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add track list and short descriptions
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layout: track | ||
track-id: 2 | ||
title: LLM Adaptation | ||
tu-leader: Maliheh Izadi | ||
jb-leader: Egor Bogomolov | ||
phd: To Be Hired | ||
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This track aims to refine generic large language models for code to suit various scenarios. By tailoring these models to the specific needs of individual users, projects, and organizations, we can ensure personalized outputs. The models will be optimized to produce legal, safe, and timely predictions and operate efficiently on low-resource devices. |
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layout: track | ||
track-id: 3 | ||
title: Interactive and Aligned IDEs | ||
tu-leader: Maliheh Izadi | ||
jb-leader: Sergey Titov | ||
phd: To Be Hired | ||
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This track aims to embed emerging large language model practices, such as code generation or code explanation, into developers' workflows without disturbing users to improve their productivity. To do so, we will study user interaction with the model in IDEs, research the user experience, and investigate how to best build trust between developers and their intelligent agents. |
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layout: track | ||
track-id: 4 | ||
title: Utilizing Runtime Information to Improve Development Processes | ||
tu-leader: Thomas Durieux | ||
jb-leader: Egor Klimov | ||
phd: To Be Hired | ||
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This track aims to seamlessly integrate runtime information into JetBrains IDEs, elevating the development experience by enhancing code quality, pinpointing and addressing performance issues, and providing precise code assistance within the IDE environment. To achieve this, we will bridge the gap between static and dynamic information within machine learning techniques. |
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layout: track | ||
track-id: 5 | ||
title: Intelligent Teaching Assistant in Programming Education | ||
tu-leader: Fenia Aivaloglou | ||
jb-leader: Anastasia Birillo | ||
phd: To Be Hired | ||
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This track aims to develop an intelligent, AI-based student assistant that provides context-informed support for programming education while stimulating knowledge transfer. We will focus specifically on how automatically generated hints can guide students toward the right solution, how students interact with the intelligent assistant, and how efficient their interactions are toward their learning objectives. |
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