Explore Projects

This project aims to improve the reliability and maintainability of the LORIS electrophysiology browser through scoped enhancements to browser behavior, validation, testing, and documentation. I plan to begin by reviewing and reproducing known issues or limitations, then implement targeted improvements in small, reviewable stages with mentor feedback. The expected deliverables include a practical browser improvement, expanded automated test coverage, and developer-facing documentation that helps make the contribution sustainable within the LORIS codebase.

The ever-growing body of data in Neuroscience, which has been enabled by the advancement of state-of-the-art techniques, calls forth collaborative attempts to make data FAIR (findable, accessible, interoperable and reusable). Consequentially, Neurodata Without Borders (NWB), an open-source software that defines components of a language for neurophysiology data, was born out of such need. NWB has thus exemplified as a sustainable biological data ecosystem, but there lies the need of converting readily available datasets into NWB format to allow for further sharibility and producibility. With that said, this project aims to (i) convert a publically available dataset into NWB format, (ii) ensure NWB datasets' compatibility with NWB explorer (NWBE), (iii) and facilitate generality of the conversion process by investigating the sources of NWBE-NWB incompatibilities and ensuring essential metadata being added. This is a medium size project (175h).

HarmonyHub is an open-source platform designed to improve music education through interactive and technology-driven learning experiences. While the current application provides note practice and visualization features, its mobile experience requires improvements in performance, responsiveness, and stability. This project focuses on strengthening the mobile application by optimizing UI responsiveness, improving performance across devices, enhancing error handling, and introducing offline capabilities. In addition, it proposes the development of a relative intonation learning module built on top of the existing chromatic tuner, enabling users to practice pitch accuracy and musical intervals with real-time feedback. Key deliverables include a fully optimized mobile experience, a functional relative intonation training system with real-time feedback, improved audio processing performance, and cross-platform stability for iOS and Android devices. The final result will be a more robust, scalable, and pedagogically effective mobile application, making music learning more accessible, interactive, and engaging.

<p>Time-series analysis is a broad, interdisciplinary field, and features for analyzing time-series datasets are ever-increasing. This has led to creating an online Django platform, CompEngine-Features, for comparing new time-series analysis features with an existing set of over 7000 time-series analysis features in the hctsa package. However, the platform lacks: support for incorporating new features contributed by users, client-side rendering of web pages, network visualizations, line plots for Empirical1000 dataset, and async views in Django, all of which are crucial for the adoption of the platform and thus its ability to have a significant impact in driving progress in time-series analysis. In this project, we will continue developing a Django online platform, CompEngine-Features, for comparing the performance of time-series analysis methods on real time-series data, including a wide range of neural dynamics.</p>

Implement an ast based behavioral filter enabling queries like `(treadmill>0 and eye is not nan) & screen == video`, and expand dataset support with brainsets-style integration of IBL Brain Wide Map and Allen Neuropixels.

<p>This proposal is for adding MPI support to GeNN. GeNN is known as a GPU-enhanced Neuronal Network simulation environment based on code generation for Nvidia CUDA. However, the limitation of GeNN is that it can only support running on a single GPU or a single shared-memory machine. This proposal is to plan for expanding GeNN to multiple machine clusters. MPI is common message passing interface and infrastructure to communicate across multiple hosts and it is natural to expand GeNN with MPI interface to achieve parallel execution on multiple hosts. The key point of this project is to balance GeNN computation simulations across MPI hierarchy between hosts and block/thread on individual GPU. Furthermore, This proposal shows potential tuning directions basing on MPI-CUDA hierarchy. To bridge the gap between the destination and implementation, this proposal also describes feasible stages for the MPI-GeNN project and narrates the candidate’s motivation, background and progressive approach. Curriculum vitae is attached to the last pages for reference. The candidate is welcome to any questions, comments or suggestions through any of the contacts, such as email, telephone, and skype.</p>

<p>The release of a new NWB version makes it necessary the reintegration of this format into the awesome open-source in-browser neuroscientific simulator, Geppetto. This integration will allow any Geppetto based application to be able to provide visualization of simulated data alongside of electrophysiology recordings.</p> <p>The purpose of this project is to make the above happen.</p>

<p>This proposal outlines the steps and experience needed for a javascript based data visualization tool for LORIS through the React framework. A method to validate uploaded data will be needed to be integrated as well with proper testing and documentation after weekly meetings and feedback from mentor(s) to ensure project deadlines are met.</p>

To Remove Bootstrap from the application and substitute the design with pure SCSS.To finish implementing the CSS global variables and add component wise CSS variables to improve the reusability of code in the application.To add CSV viewing functionality on the datasets of AnalySim to view, filter, and browse through the columns of the file. To create a feature on the Jupyter notebooks to make the Notebooks downloadable.To improve the Registration page, Explore section page, and my Dashboard page design and make the User Interface consistent throughout the application.To remove the pages and the page links that are unused in the application. To improve and redesign the project creation page. To make the design of the application consistent throughout the application to create a great User experience.

<p>This project is about integrating the resources available at the OpenWorm and DevoWorm group. This project will focus on improving the data science and machine learning infrastructure of the DevoWorm group. In this project, we need to integrate the different previous year GSoC projects under one roof. The priority this year is to improve the web interface to make it more and more user-friendly. We want to provide a general analysis and statistical tools so that researchers around the world can use these tools for their analysis. This includes the ability to incorporate new forms of analysis as well as algorithms for new types of data. The ability to extract quantitative data from the movie images is key to conducting comparative and time-series analysis. A dashboard will give researchers an edge to easily see their results and interpret them. This project will help the organization to compile its resources as well as to spread its cause i.e. - an attempt to build the first virtual organism.</p>

The Turing Way is an open source, open collaboration and community-led handbook on data science. The book is hosted online in a browsable format. Over the last four years, the book has grown significantly, making it challenging to navigate. The team created a Python package in 2022 to enhance The Turing Way's usability by enabling various access points to the book depending on the user profile or persona. But before integrating the feature provided by the package to Turing Way , there are some improvements that need to be made to allow a better user experience. The project aims at improving the Python Package to enhance the usability of The Turing Way. The following approach will be used to achieve this goal : - 1- Making browsing experience less confusing by modifying chapter links and profile tags based on a selected ‘pathway’, rather than displaying all tags associated with a chapter. 2- Adding a feature to provide descriptions for each pathway which will improve user experience allowing meaningful use of books by all profile types. 3- Implementing the solutions proposed in this application and integrating them into The Turing Way via the open source framework.

<p>Make TVB Visualization Tools compatible with Jupyter Notebooks and Google Colaboratory</p>

<p>This project is about packaging of the the virtual brain(tvb) to the scientific community, using the most used scientific software distributors such as packaging tvb to anaconda, Develop a native launcher for tvb distributions, Packaging and distribution for neurodebian , Develop a vagrantfile script for virtual machines, Develop a script( Dockerfile ) for building a docker image, And finally if time allow develop a script for building a Amazon Web Image(AMI).</p>

This project aims to further develop an open-source intracranial EEG (iEEG) analysis platform by integrating clinically relevant seizure detection and seizure onset zone (SOZ) estimation algorithms, along with enhanced visualization tools. The goal is to support clinicians and researchers through a semi-automated, transparent, and interactive workflow for epilepsy analysis. By combining automated methods with expert input, the platform will facilitate reproducible and interpretable analyses in both clinical and research settings.

<p>The project aims to provide a robust mechanism for cell tracking using 2D raw image object.Through the use of Viterbi Dynamic Programming based Algorithm it is aimed to implement an efficient Cell Tracking system aided by Multi Class Classifiers for Cell Event Probability definition.Trajectory estimate will be de-noised using modern filters such as IMM,Weiner and Multiple Channel Linear Correlation Filter.</p>

We are trying to solve the difficulty of collaboration among multiple users working on large datasets, particularly in analyzing datasets with many parameters and essential features that need to be filtered, measured, and analyzed. The solution is AnalySim, a data-sharing platform that simplifies collaboration by providing easy sharing, analysis, visualization, and collaboration capabilities on datasets. The first deliverable is the ability to embed Jupyter notebooks, Observablehq notebooks, and Google Collab notebooks on the website using an interactive panel. The second deliverable is an interactive panel or interface that displays a dataset's different types of features, including minimum, maximum, mean, number of non-zero, and the number of invalid values. The interface should show other visualization options like histograms, pie charts, multi-series line charts, pie charts, graphs, and visualizations representing data with more than two dimensions, e.g., 3D Scatter plots, 3D Mesh Plots, 3D line plots, box plots, and bubble charts. The third deliverable is the ability for users to add publications related to the datasets in a project. We will achieve this by creating a text editor page where users can add content, edit content, remove content, add tables, and add images. The text editor should allow users to add latex code so that users can publish mathematical analyses of the findings. We will also implement a page that gets the list of publications, which should contain clickable items that redirect to another page that details the publication. Finally, we will refactor some of the code to maintain a better project structure, encapsulate numerous fragments of the same code in another method to improve the readability of the code and remove deprecated methods.

<p>While running simulation on cortical surfaces we need to calculate geodesic distance as opposed to euclidean distance due to the shape of the cortical surface. The virtual brain uses geodesic_library for this calculation. The library implements the <a href="https://pdfs.semanticscholar.org/f890/2dc723ac2a49ee52efbc58947c21f8d0970b.pdf" target="_blank">original paper</a> in C++. The original source code can be found in Google Code Archive: <a href="https://code.google.com/archive/p/geodesic" target="_blank">https://code.google.com/archive/p/geodesic</a>. tvb_geodesic repository implements a cython wrapper on top of the C++ code which then is released to Pypi (<a href="https://pypi.org/project/tvb-gdist/" target="_blank">tvb-gdist</a>) and conda-forge (<a href="https://anaconda.org/conda-forge/tvb-gdist" target="_blank">Tvb Gdist</a>).</p> <p>However, the code is now outdated and users have reported various <a href="http://github.com/the-virtual-brain/tvb-geodesic/issues/" target="_blank">issues</a>. In this project, we aim to update the code and fix those issues.</p>

<p>The major goal of this project is to Extend the work of making plugin which was developed in last year. This year we need to come up with some current architecture change to incorporate learning from entire images which can be further used to image classification. Other then this we need to add some additional features selection to the existing platform and change the current GUI according to this.</p>

<p>CBS Tools are specialized Java-based image processing tools for ultra-high field MRI data. Making this potent software more easily accessible to researchers will accelerate progress in the promising field of high-resolution neuroimaging. The goal of my project is therefore to make CBS Tools available through Nipy, a popular “community of practice devoted to the use of the Python programming language in the analysis of neuroimaging data”. First steps have been made to encapsulate CBS Tools’ Java classes using the JCC package, enabling access through Python. Within the Google Summer of Code I would like to take this further and provide easy-to-use and well-documented Python interfaces for the core modules of CBS Tools. Since rapid advances in the dynamic field of high-resolution neuroimaging are to be expected, another focus of my proposal is to facilitate future contribution of other researchers. My three main objectives are (1) to provide straightforward installation routines for different platforms, (2) to create Nipype interfaces for the core modules and (3) to make the code accessible through documentation and realistic examples.</p>

<p>ImageJ is extensively used in major areas of biological and material sciences. Previously developed active segmentation platform as a plugin for ImageJ incorporate Weka toolbox-based statistical machine learning algorithms as well as deep learning techniques for trainable image segmentation. The end goal of the active segmentation platform for ImageJ is to provide researchers an extensible toolbox enabling them to select custom filters and machine learning algorithms for their research. Under the existing implementation of the active segmentation platform, it only supports users with a limited way to load the ground truth for learning ( at the moment only as of the region on interest format (ROI)). Thus, this reduced the usability of the tool and it urges the users to convert the ground truth to the specific format which is designed to be used within the application. Therefore the main contributions under this project will be to incorporate several ground-truth formats as image-based in which each pixel uniquely belongs to a particular class, partial ground truth format in which instead of the whole image and several partial boxes in an image or stack are labeled.</p>

<p>This project is about creating a responsive dashboard framework for extensive exploration, monitoring, and reviewing large neurological imaging datasets present on the XNAT server instance. This dashboard will fetch data from any XNAT instance servers and will generate highly-visualized, summarized representations of complex scientific data present on the servers and facilitate user navigation through large cohorts. This dashboard will be a light-weight, flexible, and modular framework that can adapt and change as per the new needs of the users.</p>

<p>Currently the human assistive system collects the EEG data, processes it and trains customized classifiers. With the increasing number of tested subjects, the goal is to store the rapidly growing data in a distributed storage system such as Apache Hadoop. Data processing would also be implemented on the distributed system using the MapReduce framework and its extension Apache Spark.</p> <p>The goal of this project is to create a scalable system which would enable storage of very large datasets and quick, distributed training of classifiers on those large datasets. Another goal is to provide the users with a GUI for browsing and managing the distributed filesystem as well as building full machine learning pipelines.</p>

The Active Segmentation platform for ImageJ (ASP/IJ) was developed in the scope of GSOC 2016 - 2021. The plugin provides a general-purpose environment that allows biologists and other domain experts to use transparently state-of-the-art techniques in machine learning to achieve excellent image segmentation and classification. ImageJ is a public-domain Java image processing program extensively used in life and material sciences. The program was designed with an open architecture that provides extensibility via plugins computing different filters and region descriptors (i.e. image features). The feature space and the classification results produced by the platform are stored in several separate files. The idea is that the types and values of image features and classification outcomes would be stored in an SQLite database for cross-comparisons between sessions. The candidate is required to use the SQLite database engine in order to integrate it with the GUI of ASP/IJ.