Efficient light collection and utilization are highly needed for developing effective photonic devices and materials. Nature is the master of organizing photosynthetic pigments into a densely packed state without self-quenching and conducting efficient energy transfer in a directed manner via implementing sophisticated proteins as scaffolds. The natural light-harvesting complex inspires the design of artificial photonic systems by utilizing synthetic templates to control the spatial arrangement and energy landscape of photoactive components. The self-assembled DNA nanostructures are highly programmable and intrinsically addressable, which makes them excellent templates for the precise organization of chromophores with desired complexity as artificial light-harvesting systems and photonic nanodevices for efficient photon capture and excitation energy transport. This dissertation focuses on the fundamental understanding and rational engineering of a series of artificial excitonic systems using programmable DNA architectures as templates to direct the self-assembly of cyanine dye aggregates. First, the DNA-templated pseudoisocyanine (PIC) dye aggregates were systematically studied to explore the effect of sequence and length of DNA templates on their excitonic properties. The results revealed that the PIC dye aggregates enable energy transfer along a defined track. Next, the benzothiazole cyanine dye K21 was introduced to form dye aggregates on double-stranded DNA templates. The strong inter-molecular coupling and weak sequence dependency of the K21 aggregates make it possible to mediate the efficient directional energy transfer over a distance up to 30 nm. Finally, the DNA helix-bundle structures with extended size and complicated geometries were employed to organize K21 dye as the scalable, addressable, and programmable excitonic complexes conducting sub-micron-scale directional exciton transport and serving as robust and modular building blocks to construct higher-order excitonic architectures.

The purpose of this research is to uncover connections between Chinese composer Shande Ding and Western composers, as found in the development of Ding’s compositional style.

Ding’s solo piano pieces are representative of his overall stylistic tendencies. As an innovative composer, Ding’s experiments in combining various and bold musical materials with Chinese traditional styles became the model for contemporary Chinese composers and those of the next generation.

There is little research, and few recordings of Ding’s work available in the United States. Most of the research remains in China, including his biography, styles, the analyses of specific pieces, etc. To date, Yuefang Liu has contributed the only dissertation on the merging of Eastern and Western Music Cultures that mentions Shande Ding, and it cites only Ding’s early piano works. Some articles have suggested that Ding may have been influenced by western composers, but there is no actual exploration of the proposition. This paper will offer some pedagogical suggestions by dividing Ding’s solo piano works into different levels of difficulty, along with tips on performance and teaching approaches. Both the connection between piano works of Western composers and Shande Ding, and Ding’s resultant compositional style, will be explored. It is hoped that the project will expand further interest in Ding’s solo piano works.

Visual processing in social media platforms is a key step in gathering and understanding information in the era of Internet and big data. Online data is rich in content, but its processing faces many challenges including: varying scales for objects of interest, unreliable and/or missing labels, the inadequacy of single modal data and difficulty in analyzing high dimensional data. Towards facilitating the processing and understanding of online data, this dissertation primarily focuses on three challenges that I feel are of great practical importance: handling scale differences in computer vision tasks, such as facial component detection and face retrieval, developing efficient classifiers using partially labeled data and noisy data, and employing multi-modal models and feature selection to improve multi-view data analysis. For the first challenge, I propose a scale-insensitive algorithm to expedite and accurately detect facial landmarks. For the second challenge, I propose two algorithms that can be used to learn from partially labeled data and noisy data respectively. For the third challenge, I propose a new framework that incorporates feature selection modules into LDA models.