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Antibody-Drug Conjugates (ADCs) represent a promising class of cancer therapeutics designed to selectively target cancer cells while minimizing damage to healthy cells. Unlike conventional chemotherapeutic agents, which often cause off-target effects and severe side effects, ADCs combine the specificity of monoclonal antibodies with the potency of cytotoxic payloads. This project focuses on integrating ADC research tools into DeepChem to facilitate computational exploration for researchers. My contributions include developing tutorials and implementing ADCNet, a deep learning framework tailored for ADCs, to bridge the gap between ongoing ADC research and accessible computational tools.
The goal of my project is to add OGC standard of Sensor Things API(STA) to istSOS project. The OGC SensorThings API provides an open, geospatial-enabled and unified way to interconnect the Internet of Things (IoT) devices, data, and applications over the Web. The project aims to extend interoperability and implement some of the basic APIs from STA that could be adopted in a new revised version of istSOS. FastAPI can be used for the request and response of the istSOS STA services. As PostgreSQL is used as the primary data store or data warehouse in istSOS project, data can be managed using postGIS which is a spatial database extender for PostgreSQL object-relational database. PostgREST is a standalone web server that could be used to turn the PostgreSQL database of STA data directly into a RESTful API. Finally the STA service can be integrated with the istSOS project.
<p>The Heavy Flavour AVeraging (HFLAV) group is responsible for collecting and combining measurements made at different High Energy Physics (HEP) experiments, at CERN and other particle physics laboratories, and combining them using robust statistical procedures.</p> <p>The HFLAV website provides a live snapshot of the latest data obtained from these latest publications. This project aims to improve the experience of users using the HFLAV website. These averages could be of greater value to the particle physics community if the accessibility, interactivity and visibility of the current website can be improved. Thus this project seeks to transform user interaction with HFLAV’s ‘Beauty to Charm’ averages.</p> <p>The plan to increase the accessibility is by redesigning the whole user interface, making it easier to find a relevant data point ( branching fraction) by the use of filters. As the old website is static, thus in order to make the site responsive to the user's input, the website will be refactored and the framework will be changed to Django. The application would be hosted using CERN's OpenShift service instead of the older EOS filesystem it was using.</p>
<p>Now, we have skunkworks-crow, which is the GSoC 2018 project. In this year, we need to improve the project and develop a new strategy to address the highest-value limiting factors to broaden the base, and propose a strategy to validate behavior on devices we may not have access to.</p>
Devfiles are YAML files that define remote development environments. The main part of a Devfile is the `components` section and that's where the containers required to code, build and test an application are specified. The Devfile can either include those containers defintions or reference external files such as Dockerfiles or Kubernetes manifests. [The Compose file](https://github.com/compose-spec/compose-spec/blob/master/spec.md) is a popular format in open source development projects to define runtime environments for testing the application but those cannot be referenced by a Devfile yet. The goal is to continue the work that has been started a couple of months ago to allow referencing a Compose file from a Devfile. The expected outcome is to create a PoC written in go that parses a Compose file such as [this one](https://github.com/microservices-demo/microservices-demo/blob/master/deploy/docker-compose/docker-compose.yml) using [kompose](https://github.com/kubernetes/kompose) (as a library, not as an executable) and that creates the objects corresponding to the Compose file services in a Kubernetes cluster.
<p>As given on the <a href="https://learn.bela.io/get-started-guide/say-hello-to-bela/#what-is-bela" target="_blank">official website</a>, Bela is a hardware and software system for creating beautiful interaction with sensors and sound. Bela consists of a Bela cape on top of a BeagleBone Black computer (uptil now). The idea is to enable smooth transition of the associated software to the BBAI and future TI chips.</p>
This project addresses the challenge of unreliable LLM outputs in complex health informatics workflows by building a domain-grounded Retrieval-Augmented Generation (RAG) system within JuliaHealth. It enables users to convert natural language queries into accurate, executable FunSQL.jl workflows by grounding responses in curated resources like OMOP and OHDSI. The project will deliver a modular baseline RAG pipeline, a reproducible evaluation framework to benchmark query quality, and clear documentation with an end-to-end demonstration for usability and future extension.
In this project, you will incorporate WasmEdge’s NN (Neural Network) extensions into the Python interpreter. WasmEdge provides C and Rust APIs for guest applications to access host functions in the underlying Tensorflow and PyTorch libraries. You will make those functions accessible from the CPython-based interpreter as Python wrappers. This way, Python applications can do lightweight AI inference on the WasmEdge container. - Expected outcome: * Investigate and list all C-based host function APIs for Tensorflow and PyTorch inference in WasmEdge NN. * Create CPython wrappers for those host functions. * Create high-level Python wrapper functions that are ergonomic for Python developers. * Create CI and demo apps to validate the Python wrapper API. * Create detailed documentation and tutorials.
This project focused on making patient-level prediction (PLP) more practical within the JuliaHealth ecosystem by building some of the missing foundations around OMOP CDM data. The main challenges were around data preprocessing and consistent cohort construction, which are essential before any prediction pipeline can be reliable. To address this, the project delivered two key pieces: first, a standardized Tables.jl interface in HealthBase.jl to make OMOP CDM data easier to work with and preprocess; and second, a new package, OMOPCDMFeasibility.jl, which adds tools for checking cohort feasibility and data readiness. Both were designed to work hand-in-hand with existing JuliaHealth packages like OMOPCDMCohortCreator.jl and OHDSICohortExpressions.jl. Together, these contributions provide a more reproducible, end-to-end path for building PLP models directly on OMOP CDM datasets.
In atmospheric research flight planning, one task that users have to perform is retrieving a set of plots for several flights or hundreds of time-steps during post-campaign analysis or when compiling an overview over flights of a campaign during hindcasting. Another task is retrieving similar plots of the same parameters such as map section, level etc., on a daily basis. Both of these tasks cannot be accomplished in an efficient manner using the current MSS UI. In this project, a feature would be built to let users download a number of plots in Jupyter notebooks and user scripts in an automated fashion according to the given settings. A CLI tool would also be provided for the same.
<p>Apitrace is an open source program that allows tracing, replaying, inspecting and profiling OpenGL/Direct3D calls made by any application. Last summer during GSoC 2015 there was developed an abstract interface that allows to use various profiling backends in Apitrace, also several backends (that use OpenGL extensions) were implemented then. The goal of this task is to provide a convenient interface to the new feature in qapitrace. There already exists a version of profiling view in qapitrace. It has only a limited set of old metrics and it might lack some usability features. This GUI project plans to add new metrics, as well as improving the interface overall.</p>
<p>The Human Genetics Clinic of the faculty of Medicine, University of Colombo plays a valuable role in caring for patients. Currently, contributors have finalized requirements for collecting data from patients, and developed appropriate forms to do so. However, the forms would benefit from being upgraded to be more user friendly, and become comparable to the functionality seen on the 'collect vitals' form.</p>
Currently, infinispan js client doesn't have the support of Protobuf encoded caches also Ickle is not implemented in the js client. This project aims to provide support for Protobuf encoded caches and will add support of Ickle in the js client so that client can query to the data grid using its query language. Also, API will be improved with typescript.
<p>Since SBML Level 3 is being developed as a modular format with optional SBML Level 3 packages are available to extend the focus of the core SBML representation. Deviser is a new code generation system developed to facilitate the development of SBML L3 packages by helping to automate the generation of specifications, UML diagrams and library code. The code generator currently targets libSBML only. The goal is to extend Deviser's code generator to produce Java code that can be integrated into JSBML, the pure Java API library for SBML. This will allow Deviser to target both libSBML and JSBML. By contributing to Deviser and also being an active spectator, I hope to develop the skills I already have and also incorporate some new ones.</p>
The TUF specification provides explicit guidelines for how artifacts should be hashed and later verified to guarantee their integrity. However, Content Addressable Systems like Git, IPFS (InterPlanetary File System) and OSTree have their own mechanisms for ensuring the integrity of artifacts. When TUF is used with these systems, it is redundant for it to also ensure artifact integrity. This project provides solution to this issue by delegating the responsibility of artifact integrity verification to the underlying content addressable systems themselves, while still using TUF to manage the metadata and provide additional security measures.
The proposed Personal Finance Management (PFM) system for Mifos Pay Mobile represents a strategic evolution from a transaction-centric mobile wallet into a comprehensive financial intelligence platform. While existing mobile banking solutions primarily focus on enabling payments and account access, they often lack holistic tools that empower users to understand, manage, and optimize their financial behavior. This limitation is particularly significant for users in emerging markets, where financial literacy, fragmented account ownership, and limited access to advanced financial tools hinder effective money management. This project aims to bridge that gap by introducing a unified, intelligent, and extensible PFM layer built on top of the existing Mifos Pay ecosystem.
This project aims to develop the frontend components for managing clusters in the Inventory and Transport Space (ITS). Users should be able to onboard and manage clusters through an intuitive UI. Objectives: - Develop UI components for cluster onboarding and management. - Implement validation and error handling for cluster registration. - Ensure smooth integration with backend APIs for real-time updates. - Provide a guided onboarding experience for adding new clusters. Expected Outcomes: - A fully functional ITS management UI. - Improved usability for adding and managing clusters. - Seamless backend integration for real-time data updates.
FreeBSD boasts a plethora of userspace command tools utilizing IPv4 network programming APIs and exhibiting address family dependency. With the emergence of IPv6 to address the limitations of IPv4, it's imperative to update these tools to utilize address-independent APIs, thereby facilitating seamless handling of both IPv4 and IPv6 connections.
This project proposes a comprehensive strategy to extend the CARTopiaX platform within BioDynaMo to replicate a wider range of in vitro observations relevant to CAR T-cell therapy in solid tumors. By incorporating additional agents, microenvironmental factors, and interaction rules, combined with robust parameter calibration strategies such as Bayesian optimization and evolutionary algorithms, the enhanced model aims to capture critical dynamics that influence therapeutic outcomes, providing a flexible and powerful tool for in silico hypothesis testing. If successful, the extended CARTopiaX model will serve as a reliable platform for exploring CAR T-cell therapies and has the potential to generate high-impact insights in computational cancer biology. By bridging experimental data with predictive modeling, the project could accelerate the translation of in silico findings into actionable guidance for experimental and clinical research.
<p>With beacons being lit and the specification readily being adopted by organizations all over the world, there is a need for a toolkit which guarantees compliance with the defined beacon API. The toolkit should contain an entire compliance test suite for organizations looking to implement the beacon API, as well as adapters (or protocols for creating adapters) for organizations which want to user their current data for the beacon. My proposed project will implement the toolkit as well as generate more community documentation to further simplify the beacon creation process.</p>
Package dingo is a python package that analyzes metabolic networks. It relies on high dimensional sampling with Markov Chain Monte Carlo (MCMC) methods and fast optimization methods to analyze the possible states of a metabolic network. It represents a metabolic network with a convex polytope, while the points in the interior of the polytope correspond to steady states of the network. By sampling, dingo, explores and statistically studies the flux space of the network. To perform MCMC sampling, dingo relies on the C++ library volesti. Currently, it provides MMCS algorithm which is based on Billiard Walk and on a multiphase rounding scheme. This project will provide dingo additional options for uniform sampling by exposing into it several C++ implementations of random walks that are included in volesti. Moreover, it will provide sampling from the exponential distribution using the C++ implementation of Hamiltonian Monte Carlo in volesti.
<p>Open science is a movement that promotes open access to research data, publications, and scientific collaboration. It promises to increase transparency, provide reproducibility by reusing scientific datasets and increases trust in the scientific collaboration. Blockchain fits the mindset of open science because of its transparency and decentralized nature.</p> <p>This project aims to create a Hyperledger Composer/Fabric based "Scientific Journal" which could be used to share all the research objects that were produced during the research. These objects (Docs, Videos, Images, Code, Data sets) will be stored in a decentralized manner using IPFS (Inter-Planetory File System).</p>
<p>Apache VXQuery will be a standard compliant Xquery processor implemented in java. JSONiq is a query and processing language specifically designed for the popular JSON data model. It is much similar to XQuery burrowing a large numbers of ideas from XQuery. The "JSONiq extension to XQuery" allows processing XML and JSON natively and with a single language. This project aims at adding objects to the data model of VXquery allowing it to query JSON objects.</p>
<p>This project aims to create a Graphical User Interface (GUI) for big gridded geospatial data visualization in the browser interface backed by the full power of the Python ecosystem. This GUI would allow controlled data points selection, massive rendering, data display, custom interaction, selection of fields for plotting and layout of widgets in the browser using Intake, Xarray and Pyviz collection of tools. Currently majority of geospatial data exploration happens in stand-alone applications like Panoply and NcView, tools that have limited functionality, do not provide complex analysis methods and can only be reasonably extended by the software developers on those projects. This new tool, written in Python, but presented as a dashboard in the notebook environment, will be extendable directly by researchers by using it in conjunction with tools like Dask and will also provide complex analysis methods on the data being visualized. It holds the promise of saving Earth Science and other researchers significant amounts of time since they can directly focus on visual data analysis and research rather than writing custom code to explore data.</p>