Dimah Dera specializes in robust and trustworthy modern machine learning (ML) solutions for real-world applications, including healthcare, cybersecurity, remote sensing, and surveillance systems. In the rapidly evolving landscape of artificial intelligence (AI) and autonomous systems, the integration of ML techniques has paved the way for unprecedented advancements across various domains. The robustness, safety, and reliability of AI systems have emerged as pivotal requirements. The scope of her research includes developing innovative techniques to ensure the robustness, safety, and reliability of AI systems by integrating Bayesian theory and statistical signal processing foundations into modern ML frameworks. This research highlights the intricate connections between learning Bayesian uncertainty in ML models and their robustness and safety awareness to dynamically changing environments and systems failure. This research advances theoretical and algorithmic knowledge that will transcend traditional ML and AI systems toward safe and reliable deployment of AI models in high-risk real-world applications. Dimah received the National Science Foundation (NSF) Computer and Information Science and Engineering Research Initiation Initiative (CRII) and NSF Research Experiences for Undergraduates (REU) supplement awards in 2022 for her research focusing on robust machine learning and time-series analysis. She won multiple awards, such as the Best Paper Award at the 2019 IEEE International Workshop on Machine Learning for Signal Processing (MLSP’19) and IEEE Philadelphia Sections Benjamin Franklin Key Award (2021). She publishes in the area of trustworthy, reliable, and expandable machine learning, signal and image processing and optimization.