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| Yes | | Yes | ||
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| | | How to do efficient offline training | ||
| Basic machine learning knowledge | | In this topic, you will study how to do efficient offline training for reinforcement learning | ||
| [Tingting Yuan, | | Basic programming knowledge, Basic machine learning knowledge, need coding work | ||
| | | [Tingting Yuan, tingting.yuan@cs.uni-goettingen.de] | ||
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| Yes | | Yes | ||
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| Change Detection in Satellite Image Time Series | | Change Detection in Satellite Image Time Series | ||
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| Explainable AI(XAI) / graph neural network (XGNN) | | Explainable AI(XAI) / graph neural network (XGNN) | ||
| In this topic, | | In this topic, student will study how AI models / GNNs are explained by SOTA papers. | ||
| Basic AI / GNN knowledge | | Basic AI / GNN knowledge | ||
| [Zhengze Li, zhengze.li@cs.uni-goettingen.de] | |||
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| Yes | |||
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| Anomaly Detection in Graphs | |||
| In this topic, student will read papers to learn how to detect anomaly edge/graph/subgraph… with the help of GNN. | |||
| Basic AI / GNN knowledge | |||
| [Zhengze Li, zhengze.li@cs.uni-goettingen.de] | |||
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| Yes | |||
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| The relationship between birds’ distribution and the health of the environment (Project possible) | |||
| Birds are sensitive to environmental pressures and their populations can reflect changes in the health of the environment. By analyzing the change of the distribution of birds, perhaps we may evaluate the health of the environment. | |||
| Basic Python knowledge, correlation analysis | |||
| [Zhengze Li, zhengze.li@cs.uni-goettingen.de] | | [Zhengze Li, zhengze.li@cs.uni-goettingen.de] | ||
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| Social Media Comments Network (Intern/Project/Thesis possible) | | Social Media Comments Network (Intern/Project/Thesis possible) | ||
| In this topic, | | In this topic, student will study methods to crawl the dataset from social networks(e.g. X, YouTube) and utilize social network analysis in any topic you are interested in (science/education/language…) to find out the network structure and compare the difference among different topics. | ||
| Python(Data crawling, cleaning, statistical data analysis, modeling and visualization), basic graph knowledge | | Python(Data crawling, cleaning, statistical data analysis, modeling and visualization), basic graph knowledge would be a plus | ||
| [Zhengze Li, zhengze.li@cs.uni-goettingen.de] | |||
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| No | |||
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| Influence of LLM robots in social networks (Intern/Project/Thesis possible) | |||
| In this topic, student will study methods to crawl the data of LLM robots from social networks(e.g. X, Facebook) and utilize NLP and SNA to evaluate the influence of LLM robots in a specific topic. | |||
| Python(Data crawling, cleaning, statistical data analysis, modeling and visualization), basic graph knowledge would be a plus | |||
| [Zhengze Li, zhengze.li@cs.uni-goettingen.de] | |||
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| Yes | |||
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| The life-circle of vanished scientific journals (Intern/Project/Thesis possible) | |||
| In this topic, student will mine the information of vanished/(ongoing)/top journals, try to find out the difference features(manually/ML-based method) between journals facing different destinies. | |||
| Python(Data Crawling, Cleaning, EDA, Modeling). Basic graph, XAI knowledge is a plus. | |||
| [Zhengze Li, zhengze.li@cs.uni-goettingen.de] | | [Zhengze Li, zhengze.li@cs.uni-goettingen.de] | ||
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| Traffic prediction with GNN (Intern/Project/Thesis possible) | | Traffic prediction with GNN (Intern/Project/Thesis possible) | ||
| In this topic, | | In this topic, student will study how to use XGNN to predict traffic volumn. | ||
| Python(Modeling and Visualization). Graph and XAI knowledge. | | Python(Modeling and Visualization). Graph and XAI knowledge. | ||
| [Zhengze Li, zhengze.li@cs.uni-goettingen.de] | | [Zhengze Li, zhengze.li@cs.uni-goettingen.de] | ||
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| ML/DL based industrial equipment predictive maintenance (Intern/Project/Thesis possible) | | ML/DL based industrial equipment predictive maintenance (Intern/Project/Thesis possible) | ||
| In this topic, | | In this topic, student will study how to use cutting-edge machine learning models to predict when industrial equipment need to be maintained before crashing. | ||
| Python(Cleaning, EDA, Modeling and Visualization). XAI knowledge is a plus. | | Python(Cleaning, EDA, Modeling and Visualization). XAI knowledge is a plus. | ||
| [Zhengze Li, zhengze.li@cs.uni-goettingen.de] | | [Zhengze Li, zhengze.li@cs.uni-goettingen.de] | ||
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| | | No | ||
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| | | AI for High-quality Image Restoration and Manipulation (Intern/Project/Thesis possible) | ||
| | | Image restoration and manipulation are low-level vison problems aiming to either restore the degraded images for higher perceptual quality (such as better color, contrast brightness, etc.) or manipulate image styles content for better understanding or visual-appealing effects. Moreover, such problems also plays key role for many high-level computer vision tasks, including image detection, recognition and (semantic) segmentation... In this topic, students need to follow the new trends and advances in the area of many sup-problem and explore new methods for completive or superior opportunity for academic and industrial applications. | ||
| | | Python & CV knowledge. | ||
| [Zhengze Li, zhengze.li@cs.uni-goettingen.de] | | [Zhengze Li, zhengze.li@cs.uni-goettingen.de] | ||
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| | | Lidar-based traffic flow analysis | ||
| In this topic, you will study methods to | | 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 | | Basic point cloud processing & ML knowledge | ||
| [Yanlong Huang, yanlong.huang@cs.uni-goettingen.de] | | [Yanlong Huang, yanlong.huang@cs.uni-goettingen.de] | ||
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| | | Personalized chatbot based on ChatGPT | ||
| In this topic, you will | | 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] | ||
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| Yes | | Yes | ||
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| | | Multimodal Large Language Model Evaluation for Multimodal Tasks | ||
| In this topic, you will | | 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] | | [Wenfang Wu, wenfang.wu@cs.uni-goettingen.de] | ||
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