Rensselaer Polytechnic Institute (RPI)

Information about RPI to decide whether they should join ICTBioMed

About RPI Founded in 1824, Rensselaer Polytechnic Institute is the nation’s oldest

technological research university. RPI has established six areas of research as institute priorities: biotechnology,

energy and the environment, nanotechnology, computation and information technology and media and the arts.

Some notable research centers operated by RPI are the Terahertz Research Centero Center for Biotechnology and Interdisciplinary Studies (CBIS)o Rensselaer Nanotechnology Centero New York State Center for Polymer Synthesiso Darrin Fresh Water Instituteo Center for Automation Technologies and Systemso Lighting Research Center

Office of Research Phone: 518-276-4873o Jonathan S. Dordick, Vice President for Research dordick@rpi.eduo Wolf W. von Maltzahn Associate VP for Research vonmaw@rpi.edu

Center for Computational Innovations (CCI/CCNI)

CCI was formerly called Rensselaer supercomputing center The Computational Center for Nanotechnology Innovations (CCNI), the result of a

$100 million partnership involving Rensselaer, IBM, and New York state, is designed to continue advancing semiconductor technology to the nanoscale, while also enabling key nanotechnology innovations in the fields of energy, biotechnology, arts, and medicine.

The CCNI operates heterogeneous supercomputing systems consisting of massively-parallel Blue Gene supercomputers, Power-based Linux systems, and AMD Opteron processor-based clusters. This diverse set of systems enables large-scale leading-edge computational research in both the scientific and technical arenas. This initial hardware and software configuration provides upwards of 70 TeraFLOPS of computational power with associated high-speed networking and storage.

October 2013: Rensselaer unveiled a new petascale supercomputing system, the Advanced Multiprocessing Optimized System, or AMOS. With the ability to perform more than one quadrillion calculations per second, AMOS is the most powerful university-based supercomputer in New York state and the Northeast, and among the most powerful in the world.

The Computational Center for Nanotechnology Innovations (CCNI)

The CCNI is a world class supercomputing facility that operates heterogeneous supercomputing systems consisting of massively parallel supercomputers and clusters. There is a robust software environment for the development of new applications and a production environment of design tools. The facility is connected to the rest of the world through a fiber network infrastructure.

Chris Carothers — Director Jackie Stampalia — Associate Director, Academic and

Research Computing

CCNI Research Major Research Instrumentation

o Rensselaer Researchers Receive $2.65 Million NSF Grant To Install Balanced, Green Supercomputer at CCNI Supercomputing Center.

o A new system to be installed at the Rensselaer Polytechnic Institute supercomputing center will enable exciting new research possibilities across the nation and boost the university’s international leadership in computational modeling and simulation, data science, high-performance computing, and web science.

Sustainable Environment – Actionable Data o Sustainability science is a new and growing area of research that focuses on interactions between

nature and human activities. These interactions are complex, and knowledge about them is important for guiding how government, industry, and individuals should plan for and respond to environmental and social change. Understanding fundamental principles for sustainable societies requires access to large amounts of data on natural phenomena, human behavior, and economics.

o The University of Michigan and its partners at Indiana University, Rensselaer Polytechnic Institute, and the University of Illinois will work with sustainability scientists to develop a system for managing and sharing their data. This system will enable researchers to actively and socially curate and share their own data.

https://ccni.rpi.edu/w/research/ccni

CCNI Enabled Research Multiscale Systems Engineering Schematic Multiscale Systems Engineering Concurrent model configuration during loading of a slab with a rectangular

nanoindenter (light blue region) - Computational Nanomechanics Vortex Study Adaptive Methods for Partial Differential Equations Predicting the Fate of Stem Cells Loves Me, Loves Me Not: New Method for Measuring Hydrophobicity at the

Nanoscale Rensselaer’s Lally School of Management & Technology Poised to Re-

engineer the Future of Finance Light-Speed Nanotech: Controlling the Nature of Graphene Bringing Second Life To Life: Researchers Create Character WithReasoning

Abilities of a Child Molecules to the MAX aims to boost national and global science literacy

through story, song, and fun.

Center for Biotechnology & Interdisciplinary Studies (CBIS)

Deepak Vashishth, Ph.D., Director, The CBIS is focused on research that ranges from the development

of new technologies that enhance human healthcare to energy security and sustainability to enabling scientific discoveries to be disseminated responsibly to the public.

Major multilevel, multidisciplinary research program areas include: stem cell biotechnology, drug discovery and human toxicology, drug delivery, development of alternative and renewable energy and energy storage, and computer simulations and visualization.o Biocatalysiso Molecular Bioprocessingo Biocomputation and Bioinformaticso Bio-Based Energy

The Scientific Computation Research Center (SCOREC)

SCOREC is focused on the development of the technologies necessary to enable multiscale systems engineering. Multiscale systems engineering will introduce a new paradigm in which all interacting scales important to the behavior of materials, devices, and systems will be accurately modeled and accounted for in the design optimized products and processes.

To enable the implementation of this new paradigm advanced modeling, simulation, optimization, and control technologies must be developed to provide the basis for design environments in which systematic exploration of alternative designs is supported by o a hierarchy of models that provides a consistent description of multiscale phenomena,o adaptive simulation methods that account for the scale interactions,o efficient computational analysis, optimization and control methodso the representation of uncertainty and its propagation.

Interdisciplinary team research areas o nano-composites designo vascular disease modeling o active transition of the methods and simulation technologies developed to industrial practice and

commercialization by software companies.

Bioinformatics & Computational Biology

Researchers at Rensselaer are applying computational methods to the understanding of diverse biological systems, including RNA structure, gene structure, protein structure, protein interactions, protein folding, protein design, drug design, RNA folding, chemoinformatics, molecular dynamics, phylogenetic analysis, and the brain. New computational methods are being developed including dynamic programming, support vector machines, hidden Markov models, finite element analysis, and many more.

http://www.rpi.edu/dept/bio/research/bioinfo.html

BioinformaticsBioinformatics Research Group in the Computer Science Department at Rensselaer Polytechnic Institute is comprised of 7 faculty and approx. 10 graduate students. The group is part of the larger Bioinformatics Constellation at Rensselaer, which spans over several departments, including Chemistry, Biology, Physics, and Mathematics l DEEdesign

Protein sequences may be designed to fit backbone structures using the Dead End Elimination algorithm, potentially enabling the design of novel proteins with new functions. Advisor: Chris Bystroff Protein-Protein Interactions Protein-protein interactions determine what happens in the cell. A new data structure for protein surfaces has been designed in order to make docking of proteins fast and accurate, and to enable the heirarchical clustering of all protein surfaces. Advisor: Mohammed Zaki Protein Structure Prediction Protein structure prediction is being carried out by using Hidden Markov Models to extract sequence-specific knowledge-based potentials for simplified molecules, then running molecular simulations. Advisor: Chris Bystroff

RPI websites https://ccni.rpi.edu/w/research/ccni http

://www.cs.rpi.edu/research/groups/bioinformatics/research.html

http://www.rpi.edu/dept/bio/research/bioinfo.html http://biotech.rpi.edu/ http://www.scorec.rpi.edu/about_us.php https://ccni.rpi.edu/w/research/enabled