Collaborating Authors

Robotics and AI as a Motivator for the Attraction and Retention of Computer Science Undergraduates in Canada

AAAI Conferences

Since the burst of the dot-com bubble in 2000, computer science has seen a significant decrease in enrollment in universities across North America. While this has been well-publicized in the media in the United States, Canada's numbers in this regard have been significantly worse. Within Canada, however, the Department of Computer Science at the University of Manitoba has been relatively fortunate: while a noticeable decrease has occurred, it is statistically much less than has occurred across Canada and the U.S. There are a number of reasons for this, one of which is the use of artificial intelligence (AI), and robotics in particular, as a tool for student recruitment and retention. In this paper, we examine enrollment trends of our university compared to the rest of the continent, discuss some of the reasons behind these trends, and describe how we use AI, and robotics in particular, as tools to attract and retain computer science students.

Protecting the Power Grid, and Finding Bias in Student Evaluations

Communications of the ACM

Over the past few years, a troubling hacking trend has emerged, characterized by serious intrusions into electric power infrastructures. Most of this activity has been system-mapping across several countries, ranging from the U.S. to Ireland, and on to Switzerland and Turkey. There is evidence of actual attacks, notably in Ukraine's Ivano-Frankivsk region in December 2015, when power was knocked out. The prime suspects in these intrusions appear to be Russia-friendly hacker groups known variously as "Dragonfly" and "Energetic Bear," among other names. The attention to power grids seems to have emerged hand in hand with a growing hacker interest in the broader realm of automated system controls, commonly called SCADA (supervisory control and data acquisition), whose uses are increasing across the spectrum of activities essential to a modern society's ability to function smoothly.

How To Start A Data Science Career As An Undergrad


How do I choose an internship that prepares me for a data science career as an undergraduate student? How do I choose an internship that prepares me for a data science career as an undergraduate student? I think the answer to this question really depends on the company/role/industry combination, but if your background resembles my own, I'll take a stab at the question. To start, I strongly believe that completing an internship is more valuable than an "ML related summer research project," unless that research is done in the context of a respected laboratory at your university, and you have the explicit goal of publishing a paper that will help you gain admission to top graduate programs in machine learning. With that said, while internship roles in data science at tech companies are plentiful (see, for example, What companies have data science internships for undergraduates?),

Learning to be a mentor


My first attempt came during the second year of my Ph.D. I was still trying to learn some lab techniques myself, and I wasn't sure whether I would be able to invest the time needed to train a student. But I was interested in developing my mentoring skills, and my adviser encouraged me to give it a try. The student required hand-holding and close monitoring, and it quickly became evident that the collaboration wasn't working. After similar false starts with a few more students, I ended up being reluctant to work with undergraduate researchers at all--until a new student helped me realize what is required to mentor undergraduates, and the rewards it can bring.