Our laboratory has been expanded with 10 members now. Our main research focus is on Efficient and Trustworthy Machine Learning and Large Language Models (LLMs) with Application in Health. We have the following main directions: (1) Efficient fine-tuning and inference of large language models (LLMs): The main question is to ensure if we are able to develop techniques for efficient training and inference of machine learning algorithms as well as efficient inference and fine-tuning of LLMs, considering that the require major amount of compute, memory, and energy. (2) Trustworthy large language models (LLMs): The main goal here is to ensure trust into the results and decisions made by machine learning algorithms and LLMs, considering the majority of machine learning algorithms and LLMs work on a best-effort basis. This will focus on topics, such as safety, robustness, privacy-preservation, transparency, verifiability of machine learning algorithms and LLMs. (3) Application of large language models (LLMs) in health: One of our main direction has been on application of machine learning in the healthcare domain, including with heart pathologies and brain disorders. Along this direction, we plan to work on application of LLMs in health and in collaboration with major healthtech companies in Sweden. We have several major collaborator within these projects, including Ericsson Research and early discussions with Google, AstraZeneca, etc. Our research results have been published in the top international venues, including International Conference on Machine Learning (ICML) and the AAAI Conference on Artificial Intelligence (AAAI). We have ongoing collaboration with major research institutes, including the Swiss Federal Institute of Technology (EPFL) and Oxford University.