Research Interests

Machine Learning, Big Data, Computer Security

Soft Computing

Neural Networks

Genetic Algorithms

Numerical Methods

Some of My Studentsí Research

I'm currently working on the applications of machine learning to solve a variety of problems. Of particular interest are those related to computer security (including wireless) and big data. Some examples:

Mr. Hongwei Mo, worked on the use of artificial immune systems to document classification.

Dr. Yihua Liao, worked on the issue of computer security from the point of view of insider threat.

Dr. (Ms.) Na Tang applied machine learning methods to solve the so-called inverse problems arising in data poor domains such as the health care field.

Dr. Khaled Labib applied statistical methods to study and distinguish traffic patterns during a denial of service attack.

Mr. Dustin Puim developed a method of evolving a neural net capable of playing checkers without being told what the rules of the game are.

Dr. Wenjie Hu is working on the issue of computer intrusions using a robust support vector machine he developed during his doctoral dissertation.

Dr. Subhasis Saha developed wavelet-based lossy and lossless compression algorithms

Dr. Kenrick Mock, explored the possibility of using genetic algorithms and case based reasoning to develop information filters to filter data coming on the Internet.

Dr. Walter Cedeno developed the MNC (Multi-Niche Crowding) Genetic Algorithm for finding the peaks of a multi-modal function and applied that technique to assemble the restriction-fragments using the data from chromosome 19 of the Human Genome Project.

Dr. Ivan Howitt developed a growing algorithm for radial basis function neural networks and applied this method to solve difficult problems arising in the context of digital communications.

Dr. Jay Smart used simulated annealing to study the difficult problem of visualizing the interdependencies of complex software systems by developing the most desirable "layout" of the dependency graphs.

Dr. Daniel Styer used reinforcement learning to study the difficult problem of balancing the musculo-skeletal model of a standing human.

Dr. Gyu-Sang Jang solved the difficult problem of discriminating earthquakes from underground nuclear explosions using artificial neural networks.

Dr. Art Raefsky solved a two-point boundary value problem using the finite-element method.


rvemuri@ucdavis.edu
Saturday 25 July2015