Contents:


Initiative Websites


"Data Reuse for Discovery" website

"Representational Brains and Phenotypes" website

"Physical Intelligence" website


Education Modules


Reproducible Theory-building link

Reusefulness of Data link

Explainable Methods link

Knowledge Maps (collection and discussion) link


Google Summer of Code Projects (Ethics, Society, and Technology and DevoWorm)



Ethics, Technology, and Society



A Collective Cognition Model for AI Ethics



The need for automated evaluation of real-time data is important in a number of sociotechnical contexts. Our group is looking to develop an auditing system and simulation of collective cognition that will improve open-source community sustainability. This interdisciplinary approach to AI ethics will involve both the development of a homeostatic system that encourages cooperative and altruistic interactions, and using simulated data generated through an agent-based model of open-source behaviors and interactions.

In taking a cybernetic approach, the candidate will build an analytical model that incorporates features such as general feedback loops (recurrent relationships) and causal loops (reciprocal causality). This might be in the form of a traditional boxes and arrows (input-output) model, or something more exotic such as Reinforcement Learning. Applicants might take inspiration from Mick Ashby’s ethical regulator (https://en.wikipedia.org/wiki/Ethical_regulator).

The broader goal is to build a model of cultural evolution that will encourage desired behaviors. The first part of this project will involve building a computational system to model the resources, activities, and interrelationships of an open-source community. The second part of the project will involve simulating this community using an agent-based model, which will provide the candidate with output data necessary to train and benchmark the cybernetic model.

What can I do before GSoC?

You can join the Orthogonal Lab Slack and Github, as well as attend our Saturday Morning NeuroSim meetings. You will work with our Ethics, Society, and Technology group, and interactions with your colleagues is key. You will also want to become familiar with a scientific programming approach (such as Python or Julia) to construct your cybernetic model, as well as the NetLogo platform for building agent-based models.

Requirements:

Expertise or the ability to learn Python, Julia, or Kotlin (for the cybernetic model) and Scala and Java (for the agent-based model). The ability to extract model representations from complex systems is helpful. Knowledge of open-source development practices and an interest in interdisciplinary research are a must.

Planned Effort: 350 hours. Mentors: Bradly Alicea (bradly.alicea@outlook.com), TBA

OpenWorm



GNNs as Developmental Networks

Biological development features many different types of networks: neural connectomes, gene regulatory networks, interactome networks, and anatomical networks. Using cell tracking and high-resolution microscopy, we can reconstruct the origins of these networks in the early embryo. Building on our group's past work in deep learning and pre-trained models, we look to apply graph neural networks (GNNs) to developmental biological analysis.

We seek to create graph embeddings that resemble actual biological networks found throughout development. Potential activities include growing graph embeddings using biological rules, differentiation of nodes in the network, and GNNs that generate different types of movement output based on movement seen in microscopy movies. The goal is to create a library of GNNs that can simulate developmental processes by analyzing time-series microscopy data.

DevoWorm is an interdisciplinary group engaged in both computational and biological data analysis. We have weekly meetings on Jit.si, and are a part of the OpenWorm Foundation. You may also have the chance to work with our DevoLearn (open-source pre-trained deep learning) software, in addition to adding your contributions to the DevoWorm AI library.

What can I do before GSoC?

You can ask one of the mentors to direct you to the data source and you can start working on it. Please feel free to join the OpenWorm Slack or attend our meetings to raise questions/discussions regarding your approach to the problem.

DevoWorm website: link

DevoLearn (preprint): link

DevoWorm AI: link

Skills/requirements

PyTorch/Tensorflow (PyTorch will be preferred because all our other models are on that framework already) Wrangling with video data Building a simple GUI on top of the model to run it on local systems (on Linux/windows/macOS). Basic knowledge of biology and complex networks theory would be helpful.

Planned Effort: 175 hours Mentors: Bradly Alicea (balicea@illinois.edu), TBA

Digital Microsphere

This project will build upon the specialized microscopy techniques to develop a shell composed of projected microscopy images, arranged to represent the full external surface of a sphere. This will allow us to create an atlas of the embryo’s outer surface, which in some species (e.g. Axolotl) enables us to have a novel perspective on neural development. You will build a computational tool that allows us to visualize 4D data derived from the surface of an Axolotl embryo.

What can I do before GSoC?

Build basic prototypes for this project and discuss about them with the mentors, then read these papers:

Gordon, R. (2009). Google Embryo for Building Quantitative Understanding of an Embryo As It Builds Itself. II. Progress Toward an Embryo Surface Microscope. Biological Theory, 4, 396–412.

Crawford-Young, S., Dittapongpitch, S., Gordon, R., and Harrington, K. (2018). Acquisition and reconstruction of 4D surfaces of axolotl embryos with the flipping stage robotic microscope. Biosystems, 173, 214-220.

Skills/requirements

Handling higher dimensional microscopy data (preferably also creating an API to load them as tensors for computation on the GPU). Building an intuitive GUI (or a web interface). Feature extraction (canny edges/thresholding/denoising).

Planned Effort: 175 hours Mentors: Bradly Alicea (balicea@openworm.org), Susan Crawford-Young (susan.crawfordyoung@gmail.com).


Selected Projects with Links



Educational Working Group: "Developmental Braitenberg Vehicles" Github repo

Featuring: BraGenBrain link



eLife Ambassadors initiative: "Data Reuse for Discovery" Github repo

Paper: "Data Reuse as a Prisoner’s Dilemma: the social capital of open science" preprint

"A Formal Approach to Data Recycling": grant submitted to Mozilla Foundation document (Markdown file)



Project: New Directions in Heterochrony

Paper: "Understanding Developmental Incongruity Through Dynamical Heterochrony" preprint

Notebook for Compound Heterochrony notes (reveal.js)

Paper: "Multicell systems: a compositional approach to multicellularity and embryogenesis" preprint



Project: Contextual Geometric Structures (CGS) and Cultural Evolution

Presentation: "Meta-brain Computational Models for Representing Cultural Diversity" Github repo

Working Paper on Ideological Game Theory document (Markdown file)