Samir Khan

1134 Reputation

14 Badges

11 years, 73 days

My role is to help customers better exploit our tools. I’ve worked in selling, supporting and marketing maths and simulation software for all my professional career.

I’m fascinated by the full breadth and range of application of Maple. From financial mathematics and engineering to probability and calculus, I’m always impressed by what our users do with our tools.

However much I strenuously deny it, I’m a geek at heart. My first encounter with Maple was as an undergraduate when I used it to symbolically solve the differential equations that described the heat transfer in a series of stirred tanks. My colleagues brute-forced the problem with a numerical solution in Fortran (but they got the marks because that was the point of the course). I’ve since dramatized the process in a worksheet, and never fail to bore people with the story behind it.

I was born, raised and spent my formative years in England’s second city, Birmingham. I graduated with a degree in Chemical Engineering from The University of Nottingham, and after completing a PhD in Fluid Dynamics at Herriot-Watt University in Edinburgh, I started working for Adept Scientific – Maplesoft’s partner in the UK.

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These are Posts that have been published by Samir Khan

Sometimes the obvious escapes me, and it’s only due to some chance observation that I realize the same fundamental principles are everywhere.

A short time ago, I created a simple hydraulic network in MapleSim, and after experimenting with some of the parameters, found it gave the same behaviour as an electric circuit I’d modeled earlier.

Around the time that Windows 98 was at its most popular, I used to dabble in programming Windows user interfaces with Visual C++ and the help of several thick MFC (Microsoft Foundation Class) manuals.  I wanted to create packaged (and admittedly simple) engineering applications. But for a chemical engineer with little background in Windows programming, combining math functionality with a user interface was time-consuming and cumbersome. (MFC can be arcane unless you’ve invested considerable time in learning the API.)

Later, I migrated to VB6.  Designing an interface was an order of magnitude easier, but I still had to roll many of my own math routines, or link to external libraries. While I may be interested in the mathematical mechanics of adaptive step sizing in Runge-Kutta algorithms at the intellectual level, it was secondary to my then goal.

If you were to stroll into the Application Engineering office at Maplesoft, you might be led to believe that we subsist on nothing but donuts, pizza, chocolate and coffee.  It’s even worse at this time of year when we have many more opportunities to over-consume. I try to have a balanced diet, but there are too many temptations scattered around the office (including candy at the office entrance – our receptionist, Walli, expects me at 3pm each day without fail). It doesn’t help that a virtually limitless supply of donuts are only a three minute drive away.

Resellers buy products from a manufacturer, and sell to consumers.  They are an important factor in many industries, including the one in which I work.  Maplesoft operates through a network of resellers throughout the world (apart from North America and a few other territories).  Some may suspect I’m somewhat biased in promoting the importance of resellers; I spent seven years working for Adept Scientific, Maplesoft’s partner in the UK.

The largest resellers are based in larger, better developed markets with a strong manufacturing and research base (like Cybernet and Scientific Computers in Japan and Germany).  Conversely, many smaller resellers, like Multi-On and Czech Software First in Mexico and the Czech Republic, operate in markets with significant growth potential.

Dual- and quad-core PCs are now ubiquitous.  While making your operating system a better multi-tasking environment, they’ve had a limited effect on the code that most technical professionals write.  This is largely because of the perceived difficulty of parallel programming.   The evolution , however, of high-level languages that support multi-threading throughout the 90s and beyond, removed the need to manage threads at the low level, allowing engineers to concentrate on what part of the algorithm could be run in parallel.  Given the ever-increasing complexity of systems that have to be simulated, multi-threaded programming can offer significant time savings for many the problems that can be easily parallelized (and for which time-savings of parallelization outweigh the overhead).

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