Layer-Guided Latent Reasoning (LGLR) is a study that explores whether adjusting just a few layers in a transformer model can achieve the benefits of recent reasoning techniques—like Coconut—without the high computational ...

This study explores the design and development of two adaptive interfaces—Visual and Speech Dialogue—for educational robots, guided by the CAM (Capability, Availability, Motivation) framework. Aiming to enhance parent-robot-child ...

For my Senior Thesis, I developed a VR model of the human brain in Unity, enabling users to explore brain anatomy in an immersive, interactive lab setting. The model features 141 interactive components, allowing users to ...

Vision Language Models (VLMs) have significantly advanced multimodal understanding by effectively combining visual and textual modalities for various applications, including image captioning, visual question answering, and ...

Reading is an essential yet challenging skill in child education, particularly for immigrant families navigating bilingual environments. This study explores how generative language models (LLMs) can enhance parent-child ...

Dynamic program analysis is invaluable for malware detection, debugging, and performance profiling. However, software-based instrumentation incurs high overhead and can be evaded by anti-analysis techniques. In this paper, ...

Unsupervised anomaly detection has been profoundly impacted by the advent of large-scale Vision Foundation Models (VFMs). The prevailing paradigm leverages features from a pre-trained encoder, where anomalies manifest as ...

"Multimodal large language models (MLLMs) are expected to jointly interpret vision, audio, and language, yet existing video benchmarks rarely assess fine-grained reasoning about human speech. Many tasks remain visually ...

Missing data on socioeconomic variables, such as education and occupation, is a common issue in survey studies and can be Missing Not at Random (MNAR), where the likelihood of missingness depends on the unobserved value ...