Sandeep Puthanveetil Satheesan

Bio: Sandeep Puthanveetil Satheesan is an academic researcher from University of Illinois at Urbana–Champaign. The author has contributed to research in topics: Cyberinfrastructure & Big data. The author has an hindex of 4, co-authored 11 publications receiving 41 citations. Previous affiliations of Sandeep Puthanveetil Satheesan include University at Buffalo & Urbana University.

Clowder: Open Source Data Management for Long Tail Data

Abstract: Clowder is an open source data management system to support data curation of long tail data and metadata across multiple research domains and diverse data types. Institutions and labs can install and customize their own instance of the framework on local hardware or on remote cloud computing resources to provide a shared service to distributed communities of researchers. Data can be ingested directly from instruments or manually uploaded by users and then shared with remote collaborators using a web front end. We discuss some of the challenges encountered in designing and developing a system that can be easily adapted to different scientific areas including digital preservation, geoscience, material science, medicine, social science, cultural heritage and the arts. Some of these challenges include support for large amounts of data, horizontal scaling of domain specific preprocessing algorithms, ability to provide new data visualizations in the web browser, a comprehensive Web service API for automatic data ingestion and curation, a suite of social annotation and metadata management features to support data annotation by communities of users and algorithms, and a web based front-end to interact with code running on heterogeneous clusters, including HPC resources.

Mitigation of SARS-CoV-2 Transmission at a Large Public University

Abstract: In the Fall of 2020, many universities saw extensive transmission of SARS-CoV-2 among their populations, threatening the health of students, faculty and staff, the viability of in-person instruction, and the health of surrounding communities.1, 2 Here we report that a multimodal "SHIELD: Target, Test, and Tell" program mitigated the spread of SARS-CoV-2 at a large public university, prevented community transmission, and allowed continuation of in-person classes amidst the pandemic. The program combines epidemiological modelling and surveillance (Target); fast and frequent testing using a novel and FDA Emergency Use Authorized low-cost and scalable saliva-based RT-qPCR assay for SARS-CoV-2 that bypasses RNA extraction, called covidSHIELD (Test); and digital tools that communicate test results, notify of potential exposures, and promote compliance with public health mandates (Tell). These elements were combined with masks, social distancing, and robust education efforts. In Fall 2020, we performed more than 1,000,000 covidSHIELD tests while keeping classrooms, laboratories, and many other university activities open. Generally, our case positivity rates remained less than 0.5%, we prevented transmission from our students to our faculty and staff, and data indicate that we had no spread in our classrooms or research laboratories. During this fall semester, we had zero COVID-19-related hospitalizations or deaths amongst our university community. We also prevented transmission from our university community to the surrounding Champaign County community. Our experience demonstrates that multimodal transmission mitigation programs can enable university communities to achieve such outcomes until widespread vaccination against COVID-19 is achieved, and provides a roadmap for how future pandemics can be addressed.

Brown Dog: Making the Digital World a Better Place, a Few Files at a Time

Abstract: Brown Dog is a data transformation service for auto-curation of long-tail data. In this digital age, we have more data available for analysis than ever and this trend will only increase. According to most estimates, 70--80% of this data is unstructured, and together with unsupported data formats and inaccessible software tools, in essence, this data is not either easily accessible or usable to its owners in a meaningful way. Brown Dog aims at making this data more accessible and usable by auto-curation and indexing, leveraging existing and novel data transformation tools. In this paper, we discuss the recent major component improvements to Brown Dog including transformation tools called extractors and converters; desktop, web and terminal-based clients which perform data transformations; libraries written in multiple programming languages which integrate with existing software and extend their data curation capabilities; an online tool store for users to contribute, manage and share data transformation tools and receive credit for developing them; cyberinfrastructure for deploying the system on diverse computing platforms leveraging scalability via Docker swarm; workflow management service for creatively integrating existing transformations to generate custom, reproducible workflows which meet research needs, and its data management capabilities. This paper also discusses data transformation tools developed to support some scientific and allied use cases, thereby benefiting researchers in diverse domains. Finally, we briefly discuss our future directions with regard to production deployments as well as how users can access Brown Dog to manage their un-curated unstructured data.

The VAT: enhanced video analysis

Abstract: The practice of extracting knowledge from large volumes of video data suffers from a problem of variety. Security, military, and commercial identification and retrieval are well-traveled paths for identifying very particular objects or people found in recorded footage, yet there are extremely few established technology solutions and use cases for understanding what large-scale video collections can help us discover about contemporary culture and history. This dearth is not due to a lack of imagination on the part of researchers; rather, we contend, in order to grow a common set of instruments, measures, and procedural methods, there is a need for a common gateway into content and analytics for cultural and historical experts to utilize. The Video Analysis Tableau (VAT), formerly the LSVA, is a research project aimed at establishing a software workbench for video analysis, annotation, and visualization, using both current and experimental discovery methods and built on the Clowder framework/interface. The VAT employs a host of algorithms for machine reading, in addition to spaces for user generated tagging and annotation; it is currently being expanded into a gateway project in order to foster a strong community of practice that includes researchers in a variety of disciplines.

An Architecture for Automatic Deployment of Brown Dog Services at Scale into Diverse Computing Infrastructures

Abstract: Brown Dog is an extensible data cyberinfrastructure, that provides a set of extensible and distributed data conversion and metadata extraction services to enable access and search within unstructured, un-curated and inaccessible research data across different domains of sciences and social science, which ultimately aids in supporting reproducibility of results. We envision that Brown Dog, as a data cyberinfrastructure, is an essential service in a comprehensive cyberinfrastructure which includes data services, high performance computing services and more that would enable scholarly research in a variety of disciplines that today is not yet possible. Brown Dog focuses on four initial use cases, specifically, addressing the conversion and extraction needs in the research areas of ecology, civil and environmental engineering, library and information science, and use by the general public. In this paper, we describe an architecture that supports contribution of data transformation tools from users, and automatic deployment of the tools as Brown Dog services in diverse infrastructures such as cloud or high performance computing (HPC) based on user demands and load on the system. We also present results validating the performance of the initial implementation of Brown Dog.

The Fall of One, the Rise of Many: A Survey on Multi-Biometric Fusion Methods

Abstract: Increasing operational and security demands changed biometrics by shifting the focus from single to multi-biometrics. Multi-biometrics are mandatory in the current context of large international biometric databases and to accommodate new emerging security demands. Our paper is a comprehensive survey on multi-biometrics, covering two important topics related to the multi-biometric field: fusion methods and security. Fusion is a core requirement in multi-biometric systems, being the method used to combine multiple biometric methods into a single system. The fusion section surveys recent multi-biometric schemes categorized from the perspective of fusion method. The security section is a comprehensive review of current issues, such as sensor spoofing, template security, and biometric encryption. New research trends and open challenges are discussed, such as soft, adaptive contextual-based biometrics. Finally, an implementation blueprint for a multi-biometric system is presented in the form of a list of questions to be answered when designing the system.

Longitudinal analysis of SARS-CoV-2 vaccine breakthrough infections reveal limited infectious virus shedding and restricted tissue distribution

Abstract: The global effort to vaccinate people against SARS-CoV-2 in the midst of an ongoing pandemic has raised questions about the nature of vaccine breakthrough infections and the potential for vaccinated individuals to transmit the virus. These questions have become even more urgent as new variants of concern with enhanced transmissibility, such as Delta, continue to emerge. To shed light on how vaccine breakthrough infections compare with infections in immunologically naive individuals, we examined viral dynamics and infectious virus shedding through daily longitudinal sampling in a small cohort of adults infected with SARS-CoV-2 at varying stages of vaccination. The durations of both infectious virus shedding and symptoms were significantly reduced in vaccinated individuals compared with unvaccinated individuals. We also observed that breakthrough infections are associated with strong tissue compartmentalization and are only detectable in saliva in some cases. These data indicate that vaccination shortens the duration of time of high transmission potential, minimizes symptom duration, and may restrict tissue dissemination.

TERRA-REF Data Processing Infrastructure

Abstract: The Transportation Energy Resources from Renewable Agriculture Phenotyping Reference Platform (TERRA-REF) provides a data and computation pipeline responsible for collecting, transferring, processing and distributing large volumes of crop sensing and genomic data from genetically informative germplasm sets. The primary source of these data is a field scanner system built over an experimental field at the University of Arizona Maricopa Agricultural Center. The scanner uses several different sensors to observe the field at a dense collection frequency with high resolution. These sensors include RGB stereo, thermal, pulse-amplitude modulated chlorophyll fluorescence, imaging spectrometer cameras, a 3D laser scanner, and environmental monitors. In addition, data from sensors mounted on tractors, UAVs, an indoor controlled-environment facility, and manually collected measurements are integrated into the pipeline. Up to two TB of data per day are collected and transferred to the National Center for Supercomputing Applications at the University of Illinois (NCSA) where they are processed.In this paper we describe the technical architecture for the TERRA-REF data and computing pipeline. This modular and scalable pipeline provides a suite of components to convert raw imagery to standard formats, geospatially subset data, and identify biophysical and physiological plant features related to crop productivity, resource use, and stress tolerance. Derived data products are uploaded to the Clowder content management system and the BETYdb traits and yields database for querying, supporting research at an experimental plot level. All software is open source2 under a BSD 3-clause or similar license and the data products are open access (currently for evaluation with a full release in fall 2019). In addition, we provide computing environments in which users can explore data and develop new tools. The goal of this system is to enable scientists to evaluate and use data, create new algorithms, and advance the science of digital agriculture and crop improvement.