Student Summer Internship 2025 open for UoB students now! Deadline 26th of May 2025 (Monday)
Application form download
Project 1: AI-Driven Label Recognition in Real-World Settings (details in pdf)
Project 2: Text-Image Matching (Cross-Model Retrieval) in Computer Vision (details in pdf)
How to apply and eligibility: see each project description file above.
To fill in the form:
- HESA: 0110 (it is UoB ID)
- Reference: could be the lecturers who taught you before or your allocated personal tutor.
- Starting date: relatively flexible for me, as long as you give the 2-months duration over the summer.
Application form download
Project 1: AI-Driven Label Recognition in Real-World Settings (details in pdf)
Project 2: Text-Image Matching (Cross-Model Retrieval) in Computer Vision (details in pdf)
How to apply and eligibility: see each project description file above.
To fill in the form:
- HESA: 0110 (it is UoB ID)
- Reference: could be the lecturers who taught you before or your allocated personal tutor.
- Starting date: relatively flexible for me, as long as you give the 2-months duration over the summer.
Fundamental machine learning of interest:
•Class imbalance learning, data augmentation
•Data stream learning, time series forecasting, continual learning
•Automated machine learning (AutoML)
•Distributed machine learning (Federated learning)
•Data stream learning, time series forecasting, continual learning
•Automated machine learning (AutoML)
•Distributed machine learning (Federated learning)
Machine learning in following application areas:
•Sports data analysis (e.g. GPS data, performance and strategy analysis)
•Automated software engineering (e.g. software defect prediction)
•IoT (e.g. smart cities, energy consumption, environmental monitoring)
•Multi-modal, LLM-integrated applications
•Automated software engineering (e.g. software defect prediction)
•IoT (e.g. smart cities, energy consumption, environmental monitoring)
•Multi-modal, LLM-integrated applications
Potential project topics:
Applied projects: Machine learning techniques for
1) Sports data analysis (e.g. performance and strategy analysis)
2) Multi-modal (with image, text and structured data), LLM integrated systems
3) Software defect prediction
4) Energy forecasting and smart grid
5) Environmental challenges
6) Image recognition in real-world settings
7) Image object detection and privacy protection
Research-focused projects:
1) Robust optimization handling uncertainty
2) Online data-driven optimization (keywords: surrogate models, ML, optimization, dynamic)
3) Machine learning from multiple/distributed/federated data streams (keywords: data stream learning, concept drift, distributed ML).
4) Data fusion techniques for multiple data streams (keywords: data stream learning, data fusion, distributed ML)
5) Making use of unlabelled data for class imbalance problems (keywords: semi-supervised learning, class imbalance learning)
6) Transfer learning for class imbalance problems (keywords: transfer learning, class imbalance learning)
7) Adaptive transfer learning for data streams (keywords: transfer learning, data stream learning)
8) Data stream clustering (keywords: data stream learning, clustering, non-stationarity)
9) Class evolution in data stream learning (keywords: data stream learning, class evolution)
10) Feature evolution data stream learning (keywords: data stream learning, feature evolution)
11) AutoML for non-stationary data streams (keywords: AutoML, data stream learning, concept drift)
12) AutoML for multi-label classification (keywords: AutoML, multi-label classification)
13) AutoML for class imbalance problems (keywords: AutoML, class imbalance learning)
14) AutoML for distributed data (keywords: AutoML, distributed machine learning, federated learning)
15) Multi-modal federated learning (keywords: multi-modal data, federated learning)
16) LLM-driven AutoML (keywords: AutoML, LLM)
1) Sports data analysis (e.g. performance and strategy analysis)
2) Multi-modal (with image, text and structured data), LLM integrated systems
3) Software defect prediction
4) Energy forecasting and smart grid
5) Environmental challenges
6) Image recognition in real-world settings
7) Image object detection and privacy protection
Research-focused projects:
1) Robust optimization handling uncertainty
2) Online data-driven optimization (keywords: surrogate models, ML, optimization, dynamic)
3) Machine learning from multiple/distributed/federated data streams (keywords: data stream learning, concept drift, distributed ML).
4) Data fusion techniques for multiple data streams (keywords: data stream learning, data fusion, distributed ML)
5) Making use of unlabelled data for class imbalance problems (keywords: semi-supervised learning, class imbalance learning)
6) Transfer learning for class imbalance problems (keywords: transfer learning, class imbalance learning)
7) Adaptive transfer learning for data streams (keywords: transfer learning, data stream learning)
8) Data stream clustering (keywords: data stream learning, clustering, non-stationarity)
9) Class evolution in data stream learning (keywords: data stream learning, class evolution)
10) Feature evolution data stream learning (keywords: data stream learning, feature evolution)
11) AutoML for non-stationary data streams (keywords: AutoML, data stream learning, concept drift)
12) AutoML for multi-label classification (keywords: AutoML, multi-label classification)
13) AutoML for class imbalance problems (keywords: AutoML, class imbalance learning)
14) AutoML for distributed data (keywords: AutoML, distributed machine learning, federated learning)
15) Multi-modal federated learning (keywords: multi-modal data, federated learning)
16) LLM-driven AutoML (keywords: AutoML, LLM)
Public data repositories:
UC Irvine Machine Learning Repository
http://archive.ics.uci.edu/ml/
Kaggle datasets
https://www.kaggle.com/datasets
Amazon’s AWS datasets
https://aws.amazon.com/fr/datasets/
Meta portals (they list open data repositories):
http://dataportals.org/
https://data.europa.eu/en/publications/open-data-maturity
http:// quandl.com/
https://www.quandl.com/
Wikipedia’s list of Machine Learning datasets
https://en.wikipedia.org/wiki/List_of_datasets_for_machine_learning_research
Quora.com question
https://www.quora.com/Where-can-I-find-large-datasets-open-to-the-public
Datasets subreddit
https://www.reddit.com/r/datasets/
Residential house sensor and energy data
https://www.st.ewi.tudelft.nl/~akshay/dred/
https://www.mdpi.com/1996-1073/16/16/6069
https://www.nature.com/articles/s41597-021-00921-y#Tab3
https://data.open-power-system-data.org/household_data/
https://data.openei.org/data_lakes
Facial Recognition Datasets of 2022
www.twine.net/blog/facial-recognition-datasets/
UK Data Service
https://ukdataservice.ac.uk/find-data/browse/
Fintech, A large language model for Finance
https://github.com/The-FinAI/PIXIU
Football data resources:
https://www.kaggle.com/datasets/davidcariboo/player-scores
https://github.com/eddwebster
Related research, refer to research groups: [Jesse Davie][Ulrik Brandes]
Sentiment analysis:
https://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html
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