysis of various computational models on an array of decision-making tasks. It uses STAN for Bayesian inference and supports both R and Python. hBayesDM's goal is to help interpreting experimental data through computational models using a full Bayesian statistical inference approach with MCMC sampling.

My work involved the implementation of the Q-learning algorithm for reinforcement learning tasks.

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You can check my project contributions on Github.

Gaming Reinforcement Learning with PyTorch (Super Mario Bros)

Here I used OpenAI gym tools to build a Super Mario Bros environment, and trained a Proximal Policy Optimization (PPO) algorithm with actor/critic networks being convolutional neural networks (CNNs) with PyTorch to play the game with reduced actions. I also used TensorBoard to visualize and evaluate the quality metrics of the model.

You can check the project Github.

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Multiplayer AI programming online game

Leek Wars is a multiplayer AI programming game in which you train leeks to fight each others. The language of Leek Wars is LeekScript, built in Java. Follow my leek @leakit22 ! I remain in the top 100 for the time being.

You can check the AI on Github.

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Conway's Game Of Life implemented in Python

The “game” is a zero-player game, meaning that its evolution is determined by its initial state, requiring no further input. One interacts with the Game of Life by creating an initial configuration and observing how it evolves, or, for advanced “players”, by creating patterns with particular properties. How the game works Because the Game of Life is built on a grid of nine squares, every cell has eight neighboring cells,as shown in the given figure.

A given cell (i, j) in the simulation is accessed on a grid [i][j], where i and j are the row and column indices, respectively. The value of a given cell at a given instant of time depends on the state of its neighbors at the previous time step.

Conway's Game of Life has four rules:

1) If a cell is ON and has fewer than two neighbors that are ON, it turns OFF.
2) If a cell is ON and has either two or three neighbors that are ON, it remains ON.
3) If a cell is ON and has more than three neighbors that are ON, it turns OFF.
4) If a cell is OFF and has exactly three neighbors that are ON, it turns ON.


The bulk of the graphical implementation was made using PyGame.

You can check the project on Github.

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Description

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I have been involved in We Data since 2020. We Data is a Swiss organization that shares knowledge on code in social sciences, gives statistical courses, and do coding demonstrations.

Our goal is to promote code in the social science and raise awareness of new digital technologies.

You can check the project on our Blog, Github, or on our YouTube channel.

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The goal for this project is to provide context for the image quality metrics (IQMs) shown in the MRI quality metric group reports, by showing the distribution of IQMs for your data plotted relative to a larger set of anonymized IQMs pulled from the crowdsourcing web API.

The code provided creates an online interactive data visualization app using Dash and Plotly



You can check the project on Github.

Description

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Using Shiny Server Open Source? Want to see how many people are using your apps? This app allows you to monitor the number of users on your Shiny Server by continuously parsing through your server's logs and charting the data over time.

Know thyself

Keeping track of the number of users on your Shiny Server is helpful for several reasons:

- Identifies low-traffic periods (best times to push updates or restart the server).
- Tells you if you need to pay more attention to scaling your apps (i.e. adjust server size, optimize slow apps).
- Shows which apps are most popular.
- Basic architecture.
- Deploy Shiny Server Open Source with one or more apps.
- Continuously check your server's logs and save off data on the number of users on each app (check_server.R plus a cronjob).
- Use Shiny to display the data over time (app.R).


You can check the project on Github.

Description

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This Shiny app simulates a database management system featuring functions like login/logout, save/create/delete tables, add/rename columns, etc.

Features of this app:

Back-end database: A SQLite or PostgreSQL database that stores dummy data. Authorization: Credit to the package shinyauthr which provides module functions that can be used to add an authentication layer to shiny apps.

Highlights of the functions:

- Save tables: save a store sales summary table to the database.
- Update existing tables: rename tables, rename/add columns of tables.
- Create new tables: table and column names can be customized. Provide 4 types of column to be added - integer, float, varchar(255), and boolean.
- Create entries to tables: prompt columns contained in the selected table together with their types.
- Delete tables: the action is only accessible to specific authorization.
- Robustness: Defense mechanism that prevents duplicates, invalid expressions, and conflicts with SQL keywords are set for all the input table and colunm names to ensure the smooth execution of SQL queries. Once errors are detected, prompt messages will show up suggesting possible failure reasons.


The code provided creates an online interactive data management app using R and SQL. Check the Demo here.

You can check the project on Github.

Description

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This R package is a wrapper of the ECharts library. It provides a set of functions to generate interactive charts in R.

Highlights of this package:

- Provide functions to generate interactive charts in R.
- The package is built on top of the ECharts library, which is a powerful library for data visualization.
- The package provides a set of functions that can be used to generate charts with ECharts.
- The package is still under development. More features will be added in the future.
- The package is open source.

Features of the package:

- Provides more than 20 chart types available out of the box, along with a dozen components, and each of them can be arbitrarily combined to use.
- Has a powerful rendering engine that allows you to easily switch between Canvas and SVG rendering. Progressive rendering and stream loading make it possible to render 10 million data in realtime.
- Offers professional data analysis through datasets, which support data transforms like filtering, clustering, and regression to help analyze multi-dimensional analysis of the same data.
- Has an elegant visual design that follows visualization principles and supports responsive design. Flexible configurations make it easy to customize.
- Has a healthy community that ensures the healthy development of the project and contributes a wealth of third-party extensions.
- Is accessibility-friendly with automatically generated chart descriptions and decal patterns that help users with disabilities understand the content and the stories behind the charts.




You can check the project on Github.