Research Topics

Their work encompasses two main areas: research and infrastructure development. In the research aspect, the team engages in investigating novel  techniques, algorithms, and methodologies related to ML. They explore  ways to improve ML models' performance, efficiency, accuracy, interpretability and generalization capabilities. This could involve developing new architectures, optimization algorithms, or data preprocessing techniques. The team collaborates with ML researchers and  data scientists to enhance the overall effectiveness of ML models. In terms of infrastructure development, the team focuses on designing and building the software systems and tools required to support ML research projects. This includes creating robust frameworks, libraries, and platforms that facilitate applied machine learning research. They work on optimizing and automating processes such as data preprocessing, model training, and inference to enable efficient ML workflows. Additionally, they address challenges related to data management, model  versioning, reproducibility, and performance monitoring. Overall, the RiseML research team bridges the gap between ML research and applied machine learning. They contribute to the advancement of ML techniques through research while also developing the necessary  infrastructure to enable the smooth integration with interdisciplinary  research teams.