My research interests lie in the analysis and visualization of data. We live in the era of big data revolution; the large amounts of data generated around the globe every day contain abundant information that we must extract and understand. Understanding data through visual and/or analytical means can impact human growth in a variety of disciplines, ranging from but not limited to understanding the universe to tracing our origin, from bettering the socio-economic progress to understanding the spread and causes of a disease, from taking us to Mars to optimizing business practices. The importance of data analysis and visualization is conveyed by the quote: "Without understanding data, you're just another person with an opinion!"
I am interested in playing with data of a wide variety, and contribute to enhance the fields of scientific and information visualization. A gamut of other research areas fascinate me. Given some spare time from my current work and responsibilities, I would like to be involved in Mathematical Modeling & Simulation, Probability Theory & Stochastic Processes, Markov Models & Queueing Theory, Computational Geometry, & Algorithms.
My current focus at LLNL is the research and development of Machine Learning infrastructure to support the multi-laborarory effort to understand cancerous cell growth induced by RAS protien. We are developing the first of its kind multiscale simulation of RAS biology on cell membranes to understand intricate interactions between the two at long spatial and temporal scales.
I am also developing a new in-memory representation to support adaptive representation of data, in terms of both resolution and precision, allowing many standard visualizaiton and analysis algorithms. I am also involved in the topological analysis of molecular dynamics data, where I have developed a robust topological analysis tool, TopoMS. As a part of my Ph.D. research, I developed tools for consistent feature extraction from vector field data, which can be used for a broad spectrum of applications ranging from combustion to weather, aerodynamics to cosmology, etc. I have worked extensively on theoretical and computational frameworks for robust analysis of vector fields.
Won the Best paper award at the IEEE Pacific Visualization Symposium 2016, Taipei, Taiwan
Selected as a promising young researcher to attend the 2nd Heidelberg Laureate Forum to meet with some of the preeminent scientists in mathematics and computer science in September 2014 in Heidelberg, Germany
Won a four year graduate fellowship for independent research by Lawrence Livermore National Laboratory, USA, under the Livermore Graduate Scholar Program
Won the Best paper award at the IEEE Pacific Visualization Symposium 2011, Hong Kong
2007 Won the 3rd prize worldwide in the RedHat Challenge for a technical business idea, only after Massachusetts Institute of Technology (USA) and McGill University (Canada)
2018, Oct 21: I presented "Feature Extraction and Tracking using Vector Field Decompositions" as part of "Tutorial on Recent Feature Tracking Techniques" at IEEE VIS, 2018 in Berlin, Germany. [Talk]
2018, Oct 16: I presented "Topological Exploration for Molecular and Condensed-Matter Systems" for an invited talk at the Chemistry Department at The University of Saarland, Saarbrüken, Germany. [Talk]
2018, Aug 07: I presented "Enabling Multiscale Simulations of RAS Biology using Machine Learning" at the Data Science Institute Workshop 2018, organized by LLNL. [Talk]
2018, May 06: Our paper "TopoMS: Comprehensive Topological Exploration for Molecular and Condensed-Matter Systems" appears in the Journal of Computational Chemistry, and is selected for the cover image.
2018, Mar 23: Our paper "Interactive Investigation of Traffic Congestion on Fat-Tree Networks Using TreeScope" has been accepted for publication in Computer Graphics Forum, to be presented at EuroVis 2018 on Jun 09, 2018.