Machine Learning

 

Mines machine learning group is directed by Dr. Hua Wang, and consists of multiple PhD, master, and undergraduate research assistants.

We focus on developing mathematical foundations and algorithms needed for computers to learn. Our research span the areas of machine learning and data mining, as well as their applications in a number of practical areas, such as cheminformatics, bioinformatics, medical image analysis, and computer vision. The goal of our research is to bridge the gap between computational solutions and real-world problems. Several our current research topics include:

  1. Robust learning models: Data collected from real-world problems are inevitably compromised by noises and outliers, which makes conventional learning models hard to achieve satisfactory performance. As a result, designing learning models that are insensitive to noises, particularly to outlying data points, is of great value for practice. We utilized state-of-the-art numerical methods and redesigned a number of most broadly used learning models to improve their robustness against noises.
  2. Data fusion models: In many real-world applications, data are usually collected from different sources. For example, in disease diagnosis physicians can collect different types of data to evaluate different aspects of a patient, such as the physical data, imaging data, genetic data, and so on. How to effectively integrate these data is playing a critical role in diagnosis. We have built many mixed-norm induced learning models, which can elegantly extract the most relevant information from massive raw data collected by various instruments.
  3. Learning models for big data: With the recent development in technologies, we have accumulated vast amount of data, such as those in biology, Information technology, to name a few. These data provide a wealth of information that is valuable for our lives. However, how to efficiently processing the data with either big number of samples or big number features is a very challenging problem, which has aroused a lot of interests in both academia and industries. We are actively engaged in this area and have designed learning models with low computational complexity to deal with big data problems extracted from a variety of real-world domains.

Behavioral Economics and Big Data


Affiliated Faculty

Tracy Camp, CS Division Director
Hua Wang
Hao Zhang


Sponsors

National Science Foundation


 

Collaborators and Industry Members

Indiana University School of Medicine
National Institute of Standards and Technology at Boulder (NIST Boulder)
University of Texas at Arlington

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Last Updated: 08/04/2017 08:23:15