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Facial aging is a complex process, highly dependent on multiple factors like gender, ethnicity, lifestyle, etc., making it extremely challenging to learn a global aging prior to predict aging for any individual accurately. Existing techniques often produce realistic and plausible aging results, but the re-aged images often do not resemble the person's appearance at the target age and thus need personalization. In many practical applications of virtual aging, e.g. VFX in movies and TV shows, access to a personal photo collection of the user depicting aging in a small time interval (20~40 years) is often available. However, naive attempts to personalize global aging techniques on personal photo collections often fail. Thus, we propose MyTimeMachine (MyTM), a method that combines a global aging prior with a personalized photo collection (ranging from as few as 10 images, ideally 50) to learn individualized age transformations. We introduce a novel Adapter Network that combines personalized aging features with global aging features and generates a re-aged image with StyleGAN2. We also introduce three loss functions to personalize the Adapter Network with personalized aging loss, extrapolation regularization, and adaptive w-norm regularization. Our method demonstrates strong performance on fair-use imagery of widely recognizable individuals, producing photorealistic and identity-consistent age transformations that generalize well across diverse appearances. It also extends naturally to video, delivering high-quality, temporally consistent results that closely resemble actual appearances at target ages—outperforming state-of-the-art approaches.