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Complex problems require interdisciplinary solutions. The Rensselaer Data Science Research Center develops the technologies to enable that multidisciplinary research.

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What is DSRC?

Science and engineering progress is increasingly becoming dependent on data, yet traditional data technologies were not designed for the complexity of the modern world. The RPI Data Science Research Center acquires, processes, archives, analyzes, visualizes, simulates, and disseminates complex data to close the data-to-knowledge gap.

Data Science Research Center (DSRC) brings researchers from many different disciplines to model, analyze, simulate, visualize, and secure complex data acquired from diverse domains across multiple time and length scales.

"The goal of this center is to attack difficult problems that require interdisciplinary collaborations. These problems can range from attacking a cancerous tumor to climate change. By bringing together data and experts from different disciplines and perspectives, we can greatly increase the potential of our individual research and funding."

-Bülent Yener

Meeting the Need for Data Science Research

DSRC serves as the melting pot of ideas and expertise in research areas such as computer science, biology, engineering, mathematics, physics, environmental science, library and social sciences. DSRC facilitates collaboration and interaction among not only RPI students, postdocs, and faculty but also investigators from external institutions both from academia and industrial research labs. The investigators in the center study data intensive complex problems in diverse application areas including medicine, oceanography, and networks (e.g., telecommunication, data, grid). One particular challenge the DSRC investigators take up is to bridge the gaps between mathematical sciences and life sciences by developing data driven models and algorithms.

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Partners

DSRC researchers collaborate with

Research Universities

(e.g., Mount Sinai School of Medicine)

Industrial research labs

(e.g., GE Global Research, IBM Research, General Dynamics)

Non-profit research institutions

(e.g., Woods Hole Oceanographic Research Institution, Wadsworth Research Center, MIT Lincoln Labs)

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Our Objective

The objective of DSRC is to become a center with national and international visibility, and provide support and infrastructure to its members for solving data centric and data intensive research problems by capitalizing on Rensselaer’s super computer center (CCNI) and experimental media and performing arts center (EMPAC). Members of DSRC will collaborate and interact via workshops in specific topics, group meetings, seminars, student internships at industrial research labs. DSRC will offer an educational and training program for graduate students and post-docs to prepare the next generation data scientist and engineers. The scientific focus of the center is on multiscale approaches to complex data obtained from diverse domains including biomedical, environmental, engineering and social domains. The Center aims to vertically integrate solutions to several core challenges of data science.

The current scientific focus of the center is on multiscale approaches to complex data obtained from diverse domains including biomedical, environmental, engineering and social domains.

The Center aims to vertically integrate solutions to several challenges of data science:

Data Complexity

  • High dimensionality & multimodality
  • Heterogeneity
  • Inter- and multi-disciplinary
  • Large volume and rate
  • Simulation and Visulization

  • Sensory Analytics
  • Games
  • Dynamic interactivity
  • Security and Privacy

  • Integrity
  • Access control
  • Confidentiality
  • Anonymity
  • Modeling and Analysis

  • Physics and informatics based multiscale models
  • Linear and nonlinear analysis methods
  • Learning and Knowledge Extraction

  • Supervised and unsupervised classification and learning
  • Missing data, uncertainty
  • Data Acquisition and Preprocessing

  • Finding and accessing relevant data; discovery
  • Filtering, noise reduction
  • Semantic enhancements
  • Supervised and unsupervised data.