Dimah Dera is an Endowed Assistant Professor at the Chester F. Carlson Center for Imaging Science at the Rochester Institute of Technology. She received her Ph.D. and M.S. in Electrical and Computer Engineering and M.A. in Mathematics from Rowan University. Dimah received the National Science Foundation (NSF) Computer and Information Science and Engineering Research Initiation Initiative (CRII), Award No. 2401828, in 2023 and the NSF Research Experiences for Undergraduates (REU) supplement award in 2024 for her current research focusing on robust and trustworthy machine learning. She won several research Awards at IEEE conferences and the Engineering community, such as the Best Paper Award at the 2019 IEEE International Workshop on Machine Learning for Signal Processing, the NJ Tech Council STEM Innovator to Watch Award (2019), and the IEEE Philadelphia Sections Benjamin Franklin Key Award (2021). Dimah has served as a member of the IEEE Signal Processing and Computational Intelligence Societies as well as a member of the ACM SIGHPC Association for Computing Machinery. She is the NVIDIA Deep Learning Institute (DLI) University Ambassador. Dr. Dera specializes in robust and trustworthy modern machine learning (ML) solutions for real-world applications, including healthcare, remote sensing, and surveillance systems. 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 self-awareness to dynamically changing environments and systems failure. She publishes in the area of trustworthy, reliable, and explainable machine learning, signal and image processing and optimization.