Andrew Conway
Where can a mathematics education lead you? In this series, mathematics
and statistics graduates from Australian universities write about theircareers, proving that the world is their oyster. The endeavours of Andrew
Conway, founder and former director of Silicon Genetics, eventually let
him to cross to the “Dilbert dark side.”A mathematician starts a company
When I did my PhD in Mathematics, Dilbert was just becoming popular. I was agreat fan, and strongly believed in the basic concept that the people who “do thework” — scientists and engineers — are theonly people who were really useful for society. My friends considered me as hooked inacademia — a PhD in Mathematics (working in combinatorics), and a second, recidivist, PhD in Electrical Engineering (working on helicopter navigation). Even when
I carefully considered my strengths (quantitative science, computers) and what fields
they could be used in and decided upon biochemistry, I returned to Stanford University
to do it. So how did I move to the Dilbertdark side of running a company and all that
implies?
There were of course many factors, but
the strongest was seeing how the science ofbiochemistry was being advanced by major
engineering efforts. As an example, in 1995
when I started in biochemistry, people involved with sequencing the human genome
were looking at the output of machines and— by eye — stating what they thought therelevant base would be. The problem is,this needs to be done several billion times.
Reliably. Accurately. Quickly. Preferably with quantified absolute confidence estimates. And, of course, cheaply — while
there was a lot of money available for thehuman genome project from governmentsaround the world, it dwindled to a matter ofcents when divided by several billion. Thiswas just one of the many engineering challenges involved in getting the sequences forthe human and other genomes.
For those who are not familiar with thehuman genome project, it was a multinational project to basically measure the complete DNA sequence (think chemical formula — for a molecule with billions ofatoms) of a human. Once obtained, it
was a useful resource for life scientists.
The human genome project was the mostwell known of such life science engineering
projects, but there were actually many others.
After working in this field for a while,
I realized that as an engineer I could domore for science than I ever could as a scientist. I also realized that for many typesof tools, it was more useful to make themin a company than in academia. One ideathat particularly interested me was making
software to analyse the results of a particular type of experiment called “Gene Expression”. Some colleagues had done a wonderful experiment, the first of its kind, where
they measured the activity (by some definition) of almost every gene in some yeast cells
while they grew and reproduced. This generated a table of numbers with six thousand
odd rows — each representing a gene — andsixteen columns — each representing a time
point. They came to me — as the local
mathematician — and explained what they
had done, and asked if I had any ideas what

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Andrew Conway

to do with the wonderful but daunting table of a hundred thousand numbers they hadgenerated. I looked around for data mining
tools to help, couldn’t find any sufficiently
specialised, grumbled a bit about how someone should do something about it, and thenrealised that someone should be me.
A few years later the company I founded,
Silicon Genetics had fifty odd people anda successful product in an expanding market. While my heart was still in development, I had to spend most of my time onother things — the Dilbert dark side. I evensometimes recognised myself in the butt ofvarious of the strips.
If you build a better mousetrap, the world
will not flock to your door. Most of theworld won’t know where your door is, won’t
realise that you even have a mousetrap, anddon’t really understand or care that yours
is better. Some uncooperative people won’t
even have a problem with mice!
Fortunately the way to sell software toscientists is to get other scientists to show
them how to use the software to solve theirproblems rather than showing pictures ofscantily clad smiling teenagers on televisionwaving the product around. But while oursales and marketing staff were scientists, westill had very similar issues to most businesses. We had to tell people we existed,
explain why our product could solve theirproblem (and why better than any otherproduct), teach people how to use it, support it, collect money, pay salaries, taxes,health insurance, etc., and generally keepa group of fifty odd people working together smoothly. I found new respect forgood salespeople, marketeers, accountants,lawyers, and administrators, all of whom
could make a big difference in how well acompany worked, and how happy the customers and employees could be. Of course,
I did also meet many incompetent ones afterwhom the Dilbert characters were modelled,
but there are many competent, hard working, dedicated ones as well.

I also gained a great respect for middle
management. The upper management getsthe glory (and the money), while the middle management has to deal with lots oflittle annoying problems. Also, your manager is always dumber than you. Or certainly appears that way in many contexts.
Suppose you have been working on something — in your field of speciality — for thepast week, and you need some decision. You
go to your manager, spend five minutes explaining to this less-of-a-specialist-in-yourarea-than-you what you have been thinking
about for a week, and expect a quick sensible
decision. Sometimes the manager will haveexperience, extra information, or even justthe authority to make that decision. Often
the manager will not completely understandthe problem, but still have to make a decision or suggestion.
Working out what a software productshould actually do is surprisingly difficult,and many experienced software developers
will tell you it is the main task (much ofthis is, of course, user interface). Of course
there are times when working out how to do
it is also important. Mathematics turned
out to be very important in this.
Data mining software like ours contains ofcourse a lot of statistics. Clearly one needs
to understand the statistics being used, but
it is actually important to understand thestatistics that is not being used as well —otherwise, how could you work out what
new approaches to use, or how to solve somenew problem? Even when you have worked
out what statistic you want to calculate,the logistics of how to calculate it often required a lot of mathematics to do efficiently
or without rounding problems. I only spenta tiny portion of my time really using nontrivial mathematics — but if I couldn’t doit, then I would have spent the rest of mytime doing the wrong thing!
I did find that I had to learn a lot morestatistics. As a mathematician I of course
knew a fair bit of statistics, but not nearlyenough. I’m not a statistician, but I came toMy brilliant career

the understanding that any sentence starting with “I’m not a statistician, but . . . ” isalmost certainly subtle nonsense. Statistics
is often considered unfashionable or boringas an undergraduate course — but when we
had user group meetings, the statistics talks
were always the most popular.
Eventually, Silicon Genetics became amature, successful business, and Agilent
Technologies purchased it. Now I am back243

to working on various scientific problems —none directly mathematics, but all using it.
To me, mathematics is a language. A
skilled mathematician is articulate in a language that forms the basis for much of thework that I do. It is important to be fluent
in order to think clearly and communicate
in these fields. I rarely think of myself as“using” mathematics — rather I just think
and talk in mathematics.

E-mail: andrewFromAMSGazetteReader@greatcactus.org