S Kameshwaran  S Kameshwaran photo       

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IBM Research - India, Bangalore



I am with the Mathematical Modeling and Algorithmic Solutions group in the department of Analytics and Mobile Enabled Solutions (AMES) at IBM Research - India, Bangalore. My primary research interests are in mathematical programming and algorithmic operations research. Recently I am into time series, epidemiological modeling, and spatio-temporal analytics. My recent projects are:


IBM Optimization: Mine to Ship

Mining companies have complex supply chains that start from the mining location and stretch thousands of kilometers to the end customer in a different country and continent. The logistics of moving the materials from mines to ship is composed of series of optimization problems like berth allocation, ship scheduling, stockyard scheduling, and rail scheduling, which are individually NP-hard. We have developed an application, called as IBM Optimization: Mine to Ship, for end-to-end integrated operations scheduling. The application is built on IBM ILOG ODM Enterprise with advanced features like rescheduling under deviations and disturbances, and maintenance scheduling. The modeling and computational complexity of integrated scheduling optimization is tamed using hybrid optimization technique that leverages mathematical programming and constraint programming. The application will benefit the mining companies with increased resource usage, higher throughput, reduced cost of operations, and higher revenue. More details can be found here.


Dengue Modeling and Analytics

NEA: Prevention made with data. Made with IBM

Dengue has become a major international public health concern, particularly in countries in Southeast Asia, the Americas, Africa and Western Pacific. Dengue Fever is now endemic in more than 100 countries and about half the of the world's population is at risk. As a vector-borne viral disease, the spread of dengue is attributed to the expansion of the geographic distribution of the four serotypes of dengue viruses and their vector Aedes mosquitoes. The four dominant strains of dengue viruses have progressively spread to virtually all tropical countries around the globe. No specific vaccine or pharmaceutical treatment is available, so disease control is mostly based on prevention through the eradication of vector populations. In this project, we study the transmission dynamics of dengue using advanced spatio-temporal analytics and mathematical models of epidemiology. The key objectives would be to provide early warning of potential outbreaks of dengue disease for the purpose of implementing timely control measures. The spatio-temporal analytics takes a data centric approach using statistical and data mining models for prediction. The mathematical model of dengue transmission is a multi-population model that captures the transmission dynamics between host (human) and vector (mosquito) taking into account the four strains of dengue virus and the cross infections. We use the Spatiotemporal Epidemiological Modeler (STEM) to model and simulate dengue transmission.