A5065464552- Scientific Computing and Data Management
- Distributed and Parallel Computing Systems
- Advanced Data Storage Technologies
- Cloud Computing and Resource Management
- Research Data Management Practices
- Data Quality and Management
- Parallel Computing and Optimization Techniques
- Privacy-Preserving Technologies in Data
- Peer-to-Peer Network Technologies
- Machine Learning in Materials Science
- IoT and Edge/Fog Computing
- Topic Modeling
- Computational Drug Discovery Methods
- Biomedical Text Mining and Ontologies
- Computational Physics and Python Applications
- Distributed systems and fault tolerance
- Semantic Web and Ontologies
- Advanced Database Systems and Queries
- Service-Oriented Architecture and Web Services
- Cell Image Analysis Techniques
- Software System Performance and Reliability
- Natural Language Processing Techniques
- Blockchain Technology Applications and Security
- Caching and Content Delivery
- Data Stream Mining Techniques
University of Chicago
2015-2024
Argonne National Laboratory
2015-2024
Physical Sciences (United States)
2024
University of Illinois Chicago
2019-2024
University of California, Riverside
2023
NanoTechLabs (United States)
2021
University of Southern California
2016
Victoria University of Wellington
2005-2011
Online relationships in social networks are often based on real world and can therefore be used to infer a level of trust between users. We propose leveraging these form dynamic "Social Cloud," thereby enabling users share heterogeneous resources within the context network. In addition, inherent socially corrective mechanisms (incentives, disincentives) enable cloud-based framework for long term sharing with lower privacy concerns security overheads than present traditional cloud...
High-level programming languages such as Python are increasingly used to provide intuitive interfaces libraries written in lower-level and for assembling applications from various components. This migration towards orchestration rather than implementation, coupled with the growing need parallel computing (e.g., due big data end of Moore's law), necessitates rethinking how parallelism is expressed programs. Here, we present Parsl, a scripting library that augments simple, scalable, flexible...
With the increasingly ubiquitous nature of Social networks and Cloud computing, users are starting to explore new ways interact with, exploit these developing paradigms. used reflect real world relationships that allow share information form connections between one another, essentially creating dynamic Virtual Organizations. We propose leveraging pre-established trust formed through friend within a network “Social Cloud”, enabling friends resources context network. believe combining with...
A unique archive of Big Data on Parkinson's Disease is collected, managed and disseminated by the Progression Markers Initiative (PPMI). The integration such complex heterogeneous from multiple sources offers unparalleled opportunities to study early stages prevalent neurodegenerative processes, track their progression quickly identify efficacies alternative treatments. Many previous human animal studies have examined relationship disease (PD) risk trauma, genetics, environment,...
The act of sharing scientific knowledge is rapidly evolving away from traditional articles and presentations to the delivery executable objects that integrate data computational details (e.g., scripts workflows) upon which findings rely. This envisioned coupling process essential advancing science but faces technical institutional barriers. Whole Tale project aims address these barriers by connecting computational, data-intensive research efforts with larger process—transforming discovery...
Despite the recent availability of vaccines against acute respiratory syndrome coronavirus 2 (SARS-CoV-2), search for inhibitory therapeutic agents has assumed importance especially in context emerging new viral variants. In this paper, we describe discovery a novel noncovalent small-molecule inhibitor, MCULE-5948770040, that binds to and inhibits SARS-Cov-2 main protease (Mpro) by employing scalable high-throughput virtual screening (HTVS) framework targeted compound library over 6.5...
Abstract We describe the simulated sky survey underlying second data challenge (DC2) carried out in preparation for analysis of Vera C. Rubin Observatory Legacy Survey Space and Time (LSST) by LSST Dark Energy Science Collaboration (LSST DESC). Significant connections across multiple science domains will be a hallmark LSST; DC2 program represents unique modeling effort that stresses this interconnectivity way has not been attempted before. This encompasses full end-to-end approach: starting...
Cloud computing provides a scalable platform through which large datasets can be stored and analyzed. However, because of the number storage models used rapidly increasing data sizes, it is often difficult to efficiently securely access, transfer, synchronize, share data. The authors describe approaches taken by Globus create standard interfaces common security for performing these actions on quantities These are general, allowing users access different types cloud with same ease they local...
Globus Auth is a foundational identity and access management platform service designed to address unique needs of the science engineering community. It serves broker authentication authorization interactions between end-users, providers, resource servers (services), clients (including web, mobile, desktop, command line applications, other services). thus makes it easy, for example, researcher authenticate with one credential, connect specific remote storage another identity, share data...
Social network platforms have rapidly changed the way that people communicate and interact. They enabled establishment of, participation in, digital communities as well representation, documentation exploration of social relationships. We believe `apps' become more sophisticated, it will easier for users to share their own services, resources data via networks. To substantiate this, we present a compute cloud where provisioning infrastructure occurs through "friend" In cloud, resource owners...
Abstract Modern biomedical data collection is generating exponentially more in a multitude of formats. This flood complex poses significant opportunities to discover and understand the critical interplay among such diverse domains as genomics, proteomics, metabolomics, phenomics, including imaging, biometrics, clinical data. The Big Data for Discovery Science Center taking an “-ome home” approach linkages between these disparate sources by mining existing databases proteomic genomic data,...
SUMMARY We describe Globus Genomics, a system that we have developed for rapid analysis of large quantities next‐generation sequencing genomic data. This achieves high degree end‐to‐end automation encompasses every stage data including initial retrieval from remote centers or storage (via the file transfer system); specification, configuration, and reuse multistep processing pipelines Galaxy workflow creation custom Amazon Machine Images on‐demand resource acquisition via specialized elastic...
While the Machine Learning (ML) landscape is evolving rapidly, there has been a relative lag in development of "learning systems" needed to enable broad adoption. Furthermore, few such systems are designed support specialized requirements scientific ML. Here we present Data and Hub for science (DLHub), multi-tenant system that provides both model repository serving capabilities with focus on applications. DLHub addresses two significant shortcomings current systems. First, its self-service...
Exploding data volumes and velocities, new computational methods platforms, ubiquitous connectivity demand approaches to computation in the sciences. These must enable be mobile, so that, for example, it can occur near data, triggered by events (e.g., arrival of data), offloaded specialized accelerators, or run remotely where resources are available. They also require design which monolithic applications decomposed into smaller components, that may turn executed separately on most suitable...
Powerful detectors at modern experimental facilities routinely collect data multiple GB/s. Online analysis methods are needed to enable the collection of only interesting subsets such massive streams, as by explicitly discarding some elements or directing instruments relevant areas space. Thus, required for configuring and running distributed computing pipelines—what we call flows—that link instruments, computers (e.g., analysis, simulation, artificial intelligence [AI] model training), edge...
Research process automation–the reliable, efficient, and reproducible execution of linked sets actions on scientific instruments, computers, data stores, other resources–has emerged as an essential element modern science. We report here new services within the Globus research management platform that enable specification diverse processes reusable actions, flows, such flows in heterogeneous environments. To support with broad spatial extent (e.g., from instrument to remote center) temporal...
Summary Globus, developed as software‐as‐a‐service for research data management, also provides APIs that constitute a flexible and powerful platform‐as‐a‐service to which developers can outsource management activities such transfer sharing, well identity, profile, group management. By providing these frequently important but always challenging capabilities service, accessible over the network, Globus streamlines Web application development makes it easy individuals, teams, institutions...
Broad access to the data on which scientific results are based is essential for verification, reproducibility, and extension. Scholarly publication has long been means this end. But as volumes grow, new methods beyond traditional publications needed communicating, discovering, accessing data. We describe capabilities within Globus research management service, supports of large datasets, with customizable policies different institutions researchers, ability publish directly from both locally...
Scientific applications that involve simulation ensembles can be accelerated greatly by using experiment design methods to select the best simulations perform. Methods use machine learning (ML) create proxy models of show particular promise for guiding but are challenging deploy because need coordinate dynamic mixes and tasks. We present Colmena, an open-source Python framework allows users steer campaigns providing just implementations individual tasks plus logic used choose which execute...