Honours and Masters project

Mini-CP https://www.info.ucl.ac.be/~pschaus/minicp.html is a minimal form of constraint programming solver, designed to allow for easy experimentation and learning. 

One of the most efficient approaches to discrete optimisation solving is using lazy clause generation, which is a hybrid SAT/CP approach to solving problems.  But MiniCP does not currently support this. 

The aim of this project is to understand the computations underlying animals’ choice in dynamic and changing environments. The natural environment is multisensory, dynamic and changing, requiring animals to continually adapt and update their learned knowledge of statistical regularities in the environment that signal the presence of primary needs like water, food and mates. Yet, how the brain adapts and updates itself to the non-stationary and dynamic attributes of natural environments remains unexplored.

The tennis tour is a series of tennis tournaments played globally over a calendar year, where professional tennis players compete for prize money and ranking points. The structure of the tennis tour is organised into different tiers for both men and women, including grand slam tournaments and ATP/WTA tour events. In this project we use stochastic processes to model and simulate the tour under different experimental rules.

The research project aims to investigate:

- Multi-Model Fusion with Deep Neural Networks for Future Energy Systems (Smart Grid). 

Future energy systems are envisioned to be running decentrally with full automatic control, high proportion of renewable energy (e.g., wind & solar), and abundant storage facilities. With many types of renewable energy sources are weather and climate dependent, accurate and timely prediction on reliability risks (e.g., loss of generation, voltage issues, and thermal limit violations) due to weather/climate are often necessary.

Visually discriminating the identity of multiple (similar looking) objects in a scene and creating individual tracks of their movements over time, namely multi-object tracking (MOT), is one of the basic yet most crucial vision tasks, imperative to tackle many real-world problems in surveillance, robotics/autonomous driving, health and biology.

Chatbots for mental health are shown to be helpful for preventing mental health issues and improving the wellbeing of individuals, and to ease the burden on health, community and school systems.  However, the current chatbots in this area cannot interact naturally with humans and the types of interactions are limited to short text, predefined buttons etc. In contrast, psychologists in real-world interact with patients with multiple modalities, including accustic and visual information.

The research challenge for this project is to research, prototype and evaluate approaches to automatically capture multimodal traces of team members’ activity using sensors (such as indoor positioning trackers, physiological wristbands and microphones), using learning analytics techniques to make sense of sensor data from healthcare contexts. Depending on the trajectory that you take, examples of the questions that such a project could investigate include:

In NeuroDistSys (NDS): Optimized Distributed Training and Inference on Large-Scale Distributed Systems, we aim to design and implement cutting-edge techniques to optimize the training and inference of Machine Learning (ML) models across large-scale distributed systems. Leveraging advanced AI and distributed computing strategies, this project focuses on deploying ML models on real-world distributed infrastructures, improving system performance, scalability, and efficiency by optimizing resource usage (e.g., GPUs, CPUs, energy consumption).

Chemical exchange saturation transfer (CEST) MRI provides images of molecular information and has recently been used for the detection of malignant brain tumors and the assessment of muscle tissue in cardiac infarction. Additionally, CEST has also been used to assess changes in a neurotransmitter -glutamate (Glu)- in both brain and spinal cord and has shown potential in a number of diseases including Alzheimer’s-like dementia, Parkinsonism and Huntington’s Disease and Motor neuron diseases.

This multidisciplinary project combines cutting-edge Natural Language Processing (NLP), Chinese Studies and Political Science. The project aims to develop a deeper understanding of how official discourse has developed throughout the history of the People’s Republic of China. The main focus will be on text in the People’s Daily, the largest newspaper in China and the official newspaper of the Chinese Communist Party.