Garrett is a Computer Science Ph.D. student at The University of California – Davis. 2016 is Garrett’s fourth year with the Data Science at Scale School. His research at LANL has been on big data visualization, feature extraction and analysis. In 2015 Garrett worked on milestones for several projects: fracture network visualization and analysis, data reduction and triage, and in transit visualization. Garrett’s mentors are Jim Ahrens and Jon Woodring.

Divya Banesh is a Computer Science Ph.D. student at The University of California – Davis with Professors Bernd Hamann and Mike Oskin. She earned her B.S. in Electrical Engineering and Computer Science from The University of California – Berkeley. Divya came to Los Alamos in May 2015 and has extended her stay into 2016. Her emphasis was in visualization and computer graphics. Divya’s mentors are Jim Ahrens and David Rogers.

Daniel Ben-naim is an undergraduate student at the University of California – Santa Barbara. This is his first year with the Data Science at Scale School. He will conduct research into automated pipelines for conducting perceptual tests. Currently we understand well how to conduct perception tests on Amazon Mechanical Turk (AMT), but these tests are work intensive, even though the general concepts and expertise to conduct them can be well-codified for a specific set of comparisons. Daniel will develop code that, with the answers to a few simple questions and a set of artifacts to compare, enables a user to automatically generate all supporting infrastructure content to conduct a specific set of tests on a set of data inputs. For example, by answering a few questions about images to be compared, the user would have a complete perceptual test run on AMT, and results reported back about that test. This will bridge the gap between AMT capabilities and specific perceptual testing knowledge in scientific visualization. Daniel’s mentors is David Rogers.

Wendy Caldwell is an Applied Mathematics graduaate student at Arizona State University. This is Wendy’s first year with the Data Science at Scale School. She had previously taken part in the 2015 Computational Physics Student Summer Workshop at Los Alamos. Wendy’s mentors are Sara Del Valle and Qiang Guan.

Soumya Dutta is a Ph.D. student in the Department of Computer Science and Engineering of The Ohio State University. He is working with Dr. Han-Wei Shen in the Gravity group. Before joining Ohio State, he completed his B. Tech. in Electronics and Communication Engineering from West Bengal University of Technology, Kolkata, India. He has also worked under the supervision of Dr. Bidyut B. Chaidhuri and Dr. Madhurima Chattopadhyay during his undergraduate years. This is Soumya’s second year with the Data Science at Scale School. He is researching concurrent, in transit analysis for a large-scale simulations and providing feedback to the simulations for convergence in the CESAR project. Soumya’s mentors are Jon Woodring and Emily Casleton.

Nils Feige is a Ph.D. student in Computer Science at the University of Kaiserslautern in Germany. Nils works with Professor Hans Hagen. This is his first year with the Data Science at Scale School. Nils’ mentors are Qiang Guan and John Patchett.

Felipe Horta is a Ph.D. student in Computer Engineering at New York University’s Tandon School of Engineering. He is working with Professor Claudio Silva. Felipe’s mentors are Jon Woodring and Qiang Guan.

Sebastian Klaassen is a graduate student in Computer Science at the University of Vienna with Professor Torsten Moeller. Sebastian came to Los Alamos in May 2015 and extended his stay into 2016. Sebastian researched the question ‘a local-to-global strategy is currently used for a Cinema Database, but can a quicker exploration and understanding of the data be found by starting with an overview of all sampled items?’ Sebastian’s mentors were Jim Ahrens and David Rogers.

Shaomeng (Samuel) Li is a Ph.D. student in Computer Science at the University of Oregon. He is working with Prof. Hank Childs. This is his first year with the Data Science at Scale School. Samuel’s mentors are Chris Sewell and Ollie Lo.

Jonas Lukasczyk is a Ph.D. student in Computer Science at the University of Kaiserslautern in Germany. This was his second year with the Data Science at Scale School. Jonas’ mentors are Jim Ahrens and Curt Canada.

Nina McCurdy is a Ph.D. student in Computer Science at the University of Utah with a focus on computer graphics and visualization. She is primarily interested in developing visualization tools to help scientists and scholars answer their big research questions and better understand their data. She is currently collaborating with poets/poetry scholars to develop a visualization tool in support of close reading, specifically in the context of poetry. Her Ph.D. advisor is Dr. Miriah Meyer. Nina will be visiting the the Data Science at Scale School for a few weeks this summer. Nina’s mentors are Francesca Samsel and Ollie Lo.

Jesus is a Ph.D. student at the University of California – Davis with Professor Bernd Hamann. His research at LANL involves the analysis of DNS Turbulence data with collaborator Dr. Daniel Livescu. He is also collaborating with Johns Hopkins University to further develop the Johns Hopkins Turbulence Database to support visualization of massive datasets. His research interest are visualization, wavelets and multi-resolution methods, high-performance computing, and applications to turbulence. This was Jesus’ sixth summer at Los Alamos and fourth year with the Data Science at Scale School. Jesus’ mentors are Jim Ahrens and Curt Canada.

Cameron Tauxe is an undergraduate student at the New Mexico State University. This is his first year with the Data Science at Scale School. He will conduct research into interactive methods for visualization of cloud-based large scale Cinema databases through experiments with Amazon’s ‘Lumberyard’ gaming engine. This is a coordinated effort to determine how off-the-shelf gaming engines can be utilized in cloud-based scientific data workflows to provide integrated, immediate access to large collections of complex data artifacts. We anticipate that the data handling, rendering, navigation and collaboration aspects of Amazon’s Lumberyard engine will enable us to add sparse metadata to a Cinema database and therefore enable interactive, collaborative exploration of complex data. This could have a significant impact in the near future, as we would have a well-supported, freely available service for explorations of our scientific datasets by simply defining our data artifacts with simple metadata extensions. Cameron’s mentors is David Rogers.

Karen is a Ph.D. Computational Science, Engineering and Mathematics student at the University of Texas – Austin. This was Karen’s first year with the Data Science at Scale School. Karen’s mentors are Jon Woodring and Francesca Samsel.

Tzu-Hsuan Wei is a Ph.D. Student in Computer Science at The Ohio State University. He is working with Dr. Han-Wei Shen in the Gravity group. This will be Tzu-Hsuan’s first summer at Los Alamos. His mentors will be Jon Woodring and Curt Canada.

Wathsala is a Computer Science Ph.D. student in the Scientific Computing and Imaging Institute at University of Utah. Her advisor is Dr. Valerio Pascucci and her primary research interests are in Scientific Visualization, Computer Graphics and Image Processing. Together with her advisor and Dr. Peer-Timo Bremer, she works on a topology based framework which allows interactive exploration of large-scale, time-varying datasets. Apart from that, her research also includes a scalable data-parallel halo-finder operator in PISTON (with Dr. Chris Sewell & Dr. Jim Ahrens of the Data Science at Scale team) and Ray graphs, a flexible framework for space and time exploring panoramas (with Dr. Paul Rosen). Wathsala is being supported by the Data Science at Scale team this year, but will be staying in Salt Lake City for most, if not all, of the year.

Max is making his M.Sc. in Computer Science at the University of Kaiserslautern in Germany. This is his third year with the Data Science at Scale School. Max is continuing work begun during the summer of 2014 at Los Alamos in applying advanced analysis, compression and visualization techniques to the complexissues that arise in comparing mesoscale material simulations with experimental measurements. This work touches on registration of multiple data sources, querying of large, complex datasets for scientific analysis, comparison of experiment and simulation results, and novel visualization methods for high dimensional data. He refined some initial experimental techniques into tools that can be used to quickly investigate and understand the large data that LANL’s material science experiments produce. Max’s mentors were David Rogers and Chris Sewell.

Vignesh Adhinarayanan is a fourth year Ph.D. student in the Department of Computer Science at Virginia Tech with Professor Wu-chun Feng. This was Vignesh’s first year with the Data Science at Scale School. His work was as part of the ‘Optimizing the Energy Usage and Cognitive Value of Extreme Scale Data Approaches’ (ECX) Project team. He focused on three areas; 1) establishing end-to-end workflow measurements, 2) measuring the behavior of sampling-modified workflow, and 3) modeling and optimizing a power-constrained workflow. Vignesh’s mentors were David Rogers and Scott Pakin.

Garrett is a Computer Science Ph.D. student at The University of California – Davis. This was Garrett’s third year with the Data Science at Scale School. His research at LANL has been on big data visualization, feature extraction and analysis. In 2015 Garrett worked on milestones for several projects: fracture network visualization and analysis, data reduction and triage, and in transit visualization. The first project involved complex visualization and analysis of fluid moving through fracture rock. The second project involved researching and developing a new method for ADR partitioning (analysis driven refinement) using agglomerative bottom-up clustering using flood-filling and bitmap regions. The final project involved doing concurrent, in transit analysis for a large-scale simulation and providing feedback to the simulations for convergence in the CESAR project. Garrett’s mentors were Jon Woodring and Jim Ahrens.

Zoe Ashton is an undergraduate student at Florida Institute of Technology. She is pursuing a B.A. in Humanities and a B.S. in Applied Mathematics with an emphasis in statistics. This was Zoe’s first year with the Data Science at Scale School. She studied the effectiveness of colormaps, especially those developed by Francesca Samsel. She worked with Los Alamos statisticians Joanne Wendelberger and Lawrence Ticknor to analyze task-based user study data to compare colormaps. She also worked with visiting UT-Austin data analyst Terece Turton to develop user studies that produce more statistically suitable data. Her mentors were Joanne Wendelberger and Lawrence Ticknor.

David C. Barnes is an undergraduate student majoring in Applied Mathematics at the Massachusetts Institute of Technology. This was David’s first year with the Data Science at Scale School. He worked on applying machine learning algorithms in scientific datasets. He begun by acquiring fundamental knowledge and skills on cloud computing infrastructure (e.g. OpenStack, AWS), Big Data software stacks (e.g. Hadoop/Spark), databases (SQL/NoSQL) and machine learning libraries (Mahut/MLib). He then explored using those skills in identifying/extracting significant features in our scientific datasets. David’s mentors were Chris Sewell and Ollie Lo.

Divya Banesh is a Computer Science Ph.D. student at The University of California – Davis with Professors Bernd Hamann and Mike Oskin. She earned her B.S. in Electrical Engineering and Computer Science from The University of California – Berkeley. This was Divya’s first year with the Data Science at Scale School. Her emphasis was in visualization and computer graphics. Divya’s mentors were Jim Ahrens and Curt Canada.

Anne Berres holds B.Sc. and M.Sc. Degrees in Computer Science from the Technical University of Kaiserslautern and is currently a Ph.D. student in Computer Science at the Technical University of Kaiserslautern. Her research interests include topology, differential manifolds, differential geometry, medical visualization, neural diseases and probablistic tractogrphy. This was Anne’s first year with the Data Science at Scale School. Her work focused on analysis and reduction of extreme scale data. Anne’s mentors were Jim Ahrens and John Patchett. She will be joining the Data Science at Scale team as a PostDoc in November 2015.

Ayan Biswas is a Computer Graphics and Visualization Ph.D. student at The Ohio State University with Professor Han-Wei Shen. He has worked with flow field data and particle tracing using streamlines and stream surfaces and is now looking at the time-varying multivariate data exploration and using information theory to provide some insights into the data. He is also working with turbulent flow structures and vortex visualization for the unstable time-varying complex flows. This was Ayan’s fourth summer at Los Alamos and third year with the Data Science at Scale School. In 2015 Ayan worked on developing a new parallel algorithm for visualization of streamlines. This was implemented in MPAS and tested in parallel. Ayan’s mentors were Jon Woodring and Richard Strelitz.

Claire Bowen is an applied statistics Ph.D. candidate at the University of Notre Dame. She obtained an Honors Bachelor of Science in Mathematics and Physics at Idaho State University and a Master of Science at the University of Notre Dame. Currently, she is studying statistical disclosure limitation, methods of data privacy and confidentiality, in big data, using Bayesian Statistics. This was Claire’s first year with the Data Science at Scale School. At Los Alamos she worked on applying in situ methods for exascale computing using R statistical software. Claire’s mentors were Joanne Wendelberger and Lawrence Ticknor.

Chris Bryan is a Ph.D. student at the University of California – Davis, as part of the VIDI lab with Professor Kwan-Liu Ma. This was Chris’s third year with the Data Science at Scale School. In 2015 Chris worked on creating an updated version of the high-dimensional visualization for the Cosmic emulator, either updating the existing Python version or creating a new web application. Chris’ mentors were Jon Woodring and Earl Lawrence.

Soumya Dutta is a Ph.D. student in the Department of Computer Science and Engineering of The Ohio State University. He is working with Dr. Han-Wei Shen in the Gravity group. Before joining Ohio State, he completed his B. Tech. in Electronics and Communication Engineering from West Bengal University of Technology, Kolkata, India. He has also worked under the supervision of Dr. Bidyut B. Chaidhuri and Dr. Madhurima Chattopadhyay during his undergraduate years. This was Soumya’s first year with the Data Science at Scale School. He researched concurrent, in transit analysis for a large-scale simulations and providing feedback to the simulations for convergence in the CESAR project. Soumya’s mentors were Jon Woodring and Ollie Lo.

Arnold Eatmon is a junior mathematics and geophysics dual degree major from the Cooperative Developmental Energy Program (CDEP) of Fort Valley State University. This was Arnold’s first year with the Data Science at Scale School. He built and used MPAS-Ocean to generate data for Cinema image databases that were used by other students over the summer and into the fall. Arnold’s mentors were Curt Canada and John Patchett.

Withana Kankanamalage Umayanganie (Uma) Munipal is a Computational Science Ph.D. student at the University of Texas – El Paso. She completed her Bachelor of Science degree in Mathematics and Computer Science at the University of Colombo in Sri Lanka and worked as a Software Engineer at Virtusa. This was Uma’s first year with the Data Science at Scale School. She worked with Data Science at Scale team, climate scientists and other students to advance performance analysis of in-situ, in-transit and post-processing analysis of MPAS in traditional, virtual machine and Docker environments. Uma’s mentors were David Rogers and Jon Woodring.

Sebastian Klaassen is a graduate student in Computer Science at the University of Vienna with Professor Torsten Moeller. This was his first year with the Data Science at Scale School. Sebastian researched the question ‘a local-to-global strategy is currently used for a Cinema Database, but can a quicker exploration and understanding of the data be found by starting with an overview of all sampled items?’ Sebastian’s mentors were Jim Ahrens and David Rogers.

Kewei Lu is a Computer Science Ph.D. student at The Ohio State University with Professor Han-Wei Shen. This was his first year with the Data Science at Scale School. Kewei developed portable data parallel visualization algorithms in VTK-m. Specifically, he wrote visualization filters for streamlines and stream surfaces. He also modified the original isosurface implementation using the new data model and worklets. Kewei’s mentors were Chris Sewell and Ollie Lo.

Jonas Lukasczyk is a Ph.D. student in Computer Science at the University of Kaiserslautern in Germany. This was his first year with the Data Science at Scale School. Jonas worked with Sara Del Valle and her team to analyze ebola and influenza data. Jonas’ mentors were John Patchett and Curt Canada.

Jesus is a Ph.D. student at the University of California – Davis with Professor Bernd Hamann. His research at LANL involves the analysis of DNS Turbulence data with collaborator Dr. Daniel Livescu. He is also collaborating with Johns Hopkins University to further develop the Johns Hopkins Turbulence Database to support visualization of massive datasets. His research interest are visualization, wavelets and multi-resolution methods, high-performance computing, and applications to turbulence. This was Jesus’ fifth summer at Los Alamos and third year with the Data Science at Scale School. Jesus’ mentors were Daniel Livescu and Curt Canada.

Uzma Shaikh is a Masters student in the Department of Computer and Information Technology at Purdue University. She is interested in STEM Education, System Analysis and Software Development, and Cyber-Physical Systems. This was Uzma’s first year with the Data Science at Scale School. She developed a new web interface to Cinema image databases which provided a generalized or holistic view of the system as well as a specified view of the system. Uzma’s mentors were David Rogers and Ollie Lo.

Will Usher is a Ph.D. student in the School of Computing, University of Utah with Professor Valerio Pascucci. This was his first year with the Data Science at Scale School. Will worked on writing the OpenMP backend and making general performance improvements and comparisions in VTK-m. In the area of performance measurements and improvements he added a benchmarking suite to VTK-m to compare backends and changes to backends and he migrated the default storage type to use an aligned allocator to improve CPU and MIC performance. In the area of OpenMP backend he ported Jeff Inman’s hand-vectorized MIC scan to a generic version in VTK-m, achieving somewhat comparable performance and he working on implementing a parallel quick sort for the backend as well, but still some work left to do. Will’s mentors were Chris Sewell and Ollie Lo.

Maria del Carmen Ruiz Varela is a Computer Science Ph.D. student at the University of Delaware. Before joining the University of Delaware she was a Ph.D. candidate at The University of Texas at El Paso. Maria received her B.E. degree in Computer Systems from Instituto Tecnologico de Cd. Juarez, Mexico, and a Master of Information Technology from The University of Texas at El Paso. Before pursuing graduate school full time, Maria spent ten years in the automotive industry fulfilling different roles including embedded software engineer, systems analyst, and team leader. Her main research interests are in the areas of fault tolerance and reliability of systems, emerging non-volatile memories and storages, and high performance computing (HPC). This was her first year with the Data Science at Scale School. She researched alternative architectures and augmented memory hierarchies that leverage emerging memory technologies for supercomputing, investigating how the burst buffer can improve the performance of HPC workflows such as the Multiple Prediction Across Scales – Ocean model (MPAS-O) being developed at LANL. Maria’s mentors were John Patchett and Pat Fasel.

Max is making his M.Sc. in Computer Science at the University of Kaiserslautern in Germany. This was his second year with the Data Science at Scale School. Max continued work begun last summer at Los Alamos in applying advanced analysis, compression and visualization techniques to the complexissues that arise in comparing mesoscale material simulations with experimental measurements. This work touches on registration of multiple data sources, querying of large, complex datasets for scientific analysis, comparison of experiment and simulation results, and novel visualization methods for high dimensional data. He refined some initial experimental techniques into tools that can be used to quickly investigate and understand the large data that LANL’s material science experiments produce. Max’s mentors were David Rogers and Chris Sewell.

Garrett is a Computer Science Ph.D. student at The University of California – Davis. His research at LANL is on big data visualization, feature extraction and analysis.

Ayan is a Computer Graphics and Visualization Ph.D. student at The Ohio State University with Professor Han-Wei Shen.

Chris is a Ph.D. student at the University of California – Davis, as part of the VIDI lab with Professor Kwan-Liu Ma.

Lalindra is a Ph.D. student in the School of Computing, University of Utah.

Daniel Hill is a sophmore at the University of British Columbia. He is a computer science major. Daniel works at LANL in CCS7 under David Rogers. He is working on creating interactive web interfaces for the MPAS Ocean Dataset

Sidharth is a Ph.D. student at The University of Utah with Professor Valerio Pascucci.

Evgeni is a master student of the TU Kaiserslautern and is working with Jim Ahrens and Curt Canada on the Big Data project to increase the performance of databased oriented visualization methods.

Dennis is a masters student at the university of Kaiserslautern with Professor Hans Hagen. At LANL he is working on adaptive mesh refinement under Jon Woodring.

Jesus is a Ph.D. student at the University of California – Davis with Professor Bernd Hamann. His research at LANL involves the analysis of DNS Turbulence data with collaborator Dr. Daniel Livescu. He is also collaborating with Johns Hopkins University to further develop the Johns Hopkins Turbulence Database to support visualization of massive datasets. His research interest are visualization, wavelets and multi-resolution methods, high-performance computing, and applications to turbulence

Andre is a Computer Science Ph.D. student at the University of Kaiserslautern in Germany. His research at LANL is on in-situ eddy detection in the MPAS Ocean model for global ocean simulation.

Wathsala is a Computer Science Ph.D. student in the Scientific Computing and Imaging Institute at University of Utah. Her advisor is Dr. Valerio Pascucci and her primary research interests are in Scientific Visualization, Computer Graphics and Image Processing. Together with her advisor and Dr. Peer-Timo Bremer, she works on a topology based framework which allows interactive exploration of large-scale, time-varying datasets. Apart from that, her research also includes a scalable data-parallel halo-finder operator in PISTON (with Dr. Chris Sewell & Dr. Jim Ahrens of the Data Science at Scale team) and Ray graphs, a flexible framework for space and time exploring panoramas (with Dr. Paul Rosen).

Max is making his M.Sc. in Computer Science at the University of Kaiserslautern in Germany. His research at LANL is on crystal grain visualization of near-field High-Energy X-ray Diffraction Microscopy data.

Garrett is a Computer Science Ph.D. student at The University of California – Davis. His research at LANL is on big data visualization, feature extraction and ayalysis.

Ayan is a Computer Graphics and Visualization Ph.D. student at The Ohio State University with Professor Han-Wei Shen.

Chris is a Ph.D. student at the University of California – Davis, as part of the VIDI lab with Professor Kwan-Liu Ma.

Mike received a B.S. in Computer Science from The University of Montana and a M.S. in Computer Science from The University of New Mexico before joining the Los Alamos National Lab as a Post-Masters Intern. His research during the school focused on using MongoDB to assist metadata storage for large collections of scientific data.

Kalin is a Ph.D. student in the Department of Computer Science at the Johns Hopkins University. His research interests are in the area of large scale scientific databases. He is involved with the JHU Turbulence Database project and is working on the design and deployment of a magnetohydrodynamics database.

Sidharth is a Ph.D. student at The University of Utah with Professor Valerio Pascucci.

Kien is a Ph.D. student under the supervision of Professor Juliana Freire at NYU School of Engineering in Computer Science.

Jesus is a Ph.D. student at the University of California – Davis with Professor Bernd Hamann. His research at LANL involves the analysis of DNS Turbulence data with collaborator Dr. Daniel Livescu. He is also collaborating with Johns Hopkins University to further develop the Johns Hopkins Turbulence Database to support visualization of massive datasets. His research interest are visualization, wavelets and multi-resolution methods, high-performance computing, and applications to turbulence.

Yu Su is a Ph.D. student in the Department of Computer Science and Engineering at The Ohio State University with Professor Agrawal Gagan.

Will is an undergraduate in the Computer Science department at the University of New Mexico. He likes functional programming languages, especially Erlang, Haskell, and Scheme. He also gets excited about genetic algorithms, distributed systems, programming languages, and big data.