Author: Scott McClellan

Please join the MRC on April 17 from 11AM-12PM in Room 928 of the College of Computing and Informatics for “Accelerating Artificial Intelligence for Data-Driven Discovery” a talk delivered by Shih-Chieh Hsu, PhD, University of Washington.

Abstract

As scientific datasets become progressively larger, algorithms to process this data quickly become more complex. In response, Artificial Intelligence (AI) has emerged as a solution to efficiently analyze these massive datasets. Emerging processor technologies such as graphics processing units (GPUs) and field-programmable gate arrays (FPGAs) allow AI algorithms to be greatly accelerated. The Accelerated AI Algorithms for Data-Driven Discovery (A3D3) Institute sponsored by the National Science Foundation under the Harnessing the Data Revolution program is established to enable real-time AI at scale for broad applications. In this talk, Hsu will give an overview about the challenges of high energy physics, multi-messenger astrophysics and neuroscience regarding AI across latency and throughput regimes. He will introduce various techniques for model compression using state-of-the-art techniques such as pruning and quantization for edge computing. He will demonstrate that acceleration of AI inference as a web service represents a heterogeneous computing solution. Finally Hsu will discuss how A3D3 can bring together disparate communities that are threaded by common data-intensive grand challenges to accelerate discovery in science and engineering.

Biography

Shih-Chieh Hsu, PhD is a professor in physics and adjunct professor in electrical and computer engineering at University of Washington (UW), and director of NSF HDR Institute: Accelerated Artificial Intelligence Algorithms for Data-Driven Discovery. He earned the BS/MS in physics from National Taiwan University and the PhD in Physics from University of California San Diego. He is working on experimental particle physics using proton-proton collision data from the Large Hadron Collider. His research interests range from dark matter searches with the ATLAS experiment neutrino cross-section measurements with the FASER experiment innovative artificial intelligence algorithms for data-intensive discovery and accelerated machine learning with heterogeneous computing.

Ajani Levere, a Drexel University STAR Scholar working with Drs. Jane Greenberg and David Breen presented their research imageomics at the STAR Scholars showcase on August 31, 2023. Their presentation was titled “Computational Fish Specimen Classification: Advancing Machine Learning Model Accuracy” and was part of the NSF-HDR: Biology-guided Neural Networks for Discovering Phenotypic Traits. Ajani’s research is continuing under the guidance of Dr. Greenberg. They describe their project as follows:

Digital specimen metadata is valuable for scientific research and discovery, yet sparse specimen metadata availability restricts its potential. In addition to computational efforts made to remedy this issue, Machine Learning (ML) classification was performed on a computed metadata component, the outline extracted from fish specimen images. An ML model (MLM) approach provided a computational genus classification for a given fish outline. This research improves the MLM’s ability to accurately classify fish from their 2D outlines and demonstrates the expressiveness of this computed metadata item.  

In our analysis, we inspected the outlines of the error cases, followed by a statistical review of their numerical data. We discovered our dataset limited higher MLM accuracy potential. Refactoring the dataset with a reduced feature length thus enhanced our dataset for MLM interpretability. Experimental results indicate a 96% accuracy, a 5% improvement over previous results. These results confirm the outline as a unique and highly distinguishable metadata component. Computing metadata components of this nature aids the development of a more robust metadata catalog for ML researchers.

The Metadata Reasearch Center congratulates Jane Greenberg, its Director and Founder, for receiving the Association for Information Science & Technology’s (ASIS&T) 2023 Research in Information Science Award. The award “recognizes an individual or team who has made an outstanding contribution to information science research. The award is for a systematic “program of research” in a single area at a level beyond the single study.” ASIS&T recognized Dr. Greenberg’s wide-ranging contributions, including her current positions as principal investigator on the Metadata Capital Initiative (MetaDataCAPT’L) and the NSF-funded Institute for Data Driven Dynamical Design (ID4), and the IMLS-funded project LEADING (LIS Education and Data Science Integrated Network Group). The award committee also singled out her work with the Biology-guided Neural Network (BGNN) project and the Helping Interdisciplinary Vocabulary Engineering (HIVE) tool. Please click here for the full press release from ASIS&T, “Jane Greenberg Receives Association for Information Science and Technology (ASIS&T) Research in Information Science Award.”

Scott McClellan, a second year doctoral student, presented research results to the “Data representation in materials and chemicals based on harmonised domain ontologies” birds of a feather group at the Research Data Alliance’s 20th Plenary meeting in Gothenburg, Sweden on March 21-23, 2023. His presentation, titled “Along the Border: Term Overlap Among 5 Matportal Ontologies,” focused on term overlap among a subset of ontologies maintained at the Matportal repository. It looked at how term matching algorithms for materials science semantic artifacts differed when locating terminological or URI results. His presentation stemmed from prior research done with Drs. Yuan An and Jane Greenberg and fellow graduate student Xintong Zhao. [Slides]