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Sabrina
2024-05-08 17:48:21 -03:00
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qdrant-landing/content/blog/qsoc24-interns-announcement.md
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title: "QSoC 2024: Selected Interns Announced"
title: "QSoC 2024: Announcing Our Interns!"
draft: false
slug: qsoc24-interns-announcement # Change this slug to your page slug if needed
short_description: We are pleased to announce the selection of interns for the inaugural Qdrant Summer of Code (QSoC) program. # Change this
@@ -25,11 +25,15 @@ We are excited to announce the interns selected for the inaugural Qdrant Summer
**[Jishan Bhattacharya](https://www.linkedin.com/in/j16n/): WASM-based Dimension Reduction Visualization**
Jishan will be implementing a dimension reduction algorithm in Rust, compiling it to WebAssembly (WASM), and integrating it with the Qdrant Web UI. This project aims to provide a more efficient and smoother visualization experience, enabling the handling of more data points and higher dimensions efficiently.
Jishan Bhattacharya is tasked with enhancing our vector data visualization capabilities at Qdrant by developing a dimension reduction algorithm in Rust. This algorithm will distill complex, high-dimensional data into more manageable forms for visualization. Once developed, he’ll compile this Rust code into WebAssembly (WASM), enabling its deployment directly within the Qdrant Web UI. This integration leverages Rust’s performance and memory safety features while utilizing WASM’s ability to run on multiple platforms without losing efficiency.
The result will be a more responsive UI capable of handling larger datasets and higher-dimensional data more smoothly, improving both the speed and the scalability of our visualizations.
**[Celine Hoang](https://www.linkedin.com/in/celine-h-hoang/): ONNX Cross Encoders in Python**
Celine's project focuses on porting cross-encoder ranking models, such as Sentence Transformers, ColBERT, and BGE, to the ONNX format while retaining performance. Successful completion of this project will expand Qdrant's capabilities to support ONNX models, significantly improving its versatility and efficiency in handling ranking-sensitive tasks.
Celine Hoang is working on expanding Qdrant’s model support capabilities by porting cross-encoder ranking models, such as Sentence Transformers, ColBERT, and BGE, to the ONNX format. Her project is critical for enhancing the flexibility of Qdrant in handling complex ranking tasks that require high accuracy and performance. By transitioning these models to the ONNX runtime, Celine will enable Qdrant to utilize a standardized format that supports efficient model interchange and deployment across various platforms.
This project will significantly boost Qdrant’s ability to support a broader array of machine learning applications, particularly those involving nuanced ranking processes.
We look forward to working with Jishan and Celine over the coming months and are excited to see their contributions to the Qdrant project.