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.
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.
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 RPI’s super computer center (CCNI) and
experimental media and performing art
center (EMPAC). Members of DSRC will collaborate and interact via workshops in
specific topics, group meetings, seminars, student internships at industrial
research labs. DSRC plans to offer an educational and training program for
graduate students and postdocs to prepare the next generation data scientist
and engineers.
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:
1. Data
Acquisition and Preprocessing
Finding
and accessing relevant data; discovery
Filtering,
noise reduction
Semantic
enhancements
2. Data
Complexity
High
dimensionality & multimodality
Heterogeneity
Inter-
and multi-disciplinary
Large
volume and rate
3. Modeling,
Analysis, Learning and Knowledge extraction
Physics
and informatics based multiscale models
Linear
and nonlinear analysis methods
Supervised
and unsupervised classification and learning
Missing data, uncertainty
4. Simulation,
and Visualization
Sensory
Analytics
Games
Dynamic
interactivity
5. Security
and Privacy
Integrity
Access
control
Confidentiality
Anonymity
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