Final Doctoral Recital

Piano, Matthew Barnson, François Couperin, Claude Debussy, Jean-Philippe Ramea

Final Doctoral Recital

Percussion, John Ling, Ross Lewicki, Sara Cassey Please see Additional Documents for Recital Progra

Final Doctoral Recital

Piano, Alban Berg, Alexander Scriabin, Charles Ives, Robert Schumann. Please see Additional Documents for Recital Program

Long Island Sound Reef GIS Data

High-resolution backscatter and bathymetric maps created by multibeam sonar surveys were used to identify different seafloor bottom types within existing, potentially expanded, and newly proposed reef areas in New York waters. Existing sites included Smithtown in Long Island Sound (LIS), and Rockaway, Atlantic Beach, Hempstead, Yellowbar, Kismet, Fire Island, Twelve Mile along the South Shore. Potential expansions are proposed on the South Shore for McAllister, Moriches, and Shinnecock reefs in addition to a new site called Sixteen Fathom. In Long Island Sound, new sites are proposed for Huntington/Oyster Bay, Port Jefferson/Mount Sinai, and Mattituck. Grab samples were collected within these areas to characterize sediment properties and macrofauna. Multivariate analysis was used to identify important factors explaining variations in community structure. Sites within Long Island Sound had 3 to 10 bottom types (i.e., acoustic provinces), but sediments and benthic community structure was characterized by greater among site variation compared to within site variability. Sites along the South Shore had 4 to 12 bottom types (acoustic provinces), and although sediments were mostly sandy, there was substantial within site variation in benthic community structure. This research dataset contains only the Long Island Sound GIS files. A second research dataset for the South Shore GIS was also deposited to Academic Commons

Modularizing Design Space Exploration And Optimizing Compilation-Time Of Deep Learning Workloads Using Tensor Compilers

Recently, server-edge-based hybrid computing has received considerable attention as a promising means to provide Deep Learning (DL) based services. However, due to the limited computation capability of the data processing units (such as CPUs, GPUs, and specialized accelerators) in edge devices, using the devices’ limited resources efficiently is a challenge that affects deep learning-based analysis services. This has led to the development of several inference compilers, such as TensorRT, TensorFlow Lite, Glow, and TVM, which optimize DL inference models specifically for edge devices. These compilers operate on the standard DL models available for inferencing in various frameworks, e.g., PyTorch, TensorFlow, Caffe, and MxNet, and transform them into a corresponding lightweight model by analyzing the computation graphs and applying various optimizations at different stages. These high-level optimizations are applied using compiler passes before feeding the resultant computation graph for low-level and hardware-specific optimizations. With advancements in DNN architectures and backend hardware, the search space of compiler optimizations has grown manifold. Including passes without the knowledge of the computation graph leads to increased execution time with a slight influence on the intermediate representation. This report presents a detailed performance study of TensorFlow Lite (TFLite) and TensorFlow TensorRT (TF-TRT) using commonly employed DL models on varying degrees of hardware platforms. The work compares throughput, latency performance, and power consumption. The integrated TF-TRT performs better at the high-precision floating point on different DL architectures, especially with GPUs using tensor cores. However, it loses its edge for model compression to TFLite at low precision. TFLite, primarily designed for mobile applications, performs better with lightweight DL models than deep neural network-based models. Further, we understood that benchmarking and auto-tuning the tensor program generation is challenging with emerging hardware and software stacks. Hence, we offer a modular and extensible framework to improve benchmarking and interoperability of compiler optimizations across diverse and continually emerging software, hardware, and data from servers to embedded devices. We propose HPCFair, a modular, extensible framework to enable AI models to be Findable, Accessible, Interoperable and Reproducible (FAIR). It enables users with a structured approach to search, load, save and reuse the models in their codes. We present our framework’s conceptual design and implementation and highlight how it can seamlessly integrate into ML-driven applications for high-performance computing applications and scientific machine-learning workloads. Lastly, we discuss the relevance of neural-architecture-aware pass selection and ordering in DL compilers. We provide a methodology to prune the search space of the phase selection problem. We use TVM as a compiler to demonstrate the experimental results on Nvidia A100 and GeForce RTX 2080 GPUs, establishing the relevance of neural architecture-aware selection of optimization passes for DNNs DL compilers. Experimental evaluation with seven models categorized into four architecturally different classes demonstrated performance gains for most neural networks. For ResNets, the average throughput increased by 24% and 32% for TensorFlow and PyTorch frameworks, respectively. Additionally, we observed an average 15% decrease in the compilation time for ResNets, 45% for MobileNet, and 54% for SSD-based models without impacting the throughput. BERT models showed an improvement with over 90% reduction in the compile time

Final Doctoral Recital

Bassoon, Ernesto Balarezo Urmeneta, Ann K. Gebuhr, Michele Abondano, Valeria Rubi, Peter Van Zandt Lane, Gregg August, Paquito D\u27Rivera, Astor Piazzolla. Please see Additional Documents for Recital Program

Reef Benthic Fauna and Sediment Characterization

High-resolution backscatter and bathymetric maps created by multibeam sonar surveys were used to identify different seafloor bottom types within existing, potentially expanded, and newly proposed reef areas in New York waters. Existing sites included Smithtown in Long Island Sound (LIS), and Rockaway, Atlantic Beach, Hempstead, Yellowbar, Kismet, Fire Island, Twelve Mile along the South Shore. Potential expansions are proposed on the South Shore for McAllister, Moriches, and Shinnecock reefs in addition to a new site called Sixteen Fathom. In Long Island Sound, new sites are proposed for Huntington/Oyster Bay, Port Jefferson/Mount Sinai, and Mattituck. Grab samples were collected within these areas to characterize sediment properties and macrofauna. Multivariate analysis was used to identify important factors explaining variations in community structure. Sites within Long Island Sound had 3 to 10 bottom types (i.e., acoustic provinces), but sediments and benthic community structure was characterized by greater among site variation compared to within site variability. Sites along the South Shore had 4 to 12 bottom types (acoustic provinces), and although sediments were mostly sandy, there was substantial within site variation in benthic community structure

Final Doctoral Recital

Oboe, Nicolas Chedeville, Ulysses Kay, Charlotte Bray, Nguyen Phuc Linh, Miguel del Aguila. Please see Additional Documents for Recital Program

Distributed Networks of Listening and Sounding: 20 Years of Telematic Musicking

This paper traces a twenty-year arc of my performance and compositional practice in the medium of telematic music, focusing on a distinct approach to fostering interdependence and emergence through the integration of listening strategies, electroacoustic improvisation, pre-composed structures, blended real/virtual acoustics, networked mutual-influence, shared signal transformations, gesture-concepts and machine agencies. Communities of collaboration and exchange over this time period are discussed, which span both pre- and post-pandemic approaches to the medium that range from metaphors of immersion and dispersion to diffraction

Using Focus Groups to Understand Sorority and Fraternity Life and Inform Survey Design

Fraternity and sorority experiences can support or challenge the undergraduate student experience related to student learning and development (Sasso et al., 2020a, 2020b). There are concerns that researchers, advisors, and practitioners can pay attention to in order to enhance healthy chapter cultures or intervene when concerns arise. The article explores the process of revising the Fraternity and Sorority Experience Survey (FSES) using focus group findings to inform survey revision and practice. The FSES is organized around five themes–Learning, Values, Alcohol/Social Issues, Operations, and Community–and measures student perceptions and experiences. Implications for practice are included about instrument revision and how it informs assessment decision-making