Dr. Dimah Dera | Robotic Navigation | Best Researcher Award
Professor | Rochester Institute of Technology | United States
Dr. Dimah Dera has established a strong research profile in the fields of machine learning, artificial intelligence, and signal processing with a particular focus on uncertainty quantification, explainable AI, and the reliability of deep learning models in high-stakes applications. Her work significantly contributes to improving the robustness, interpretability, and decision-support capabilities of AI systems used in diverse domains such as intelligent transportation and medical imaging. One of her notable contributions includes advancements in transportation data analytics through the integration of machine learning techniques to enhance intelligent transportation systems, improving system efficiency and safety. She has also co-developed influential methodologies for robust explainability, offering extensive insights into gradient-based attribution techniques that help ensure transparency and trust in deep neural networks. Her research on uncertainty propagation in convolutional neural networks has resulted in models like PremiUm-CNN, which provides enhanced predictive confidence and performance reliability. Additionally, she has contributed to failure detection mechanisms in medical imaging systems, advancing the safety and diagnostic accuracy of AI models applied in healthcare environments. With her impactful publications in high-quality international journals and conferences, strong citation record, and multidisciplinary research collaborations, Dr. Dera consistently demonstrates innovative thinking and a commitment to addressing real-world challenges through AI. Her work not only advances core scientific understanding but also ensures practical translation of research outcomes into sectors where reliability, transparency, and robustness are essential, reflecting her leadership potential and suitability for recognition through a Best Researcher Award.
Featured Publications
Bhavsar, P., Safro, I., Bouaynaya, N., Polikar, R., & Dera, D. (2017). Machine learning in transportation data analytics. Data Analytics for Intelligent Transportation Systems, 283–307.
Nielsen, I. E., Dera, D., Rasool, G., Ramachandran, R. P., & Bouaynaya, N. C. (2022). Robust explainability: A tutorial on gradient-based attribution methods for deep neural networks. IEEE Signal Processing Magazine, 39(4), 73–84.
Dera, D., Bouaynaya, N. C., Rasool, G., Shterenberg, R., & Fathallah-Shaykh, H. M. (2021). PremiUm-CNN: Propagating uncertainty towards robust convolutional neural networks. IEEE Transactions on Signal Processing, 69, 4669–4684.
Dera, D., Rasool, G., & Bouaynaya, N. (2019). Extended variational inference for propagating uncertainty in convolutional neural networks. Proceedings of the 2019 IEEE 29th International Workshop on Machine Learning for Signal Processing.
Ahmed, S., Dera, D., Hassan, S. U., Bouaynaya, N., & Rasool, G. (2022). Failure detection in deep neural networks for medical imaging. Frontiers in Medical Technology, 4, 919046.