Chandrika Kamath is a data scientist at Lawrence Livermore National Laboratory, where she is involved in the analysis of data from scientific simulations, experiments, and observations. Her early interest in mathematical algorithms, and their efficient implementation, led her to the fields of numerical methods and high performance computing. A few years into her career, the need for scalable algorithms to cluster Web documents, and the connection between SVD and PCA, resulted in a serendipitous introduction to the field of data mining. Realizing the potential of this field in science and engineering applications, Chandrika changed her career focus. For the last twenty-five years, she has enjoyed applying her interests and expertise in mathematics and computer science to solve problems where the challenges posed by the size of the data, whether tiny or massive, are matched by the complexity of the data.
My work on classification of orbits in Poincare maps has been published in the International Journal of Data Science and Analytics (open access). – 27Nov2022
I finally completed the long overdue technical report on classification of orbits in Poincare maps. This report has the unfortunate distinction of being the one with the longest time between solving a problem and summarizing the results. This unusual data set was the smallest and most challenging data set I have analyzed and I thought the work should be documented. Better late than never! – 07Aug2022
My epic paper on intelligent sampling in surrogate modeling, hyperparameter optimization, and data analysis was published in Journal of Machine Learning with Applications (open access). This was another paper that took a while to see the light of day, but I think is the better for it. – 07Aug2022
In early 2022, my web service provider, under new ownership, changed the software being used to create web pages. I could either redo my web pages or never update them. The latter option begged the question of why one would have a web page in the first place. As my old pages were looking “worn”, like a digital version of faded wallpaper, I figured it was time for an update. Enjoy! – 30Mar2022