ESE

Machine learning has been widely adopted to medical imaging research, yet it suffers from domain shift for real world applications. Due to the heterogeneity of medical data, machine learning-based diagnostic models are also prone to biases. In this thesis, we start from arguing the necessity of domain adaptation to achieve the optimal performance for each […]

Graphs are powerful mathematical tools that enable modeling of complex systems. Graph machine learning exploits possibly unknown data structures, which provides a unified approach to tackle a wide variety of problems. However useful in practice, graph machine learning solutions tend to suffer from three main limitations: they do not scale with the size of the […]

Aluminum scandium nitride (AlScN), a ternary alloy by doping scandium into aluminum nitride (AlN), has circumspectly gained recognition through the last decade as one of the promised materials in forming the next generation radio frequency (RF) filters and resonators - core components in the wireless telecommunication systems that interconnect today’s world. This, coupled with the […]

The limitations of silicon electronic devices increasingly constrain the performance of silicon integrated circuits (ICs) and their use in new applications. Next-generation devices with exceptional performance and new functionalities have been realized using two-dimensional materials such as graphene. For example, graphene Hall-effect sensors (GHSs) greatly outperform commercial silicon magnetic-field sensors and could significantly improve the […]