Undergraduate Research Projects in Data Analytics

Below is a list of the current undergraduate research projects in data analytics:

Panos Pardalos, Ph.D.

Networks in Brain Research

Email: pardalos@ufl.edu
Ph.D. Student Mentor(s): Antony Kocheturov, antrubler@gmail.com
Terms Available: Fall, Spring, Summer
Student Level: Junior, Senior; 2 students per semester
Prerequisites:  None, but some knowledge on Data Analysis, and an interest on new subjects will help
Credit:  0-3credits via EGN 4912
Stipend: none unless selected for University Scholars
Application Requirements: Resume, faculty interview, visit my web page for related work; email all materials in one pdf file to Panagote (Panos) Pardalos
Application Deadline: Any time throughout the year – but beginning of semesters is typically best
Website:
  www.ise.ufl.edu/pardalos
Project Description: Many large (and massive) data-sets in neuroscience can be represented as a network. In these networks, certain attributes are associated with vertices and edges. The analysis of these networks often provides useful information about the internal structure of the datasets they represent.  We are working on several networks for the epileptic brain, the Parkinson brain, and in general we use Networks to study brain dynamics.

Information Theory and Network Robustness

Email: pardalos@ufl.edu
Ph.D. Student Mentor(s): Arsenios Tsokas, artsokas@gmail.com
Terms Available: Fall, Spring, Summer
Student Level: Junior, Senior; 2 students per semester
Prerequisites:  None, but computer skills will help.
Credit:  0-3credits via EGN 4912
Stipend: none unless selected for University Scholars
Application Requirements: Resume, faculty interview, visit my web page for related work; email all materials in one pdf file to Panagote (Panos) Pardalos
Application Deadline: Any time throughout the year – but beginning of semesters is typically best
Website:
  www.ise.ufl.edu/pardalos
Project Description: A crucial challenge in network theory is the study of the robustness of a network when facing a sequence of failures.  We proposed a novel methodology to measure the robustness of a network to component failures or targeted attacks based on Information Theory, that considers measurements of the structural changes caused by failures of the network’s components providing a dynamical information about the topological damage. The methodology is comprehensive enough to be used with different probability distributions and provides a dynamic profile that shows the response of the network’s topology to each event, quantifying the vulnerability of these intermediate topologies. There are many applications for the smart grid, evacuation and disaster recovery, security, and biomedicine.