Dimah Dera Headshot

Dimah Dera

Assistant Professor

Chester F. Carlson Center for Imaging Science
College of Science

5854752454
Office Location

Dimah Dera

Assistant Professor

Chester F. Carlson Center for Imaging Science
College of Science

Bio

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) award in 2023 and the NSF Research Experiences for Undergraduates (REU) supplement award in 2024. 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 the IEEE Benjamin Franklin Key Award (2021). She publishes in the area of trustworthy, reliable, and expandable machine learning, signal and image processing and optimization.

Currently Teaching

IMGS-210
4 Credits
The goal of this course is to give students an appreciation of the importance of mathematics in imaging, and provide an introduction to the relevant mathematical methods to enable students to address important imaging problems. The course covers topics that include geometry, linear algebra, multivariable calculus, probability and statistics, and information theory.
IMGS-362
3 Credits
This course is considers the more advanced concepts of digital image processing. The topics include image reconstruction, noise sources and techniques for noise removal, information theory, image compression, video compression, wavelet transformations, frequency-domain based applications, morphological operations, and modern digital image watermarking and steganography algorithms. Emphasis is placed on applications and efficient algorithmic implementation using the student’s computer programming language of choice, technical presentation, and technical writing.
IMGS-599
1 - 4 Credits
This course is a faculty-directed tutorial of appropriate topics that are not part of the formal curriculum. The level of study is appropriate for student in any of their years of study.
IMGS-789
1 - 3 Credits
This is a graduate-level course on a topic that is not part of the formal curriculum. This course is structured as an ordinary course and has specific prerequisites, contact hours, and examination procedures.
IMGS-790
1 - 6 Credits
Masters-level research by the candidate on an appropriate topic as arranged between the candidate and the research advisor.
IMGS-799
1 - 4 Credits
This course is a faculty-directed tutorial of appropriate topics that are not part of the formal curriculum. The level of study is appropriate for student in their graduate studies.
IMGS-890
1 - 6 Credits
Doctoral-level research by the candidate on an appropriate topic as arranged between the candidate and the research advisor.
IMGS-891
0 Credits
Continuation of Thesis