Catching Bad Guys with Math

Thoughts from a retired cybersecurity AI leader.

recent posts

about

Biography

  • Cross-Platform Native Development when Design Matters: Less Expensive than You Think

    I have long been fascinated by cross-platform mobile native development. I will not re-hash the argument for and against mobile development using native technologies versus HTML5-centric technologies (including Cordova); these have been well-articulated by many others. I will say that when user experience and design matters, I am firmly a fan of native development.

    While developing the IBM Cognos Mobile client, which had to support multiple mobile platforms, one of the things we discovered is that it actually took fewer lines of code to develop native cross-platform applications, compared to using HTML5. We built both a web version of Cognos Mobile that supported multiple mobile devices, as well as multiple native versions of Cognos Mobile, so we were able to directly count lines of code and compare.

    Why was this surprising result the case? Because we wanted a native-like experience on each smartphone and tablet, we had to write a lot of device-specific code regardless. It didn’t matter whether it was custom CSS and JS or platform-specific Objective C or C++: it turned out to be just as much work to implement a device-specific design using HTML5.

    To be fair, this may not always be in the case. In particular, for Cognos Mobile:

    • Cognos Mobile is a business intelligence application that has a lot of client-side code that was not related to the interface, and
    • We developed in-house technology to translate non-UI code (like business logic) across mobile platforms, so we were able to re-use significant portions of the codebase.

    So it matters (of course) on your application. I would imagine the situation is dramatically different for (say) a video game versus a simple forms application. It also matters if you are able to easily reuse native code between platforms. While we developed in-house technology at IBM, for most of us, this means re-using C/C++/Objective C code.

    Regardless, what I learned from this experience is that when design matters, it’s probably less expensive than you think to stick with native approaches. And really, how else did you expect to get Super Monkey Ball? 😉

  • Another Book!

    I’m incredibly proud to have contributed to another book! This time it’s a collection of research articles of importance to future directions in Business Intelligence, directly from some amazing university researchers. The book covers a broad range of research topics, from BI modelling, to information extraction, to information visualization.

    Perspectives on BI (2013) Cover Small

    Perspectives on Business Intelligence. Raymond T. Ng, Patricia C. Arocena, Denilson Barbosa, Giuseppe Carenini, Luiz Gomes Jr., Stephan Jou, Rock Anthony Leung, Evangelos Milios, Renee J. Miller, John Mylopoulos, Rachel A. Pottinger, Frank Tompa and Eric Yu. Morgan and Claypool Publishers, 2013

    The book resulted from my amazing five years involved with the NSERC Business Intelligence Network, and I’m humbled to be on the cover and even be mentioned with these outstanding Canadian researchers. They are all world-class folks.

  • Online lecture series from IBM on analytics

    One of the things that kept me very busy while at IBM was education. Everyone knew that analytics was important, but surprisingly few people knew exactly what analytics was. (My boss and CTO of IBM Business Analytics, the wonderful Brenda Dietrich, had a great line: Do you remember when “analytics” was just called “math”?) There was a lack of understanding around the basics and foundation of analytics, and yet that knowledge was critical for (say) development teams to understand what was possible and how to truly incorporate analytics into their software.

    As a result, my colleague and friend Jean-Francois Puget and I set out to create a series of recorded lectures on the many areas of analytics, from basic descriptive statistics, to predictive models, to optimization, to machine learning, to parallel computation to image and video analytics. We did not want lectures on products or solutions: we wanted people to learn about the science of analytics. At the same time, we did not want things to get too deeply technical or mathematical: our audience, while technical, were developers who really just needed a high level understanding and could then follow up from there.

    Internally, our lecture series broke all kinds of attendance records, becoming one of the most well-attended talks ever within IBM. Clearly there was a need for this information assembled together in one place!

    I am thrilled that we are now able to make this video lecture series available, at no charge, to anyone who wants it. I’m biased, of course, but I think the content is awesome.

    You can read more about the lecture series on Jean-Francois’s blog and on AnalyticsZone. Kudos to the people back at IBM who are continuing to drive this effort while I am on sabbatical — you know who you are. 🙂