Timothy M. Cockerill

Bio: Timothy M. Cockerill is an academic researcher from University of Texas at Austin. The author has contributed to research in topics: Quantum well & Semiconductor laser theory. The author has an hindex of 18, co-authored 54 publications receiving 3313 citations. Previous affiliations of Timothy M. Cockerill include University of Illinois at Urbana–Champaign.

XSEDE: Accelerating Scientific Discovery

Abstract: Computing in science and engineering is now ubiquitous: digital technologies underpin, accelerate, and enable new, even transformational, research in all domains. Access to an array of integrated and well-supported high-end digital services is critical for the advancement of knowledge. Driven by community needs, the Extreme Science and Engineering Discovery Environment (XSEDE) project substantially enhances the productivity of a growing community of scholars, researchers, and engineers (collectively referred to as "scientists"' throughout this article) through access to advanced digital services that support open research. XSEDE's integrated, comprehensive suite of advanced digital services federates with other high-end facilities and with campus-based resources, serving as the foundation for a national e-science infrastructure ecosystem. XSEDE's e-science infrastructure has tremendous potential for enabling new advancements in research and education. XSEDE's vision is a world of digitally enabled scholars, researchers, and engineers participating in multidisciplinary collaborations to tackle society's grand challenges.

DesignSafe: New Cyberinfrastructure for Natural Hazards Engineering

Abstract: Natural hazards engineering plays an important role in minimizing the effects of natural hazards on society through the design of resilient and sustainable infrastructure. The DesignSafe cy...

TeraGrid: Analysis of organization, system architecture, and middleware enabling new types of applications

Abstract: The TeraGrid project has been supported through a variety of funding and in-kind con- tributions in addition to multiple grants from the National Science Foundation. State support has come from the states of California, Illinois, Indiana, Pennsylvania, and Texas. Institutional support has come from Carnegie Melon University, Indiana Uni- versity, Purdue University, University of California-San Diego, University of Chicago, University of Illinois at Urbana-Champaign, University of Pittsburgh, the University of North Carolina, California Institute of Technology, and the University of Texas. Cor- porate support has come from Cray, Dell, IBM, Lilly Endowment, Qwest Communica- tions, and Sun Microsystems. Several hundred staff members from partner institutions contribute to the TeraGrid facility.

Jetstream: a self-provisioned, scalable science and engineering cloud environment

Abstract: Jetstream will be the first production cloud resource supporting general science and engineering research within the XD ecosystem. In this report we describe the motivation for proposing Jetstream, the configuration of the Jetstream system as funded by the NSF, the team that is implementing Jetstream, and the communities we expect to use this new system. Our hope and plan is that Jetstream, which will become available for production use in 2016, will aid thousands of researchers who need modest amounts of computing power interactively. The implementation of Jetstream should increase the size and disciplinary diversity of the US research community that makes use of the resources of the XD ecosystem.

A distributed feedback ridge waveguide quantum well heterostructure laser

Abstract: A method for incorporating distributed feedback in a ridge waveguide laser by means of lateral gratings and a single growth step is discussed. The necessary Bragg condition for distributed feedback is satisfied by etching gratings along the ridge in the top confining layer of the laser on either side of the contact stripe. Both Fabry-Periot modes and a single emission peak away from the peak of the gain profile are observed in lasers with cleaved facets. The Bragg reflection emission peak does not shift with increasing drive current, which is characteristic of a distributed-feedback (DFB) laser. >

XSEDE: Accelerating Scientific Discovery

Abstract: Computing in science and engineering is now ubiquitous: digital technologies underpin, accelerate, and enable new, even transformational, research in all domains. Access to an array of integrated and well-supported high-end digital services is critical for the advancement of knowledge. Driven by community needs, the Extreme Science and Engineering Discovery Environment (XSEDE) project substantially enhances the productivity of a growing community of scholars, researchers, and engineers (collectively referred to as "scientists"' throughout this article) through access to advanced digital services that support open research. XSEDE's integrated, comprehensive suite of advanced digital services federates with other high-end facilities and with campus-based resources, serving as the foundation for a national e-science infrastructure ecosystem. XSEDE's e-science infrastructure has tremendous potential for enabling new advancements in research and education. XSEDE's vision is a world of digitally enabled scholars, researchers, and engineers participating in multidisciplinary collaborations to tackle society's grand challenges.

The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2018 update

Abstract: Galaxy (homepage: https://galaxyproject.org, main public server: https://usegalaxy.org) is a web-based scientific analysis platform used by tens of thousands of scientists across the world to analyze large biomedical datasets such as those found in genomics, proteomics, metabolomics and imaging. Started in 2005, Galaxy continues to focus on three key challenges of data-driven biomedical science: making analyses accessible to all researchers, ensuring analyses are completely reproducible, and making it simple to communicate analyses so that they can be reused and extended. During the last two years, the Galaxy team and the open-source community around Galaxy have made substantial improvements to Galaxy's core framework, user interface, tools, and training materials. Framework and user interface improvements now enable Galaxy to be used for analyzing tens of thousands of datasets, and >5500 tools are now available from the Galaxy ToolShed. The Galaxy community has led an effort to create numerous high-quality tutorials focused on common types of genomic analyses. The Galaxy developer and user communities continue to grow and be integral to Galaxy's development. The number of Galaxy public servers, developers contributing to the Galaxy framework and its tools, and users of the main Galaxy server have all increased substantially.

How to… home page

Abstract: Home PURPOSE OF THE CENTER: To develop the center to address state-of-the-art research, create innovating educational programs, and support technology transfers using commercially viable results to assist the Army Research Laboratory to develop the next generation Future Combat System in the telecommunications sector that assures prevention of perceived threats, and Non Line of Sight/Beyond Line of Sight lethal support.

Automation of the CHARMM General Force Field (CGenFF) II: Assignment of Bonded Parameters and Partial Atomic Charges

Abstract: Molecular mechanics force fields are widely used in computer-aided drug design for the study of drug candidates interacting with biological systems. In these simulations, the biological part is typically represented by a specialized biomolecular force field, while the drug is represented by a matching general (organic) force field. In order to apply these general force fields to an arbitrary drug-like molecule, functionality for assignment of atom types, parameters, and partial atomic charges is required. In the present article, algorithms for the assignment of parameters and charges for the CHARMM General Force Field (CGenFF) are presented. These algorithms rely on the existing parameters and charges that were determined as part of the parametrization of the force field. Bonded parameters are assigned based on the similarity between the atom types that define said parameters, while charges are determined using an extended bond-charge increment scheme. Charge increments were optimized to reproduce the cha...

Anomaly Detection with Robust Deep Autoencoders

Abstract: Deep autoencoders, and other deep neural networks, have demonstrated their effectiveness in discovering non-linear features across many problem domains. However, in many real-world problems, large outliers and pervasive noise are commonplace, and one may not have access to clean training data as required by standard deep denoising autoencoders. Herein, we demonstrate novel extensions to deep autoencoders which not only maintain a deep autoencoders' ability to discover high quality, non-linear features but can also eliminate outliers and noise without access to any clean training data. Our model is inspired by Robust Principal Component Analysis, and we split the input data X into two parts, $X = L_{D} + S$, where $L_{D}$ can be effectively reconstructed by a deep autoencoder and $S$ contains the outliers and noise in the original data X. Since such splitting increases the robustness of standard deep autoencoders, we name our model a "Robust Deep Autoencoder (RDA)". Further, we present generalizations of our results to grouped sparsity norms which allow one to distinguish random anomalies from other types of structured corruptions, such as a collection of features being corrupted across many instances or a collection of instances having more corruptions than their fellows. Such "Group Robust Deep Autoencoders (GRDA)" give rise to novel anomaly detection approaches whose superior performance we demonstrate on a selection of benchmark problems.