- Start Date:
June 24, 2014
- Event Start Time:
1:30 PM
- Event End Time:
2:30 PM
- Organizers:
Gene Fiorini
- Seminar Series:
REU Seminar
- Presenter(s):
Frank Hsu - Fordham University
- Event Location:
DIMACS Seminar room
- Abstract:
Processing data and analyzing information in order to solve problems , make decisions, or discover knowledge in science, technology, society, medicine , health care, eduction and business often involves fusion of data or combination of information from multiple sensors , sources, and systems. Although it is widely known that fusion of information can improve results tremendously, the issue of "when and how" remains a challenging problem. This happens in regression, machine learning, data mining, knowledge discovery, and other fields in computing, informatics and analytics. “Combinatorial Fusion Algorithm (CFA)”, a recently developed information fusion paradigm, uses the method and practice of multiple scoring systems (MSS) - each having a score function, a rank function and a rank-score characteristics (RSC) function. It employs combinatorial methods to model the system space and the rank-score characteristic (RSC) function to measure information diversity between two systems. CFA uses a unique paradigm which integrates computational, mathematical and statistical approaches as well as cognitive neuroscience. In this talk, I will discuss CFA and its impact on a variety of application domains including science and technology (target tracking and computer vision, information retrieval & internet search); biomedical informatics (virtual screening & drug discovery, protein structure prediction and ChIP-seq analytics); and cognitive neuroscience (brain informatics and affective computing).
