Research and Education in Computational Science and Engineering

This report presents challenges, opportunities, and directions for computational science and engineering (CSE) research and education for the next decade. Over the past two decades the field of CSE has penetrated both basic and applied research in academia, industry, and laboratories to advance discovery, optimize systems, support decision-makers, and educate the scientific and engineering workforce. Informed by centuries of theory and experiment, CSE performs computational experiments to answer questions that neither theory nor experiment alone is equipped to answer. CSE provides scientists and engineers with algorithmic inventions and software systems that transcend disciplines and scales. CSE brings the power of parallelism to bear on troves of data. Mathematics-based advanced computing has become a prevalent means of discovery and innovation in essentially all areas of science, engineering, technology, and society, and the CSE community is at the core of this transformation. However, a combination of disruptive developments---including the architectural complexity of extreme-scale computing, the data revolution and increased attention to data-driven discovery, and the specialization required to follow the applications to new frontiers---is redefining the scope and reach of the CSE endeavor. With these many current and expanding opportunities for the CSE field, there is a growing demand for CSE graduates and a need to expand CSE educational offerings. This need includes CSE programs at both the undergraduate and graduate levels, as well as continuing education and professional development programs, exploiting the synergy between computational science and data science. Yet, as institutions consider new and evolving educational programs, it is essential to consider the broader research challenges and opportunities that provide the context for CSE education and workforce development

Phase-based video motion processing

We introduce a technique to manipulate small movements in videos based on an analysis of motion in complex-valued image pyramids. Phase variations of the coefficients of a complex-valued steerable pyramid over time correspond to motion, and can be temporally processed and amplified to reveal imperceptible motions, or attenuated to remove distracting changes. This processing does not involve the computation of optical flow, and in comparison to the previous Eulerian Video Magnification method it supports larger amplification factors and is significantly less sensitive to noise. These improved capabilities broaden the set of applications for motion processing in videos. We demonstrate the advantages of this approach on synthetic and natural video sequences, and explore applications in scientific analysis, visualization and video enhancement.Shell ResearchUnited States. Defense Advanced Research Projects Agency. Soldier Centric Imaging via Computational CamerasNational Science Foundation (U.S.) (CGV-1111415)Cognex CorporationMicrosoft Research (PhD Fellowship)American Society for Engineering Education. National Defense Science and Engineering Graduate Fellowshi

Discovering Structure in the Space of fMRI Selectivity Profiles

We present a method for discovering patterns of selectivity in fMRI data for experiments with multiple stimuli/tasks. We introduce a representation of the data as profiles of selectivity using linear regression estimates, and employ mixture model density estimation to identify functional systems with distinct types of selectivity. The method characterizes these systems by their selectivity patterns and spatial maps, both estimated simultaneously via the EM algorithm. We demonstrate a corresponding method for group analysis that avoids the need for spatial correspondence among subjects. Consistency of the selectivity profiles across subjects provides a way to assess the validity of the discovered systems. We validate this model in the context of category selectivity in visual cortex, demonstrating good agreement with the findings based on prior hypothesis-driven methods.McGovern Institute Neurotechnology (MINT) ProgramNational Institutes of Health (U.S.) (Grant NIBIB NAMIC U54-EB005149)National Institutes of Health (U.S.) (Grant NCRR NAC P41-RR13218)National Eye Institute (grant 13455)National Science Foundation (U.S.) (grant CAREER 0642971)Collaborative Research in Computational Neuroscience (IIS/CRCNS 0904625)Deshpande Center for Technological Innovation (MIT HST Catalyst grant)American Society for Engineering Education. National Defense Science and Engineering Graduate Fellowshi

From the simplest equations of Hydrodynamics to science and engineeringmodeling skills

[EN] The development of modeling skills is a very important issue in Science teaching nowadays. The present workillustrates how, from the simplest equations of hydrodynamics, it is possible to contribute to this end. Bernoulliand continuity equations are included in Physics syllabi of secondary and university levels, and can be seen as alinking between general and professional education. By means of the proposed project, students are taken throughgeneral stages which are usually present in any engineering project or research work based on modeling and simu-lation. such as the formulation of the problem, the statement of the Physics model, a computational simulationand the comparison between theory and experiments. This kind of project allows for the development of modelingskills and also to some other typical skills of the scientist's and engineer's pro les nowadays, such as tting andgraphing analysis. It is common to see that secondary and rst year university courses do not contribute muchto the formation of modeling skills, instead they rather contribute to particular skills from the perspective of thedi erent subjects. On the other hand, students are usually more motivated for the modeling of real world situationsthan for idealized ones.[ES] El desarrollo de habilidades relacionadas con la modelación es un aspecto esencial en la enseñanza de las ciencias hoy en día. El presente trabajo ilustra una propuesta de cómo desarrollar habilidades de modelación físico-matemáticas desde las ecuaciones más simples de la hidrodinámica, es decir, la ecuación de Bernoulli y la ecuación de continuidad. Estas ecuaciones representan la conservación de la energía y de la masa, respectivamente, y están presentes comúnmente en los programas de Física para la Enseñanza Secundaria y Universidad. A través del proyecto propuesto, el estudiante transita a través de etapas generales usualmente presentes en los proyectos de innovación ingenieril o de investigación, es decir, el surgimiento de la idea inicial, el planteamiento del modelo físico, la exploración computacional del mismo, y la comparación con medidas experimentales. El proyecto presentado hace uso directo de habilidades tales como la realización de ajustes y análisis gráficos, típicas en los perfiles de ingenieros e investigadores en la actualidad. Por otro lado, los estudiantes presentan más motivación por aquellas situaciones más cercanas a la realidad que por las muy idealizadas.This work has been partially supported by funds of the Interdisciplinar Modeling Group InterTech from the Universitat Politècnica de València, Spain.Castro-Palacio, JC.; Velázquez-Abad, L.; Perea, MH.; Navarro-Pardo, E.; Acosta-Iglesias, D.; Fernández-De-Córdoba-Castellá, P. (2017). Desarrollo de habilidades de modelación desde las ecuaciones más simples de la Hidrodinámica. Modelling in Science Education and Learning. 10(2):211-222. doi:10.4995/msel.2017.7143SWORD211222102Mendonça, P. C. C., & Justi, R. (2013). The Relationships Between Modelling and Argumentation from the Perspective of the Model of Modelling Diagram. International Journal of Science Education, 35(14), 2407-2434. doi:10.1080/09500693.2013.811615Chapman S.J. (2003). Fortran 90/95 for Scientists and Engineers, 2nd Ed. McGraw-Hill Series in General Engineering.Fishbane P.M., Gasiorowicz, S. & Thornton S. (1996). Physics for scientists and engineers. Prentice Hall.Justi, R. S., & Gilbert, J. K. (2002). Science teachers’ knowledge about and attitudes towards the use of models and modelling in learning science. International Journal of Science Education, 24(12), 1273-1292. doi:10.1080/09500690210163198Nair, C. S., Patil, A., & Mertova, P. (2009). Re-engineering graduate skills – a case study. European Journal of Engineering Education, 34(2), 131-139. doi:10.1080/03043790902829281Patil A.S. (2005). The global engineering criteria for the development of a global engineering profession. World Transaction on Engineering Education 4(1), 49-52.Radcliffe D.F. (2005). Innovation as a meta attribute for graduate engineers. International Journal of Engineering Education 21(2), 194-199.Resnick R., Halliday D., & Krane K. (1999). Physics. 4th Ed. Mexico: CECSA.Wedelin, D., Adawi, T., Jahan, T., & Andersson, S. (2015). Investigating and developing engineering students’ mathematical modelling and problem-solving skills. European Journal of Engineering Education, 40(5), 557-572. doi:10.1080/03043797.2014.987648Wellington P., Thomas I., Powell I., & Clarke B. (2002). Authentic assessment applied to engineering and business undergraduate consulting teams. International Journal of Engineering Education 18(2), 168-179

Genomic mining of prokaryotic repressors for orthogonal logic gates

Genetic circuits perform computational operations based on interactions between freely diffusing molecules within a cell. When transcription factors are combined to build a circuit, unintended interactions can disrupt its function. Here, we apply 'part mining' to build a library of 73 TetR-family repressors gleaned from prokaryotic genomes. The operators of a subset were determined using an in vitro method, and this information was used to build synthetic promoters. The promoters and repressors were screened for cross-reactions. Of these, 16 were identified that both strongly repress their cognate promoter (5- to 207-fold) and exhibit minimal interactions with other promoters. Each repressor-promoter pair was converted to a NOT gate and characterized. Used as a set of 16 NOT/NOR gates, there are >10[superscript 54] circuits that could be built by changing the pattern of input and output promoters. This represents a large set of compatible gates that can be used to construct user-defined circuits.United States. Air Force Office of Scientific Research (Award FA9550-11-C-0028)American Society for Engineering Education. National Defense Science and Engineering Graduate Fellowship (32 CFR 168a)United States. Defense Advanced Research Projects Agency. Chronical of Lineage Indicative of Origins (N66001-12-C-4016)United States. Office of Naval Research (N00014-13-1-0074)National Institutes of Health (U.S.) (GM095765)National Science Foundation (U.S.). Synthetic Biology Engineering Research Center (SA5284-11210

IGERT: Predoctoral Training in Functional Genomics of Model Organisms

The objective of this IGERT project is to initiate an interdisciplinary, inter-institutional degree program in Functional Genomics of Model Organisms supported by an interactive faculty from the University of Maine, the Jackson Laboratory, and the Maine Medical Center Research Institute. The major challenge for biological and biomedical research for the foreseeable future is to understand how the information encoded within a genome determines the development and functioning of a living organism. To move from the level of DNA sequence to an understanding of the molecular interplay producing the final traits of an individual will require a continuum of experimental approaches ranging from experimental genomics, molecular biology, and novel biophysical methodologies, to advanced data screening schemes and computational techniques. Traditional alignments of the biologically based disciplines will be insufficient to solve the complex problems associated with functional genomics. Genome projects, regardless of the organism, will rely increasingly on the physical and computational sciences. The increased need for interdisciplinary research will require scientists trained to work interactively in multiple disciplines. This program introduces a new educational paradigm, developed to train students to move freely among the disciplines needed to investigate genome function. Students receive training in the biological, physical and computational sciences through a combination of core and advanced courses, intensive workshops, and research seminars. Emphasis is placed on a high-quality research environment and a tutorial relationship between the student and her/his mentors and program committee. Central to the students\u27 training in interdisciplinary research will be the use of a paired mentoring system, a concept referred to as twinning. The primary mentor plays a role similar to the traditional graduate advisor and comes from the student\u27s primary area of research. The secondary mentor comes from a second discipline, and each student develops a research project dependent upon interdisciplinary collaborations.IGERT is an NSF-wide program intended to meet the challenges of educating U.S. Ph.D. scientists and engineers with the multidisciplinary backgrounds and the technical, professional, and personal skills needed for the career demands of the future. The program is intended to catalyze a cultural change in graduate education by establishing innovative new models for graduate education and training in a fertile environment for collaborative research that transcends traditional disciplinary boundaries. In the fifth year of the program, awards are being made to twenty-one institutions for programs that collectively span the areas of science and engineering supported by NSF

Research and Education in Computational Science and Engineering

Over the past two decades the field of computational science and engineering (CSE) has penetrated both basic and applied research in academia, industry, and laboratories to advance discovery, optimize systems, support decision-makers, and educate the scientific and engineering workforce. Informed by centuries of theory and experiment, CSE performs computational experiments to answer questions that neither theory nor experiment alone is equipped to answer. CSE provides scientists and engineers of all persuasions with algorithmic inventions and software systems that transcend disciplines and scales. Carried on a wave of digital technology, CSE brings the power of parallelism to bear on troves of data. Mathematics-based advanced computing has become a prevalent means of discovery and innovation in essentially all areas of science, engineering, technology, and society; and the CSE community is at the core of this transformation. However, a combination of disruptive developments---including the architectural complexity of extreme-scale computing, the data revolution that engulfs the planet, and the specialization required to follow the applications to new frontiers---is redefining the scope and reach of the CSE endeavor. This report describes the rapid expansion of CSE and the challenges to sustaining its bold advances. The report also presents strategies and directions for CSE research and education for the next decade.Comment: Major revision, to appear in SIAM Revie

Educating and Training Accelerator Scientists and Technologists for Tomorrow

Accelerator science and technology is inherently an integrative discipline that combines aspects of physics, computational science, electrical and mechanical engineering. As few universities offer full academic programs, the education of accelerator physicists and engineers for the future has primarily relied on a combination of on-the-job training supplemented with intense courses at regional accelerator schools. This paper describes the approaches being used to satisfy the educational interests of a growing number of interested physicists and engineers.Comment: 19 pages, 3 figure