Autonomous driving has shown significant progress in recent years. The combination of advanced sensors, ample data, and machine learning algorithms has led to the deployment of autonomous vehicles (AVs) in cities like Los Angeles, San Francisco, and Phoenix. While not all humans can drive perfectly, AVs should be able to plan, adapt, and react to environmental disturbances, including irrational human drivers. My research focuses on applying reinforcement learning (RL) techniques to validate AV-related cyber-physical systems (CPS) in realistic environments. I develop a custom RL environment that simulates highway driving scenarios with multiple vehicles. This environment includes a CPS model of adaptive cruise control (ACC), a lane-changing model (MOBIL), and an adversarial agent that learns to drive irrationally. My work extends interpretable RL techniques to continuous control tasks like autonomous driving.

Detection of melanocytes serves as a critical prerequisite in assessing melanocytic growth patterns when diagnosing melanoma and its precursor lesions on skin biopsy specimens. However, this detection is challenging due to the visual similarity of melanocytes to other cells in routine Hematoxylin and Eosin (H&E) stained images, leading to the failure of current nuclei detection methods. Stains such as Sox10 can mark melanocytes, but they require an additional step and expense and thus are not regularly used in clinical practice. To address these limitations, we introduce VSGD-Net, a novel detection network that learns melanocyte identification through virtual staining from H&E to Sox10. The method takes only routine H&E images during inference, resulting in a promising approach to support pathologists in the diagnosis of melanoma. To the best of our knowledge, this is the first study that investigates the detection problem using image synthesis features between two distinct pathology stainings. Extensive experimental results show that our proposed model outperforms state-of-the-art nuclei detection methods for melanocyte detection. The source code and pre-trained model are available at: https://github.com/kechunl/VSGD-Net.