NET Wiki - User contributions [en]

Seminar on Internet Technologies (Summer 2026)

2026-04-30T02:21:14Z

Lenke1:

== Details ==

{{CourseDetails
|credits=5 ECTS (BSc/MSc AI); 5 (ITIS)
|module=M.Inf.1124
|lecturer=[http://user.informatik.uni-goettingen.de/~fu Prof. Xiaoming Fu]
|ta =[http://www.net.informatik.uni-goettingen.de/?q=people/jiaquan-zhang MSc. Jiaquan Zhang];
|'''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]
|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]
}}

==Announcement==
No open talk. You can contact your topic advisor to schedule a 1V1 meeting or talk.

==Course description==

This course covers selected topics on up-to-date Internet technologies and research. Each student chooses a topic, does a presentation, and writes a report on it. Besides the introduction meeting, there are no regular meetings, lectures, or classes for this course. The purpose of this course is to familiarize the students with new technologies, enable the independent study of a specific topic, and train presentation and writing skills.

The informational meeting at the beginning of the course will cover some guidelines on scientific presenting and writing.

Due to the topic advisors' workload limitation, we could only provide limited topics, and the topic assignment will be on the basis of the first come first serve principle. Please contact the topic advisor directly for the topic availability.

Note: Participants in the seminar only need to register for the exam before the end of the course.

==Passing requirements==
*There will be 2 milestones before the presentations that the students should pass before they register for the course.
**Intro milestone where the adviser makes sure that the student starts to work on the topic and follows an accepted methodology.
**Midterm milestone. (ex. programming tasks are done etc... )

*Actively and frequently participate in the project communication with the topic advisor
**This accounts for 20% of your grade.
* Present the selected topic '''offline''' (20 min. presentations + 10 min. Q&A).
** This accounts for 40% of your grade.
* Write a report on the selected topic (6-8 pages) (LaTeX Template:[https://www.overleaf.com/latex/templates/template-sobraep-english/vnqtqpynnymb]).
** This accounts for 40% of your grade.
* Please check the [[#Schedule]] and adhere to it.

==Schedule==
*Exam Registration Deadline: 15.07.2026 (Exam includes final presentation and report)
*Final Presentation Deadline: 10.08.2026
*Report Submission Deadline: 25.08.2026 (23:59)

== Topics ==

{| align="center" class="wikitable sortable" {{Prettytable}}
|-
|{{Hl2}} |'''Topic'''
|{{Hl2}} |'''Description'''
|{{Hl2}} |'''Prerequisites'''
|{{Hl2}} |'''Topic Advisor'''
|{{Hl2}} |'''Readings'''
|{{Hl2}} |'''Available'''
|-
|-
| Disaster Monitoring
| In this topic, you will study methods to monitor disaster scenarios (e.g. floodings) with aerial images.
| Basic machine learning knowledge
| [Fabian Wölk, fabian.woelk@cs.uni-goettingen.de]
|
| No
|-
|-
| Lidar-based traffic flow analysis
| In this topic, you will study methods to analyze traffic usage on roads, e.g., in terms of traffic flow, speed, and density to identify patterns and trends.
| Basic point cloud processing & ML knowledge
| [Yanlong Huang, yanlong.huang@cs.uni-goettingen.de]
|
| Yes
|-
|-
|-
|-
| 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]
|
| Yes
|-
|-
| Long-Video Understanding and Video Question Answering
| 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.
| Vision-language models & Large language model
| [Haihan Zhang, haihan.zhang@cs.uni-goettingen.de]
|
| Yes
|-
|-
| 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]
|
| Yes
|-
|-
| 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
|-
|-
|}

==Workﬂow==

=== 1. Select a topic ===
Each student needs to choose a topic from the list. You can start to work on your selected topic '''at any time'''. However, please make sure to '''notify the advisor''' of your selected topic in advance, because you might be refused by the advisor if someone has registered on the same topic.

=== 2. Get your work advised ===
Each topic has an advisor, who will help you to solve problems regarding the topic. Please do not hesitate to contact your advisor.
It is recommended (and not mandatory) that you can schedule a skype or zoom meeting with your advisor right after you select your topic. Your advisor will give you some useful guidance and suggestions, which will help you to gain more from this course.

=== 3. Approach your topic ===

* By choosing a topic, you will get a direction of elaboration.
* You may work in different styles, for example:
** Survey: Basic introduction, an overview of the ﬁeld; general problems, methods, approaches.
** Specific problem: Detailed introduction, details about the problem, and the solution.
* Based on the research, you should have your own ideas on your topic.

=== 4. Prepare presentation ===

* Present your topic to the audience (in English).
* The final presentation will be conducted '''offline'''.
* 20 minutes of presentation followed by 10 minutes of discussion.

You need to present your topic to an audience of students and other interested people (usually the [http://www.net.informatik.uni-goettingen.de/ NET] group members). Your presentation should include your general idea of your topic and highlight interesting problems and solutions. You must finish your presentation within a limited time. You have 20 minutes to present your topic followed by 10 minutes of discussion. It is highly recommended to send your slides to your topic advisor in advance, he/she will give you help with your presentation.

Hints for preparing the presentation:
If your topic includes many aspects, and 20 minutes is too short for you to introduce them all, it is recommended to focus on one certain important aspect. Besides, you can discuss with your advisor, he/she will help you to reduce the content.
Please make sure to ﬁnish your presentation in time.

Suggestions for preparing the slides:
No more than 20 pages/slides.
Get your audiences to quickly understand the general idea.
Figures, tables, and animations are better than sentences.
Don't forget a summary of the topic and your ideas.

=== 5. Write a report ===

* Present the problem with its background.
* Detail the approaches, techniques, and methods to solve the problem.
* Evaluate and assess those approaches (e.g., pros and cons).
* Give a short outlook on potential future developments.

The report must be written in English according to common guidelines for scientific papers, between 6 and 8 pages of content (excluding bibliography, etc.).
Please note that you can not directly copy content from papers or webpages, as this will be considered plagiarism. All quoted images and tables need to indicate their source.

=== 6. Course schedule===
There are no regular meetings, lectures, or classes for this course. The work is expected to be done by yourself with the assistance of your topic advisor. Please follow the [[#Schedule]] to take appropriate actions.

[[Category:Courses]]

Seminar on Internet Technologies (Summer 2026)

2026-04-21T09:30:36Z

Lenke1:

== Details ==

{{CourseDetails
|credits=5 ECTS (BSc/MSc AI); 5 (ITIS)
|module=M.Inf.1124
|lecturer=[http://user.informatik.uni-goettingen.de/~fu Prof. Xiaoming Fu]
|ta =[http://www.net.informatik.uni-goettingen.de/?q=people/jiaquan-zhang MSc. Jiaquan Zhang];
|'''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]
|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]
}}

==Announcement==
No open talk. You can contact your topic advisor to schedule a 1V1 meeting or talk.

==Course description==

This course covers selected topics on up-to-date Internet technologies and research. Each student chooses a topic, does a presentation, and writes a report on it. Besides the introduction meeting, there are no regular meetings, lectures, or classes for this course. The purpose of this course is to familiarize the students with new technologies, enable the independent study of a specific topic, and train presentation and writing skills.

The informational meeting at the beginning of the course will cover some guidelines on scientific presenting and writing.

Due to the topic advisors' workload limitation, we could only provide limited topics, and the topic assignment will be on the basis of the first come first serve principle. Please contact the topic advisor directly for the topic availability.

Note: Participants in the seminar only need to register for the exam before the end of the course.

==Passing requirements==
*There will be 2 milestones before the presentations that the students should pass before they register for the course.
**Intro milestone where the adviser makes sure that the student starts to work on the topic and follows an accepted methodology.
**Midterm milestone. (ex. programming tasks are done etc... )

*Actively and frequently participate in the project communication with the topic advisor
**This accounts for 20% of your grade.
* Present the selected topic '''offline''' (20 min. presentations + 10 min. Q&A).
** This accounts for 40% of your grade.
* Write a report on the selected topic (6-8 pages) (LaTeX Template:[https://www.overleaf.com/latex/templates/template-sobraep-english/vnqtqpynnymb]).
** This accounts for 40% of your grade.
* Please check the [[#Schedule]] and adhere to it.

==Schedule==
*Exam Registration Deadline: 15.07.2026 (Exam includes final presentation and report)
*Final Presentation Deadline: 10.08.2026
*Report Submission Deadline: 25.08.2026 (23:59)

== Topics ==

{| align="center" class="wikitable sortable" {{Prettytable}}
|-
|{{Hl2}} |'''Topic'''
|{{Hl2}} |'''Description'''
|{{Hl2}} |'''Prerequisites'''
|{{Hl2}} |'''Topic Advisor'''
|{{Hl2}} |'''Readings'''
|{{Hl2}} |'''Available'''
|-
|-
| Disaster Monitoring
| In this topic, you will study methods to monitor disaster scenarios (e.g. floodings) with aerial images.
| Basic machine learning knowledge
| [Fabian Wölk, fabian.woelk@cs.uni-goettingen.de]
|
| No
|-
|-
| Lidar-based traffic flow analysis
| In this topic, you will study methods to analyze traffic usage on roads, e.g., in terms of traffic flow, speed, and density to identify patterns and trends.
| Basic point cloud processing & ML knowledge
| [Yanlong Huang, yanlong.huang@cs.uni-goettingen.de]
|
| Yes
|-
|-
|-
|-
| 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]
|
| Yes
|-
|-
| Long-Video Understanding and Video Question Answering
| 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.
| Vision-language models & Large language model
| [Haihan Zhang, haihan.zhang@cs.uni-goettingen.de]
|
| Yes
|-
|-
| 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]
|
| Yes
|-
|-
| 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]
|
| No
|-
|-
|}

==Workﬂow==

=== 1. Select a topic ===
Each student needs to choose a topic from the list. You can start to work on your selected topic '''at any time'''. However, please make sure to '''notify the advisor''' of your selected topic in advance, because you might be refused by the advisor if someone has registered on the same topic.

=== 2. Get your work advised ===
Each topic has an advisor, who will help you to solve problems regarding the topic. Please do not hesitate to contact your advisor.
It is recommended (and not mandatory) that you can schedule a skype or zoom meeting with your advisor right after you select your topic. Your advisor will give you some useful guidance and suggestions, which will help you to gain more from this course.

=== 3. Approach your topic ===

* By choosing a topic, you will get a direction of elaboration.
* You may work in different styles, for example:
** Survey: Basic introduction, an overview of the ﬁeld; general problems, methods, approaches.
** Specific problem: Detailed introduction, details about the problem, and the solution.
* Based on the research, you should have your own ideas on your topic.

=== 4. Prepare presentation ===

* Present your topic to the audience (in English).
* The final presentation will be conducted '''offline'''.
* 20 minutes of presentation followed by 10 minutes of discussion.

You need to present your topic to an audience of students and other interested people (usually the [http://www.net.informatik.uni-goettingen.de/ NET] group members). Your presentation should include your general idea of your topic and highlight interesting problems and solutions. You must finish your presentation within a limited time. You have 20 minutes to present your topic followed by 10 minutes of discussion. It is highly recommended to send your slides to your topic advisor in advance, he/she will give you help with your presentation.

Hints for preparing the presentation:
If your topic includes many aspects, and 20 minutes is too short for you to introduce them all, it is recommended to focus on one certain important aspect. Besides, you can discuss with your advisor, he/she will help you to reduce the content.
Please make sure to ﬁnish your presentation in time.

Suggestions for preparing the slides:
No more than 20 pages/slides.
Get your audiences to quickly understand the general idea.
Figures, tables, and animations are better than sentences.
Don't forget a summary of the topic and your ideas.

=== 5. Write a report ===

* Present the problem with its background.
* Detail the approaches, techniques, and methods to solve the problem.
* Evaluate and assess those approaches (e.g., pros and cons).
* Give a short outlook on potential future developments.

The report must be written in English according to common guidelines for scientific papers, between 6 and 8 pages of content (excluding bibliography, etc.).
Please note that you can not directly copy content from papers or webpages, as this will be considered plagiarism. All quoted images and tables need to indicate their source.

=== 6. Course schedule===
There are no regular meetings, lectures, or classes for this course. The work is expected to be done by yourself with the assistance of your topic advisor. Please follow the [[#Schedule]] to take appropriate actions.

[[Category:Courses]]

Seminar on Internet Technologies (Summer 2026)

2026-04-17T03:45:52Z

Lenke1:

== Details ==

{{CourseDetails
|credits=5 ECTS (BSc/MSc AI); 5 (ITIS)
|module=M.Inf.1124
|lecturer=[http://user.informatik.uni-goettingen.de/~fu Prof. Xiaoming Fu]
|ta =[http://www.net.informatik.uni-goettingen.de/?q=people/jiaquan-zhang MSc. Jiaquan Zhang];
|'''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]
|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]
}}

==Announcement==
No open talk. You can contact your topic advisor to schedule a 1V1 meeting or talk.

==Course description==

This course covers selected topics on up-to-date Internet technologies and research. Each student chooses a topic, does a presentation, and writes a report on it. Besides the introduction meeting, there are no regular meetings, lectures, or classes for this course. The purpose of this course is to familiarize the students with new technologies, enable the independent study of a specific topic, and train presentation and writing skills.

The informational meeting at the beginning of the course will cover some guidelines on scientific presenting and writing.

Due to the topic advisors' workload limitation, we could only provide limited topics, and the topic assignment will be on the basis of the first come first serve principle. Please contact the topic advisor directly for the topic availability.

Note: Participants in the seminar only need to register for the exam before the end of the course.

==Passing requirements==
*There will be 2 milestones before the presentations that the students should pass before they register for the course.
**Intro milestone where the adviser makes sure that the student starts to work on the topic and follows an accepted methodology.
**Midterm milestone. (ex. programming tasks are done etc... )

*Actively and frequently participate in the project communication with the topic advisor
**This accounts for 20% of your grade.
* Present the selected topic '''offline''' (20 min. presentations + 10 min. Q&A).
** This accounts for 40% of your grade.
* Write a report on the selected topic (6-8 pages) (LaTeX Template:[https://www.overleaf.com/latex/templates/template-sobraep-english/vnqtqpynnymb]).
** This accounts for 40% of your grade.
* Please check the [[#Schedule]] and adhere to it.

==Schedule==
*Exam Registration Deadline: 15.07.2026 (Exam includes final presentation and report)
*Final Presentation Deadline: 10.08.2026
*Report Submission Deadline: 25.08.2026 (23:59)

== Topics ==

{| align="center" class="wikitable sortable" {{Prettytable}}
|-
|{{Hl2}} |'''Topic'''
|{{Hl2}} |'''Description'''
|{{Hl2}} |'''Prerequisites'''
|{{Hl2}} |'''Topic Advisor'''
|{{Hl2}} |'''Readings'''
|{{Hl2}} |'''Available'''
|-
|-
| Disaster Monitoring
| In this topic, you will study methods to monitor disaster scenarios (e.g. floodings) with aerial images.
| Basic machine learning knowledge
| [Fabian Wölk, fabian.woelk@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Lidar-based traffic flow analysis
| In this topic, you will study methods to analyze traffic usage on roads, e.g., in terms of traffic flow, speed, and density to identify patterns and trends.
| Basic point cloud processing & ML knowledge
| [Yanlong Huang, yanlong.huang@cs.uni-goettingen.de]
|
| Yes
|-
|-
|-
|-
| 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]
|
| Yes
|-
|-
| Long-Video Understanding and Video Question Answering
| 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.
| Vision-language models & Large language model
| [Haihan Zhang, haihan.zhang@cs.uni-goettingen.de]
|
| Yes
|-
|-
| 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]
|
| Yes
|-
|-
| 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]
|
| No
|-
|-
|}

==Workﬂow==

=== 1. Select a topic ===
Each student needs to choose a topic from the list. You can start to work on your selected topic '''at any time'''. However, please make sure to '''notify the advisor''' of your selected topic in advance, because you might be refused by the advisor if someone has registered on the same topic.

=== 2. Get your work advised ===
Each topic has an advisor, who will help you to solve problems regarding the topic. Please do not hesitate to contact your advisor.
It is recommended (and not mandatory) that you can schedule a skype or zoom meeting with your advisor right after you select your topic. Your advisor will give you some useful guidance and suggestions, which will help you to gain more from this course.

=== 3. Approach your topic ===

* By choosing a topic, you will get a direction of elaboration.
* You may work in different styles, for example:
** Survey: Basic introduction, an overview of the ﬁeld; general problems, methods, approaches.
** Specific problem: Detailed introduction, details about the problem, and the solution.
* Based on the research, you should have your own ideas on your topic.

=== 4. Prepare presentation ===

* Present your topic to the audience (in English).
* The final presentation will be conducted '''offline'''.
* 20 minutes of presentation followed by 10 minutes of discussion.

You need to present your topic to an audience of students and other interested people (usually the [http://www.net.informatik.uni-goettingen.de/ NET] group members). Your presentation should include your general idea of your topic and highlight interesting problems and solutions. You must finish your presentation within a limited time. You have 20 minutes to present your topic followed by 10 minutes of discussion. It is highly recommended to send your slides to your topic advisor in advance, he/she will give you help with your presentation.

Hints for preparing the presentation:
If your topic includes many aspects, and 20 minutes is too short for you to introduce them all, it is recommended to focus on one certain important aspect. Besides, you can discuss with your advisor, he/she will help you to reduce the content.
Please make sure to ﬁnish your presentation in time.

Suggestions for preparing the slides:
No more than 20 pages/slides.
Get your audiences to quickly understand the general idea.
Figures, tables, and animations are better than sentences.
Don't forget a summary of the topic and your ideas.

=== 5. Write a report ===

* Present the problem with its background.
* Detail the approaches, techniques, and methods to solve the problem.
* Evaluate and assess those approaches (e.g., pros and cons).
* Give a short outlook on potential future developments.

The report must be written in English according to common guidelines for scientific papers, between 6 and 8 pages of content (excluding bibliography, etc.).
Please note that you can not directly copy content from papers or webpages, as this will be considered plagiarism. All quoted images and tables need to indicate their source.

=== 6. Course schedule===
There are no regular meetings, lectures, or classes for this course. The work is expected to be done by yourself with the assistance of your topic advisor. Please follow the [[#Schedule]] to take appropriate actions.

[[Category:Courses]]

AI-Empowered Networking and Mobile Communications(Summer 2026)

2026-04-07T07:25:23Z

Lenke1:

== Details ==
{{CourseDetails
|credits=5ECTS
|module= M.Inf.1223.Mp: Advanced Topics in Computer Networks
B.Inf.1702.Mp: Vertiefung Computersysteme

M.Inf.1120.Mp: Mobilkommunikation

M.Inf.121.1: Mobilkommunikation I

M.Inf.225.Mp: Ausgewählte Themen der Mobilkommunikation

Note: You can choose any of them to attend this course, but only one! Please note that enrolling in the same course more than once will not grant additional credits.
|lecturer=[http://www.net.informatik.uni-goettingen.de/people/xiaoming_fu Prof. Xiaoming Fu]
|ta= Fabian Wölk
|time=Thursdays, 10-12am.
|place=IFI 2.101
|univz=[]
}}

==Course description==
This lecture will introduce advanced concepts of computer networking to interested students. Topics include:
*Information Centric Network
*AI meets Networking
*Segment Routing
*Intelligent Transportation Application based on V2I Networking
*Social Network Analysis
*Multimodal Sentiment Analysis
*Bio-inspired Networking

For each topic, basic structures, features and applied techniques will be taught.

If you have any questions, please contact Fabian Wölk (fabian.woelk@cs.uni-goettingen.de)

==Schedule (Tentative)==
{| {{Prettytable|width=}}
|-
|{{Hl2}} |'''Date'''
|{{Hl2}} |'''Topic'''
|{{Hl2}} |'''Lecturer'''
|{{Hl2}} |'''Slides'''
|-
| align="right" | 16.04.2026 (10:00-12:00am)
| Introduction / Segment Routing I
| Fabian
|
|-
| align="right" | 23.04.2026 NO LECTURE
| (GIRL'S DAY)
|
|
|-
| align="right" | 30.04.2026 (10:00-12:00am)
| Segment Routing II
| Fabian
|
|-
| align="right" | 07.05.2026 (10:00-12:00am)
| Information Centric Network I
| Prof. Fu
|
|-
| align="right" | 14.05.2026 NO LECTURE
| (PUBLIC HOLIDAY)
|
|
|-
| align="right" | 21.05.2026 (10:00-12:00am)
| Information Centric Network II
| Prof. Fu
|
|-
| align="right" | 28.05.2026 (10:00-12:00am)
| From Words to Vision: A Journey Through Multimodal Sentiment Analysis
| Wenfang Wu
|
|-
| align="right" | 04.06.2026 (10:00-12:00am)
| Sign language translation
| Wenfang Wu
|
|-
| align="right" | 11.06.2026 (10:00-12:00am)
| Intelligent Transportation Application based on V2I Networking
| Yanlong Huang
|
|-
| align="right" | 18.06.2028 (10:00-12:00am)
| TBA
| Yanlong Huang
|
|-
| align="right" | 25.06.2026 (10:00-12:00am)
| AI and Social Network Analysis
| Huilian Sophie Qiu
|
|-
| align="right" | 02.07.2026 (10:00-12:00am)
| Social Network Analysis
| Huilian Sophie Qiu
|
|-
| align="right" | 09.07.2026 (10:00-12:00am)
| Large Language Model Chain of Thought Compression for Efficient Reasoning
| Hao Xu
|
|-
| align="right" | 16.07.2026 (10:00-12:00am)
| Digital Twin Networks for LiDAR Traffic Systems
| Jan Lenke
|
|-
| align="right" | TBA
| Written Examination
|
|
|-
|}

==Prerequisites==
* Computer Science I, II; Computer Networks

==References===
* Yang, S., N. He, F. Li, and X. Fu, Resource Allocation in Network Function Virtualization: Problems, Models and Algorithms, Singapore: Springer, August 2022.

* James Kurose, Keith Ross, Computer Networking: A Top-Down Approach. 8th Edition, Pearson, June 2021

[[Category:Courses]]

Seminar on Internet Technologies (Summer 2026)

2026-03-26T12:41:50Z

Lenke1:

== Details ==

{{CourseDetails
|credits=5 ECTS (BSc/MSc AI); 5 (ITIS)
|module=M.Inf.1124
|lecturer=[http://user.informatik.uni-goettingen.de/~fu Prof. Xiaoming Fu]
|ta =[http://www.net.informatik.uni-goettingen.de/?q=people/jiaquan-zhang MSc. Jiaquan Zhang];
|'''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]
|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]
}}

==Announcement==
No open talk. You can contact your topic advisor to schedule a 1V1 meeting or talk.

==Course description==

This course covers selected topics on up-to-date Internet technologies and research. Each student chooses a topic, does a presentation, and writes a report on it. Besides the introduction meeting, there are no regular meetings, lectures, or classes for this course. The purpose of this course is to familiarize the students with new technologies, enable the independent study of a specific topic, and train presentation and writing skills.

The informational meeting at the beginning of the course will cover some guidelines on scientific presenting and writing.

Due to the topic advisors' workload limitation, we could only provide limited topics, and the topic assignment will be on the basis of the first come first serve principle. Please contact the topic advisor directly for the topic availability.

Note: Participants in the seminar only need to register for the exam before the end of the course.

==Passing requirements==
*There will be 2 milestones before the presentations that the students should pass before they register for the course.
**Intro milestone where the adviser makes sure that the student starts to work on the topic and follows an accepted methodology.
**Midterm milestone. (ex. programming tasks are done etc... )

*Actively and frequently participate in the project communication with the topic advisor
**This accounts for 20% of your grade.
* Present the selected topic '''offline''' (20 min. presentations + 10 min. Q&A).
** This accounts for 40% of your grade.
* Write a report on the selected topic (6-8 pages) (LaTeX Template:[https://www.overleaf.com/latex/templates/template-sobraep-english/vnqtqpynnymb]).
** This accounts for 40% of your grade.
* Please check the [[#Schedule]] and adhere to it.

==Schedule==
*Exam Registration Deadline: 15.07.2026 (Exam includes final presentation and report)
*Final Presentation Deadline: 10.08.2026
*Report Submission Deadline: 25.08.2026 (23:59)

== Topics ==

{| align="center" class="wikitable sortable" {{Prettytable}}
|-
|{{Hl2}} |'''Topic'''
|{{Hl2}} |'''Description'''
|{{Hl2}} |'''Prerequisites'''
|{{Hl2}} |'''Topic Advisor'''
|{{Hl2}} |'''Readings'''
|{{Hl2}} |'''Available'''
|-
|-
| Disaster Monitoring
| In this topic, you will study methods to monitor disaster scenarios (e.g. floodings) with aerial images.
| Basic machine learning knowledge
| [Fabian Wölk, fabian.woelk@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Lidar-based traffic flow analysis
| In this topic, you will study methods to analyze traffic usage on roads, e.g., in terms of traffic flow, speed, and density to identify patterns and trends.
| Basic point cloud processing & ML knowledge
| [Yanlong Huang, yanlong.huang@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Personalized chatbot based on ChatGPT
| In this topic, you will learn about ChatGPT and learn to use OpenAI ChatGPT API to create a personalized chatbot.
| NLP & ChatGPT
| [Wenfang Wu, wenfang.wu@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Multimodal Large Language Model Evaluation for Multimodal Tasks
| 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.
| Large Language Model & multimodal setting
| [Wenfang Wu, wenfang.wu@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Long-Video Understanding and Video Question Answering
| 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.
| Vision-language models & Large language model
| [Haihan Zhang, haihan.zhang@cs.uni-goettingen.de]
|
| Yes
|-
|-
| 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]
|
| Yes
|-
|-
| 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
|-
|-
|}

==Workﬂow==

=== 1. Select a topic ===
Each student needs to choose a topic from the list. You can start to work on your selected topic '''at any time'''. However, please make sure to '''notify the advisor''' of your selected topic in advance, because you might be refused by the advisor if someone has registered on the same topic.

=== 2. Get your work advised ===
Each topic has an advisor, who will help you to solve problems regarding the topic. Please do not hesitate to contact your advisor.
It is recommended (and not mandatory) that you can schedule a skype or zoom meeting with your advisor right after you select your topic. Your advisor will give you some useful guidance and suggestions, which will help you to gain more from this course.

=== 3. Approach your topic ===

* By choosing a topic, you will get a direction of elaboration.
* You may work in different styles, for example:
** Survey: Basic introduction, an overview of the ﬁeld; general problems, methods, approaches.
** Specific problem: Detailed introduction, details about the problem, and the solution.
* Based on the research, you should have your own ideas on your topic.

=== 4. Prepare presentation ===

* Present your topic to the audience (in English).
* The final presentation will be conducted '''offline'''.
* 20 minutes of presentation followed by 10 minutes of discussion.

You need to present your topic to an audience of students and other interested people (usually the [http://www.net.informatik.uni-goettingen.de/ NET] group members). Your presentation should include your general idea of your topic and highlight interesting problems and solutions. You must finish your presentation within a limited time. You have 20 minutes to present your topic followed by 10 minutes of discussion. It is highly recommended to send your slides to your topic advisor in advance, he/she will give you help with your presentation.

Hints for preparing the presentation:
If your topic includes many aspects, and 20 minutes is too short for you to introduce them all, it is recommended to focus on one certain important aspect. Besides, you can discuss with your advisor, he/she will help you to reduce the content.
Please make sure to ﬁnish your presentation in time.

Suggestions for preparing the slides:
No more than 20 pages/slides.
Get your audiences to quickly understand the general idea.
Figures, tables, and animations are better than sentences.
Don't forget a summary of the topic and your ideas.

=== 5. Write a report ===

* Present the problem with its background.
* Detail the approaches, techniques, and methods to solve the problem.
* Evaluate and assess those approaches (e.g., pros and cons).
* Give a short outlook on potential future developments.

The report must be written in English according to common guidelines for scientific papers, between 6 and 8 pages of content (excluding bibliography, etc.).
Please note that you can not directly copy content from papers or webpages, as this will be considered plagiarism. All quoted images and tables need to indicate their source.

=== 6. Course schedule===
There are no regular meetings, lectures, or classes for this course. The work is expected to be done by yourself with the assistance of your topic advisor. Please follow the [[#Schedule]] to take appropriate actions.

[[Category:Courses]]

Data Science in Smart City (Summer 2026)

2026-03-04T13:10:21Z

Lenke1:

{{Announcement|Note: The primary platform for communication in this course will be StudIP. All materials will be uploaded there.}}

== Details ==
{{CourseDetails
|credits=180h, 6 ECTS
|module=M.Inf.1800 Fortgeschrittenen Praktikum Computernetzwerke
|lecturer=[http://134.76.18.81/?q=people/prof-dr-xiaoming-fu Prof. Xiaoming Fu]; [http://www.net.informatik.uni-goettingen.de/?q=people/yanlong-huang Yanlong Huang]
|ta=Yanlong Huang, Jan Lenke
|time=Monday 8:00 - 10:00am
|place= [https://univz.uni-goettingen.de/qisserver/rds;jsessionid=A0D213EBDDAF9A0A0BB5BBA4B3F5E795.s44?state=verpublish&status=init&vmfile=no&moduleCall=webInfo&publishConfFile=webInfoRaum&publishSubDir=raum&keep=y&raum.rgid=8903 Room 1.101, Goldschmidtstr. 7 (Informatik u.Stochastik)]
|univz=[https://www.studip.uni-goettingen.de/dispatch.php/course/details?sem_id=00c43797ae27491ab2fce12f8421056f]
}}

==Course Organization==
In this course, you will complete several practical tasks in the realm of data analysis. These tasks can include both exploratory (descriptive) data analysis as well as the application of machine learning algorithms to specific datasets.

While the focus of the course is strongly practical, to support students, the course will provide lectures on different aspects of practical machine learning in the early stages of the course, including:

* Introduction to the practical data science pipeline
* Exploratory data analysis
* The Python Data Science stack
* Video Analytics
* Advanced algorithms for Data Science
* Parameter tuning for predictive models

The goal of this course is to:

* Help students to further understand computer networks and data science knowledge.
* Help students to use computer science knowledge to build a practical AI system.
* Guide students to utilize knowledge to improve the performance of the system.

In this course, each student (max. number 30) needs to:

* Read state-of-art papers.
* Use programming to build systems including computer vision algorithms, embedded design programs.
* Learn how to analyze city public transport sensor data.

Students need to finish three tasks by specific deadlines throughout the course. Note that this course thus requires a continuous effort throughout the whole semester.
A final report needs to be submitted at the end of the semester.

==Prerequisites==
*You are ''highly recommended'' to have completed a course on Data Science (e.g., "[https://www.swe.informatik.uni-goettingen.de/lectures/data-science-and-big-data-analytics-ws2015 Data Science and Big Data Analytics" taught by Dr. Steffen Herbold] or the Course "Machine Learning" by Stanford University) before entering this course. You need to be familiar with basic statistics (distributions, p/t/z-tests, etc.), a range of machine learning algorithms (linear/logistic/lasso regression, k-means clustering, k-NN classification etc.), computer networking, and mobile communications.
*Knowledge of any of the following languages: Python (course language), R, Matlab or any language that features proper machine learning libraries

==Schedule==
{| {{Prettytable|width=}}
|-
|{{Hl2}} |'''When?'''
|{{Hl2}} |'''What?'''
|-
| align="left" | 13.04.2026
| Lecture 1 The Data Science Pipeline (online)
|-
| align="left" | 20.04.2026
| Lecture 2 Python Stack & Release of Task 1 (online)
|-
| align="left" | 27.04.2026
| Lecture 3 GNN (online)
|-
| align="left" | 04.05.2026
| Intermediate meeting of Task 1 (online)
|-
| align="left" | 11.05.2026
| No Lecture, Task 1 report submission (Before 10PM)
|-
| align="left" | 18.05.2026
| Lecture 4 3D Reconstruction
|-
| align="left" | 25.05.2026
| NO LECTURE (PUBLIC HOLIDAY)
|-
| align="left" | 01.06.2026
| Lecture 5 3D Object Detection & Release of Task 2
|-
| align="left" | 08.06.2026
| Intermediate meeting of Task 2
|-
| align="left" | 15.06.2026
| No Lecture, Task 2 report submission (Before 10PM)
|-
| align="left" | 22.06.2026
| Release of Task 3
|-
| align="left" | 29.06.2026
| Intermediate meeting Task 3 (online)
|-
| align="left" | TBD
| Final Presentation
|-
| align="left" | TBD
| Final Report Submission (Before 10PM)
|}

'''Where?''': [https://www.geodata.uni-goettingen.de/lageplan/?ident=2412_1_1.OG_1.101 Room 1.101, Goldschmidtstr. 7 (Informatik u.Stochastik)]

==Grading==
** Task 1: 25%
** Task 2: 25%
** Task 3: 50% (Presentation: 20%, Report&Code: 30%)

* Presentation:
**Present on your work with a slide to the audience (in English).
**20 minutes of presentation followed by 10 minutes Q&A.
Suggestions for preparing the slides: Get your audiences to quickly understand the general idea. Figures, tables, and animations are better than sentences. Don't forget a summary of your ideas and contributions.
All quoted images, tables and text need to indicate their source.
Note: The team needs to clearly introduce the division of their work, and both team members need to present their respective work and answer questions.

* Final report:
The report must be written in English according to common guidelines for scientific papers, 6-8 pages(excluding bibliography, etc.) in double-column latex(LaTeX Template:[https://www.overleaf.com/latex/templates/template-sobraep-english/vnqtqpynnymb]).
Please note that you can not directly copy content from papers or webpages, as this will be considered plagiarism, and we will treat it seriously. All quoted images and tables need to indicate their source.
The source code, data (or URL of data) and a manual should be uploaded with the report.

Data Science in Smart City (Summer 2026)

2026-03-04T12:20:50Z

Lenke1: Created page with "{{Announcement|Note: The primary platform for communication in this course will be StudIP. All materials will be uploaded there.}} == Details == {{CourseDetails |credits=180h, 6 ECTS |module=M.Inf.1800 Fortgeschrittenen Praktikum Computernetzwerke |lecturer=[http://134.76.18.81/?q=people/prof-dr-xiaoming-fu Prof. Xiaoming Fu]; [http://www.net.informatik.uni-goettingen.de/?q=people/yanlong-huang Yanlong Huang] |ta=Yanlong Huang, Jan Lenke |time=Monday 10:00 - 12:00am |p..."

{{Announcement|Note: The primary platform for communication in this course will be StudIP. All materials will be uploaded there.}}

== Details ==
{{CourseDetails
|credits=180h, 6 ECTS
|module=M.Inf.1800 Fortgeschrittenen Praktikum Computernetzwerke
|lecturer=[http://134.76.18.81/?q=people/prof-dr-xiaoming-fu Prof. Xiaoming Fu]; [http://www.net.informatik.uni-goettingen.de/?q=people/yanlong-huang Yanlong Huang]
|ta=Yanlong Huang, Jan Lenke
|time=Monday 10:00 - 12:00am
|place= [https://univz.uni-goettingen.de/qisserver/rds;jsessionid=A0D213EBDDAF9A0A0BB5BBA4B3F5E795.s44?state=verpublish&status=init&vmfile=no&moduleCall=webInfo&publishConfFile=webInfoRaum&publishSubDir=raum&keep=y&raum.rgid=8903 Room 1.101, Goldschmidtstr. 7 (Informatik u.Stochastik)]
|univz=[https://www.studip.uni-goettingen.de/dispatch.php/course/details?sem_id=00c43797ae27491ab2fce12f8421056f]
}}

==Course Organization==
In this course, you will complete several practical tasks in the realm of data analysis. These tasks can include both exploratory (descriptive) data analysis as well as the application of machine learning algorithms to specific datasets.

While the focus of the course is strongly practical, to support students, the course will provide lectures on different aspects of practical machine learning in the early stages of the course, including:

* Introduction to the practical data science pipeline
* Exploratory data analysis
* The Python Data Science stack
* Video Analytics
* Advanced algorithms for Data Science
* Parameter tuning for predictive models

The goal of this course is to:

* Help students to further understand computer networks and data science knowledge.
* Help students to use computer science knowledge to build a practical AI system.
* Guide students to utilize knowledge to improve the performance of the system.

In this course, each student (max. number 30) needs to:

* Read state-of-art papers.
* Use programming to build systems including computer vision algorithms, embedded design programs.
* Learn how to analyze city public transport sensor data.

Students need to finish three tasks by specific deadlines throughout the course. Note that this course thus requires a continuous effort throughout the whole semester.
A final report needs to be submitted at the end of the semester.

==Prerequisites==
*You are ''highly recommended'' to have completed a course on Data Science (e.g., "[https://www.swe.informatik.uni-goettingen.de/lectures/data-science-and-big-data-analytics-ws2015 Data Science and Big Data Analytics" taught by Dr. Steffen Herbold] or the Course "Machine Learning" by Stanford University) before entering this course. You need to be familiar with basic statistics (distributions, p/t/z-tests, etc.), a range of machine learning algorithms (linear/logistic/lasso regression, k-means clustering, k-NN classification etc.), computer networking, and mobile communications.
*Knowledge of any of the following languages: Python (course language), R, Matlab or any language that features proper machine learning libraries

==Schedule==
{| {{Prettytable|width=}}
|-
|{{Hl2}} |'''When?'''
|{{Hl2}} |'''What?'''
|-
| align="left" | TODO
| Lecture 1 The Data Science Pipeline
|-
| align="left" | TODO
| Lecture 2 Python Stack & Release of Task 1
|-
| align="left" | TODO
| Lecture 3 GNN
|-
| align="left" | TODO
| Intermediate meeting of Task 1
|-
| align="left" | TODO
| No Lecture, Task 1 report submission (Before 10PM)
|-
| align="left" | TODO
| Lecture 4 3D Reconstruction
|-
| align="left" | TODO
| Lecture 5 3D Object Detection & Release of Task 2
|-
| align="left" | TODO
| Intermediate meeting of Task 2
|-
| align="left" | TODO
| No Lecture, Task 2 report submission (Before 10PM)
|-
| align="left" | TODO
| Release of Task 3
|-
| align="left" | TODO
| Intermediate meeting Task 3
|-
| align="left" | TODO
| Final Presentation
|-
| align="left" | TODO
| Final Report Submission (Before 10PM)
|}

'''Where?''': [https://www.geodata.uni-goettingen.de/lageplan/?ident=2412_1_1.OG_1.101 Room 1.101, Goldschmidtstr. 7 (Informatik u.Stochastik)]

==Grading==
** Task 1: 25%
** Task 2: 25%
** Task 3: 50% (Presentation: 20%, Report&Code: 30%)

* Presentation:
**Present on your work with a slide to the audience (in English).
**20 minutes of presentation followed by 10 minutes Q&A.
Suggestions for preparing the slides: Get your audiences to quickly understand the general idea. Figures, tables, and animations are better than sentences. Don't forget a summary of your ideas and contributions.
All quoted images, tables and text need to indicate their source.
Note: The team needs to clearly introduce the division of their work, and both team members need to present their respective work and answer questions.

* Final report:
The report must be written in English according to common guidelines for scientific papers, 6-8 pages(excluding bibliography, etc.) in double-column latex(LaTeX Template:[https://www.overleaf.com/latex/templates/template-sobraep-english/vnqtqpynnymb]).
Please note that you can not directly copy content from papers or webpages, as this will be considered plagiarism, and we will treat it seriously. All quoted images and tables need to indicate their source.
The source code, data (or URL of data) and a manual should be uploaded with the report.