To make the research center truly interdisciplinary, the organization will be discipline-agnostic, and focus more on the methods to process large amounts of data. This way, researchers across domains will be encouraged to learn from each other and to cross pollinate. We will bring people together from all Colleges across the campus to develop non-discipline specific tools in the form of:

1)  Statistical Methods: Convert research questions into quantitative hypotheses. Single and multivariate regressions as well as interpreting the meaning of the outcomes.

2)  Machine Learning and Predictive Modeling: – Develop, design and apply advanced techniques such as (deep) neural networks, random forests and other machine learning algorithms. 

3)  Analytical Modeling: In many cases, the understanding of a phenomenon can be improved by a simpler analytical model with clear causes, mechanisms, and effects. Even if this goes at the expense of model accuracy, the improved understanding may lead to higher applicability beyond the data set.

4)  Numerical Modeling: Numerical modeling has long been a key component of some of the exact sciences (e.g., fluid dynamics, chemical engineering), but those same techniques can be applicable to any problem where the fundamental mechanisms are understood, but the implications too complex to oversee. 

5)  Data Visualization and Presentation : Without a proper presentation, research results mean nothing. In recent years, advanced data presentation has become much more accessible and visible, as noted by visualization packages in Python and Matlab. Share tools and best practices to make these visualizations, and to critique each other’s work. 

Modern computing tools, such as R, Python and Matlab will be used to develop our analysis methods. Indeed, it is one of the goals of the center to make these tools more accessible to researchers across campus, through collaborations, teaching, and through the development of tools that make advanced statistics more accessible to researchers. 


ADAM intends to be an inclusive Center, with researchers from any domain welcome to join and share expertise. Examples of research domains that would fit well within our center include (but are not limited to):

1.  Materials and Manufacturing: Find predictive functional forms and develop novel tools for detecting trends that govern the underlying behavior of a large data set. Using real-world data to guide strategies for understanding and prevention. Develop novel numerical algorithms for studying advanced materials, manufacturing, and synthetic particle systems.

2.  Financial and Economic Modeling: Develop novel models and statistical approaches for analyzing financial and economic data to identify underlying trends in risk assessment and market growth.

3.  Biomedical applications: Analyze vast array of medical data to develop novel patient procedures and develop preventative medical approaches. Use biostatistics to identify hidden trends.

4.  Food Safety: Develop models capable of risk assessment and cross-contamination prediction in the poultry and produce industry.

5.  Environmental Sciences: Develop quantitative models that foster understanding of the environment and changes therein.

6.  Education: Develop predictive models of student success, improve learning across the K-12 and tertiary curriculum. This includes not only externally fundable research, but also development of tools for internal use at CSU or for investment in CSU’s pipeline through Cleveland’s public school system.

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