Our Audience

Our workshop attendees include postgraduate students, early career researchers, postdocs, undergraduates, cademic and non-academic staff, including those working in government or industry, and people working in library- and information-related roles. The learner profiles below provide examples of the diverse domain backgrounds, levels of computational experience, and career stages of our learners.

Bhadra Basepair

Bhadra Basepair received a B.Sc. in biochemistry five years ago. She has worked since then for Genes'R'Us, a biotech firm with labs in four countries. She completed a Java programming course as an undergraduate, and a bioinformatics course using R as a senior, but has no other training in programming.

Bhadra and her colleagues are developing fuzzy pattern-matching algorithms for finding similarities between DNA records in standard databases. To help other Genes'R'Us researchers, and to test her group's heuristics, Bhadra runs queries that people send to her by email. These queries arrive as the subject lines of messages, in their bodies, as attachments, or as links to pages on the company's firewalled file-sharing site. Bhadra saves them in files called search/a.in, search/b.in, and so on, then edits them to add query parameters. (She almost never accidentally overwrites one query with another…)

Once a week, Bhadra runs all of the queries that have piled up to create output files with matching names like search/a.out, then emails those back to whoever sent the original query. (She almost never sends someone the wrong file…) Once every couple of months, she creates another directory called something like may2013 and copies the input and output files she has accumulated into it. She would eventually like to do some statistical analysis on these, but hasn't been able to find time.

Bhandra is quite near-sighted, and uses high-contrast fonts on her computer and a portable text magnifier when reading paper documents.

Fan Fullerene

Fan Fullerene is a graduate student in chemistry who is working as a lab technician to help cover his family's living costs. His only programming experience is a general first-year introduction to computational science using Python.

Fan's supervisor is studying the production of fullerenes (also known as "buckyballs"). Each set of experiments involves testing a sample at 20 different temperatures and 15 different pressures. Using a machine borrowed from a collaborating lab, Fan can run all temperature and pressure combinations in one job, but must upload a parameter file to the machine to do this. The temperatures and pressures to be used vary from sample to sample, so Fan now has two dozen different parameter files, each containing 300 lines of control information that he fervently hopes is correct.

The machine sends these files to Fan once the experiment is completed. Fan analyses them by opening Excel, copying and pasting the data into a spreadsheet, then creating a chart using the chart wizard. He then saves the chart as a PNG file on the group's web site, along with the original data file.

Fan and his wife have had two children arrive while in graduate school, and his research progress is behind that of his peers. He is very nervous about finishing his PhD and suffers from undiagnosed depression.

Helen Helmet

Helen Helmet, a Ph.D. student in mechanical engineering, is currently doing a six-month internship at an engineering firm that makes carbon-fiber helmets for firefighters and other emergency service personnel. Her undergraduate courses included an introduction to scientific computing using MATLAB and a robotics course that used C. She learned some Python during a co-op placement between her junior and senior years, and used it again in a graduate course on finite elements.

Helen's task is to model the non-combustive thermal degradation (otherwise known as "melting") of candidate materials. Her starting point is a 4,000-line Python program that her supervisor wrote six years ago. She is currently trying to replace the mesh deformation functions with new ones that can handle non-uniform meshes. She sometimes writes, runs, and deletes sections of code three or four times before she is satisfied.

Helen tests her program by writing the total heat content of the mesh at each time step to a file. She then loads this data into a separate Python program to graph the percentage differences between these values and the ones produced by the original program for six sample problems. Right now, the difference is less than 5% for five test cases, but 30% for the sixth. Helen has added hundreds of print statements to the program to try to track down the bug, but still doesn't know where it is.

Helen has been diagnosed with coeliac disease.

Mehrdad Mapping

Mehrdad Mapping is a graduate student studying bark beetle infestations in the Canadian taiga. He has never taken a programming course, but used SAS in an undergraduate statistics course.

For the last three years, Mehrdad has spent six weeks every autumn counting beetle bores in pine trees in the Yukon and Alaska. He now has a spreadsheet with 5,000 entries, each recording the location and time of a measurement, the number of bores found, the moisture and acidity of the soil, and several other values. He also has two hundred text files containing 7,000 measurements that his supervisor made in the same regions in the 1970s and 1980s. His task now is to clean up and analyse both sets of measurements so that he can start to correlate changes in bark beetle distribution with changes in climate.

In high school, Mehrdad was diagnosed with Attention Deficit Disorder, which he learned to manage.