Scientists with an edge at computational prowess and mathematical techniques are in great demand ever since information technology has taken a central position in modern society. Here we discuss a couple of themes where data analytics and stochastic modeling tools have shown impressive track record for young graduates in the physical sciences to dream for that 'incredible career path'. Sound logic, experimental precision and computational powers have been increasingly harnessed to build efficient systems and arrive at prudent decisions. Godel's incompleteness theorem asserts that no formal theory that contains arithmetic can be both complete and consistent at the same time. Hence we are forced to carry out scientific computation using advanced numerical schemes using as many data points as available for better precision.
[Refer: Computing over the reals: Where Turing Meets Newton-BY LENOR BLUM]
In another direction we also require computational tools for market analysis, trading and investment analysis. Taking a cue at stochastic environments involved in quantum physics, the academia and industry recognized the value of engineering financial decisions using powerful mathematics. To narrate the story in a nutshell, financial decision making invariably involves numerical calculations, spreadsheet models for simple what-if- analysis, cost of capital calculation and business valuations. Of late research trends in risk management require sophisticated stochastic analysis. For instance asset price dynamics, evolutionary trends in commodity prices, interest rate analysis and derivative products. We shall quote A.Brown (of the Morgan Stanley group) "In the olden days of risk management you could take decisions looking at the data while now we have tons of millions of positions and hundreds of thousands of market factors Each position can have so many attributes and each market factor has thousands of data points and the market factors themselves are becoming very abstract, we dont have prices in there any more we have implied volatalities and implied correlations"
[Ref: Convergence of Technology and Pure Studies-Bulletin of Science Popularization]
-- J V Ramana Raju
[Refer: Computing over the reals: Where Turing Meets Newton-BY LENOR BLUM]
In another direction we also require computational tools for market analysis, trading and investment analysis. Taking a cue at stochastic environments involved in quantum physics, the academia and industry recognized the value of engineering financial decisions using powerful mathematics. To narrate the story in a nutshell, financial decision making invariably involves numerical calculations, spreadsheet models for simple what-if- analysis, cost of capital calculation and business valuations. Of late research trends in risk management require sophisticated stochastic analysis. For instance asset price dynamics, evolutionary trends in commodity prices, interest rate analysis and derivative products. We shall quote A.Brown (of the Morgan Stanley group) "In the olden days of risk management you could take decisions looking at the data while now we have tons of millions of positions and hundreds of thousands of market factors Each position can have so many attributes and each market factor has thousands of data points and the market factors themselves are becoming very abstract, we dont have prices in there any more we have implied volatalities and implied correlations"
[Ref: Convergence of Technology and Pure Studies-Bulletin of Science Popularization]
-- J V Ramana Raju
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