Another Ada Lovelace Day Post – CS Role Model

I posted earlier for Ada Lovelace Day about LinuxChix.org as a great resource for women in technology, and now I'm getting into the groove and want to add another post, this time about my first female role model in computer science.

Dr. Neelima Shrikhande is a professor of computer science at Central Michigan University. At the time I was working on my MS, she was the only female professor in the department. I never had an indication that she views herself as a role model for the few women studying computer science there, but she is definitely a role model.

She's a super intelligent and focused woman for whom I have a lot of respect. According to the cmich.edu website, she "is an authority on computer vision and artificial intelligence. She studies how to make computers capable of seeing things and understanding pictures."

I had her for only one class, my compiler class, but she really opened up the world of computer science for me with that class. It was a hard and life-consuming class, but I loved it more than any other class and even used what I learned for my thesis. I now have a life-long fascination with compilers and virtual machines because of that class and I still have my dragon book. At the time, I never thought about this, but I imagine that class was at least as hard to teach as it was to take, but she held up to the challenge seamlessly.

Thanks, Dr. Shrikhande, for being such a sharp, successful role model in computer science.

Shameless plug: the CMU CS department is a great place for an education!

A Tale About Problems of Scale

I earned some new bragging rights this week and had fun doing it. I love computer science!

A tale about iteration, unexpectedly large data sets, and time...

About a month ago, a large file of data unexpectedly needed to be processed, using some pre-existing code written several years ago. This code happened to be written in Java, but this story could apply to most any commonly used programming language. It was a batch process, so speed wasn't of the utmost essence, but at the same time it shouldn't run on and on and one because other files also needed to be processed. The data set was 500K records, whereas the larger data sets normally are in the 20-30K range, so this was more than 10x the norm.

Everything seemed to be going fine. The file was read into a database and some processing had been done on it and the time had come to write out a result file. Along with results, the original programmer had wanted some statistics, so there was a quick iteration through all of the records to gather the statistics and then the results would be written into the file. It seemed straightforward and I didn't expect anything to go wrong. When things go wrong on files, my experience has been that they usually go wrong earlier on.