Please join the Boston IEEE Reliability Chapter for the following Technical Presentation on June 11, 2025! If attending in person, you must show a valid photo ID at the MIT LL gate, at 244 Wood St, Lexington, MA. State that you are attending the IEEE meeting in the Main Cafeteria. If attending remotely, see the Zoom link in the "Location" section below. Detailed agenda is at the bottom of this web page. Abstract: System reliability and resilience are crucial for ensuring dependable performance, especially in response to evolving demands and unexpected disruptions. Traditional reliability models, such as the Non-Homogeneous Poisson Process (NHPP), are widely used to predict defect occurrence based on testing time or effort. However, these models often fail to capture the complexities of real-world systems. Resilience engineering, which focuses on a system's ability to respond to and recover from shocks, has gained significant attention as a complementary approach to traditional reliability methods. Although statistical models provide foundational insights, their rigid assumptions can limit flexibility and fail to capture dynamic patterns in defect occurrence and recovery processes. Conversely, machine learning methods, such as neural networks, offer the potential to model intricate dependencies and non-linear trends. However, these models often require extensive data, which may not always be available in resilience engineering contexts, and they can lack robustness in long-term predictions. This limitation underscores the need for integrated approaches that effectively tackle the challenges of modeling resilience in systems experiencing various types and intensities of shocks. To address these challenges, this talk explores hybrid approaches that enhance defect prediction in both regression and classification tasks and improve resilience assessment. We introduce flexible time series techniques that account for multiple stressors and recovery patterns. By integrating machine learning and statistical methods, this presentation aims to advance the assessment of both reliability and resilience in systems, providing robust, adaptable models capable of predicting defects and tracking recovery under complex conditions. Speaker(s): Fatemeh Salboukh, Agenda: 5:00 pm doors open, for networking. Arriving earlier is welcome. 5:30 pm: Pizza, salad, and refreshments are scheduled to arrive, while networking continues. 6:00 pm: A plaque presentation, followed by an introduction to the presentation, followed by the formal presentation. . Bldg: Main Cafeteria, 244 Wood Street, Lexington, Massachusetts, United States, 02420, Virtual: https://events.vtools.ieee.org/m/486009