Under construction... .
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paper is available here) The method is further extended to study the global distribution of small particles (PM2.5).
We also applied data fusion of active and passive sensor to obtain regional PM2.5 map. More descriptions and paper link http://www.nabinkm.com/2014/01/assessing-surface-pm25-estimates-using.html
One of my mentee was rewarded the first prize (News, link).
We have applied machine learning technique to downscale meteorological variables of interest over the northeast USA. The results were presented in the machine learning conference organized by NYAS in NYC. Please see an example below.
Please follow the following link to watch a demo.
Using the technique of EOF method and time series information, we could infer incomplete ionosphere data. Paper here ...
My paper on sensor characterization using mixture of Gaussian model is available here (pdf 110K). I also designed entropy-based search algorithm for experimental design (http://arxiv.org/abs/1008.4973, and http://arxiv.org/abs/1111.3421).
I have been working on projects analyzing remote sensing satellite data, and applying various machine-learning techniques. In conjunction with Dr. Rebecca J. Allee (NOAA), we analyzed the remote sensing data to classify the water column component in the Gulf of Mexico. We were able to provide a consistent machine learning classification to the remote sensing data, and establish a correspondence between the Coastal and Marine Ecological Classification Scheme (CMECS) and the ML results. The results are available at URL: http://www.tinyurl.com/SOMGOM2
This helped delineate the water preference for commercial fish species. This could be used by decision makers for conservation, and fishermen to decide on where to go for fishing as well.
Figure showing the ML classification of North Gulf of Mexico. The result was used to delineate the water type preference for commercial fish species.
In UT Dallas, I worked on the societal applications of Machine Learning to build conceptual understanding of physical phenomena from real-world data. I have been working with big as well as collaboration projects. Please click on the list for details.
Oil sleek detection from space is a challenging task due to the fact that the reflective properties of oil on water are similar. Sun-glint methods are often used to visually identify the sleek. We explored ML techniques to identify the oil sleek in Gulf of Mexico, and were successful to some extent. In the future, integrating the social media with remote sensing data will help establish an alert system to detect the oil sleek.