This page describes my research in data mining algorithms, that is, solution techniques for specific tasks in data analysis. Unlike the area of applications, where multiple challenges often have to be addressed simultaneously, a focus on the algorithms enables me to consider, in isolation, each of the many challenges encountered in real applications. I can create computationally efficient solutions that are appropriate to the size of the data, can process the variation in the data, and are robust to the settings of parameters of the algorithms. It is also an opportunity to advance the state of the art in analysis algorithms.
More details on my research in algorithms is available on the following pages:
- Surrogates and sampling for simulations
- Intelligent sampling
- Regression with small data sets
- Interpreting the solution to inverse problems
- Independent-block Gaussian process
- Compressing unstructured simulation data
- Intelligent exploration of large-scale data
- Analysis of time series data from sensors
- Dimension reduction for scientific applications
- ASPEN- Approximate SPlitting for ENsembles
- Tracking moving objects in simulations and video
- PDEs in image processing
- Sapphire scientific data mining software
Sampling and surrogates for simulations: Analysis of data from computer simulations presents unique opportunities for data analysis. Simulations of complex physical phenomena can be very expensive to run, but at the same time, we have greater control over the data we choose to generate. For the last fifteen years, I have been interested in how we can combine the generation and the analysis of the data to reduce the cost of gaining insight into the phenomenon being simulated. Two ideas play a role – sampling, where we carefully select the values of the input parameters at which to run the simulation, and surrogate models, which are fast, but approximate, alternatives to the simulation. By combining these ideas suitably, we can identify viable regions in the input space; solve inverse problems, where we seek the input parameters that result in specific output values with associated uncertainties; design experiments; and progressively refine our understanding of the phenomenon being simulated.
Select publications (available from Google Scholar):
- Chandrika Kamath, Intelligent sampling for surrogate modeling, hyperparameter optimization, and data analysis, Machine Learning with Applications, Volume 9, 15 September 2022, 100373, https://doi.org/10.1016/j.mlwa.2022.100373
- Chandrika Kamath, “Intelligent Sampling for Surrogate Modeling, Hyperparameter Optimization, and Data Analysis, ” LLNL Technical Report LLNL-TR-829837, December 2021.
- Chandrika Kamath, Juliette Franzman, and Ravi Ponmalai, “Data mining for faster, interpretable solutions to inverse problems: A case study using additive manufacturing,” Machine Learning with Applications, Volume 6, 15 December 2021, https://doi.org/10.1016/j.mlwa.2021.100122.
- C. Kamath and Y.J. Fan, “Regression with Small Data Sets: A Case Study using Code Surrogates in Additive Manufacturing,” Knowledge and Information Systems Journal, Volume 57, Number 2, November 2018, pp. 475-493.
- C. Kamath, “On the use of data mining to build high-density, additively-manufactured parts,” invited book chapter, Information Science for Materials Discovery and Design, T. Lookman, F. Alexander, and K. Rajan (eds.) in Springer Series in Materials Science, 225, pp 141-155, 2016.
- C.Kamath, “Data Mining and Statistical Inference in Selective Laser Melting”, International Journal of Advanced Manufacturing Technology, Volume 86, Issue 5, pp 1659–1677, September 2016. Appeared online 11 January, 2016. http://dx.doi.org/10.1007/s00170-015-8289-2
Sampling for surrogate models, hyperparameter optimization, and data analysis: The quality of a surrogate model depends not only on the model used, but also the sample points at which the training data are generated. Often, the focus is on the initial set of sample points used to create the model. But, as the practical use of these models increases, there is a need for algorithms that not only generate space-filling samples, but also support progressive and incremental sampling. We explore algorithms used in various disciplines and evaluate them in the context of how well they support the many needs of modern surrogate modeling, the closely related task of hyperparameter optimization, and data analysis in general.
Select publications (available from Google Scholar):
- Chandrika Kamath, Intelligent sampling for surrogate modeling, hyperparameter optimization, and data analysis, Machine Learning with Applications, Volume 9, 15 September 2022, 100373, https://doi.org/10.1016/j.mlwa.2022.100373
- Chandrika Kamath, “Intelligent Sampling for Surrogate Modeling, Hyperparameter Optimization, and Data Analysis, ” LLNL Technical Report LLNL-TR-829837, December 2021.