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Seminar on Internet Technologies (Summer 2026): Difference between revisions

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|'''Please read this introduction slide [https://docs.google.com/presentation/d/13hmKYBmB4tbTFNeK1GvBAs1qZntMYo75o8ycb1NgYXI/edit?usp=sharing]. If there is any question, please contact teaching assistants.'''
|'''Please read this introduction slide [https://docs.google.com/presentation/d/13hmKYBmB4tbTFNeK1GvBAs1qZntMYo75o8ycb1NgYXI/edit?usp=sharing]. If there is any question, please contact teaching assistants.'''
|ta = Hao Xu[hao.xu@cs.uni-goettingen.de]
|ta = Hao Xu[hao.xu@cs.uni-goettingen.de]
|time='''Please read this introduction slide [https://docs.google.com/presentation/d/13hmKYBmB4tbTFNeK1GvBAs1qZntMYo75o8ycb1NgYXI/edit?usp=sharing]. If there is any question, please contact teaching assistants.'''
|time='''Please read this introduction slide [https://docs.google.com/presentation/d/1I9Pa7y0ATtHM2KDdilgyIsOluhjDntqJn-EXzNdG_Hw/edit?usp=sharing]. If there is any question, please contact teaching assistants.'''
|univz=[https://studip-ecampus.uni-goettingen.de/dispatch.php/course/details/index/4f4ce922cd439f8a00f299fec776c727]
|univz=[https://studip-ecampus.uni-goettingen.de/dispatch.php/course/details/index/4f4ce922cd439f8a00f299fec776c727]
}}
}}
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==Schedule==
==Schedule==
*Exam Registration Deadline: 15.07.2026 (Exam includes final presentation and report)
*Final Presentation Deadline: 18.08.2026
*Report Submission Deadline: 03.09.2026 (23:59)


== Topics ==
== Topics ==
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| [Fabian Wölk, fabian.woelk@cs.uni-goettingen.de]
| [Fabian Wölk, fabian.woelk@cs.uni-goettingen.de]
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| Yes
| No
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| Personalized chatbot based on ChatGPT
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| In this topic, you will learn about ChatGPT and learn to use OpenAI ChatGPT API to create a personalized chatbot.
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| NLP & ChatGPT
| Sign language translation
| This topic focuses on assessing the performance and effectiveness of large language models in handling sign language translation tasks, which involve converting between sign language (visual modality) and spoken language (text or audio). In this topic, you will gain insights into various sign language translation models and multimodal frameworks, and acquire knowledge about a wide range of tasks, including sign language recognition and natural language generation. Additionally, you will become proficient in implementing evaluations related to these tasks.
| Large Language Model & multimodal setting
| [Wenfang Wu, wenfang.wu@cs.uni-goettingen.de]
| [Wenfang Wu, wenfang.wu@cs.uni-goettingen.de]
|  
|  
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| Multimodal Large Language Model Evaluation for Multimodal Tasks
| Long-Video Understanding and Video Question Answering
| This topic focuses on assessing the performance and effectiveness of large language models in handling tasks that involve multiple modalities, such as text, images, and audio. It involves the evaluation of these large models using specialized multimodal datasets, considering both quantitative metrics and qualitative analysis. In this topic, you will gain insights into various large models, including GPT-4, and acquire knowledge about a wide range of multimodal tasks. Additionally, you will become proficient in implementing evaluations related to these tasks.
| In this topic, you will study methods for understanding long videos and for answering questions based on video content. This includes long-range temporal modeling, multimodal video understanding, and question answering over complex video sequences.
| Large Language Model & multimodal setting
| Vision-language models & Large language model
| [Wenfang Wu, wenfang.wu@cs.uni-goettingen.de]
| [Haihan Zhang, haihan.zhang@cs.uni-goettingen.de]
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| Yes
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|-
| CoT Compression for Efficient Reasoning
| In this topic, you will study methods to improve the efficiency of Chain-of-Thought (CoT) reasoning in large language models by reducing redundant or verbose reasoning steps. This includes techniques such as summarization-based compression, iterative reasoning, and latent reasoning representations. You will explore how compression affects reasoning accuracy, computational cost, and attention mechanisms, and implement approaches to balance efficiency and performance.
| Large Language Models & NLP (familiarity with Transformer architecture is recommended)
| [Hao Xu, hao.xu@cs.uni-goettingen.de]
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| Yes
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| Optimizing Kubernetes for Edge Environments
| In this topic, you will explore methods to optimize Kubernetes for edge environments, focusing on addressing challenges such as resource management, fault tolerance or network constraints. This includes investigating existing solutions and identifying gaps by evaluating the effectiveness of these approaches using metrics such as deployment time, resource utilization and application performance.
| Understanding of Kubernetes, containers and programming skills in relevant languages (e.g., Python, Go)
| [Jan Lenke, jan.lenke@cs.uni-goettingen.de]
|  
|  
| Yes
| Yes
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