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Advanced Topics in AI for Computing and Networking (Winter 2026/2027)

Wwu1 — Mon, 24 Aug 2026 14:57:12 GMT

List of Papers

← Older revision		Revision as of 16:57, 24 August 2026
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	==List of Papers ==		==List of Papers ==
	1. ~~InftyThink~~: ~~Breaking the Length Limits of Long~~-~~Context Reasoning in Large Language Models~~ [https://arxiv.org/~~abs~~/2503.~~06692~~]		1. DSV-LFS: Unifying LLM-Driven Semantic Cues with Visual Features for Robust Few-Shot Segmentation [https://arxiv.org/pdf/2503.04006]

	2. ~~Emergent Hierarchical Reasoning in LLMs through Reinforcement~~ Learning [https://arxiv.org/~~abs~~/~~2509~~.~~03646~~]		2. STPro: Spatial and Temporal Progressive Learning for Weakly Supervised Spatio-Temporal Grounding [https://arxiv.org/pdf/2502.20678]

	3. ~~Language Models Resist Alignment~~: ~~Evidence From Data Compression~~ [https://arxiv.org/~~abs~~/~~2406~~.~~06144~~]		3. Crab: A Unified Audio-Visual Scene Understanding Model with Explicit Cooperation [https://arxiv.org/pdf/2503.13068]

	4. A ~~Theory of Response Sampling in LLMs: Part Descriptive and Part Prescriptive~~ [https://arxiv.org/~~abs~~/~~2402~~.~~11005~~]		4. WeGen: A Unified Model for Interactive Multimodal Generation as We Chat [https://arxiv.org/pdf/2503.01115]

	5. ~~Fairness through Difference Awareness~~: ~~Measuring Desired Group Discrimination in LLMs~~ [https://arxiv.org/~~abs~~/~~2502~~.~~01926~~]		5. M3-VOS: Multi-Phase, Multi-Transition, and Multi-Scenery Video Object Segmentation [https://arxiv.org/pdf/2412.13803]

	6. BlueLM-V-3B: Algorithm and System Co-Design for Multimodal Large Language Models on Mobile Devices [https://arxiv.org/pdf/2411.11909]		6. BlueLM-V-3B: Algorithm and System Co-Design for Multimodal Large Language Models on Mobile Devices [https://arxiv.org/pdf/2411.11909]
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	20. Graph Contrastive Learning via Interventional View Generation [https://dl.acm.org/doi/pdf/10.1145/3589334.3645687]		20. Graph Contrastive Learning via Interventional View Generation [https://dl.acm.org/doi/pdf/10.1145/3589334.3645687]


	==Schedule==		==Schedule==

Advanced Topics in AI for Computing and Networking (Winter 2026/2027)

Wwu1 — Mon, 24 Aug 2026 14:55:22 GMT

Created page with "== Details == {{CourseDetails |credits=5 ECTS |module=M.Inf.1123 |lecturer=[http://134.76.18.81/?q=people/prof-dr-xiaoming-fu Prof. Xiaoming Fu]; |ta=Wenfang Wu; Hao Xu |time=Thursday 14:00-16:00 |place= }} ==Announcements== Please contact me by email: wenfang.wu@cs.uni-goettingen.de if you have any questions. ==Course Overview== The purpose of this seminar is to discuss some advanced topics in '''computer networks'''. This course is a '''theory-oriented''' research s..."

New page

== Details ==

{{CourseDetails
|credits=5 ECTS
|module=M.Inf.1123
|lecturer=[http://134.76.18.81/?q=people/prof-dr-xiaoming-fu Prof. Xiaoming Fu];
|ta=Wenfang Wu; Hao Xu
|time=Thursday 14:00-16:00
|place=
}}

==Announcements==
Please contact me by email: wenfang.wu@cs.uni-goettingen.de if you have any questions.

==Course Overview==
The purpose of this seminar is to discuss some advanced topics in '''computer networks'''. This course is a '''theory-oriented''' research seminar (5 ECTS, 2 SWS), held on a weekly base and comprises the following components:
* Weekly Presentation + Weekly Paper Reading and Discussion 40%
* Final Presentation 40%
* Final Report 20%

The material in the seminar is mainly drawn from the research literature in top journals/conferences, like ToN,TMC, TPDS, SIGCOMM, SIGMETRICS, INFOCOM, MOBICOM, MOBIHOC, WWW, CoNEXT.

==Requirements==
* Each participant is required to read the assigned paper before the seminar and prepare the review of the paper, which should include the following parts:
** Summary of the paper
** Pros and cons of the paper (your conclusion)
** '''NOTE!! Every participant should provide the paper review BEFORE the seminar (23:59 on Wedesday). => the review form is available at [[http://user.informatik.uni-goettingen.de/~fu/Paper_Review_Form_ATCN_WS201112.doc Paper_Review_Form_ATCN_WS201112.doc]]
* During each weekly seminar, one participant is assigned for presenting the paper (each presentation lasts for ~20 minutes) and the list of pros and cons are discussed by all the participants.
* In the middle of the semester, everyone is requested to prepare:
** Final report: Essay (5~6 pages, double columns, IEEE format) for your chosen research topic, which contains a comprehensive literature survey + a detailed discussion of some key enabling technologies
** Final presentation: each presentation lasts for ~20 minutes, plus ~10 minutes Q&A

==List of Papers ==
1. InftyThink: Breaking the Length Limits of Long-Context Reasoning in Large Language Models [https://arxiv.org/abs/2503.06692]

2. Emergent Hierarchical Reasoning in LLMs through Reinforcement Learning [https://arxiv.org/abs/2509.03646]

3. Language Models Resist Alignment: Evidence From Data Compression [https://arxiv.org/abs/2406.06144]

4. A Theory of Response Sampling in LLMs: Part Descriptive and Part Prescriptive [https://arxiv.org/abs/2402.11005]

5. Fairness through Difference Awareness: Measuring Desired Group Discrimination in LLMs [https://arxiv.org/abs/2502.01926]

6. BlueLM-V-3B: Algorithm and System Co-Design for Multimodal Large Language Models on Mobile Devices [https://arxiv.org/pdf/2411.11909]

7. SymDPO: Boosting In-Context Learning of Large Multimodal Models with Symbol Demonstration Direct Preference Optimization [https://ieeexplore.ieee.org/document/10228858]

8. Energy-Efficient 360-Degree Video Streaming on Multicore-Based Mobile Devices [https://ieeexplore.ieee.org/document/10228863]

9. Hawkeye: A Dynamic and Stateless Multicast Mechanism with Deep Reinforcement Learning [https://ieeexplore.ieee.org/document/10228869]

10. WiseCam: Wisely Tuning Wireless Pan-Tilt Cameras for Cost-Effective Moving Object Tracking [https://ieeexplore.ieee.org/document/10228926]

11. Jupiter: Fast and Resource-Efficient Collaborative Inference of Generative LLMs on Edge Devices [https://arxiv.org/pdf/2504.08242]

12. Joint Optimization of Prompt Security and System Performance in Edge-Cloud LLM Systems [https://arxiv.org/pdf/2501.18663]

13. iRadar: Synthesizing Millimeter-Waves from Wearable Inertial Inputs for Human Gesture Sensing [https://arxiv.org/pdf/2412.15980]

14. Mell: Memory-Efficient Large Language Model Serving via Multi-GPU KV Cache Management [https://arxiv.org/pdf/2501.06709]

15. SPIN: Accelerating Large Language Model Inference with Heterogeneous Speculative Models [https://arxiv.org/pdf/2503.15921]

16. Quark: Implementing Convolutional Neural Networks Entirely on Programmable Data Plane [https://arxiv.org/pdf/2501.15100]

17. PDStream: Slashing Long-Tail Delay in Interactive Video Streaming via Pseudo-Dual Streaming [https://arxiv.org/pdf/2502.04691]

18. Understanding Human Preferences: Towards More Personalized Video to Text Generation [https://dl.acm.org/doi/10.1145/3589334.3645711]

19. Multimodal Relation Extraction via a Mixture of Hierarchical Visual Context Learners [https://dl.acm.org/doi/abs/10.1145/3589334.3645603]

20. Graph Contrastive Learning via Interventional View Generation [https://dl.acm.org/doi/pdf/10.1145/3589334.3645687]

==Schedule==
W1: Open Talk (30.10)

W2: Select papers and create schedule

W4:

W6:

W8:

W10:

W12:

W14:

'''!! xx.xx deadline for registration on Flexnow'''

Final Presentation (xx.01)
*'''Paper Title''':
*'''Paper Title''':

Report deadline

==Final Presentations & Report==

*Final Registration in '''FlexNow''': To Be Announced (TBA).

*Final Presentation:
**Each for ~20 minutes, plus ~20 minutes Q&A

*Final Presentation Slots:
**To Be Announced (TBA)

*Final Report:
**Essay (~6 pages, double column, IEEE format: https://journals.ieeeauthorcenter.ieee.org/create-your-ieee-journal-article/authoring-tools-and-templates/ieee-article-templates/templates-for-transactions/)
**Due by 23:59pm 15th February.

[[Category:Courses]]

Seminar on Internet Technologies (Summer 2026)

Hxu4 — Mon, 24 Aug 2026 09:38:20 GMT

Schedule

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	==Schedule==		==Schedule==
	*Exam Registration Deadline: 15.07.2026 (Exam includes final presentation and report)		*Exam Registration Deadline: 15.07.2026 (Exam includes final presentation and report)
	*Final Presentation Deadline: 10.08.2026		*Final Presentation Deadline: 18.08.2026
	*Report Submission Deadline: 25.08.2026 (23:59)		*Report Submission Deadline: 03.09.2026 (23:59)

	== Topics ==		== Topics ==

Computer Networks (Winter 2026/2027)

Yanlong.huang01 — Wed, 19 Aug 2026 13:01:43 GMT

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	\|lecturer=[http://134.76.18.81/?q=people/prof-dr-xiaoming-fu Prof. Xiaoming Fu]		\|lecturer=[http://134.76.18.81/?q=people/prof-dr-xiaoming-fu Prof. Xiaoming Fu]
	\|ta= [http://134.76.18.81/?q=people/yanlong-huang Yanlong Huang, MSc.] [http://134.76.18.81/?q=people/wenfang-wu Wenfang Wu, MSc.]		\|ta= [http://134.76.18.81/?q=people/yanlong-huang Yanlong Huang, MSc.] [http://134.76.18.81/?q=people/wenfang-wu Wenfang Wu, MSc.]
	\|time=Lecture: ~~Thursday~~, 10am-12pm, Exercise: Thursday, 12pm-1pm		\|time=Lecture: Friday, 10am-12pm, Exercise: Thursday, 12pm-1pm
	\|place= Lecture~~: MN09;~~ Exercise: Institut für Informatik / Mathematische Stochastik (IMS) -1.101		\|place= Lecture & Exercise: Institut für Informatik / Mathematische Stochastik (IMS) -1.101
	\|\|univz=		\|\|univz=
	}}		}}
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	\|{{Hl2}} \|'''Solutions'''		\|{{Hl2}} \|'''Solutions'''
	\|-		\|-
	\| align="right" \| 29.10.2026		\| align="right" \| 30.10.2026
	\| Introduction & Layering		\| Introduction & Layering
	\|		\|
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	\|		\|
	\|-		\|-
	\| align="right" \| 05.11.2026		\| align="right" \| 06.11.2026
	\| Application Layer		\| Application Layer
	\|		\|
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	\|		\|
	\|-		\|-
	\| align="right" \| 12.11.2026		\| align="right" \| 13.11.2026
	\| Transport Layer I		\| Transport Layer I
	\|		\|
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	\|		\|
	\|-		\|-
	\| align="right" \| 19.11.2026		\| align="right" \| 20.11.2026
	\| Transport Layer II		\| Transport Layer II
	\|		\|
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	\|		\|
	\|-		\|-
	\| align="right" \| 26.11.2026		\| align="right" \| 27.11.2026
	\| Network Layer I		\| Network Layer I
	\|		\|
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	\|		\|
	\|-		\|-
	\| align="right" \| 03.12.2026		\| align="right" \| 04.12.2026
	\| Network Layer II		\| Network Layer II
	\|		\|
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	\|		\|
	\|-		\|-
	\| align="right" \| 10.12.2026		\| align="right" \| 11.12.2026
	\| Link Layer I		\| Link Layer I
	\|		\|
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	\|		\|
	\|-		\|-
	\| align="right" \| 17.12.2026		\| align="right" \| 18.12.2026
	\| Link Layer II		\| Link Layer II
	\|		\|
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	\|		\|
	\|-		\|-
	\| align="right" \| 14.01.2027		\| align="right" \| 15.01.2027
	\| Networked Multimedia		\| Networked Multimedia
	\|		\|
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	\|		\|
	\|-		\|-
	\| align="right" \| 21.01.2027		\| align="right" \| 22.01.2027
	\| Quality of Service		\| Quality of Service
	\|		\|
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	\|		\|
	\|-		\|-
	\| align="right" \| 28.01.2027		\| align="right" \| 29.01.2027
	\| Network Security I		\| Network Security I
	\|		\|
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	\|		\|
	\|-		\|-
	\| align="right" \| 04.02.2027		\| align="right" \| 05.02.2027
	\| Network Security II		\| Network Security II
	\|		\|
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	\| align="right" \| TBA		\| align="right" \| TBA
	\| Final Exam		\| Final Exam
	\| Time: 11.03.2027, 12-14		\| Time: 12.03.2027, 12-14
	\| Room: TBD		\| Room: TBD
	\|		\|

Computer Networks (Winter 2026/2027)

Hyanlon — Tue, 18 Aug 2026 12:40:46 GMT

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	\|ta= [http://134.76.18.81/?q=people/yanlong-huang Yanlong Huang, MSc.] [http://134.76.18.81/?q=people/wenfang-wu Wenfang Wu, MSc.]		\|ta= [http://134.76.18.81/?q=people/yanlong-huang Yanlong Huang, MSc.] [http://134.76.18.81/?q=people/wenfang-wu Wenfang Wu, MSc.]
	\|time=Lecture: Thursday, 10am-12pm, Exercise: Thursday, 12pm-1pm		\|time=Lecture: Thursday, 10am-12pm, Exercise: Thursday, 12pm-1pm
	\|place= MN09		\|place= Lecture: MN09; Exercise: Institut für Informatik / Mathematische Stochastik (IMS) -1.101
	\|\|univz=		\|\|univz=
	}}		}}
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	\| Final Exam		\| Final Exam
	\| Time: 11.03.2027, 12-14		\| Time: 11.03.2027, 12-14
	\| Room: ~~MN08~~		\| Room: TBD
	\|		\|
	\|-		\|-

Computer Networks (Winter 2026/2027)

Hyanlon — Tue, 18 Aug 2026 11:00:49 GMT

Created page with "== Details == {{CourseDetails |credits=120h, 4 ECTS (old PO), 5 ECTS (new PO) |module=B.Inf.902: Telematik (old), B.Inf.1204.Telematik/Computernetzwerke (new) |lecturer=[http://134.76.18.81/?q=people/prof-dr-xiaoming-fu Prof. Xiaoming Fu] |ta= [http://134.76.18.81/?q=people/yanlong-huang Yanlong Huang, MSc.] [http://134.76.18.81/?q=people/wenfang-wu Wenfang Wu, MSc.] |time=Lecture: Thursday, 10am-12pm, Exercise: Thursday, 12pm-1pm |place= MN09 ||univz= }} ==Course descr..."

New page

== Details ==
{{CourseDetails
|credits=120h, 4 ECTS (old PO), 5 ECTS (new PO)
|module=B.Inf.902: Telematik (old), B.Inf.1204.Telematik/Computernetzwerke (new)
|lecturer=[http://134.76.18.81/?q=people/prof-dr-xiaoming-fu Prof. Xiaoming Fu]
|ta= [http://134.76.18.81/?q=people/yanlong-huang Yanlong Huang, MSc.] [http://134.76.18.81/?q=people/wenfang-wu Wenfang Wu, MSc.]
|time=Lecture: Thursday, 10am-12pm, Exercise: Thursday, 12pm-1pm
|place= MN09
||univz=
}}

==Course description==
Principles and basic understanding of computer networking, with an emphasis on the Internet. Topics include: the concepts and components of computer networks, packet switching, layered architectures, TCP/IP, error control, window flow control, local area networks, network layer and mobility, transport layer and congestion control, Quality of Service and multimedia networking, network management and security, and an introduction to current research topics.
After this course students should have general knowledge on basic concepts of networking, how the Internet works and basic network programming.

==Preliminary Schedule==
{| {{Prettytable|width=}}
|-
|{{Hl2}} |'''Date'''
|{{Hl2}} |'''Topic'''
|{{Hl2}} |'''Slides'''
|{{Hl2}} |'''Excercises'''
|{{Hl2}} |'''Solutions'''
|-
| align="right" | 29.10.2026
| Introduction & Layering
|
|
|
|-
| align="right" | 05.11.2026
| Application Layer
|
|
|
|-
| align="right" | 12.11.2026
| Transport Layer I
|
|
|
|-
| align="right" | 19.11.2026
| Transport Layer II
|
|
|
|-
| align="right" | 26.11.2026
| Network Layer I
|
|
|
|-
| align="right" | 03.12.2026
| Network Layer II
|
|
|
|-
| align="right" | 10.12.2026
| Link Layer I
|
|
|
|-
| align="right" | 17.12.2026
| Link Layer II
|
|
|
|-
| align="right" | 14.01.2027
| Networked Multimedia
|
|
|
|-
| align="right" | 21.01.2027
| Quality of Service
|
|
|
|-
| align="right" | 28.01.2027
| Network Security I
|
|
|
|-
| align="right" | 04.02.2027
| Network Security II
|
|
|
|-
| align="right" | TBA
| Final Exam
| Time: 11.03.2027, 12-14
| Room: MN08
|
|-
|}

==Textbook==
* J. Kurose and K. Ross, [https://media.pearsoncmg.com/bc/abp/cs-resources/products/product.html#product,isbn=0133594149 , "Computer Networking: A Top-Down Approach"], 7th Edition, 2017
* Andrew S. Tanenbaum, David J. Wetherall, [https://www.pearson.com/us/higher-education/program/Tanenbaum-Computer-Networks-5th-Edition/PGM270019.html, Computer Networks], 5th Edition， 2011

==Old exam sheets==
The following old exam sheets are provided for ''self-study'' purposes. They will not be discusses during an exercise session, nor will we provide answer sheets. However, you can email questions regarding a particular exercise to the TA's to be considered during the Q&A session at the end of the semester.
* [[Media:ComputerNetworks_ws2009_exam.pdf | Exam sheet winter semester 2009]]
* [[Media:ComputerNetworks_SS2010_exam.pdf | Exam sheet summer semester 2010]]

==Additional References==
* W. Richard Stevens, [http://www.kohala.com/start/tcpipiv1.html "TCP/IP Illustrated, Volume 1: The Protocols"], Addison-Wesley, 1994.
* W. Richard Stevens, [http://www.kohala.com/start/unpv12e.html "UNIX Network Programming, Volume 1: Networking APIs"], 2nd edition, Prentice Hall, 1997.

== Other Resources==
* Movie: "Warriors of the Net" [http://www.warriorsofthe.net/]

==Prerequisites==
* Computer Science I, II; basic familiarity with UNIX and C.

[[Category:Courses]]

Data Science in Smart City (Winter 2026/2027)

Lenke1 — Tue, 18 Aug 2026 09:41:38 GMT

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/jan-lenke Jan Lenke] |ta=TODO |time=Monday 8:00 - 10:00am |place=Online [https://meet.aca..."

New page

{{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/jan-lenke Jan Lenke]
|ta=TODO
|time=Monday 8:00 - 10:00am
|place=Online [https://meet.academiccloud.de/gl/rooms/gxc-xon-uyk-zo6/join]
|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)
|}

==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.

Teaching

Hyanlon — Fri, 07 Aug 2026 13:43:44 GMT

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			== Winter Semester 2026/2027 ==
			* [[Computer Networks (Winter 2026/2027)\| Computer Networks (Winter 2026/2027)]] (BSc) (Yanlong, Wenfang)

			* [[Seminar on Internet Technologies (Winter 2026/2027) \| Seminar on Internet Technologies (Winter 2026/2027)]](MSc, BSc) (Hao)

			* [[Data Science in Smart City (Winter 2026/2027) ]](MSc) (Jan, Yanlong/student assistant)

			* [[Advanced Topics in AI for Computing and Networking (Winter 2026/2027) \| Advanced Topics in AI for Computing and Networking (Winter 2026/2027)]] (MSc, BSc) (Wenfang)

			* [[Practical Course Networking Lab (Winter 2026/2027) \| Practical Course Networking Lab (Winter 2026/2027) ]] (BSc) (Haihan)

	== Summer Semester 2026 ==		== Summer Semester 2026 ==
	* [[Social Networks and Big Data Methods (Summer 2026) ]](MSc, BSc) (Sophie)		* [[Social Networks and Big Data Methods (Summer 2026) ]](MSc, BSc) (Sophie)