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Data Science Research Projects at Illinois Tech

The following is a selected list of data science research projects in progress at Illinois Tech:

  • A system that creates a small 3D object that summarizes a meeting or other conversation, used to remember, communicate, or analyze what happened
  • Adaptive numerical algorithms with guarantees of success
  • Analysis of massive longitudinal data obtained from accelerometers or biosensors
  • Application of structural equation modeling to meta-analytic findings
  • Applications of Big Data to psychological research
  • Automated analysis and visualization of brain structure from 3D brain imaging data
  • Automated social media analysis for public health and natural disaster informatics and response
  • Automatically turning extremely massive text streams into comprehensible narratives
  • Categorical data analysis applied to the evaluation of employment discrimination
  • Computer-based adaptive testing
  • Designing large scale sustainable cyberphysical sensor networks
  • Determining optimal dimensionality of personality measures
  • Developing better text analysis methods applicable to comments on employee surveys
  • Developing dedicated hardware interfaces for data visualization, so that people can carry out analytical tasks off-screen
  • Effective data collection and experimental design for machine learning and engineering
  • Efficient analysis of non-linear, high-dimensional computer simulation data
  • Efficient non-asymptotic goodness-of-fit tests for log-linear models based on Markov bases
  • High-performance data-intensive computing infrastructure using standard hardware
  • Identifying author ideology from textual analysis
  • Improving detection of physiological structures in MRI and CT scans
  • Improving efficiency and accuracy in Bayesian modeling and computation
  • Kriging for approximating functions of several variables, often to construct surrogates for computer experiments
  • Machine learning for extracting meaningful information from unstructured text
  • Maximum likelihood estimation for model parameters of multimodal likelihood functions
  • Mediating objects or other forms of technology that can be used to help people comply with their own best intentions, such as to improve adherence to medication
  • Meta-analysis methods that account for differences in research design
  • Methods for assessing measurement equivalence, i.e., do people from different cultural groups, languages, etc. interpret and respond to test or survey items differently?
  • Modeling Methodologies for Smart Grid Control System Security
  • Modeling, simulating, and analyzing random graphs and networks for sound statistical inference
  • Pervasive provenance in databases for improved trust and reliability
  • Program evaluation related to Human Resources practices
  • Process monitoring and quality control focusing on manufacturing engineering
  • Regression with emphasis on suppression and determining which substantive claims can and cannot be supported
  • Rigorous methods of hypothesis testing for integrated end-to-end network security
  • Scalable learning of very large graphical models for very large data
  • Social network analysis, with application to the development of communication in infants
  • Space filling design of experiments, i.e. how to sample phenomena to get the most information
  • Statistical analysis of very large corpora to understand conceptual metaphors
  • Structural equation modeling  with emphasis on the use of parcels associated with the measurement models
  • The IIT Wide Spectrum Radio-Frequency Observatory
  • User interaction and transparency for more effective machine learning
  • Using mobile devices to collect longitudinal survey responses or passive data (e.g. fitbit, biosensors)
  • Visual languages that represent instrument data in a readable alphabet that can be used to scan and understand output sequences more quickly and easily
  • Visualization of mental models, with applications to psychotherapy 

Data Science Survey Main Content

For more information about Data Science at IIT, contact the Director of Data Science.