The first event of the 2021/2022 BIRS Career & Innovation Hub (CIH) is a 3-day data science focused event that will take place online from November 12 – 14, 2021. The event is presented in partnership with the PIMS M2PI program and Hackhub.

The event will provide students an opportunity to interact with employers and industry leaders through virtual programs, prove their skill in a data science focused hackathon, and attend workshops with a practical focus on today’s data science job market . This event is free to attend but registration is required.

This event has concluded but many of the presentations were recorded.

Schedule

(all times MT – Calgary)

Friday, November 12

10AM – 1PM Data Science in Action

1:30PM – 3:00PM Writing Resumes that Get Read

Saturday, November 13

10AM – 10:10AM Hackathon Introduction

10:10AM – 10:30AM Keynote Address from Dr. Raymond Ng

10:30AM Hackathon begins

1:00PM – 1:45PM Guest Speaker Megan Dewar

2:00PM – 2:45PM Guest Speaker Frank Maurer

3:00PM – 5:05PM Job Recruiting and Information Session

Sunday, November 14

12PM – 2PM Hackathon Final Presentations and Awards Announcement

Programs

Data Science in Action

Data is everywhere. It is being collected when we browse the web, it is an important and expanding part of modern journalism, and companies want to mine their databases hoping to find a competitive advantage.

In this workshop, we welcome leaders from academia and industry to discuss the impact of the data science revolution on their field. We will discuss how diverse domains, such as cryptography and political analysis, have used this abundance of data to create new insights. By seeing data science in action, students, researchers, and industry people alike will learn about exciting new initiatives in their field and potential for new collaborations across fields.

Trevor Campbell (10-10:30)
Jennifer Bodnarchuk (10:30-11)
Robyn Ritchie (11-11:30)
William Spat (11:30-12)
Benoit Hamelin (12-12:30)
Nur Zincir-Heywood (12:30-1)

Speakers:

Jennifer Bodnarchuk

Senior Data Scientist, City of Winnipeg

Jen is the Senior Data Scientist for the City of Winnipeg and was the second data scientist hired at the City, in 2019. Prior to working at the City, she spent 12 years working as a statistical analyst and senior planning analyst at Manitoba Liquor & Lotteries. She earned a PhD from the Department of Psychology at the U of M in 2005 and a Master of Data Science from Northwestern University in Chicago in 2016.

The City of Winnipeg’s Data Innovation: A Diversity Dashboard

Jennifer and colleagues from the City of Winnipeg were recently selected as the Canadian participants in a City Incubator program designed to support data innovations in cities around the world. The City of Winnipeg is using their data to develop a Diversity Dashboard. Jennifer will share the latest progress, lessons learned so far, and information about how data science works in city governments.

Nur Zincir-Heywood

Professor, Dalhousie University

Dr. Nur Zincir-Heywood is a University Research Professor and a Professor of Computer Science at Dalhousie University. Her research interests include machine learning for cyber security, network and service analysis. She has published over 200 fully reviewed papers and has been a recipient of multiple best paper awards. She serves as an Associate Editor of the IEEE Transactions on Network and Service Management and Wiley International Journal of Network Management. She also promotes information communication technologies to wider audiences as a tech columnist for CBC Information Morning and a board member on CS-Can/INFO-Can.

Abstract

Advances in Artificial Intelligence (AI) and Data science technologies are not only the fastest growing areas but also provide endless possibilities in many different disciplines including networks, services and cyber security. Any user who has a tablet or smartphone can tangibly experience advances in AI via their social media apps, cameras and digital assistants. The goal of this talk is to share some practical insights while exploring the opportunities and challenges at the intersection of AI and cyber security from management to monitoring to analysis of data in the era of highly connected devices.

William Spat

CEO, IOTO International

Dr William Spat is founder and CEO of IOTO International Inc. a Vancouver-based research & development firm founded in 2003. William’s recent work focuses on developing performance metrics for politics. Starting with big data and natural language processing tools, and inspired by remarkable progress in sports analytics, Dr Spat’s goal is to productize standard quantitative measurements for political performance across western democracies.

Dr Spat holds a PHD in philosophy from University of Edinburgh, where he studied as Vans Dunlop Scholar after being awarded an honours degree in philosophy from University of British Columbia. Time spent as pensionnaire étranger at the École Normale Supérieure in Paris in 1989 – the year that Nicolas de Condorcet’s remains were transferred to the Pantheon – may have sparked Dr Spat’s interest in applying mathematics to democracies. Or not: while the Marquis de Condorcet is of course known today for his methods, paradoxes, and theorems associated with voting, Dr Spat is more interested in the application of quantificational methods to political performance in periods between elections. And so, the topic of his talk today is:

Measuring political performance between elections: applying analytics to a bare-knuckle contact sport

Philosophers really like to start with Aristotle, so I’m going to start with a quote from Aristotle’s Nicomachean Ethics, Book I. Then I’m going to spend twenty minutes talking about four things: 1) political things that can be measured; 2) where we can look for precision in politics; 3) what tools we use to get the same result every time we measure political things; and 4) the importance of good visualization techniques in making the abstruse more easily understood. This last is a key skill in showing the value of your theoretical capabilities to broad and commercial audiences.

Robyn Ritchie

PhD student, Simon Fraser University

Robyn Ritchie completed her undergraduate and master’s degree in Statistics at the University of Manitoba under the supervision of Dr. Alexandre Leblanc. She has a love for sports and is continuing her education at Simon Fraser University pursuing a PhD in Statistics where she hopes to revolutionize the game of curling with sports analytics under the supervision of Dr. Thomas Loughin and Dr. Alexandre Leblanc. Robyn has worked with soccer analytics throughout her masters where she worked on semiparametric estimation of scoring rates for various teams in the English Premier League, as well as comparing home and away performances and scoring patterns throughout additional time.

A discussion of various advances in sports analytics

Statistics has revolutionized many different aspects of sports. We can track player movements, predict game outcomes and even make suggestions, backed by strong statistical evidence, advising certain plays and lineups to improve individual and team performances. The development of sports analytics has given rise to new possibilities and elevated the level of competition in many sports. We review the different types of data available in sports analytics and discuss the collection process of “free” data that is available online using R and Python. We look at how sports analytics can be used to study and improve various aspects in sports such as soccer, football and curling.

Trevor Campbell

Assistant professor, UBC Department of Statistics

Trevor Campbell is an assistant professor in the Department of Statistics at the University of British Columbia. His research focuses on automated, scalable Bayesian inference algorithms, Bayesian nonparametrics, streaming data, and Bayesian theory. He was previously a postdoctoral associate advised by Tamara Broderick in the Computer Science and Artificial Intelligence Laboratory (CSAIL) and Institute for Data, Systems, and Society (IDSS) at MIT, a Ph.D. candidate under Jonathan How in the Laboratory for Information and Decision Systems (LIDS) at MIT, and before that he was in the Engineering Science program at the University of Toronto.

Abstract

Modern models in statistical machine learning continue to grow in complexity, and traditional inference algorithms struggle to explore their full uncertainty landscape. Parallel tempering (PT) is an algorithm that has recently re-emerged as a candidate to address this challenge. PT operates by creating a path of models from a simple “reference model” to the desired complex target model, and then using the simpler models along the path to help sample from the target complex model. The performance of PT depends heavily on the particular path of models one chooses. Past work on PT used only simple linear paths; in this talk I’ll show that this path performs poorly in common applications. To address this issue, I’ll present an extension of the PT framework to general families of paths, formulate the choice of path as an optimization problem that admits tractable gradient estimates, and present a flexible new family of spline interpolation paths for use in practice. Theoretical and empirical results will demonstrate that the proposed methodology breaks previously-established upper performance limits for traditional paths.

Benoit Hamelin

Researcher, Government of Canada

After completing a masters in computer science at Université de Sherbrooke and a PhD in medical imaging at École Polytechnique de Montréal, I have taken a sharp turn into cybersecurity. I have played roles of software developer, manager and executive in a few Montreal start-ups, until I slid back into a research role through a joint research project between Element AI and Communications Security Establishment (CSE). I am taking the fight to the windmills in a bid to solve cyber defense, and I believe the best partner to achieve this is the Canadian Centre for Cyber Security, part of CSE. I work now in its fundamental research laboratory, the Tutte Institute for Mathematics and Computing.

Abstract

While current cyber defense processes work well for addressing cyber threats, they are expensive, labor-intensive, and strain against human cognitive limits. Despite years of data science research over cyber security applications, both in academia and industry, the field has yet to revolution the practice of cyber defense. In this talk, I show how well-established data embedding techniques can provide visualization tools that stand to support and enhance the abilities of cyber analysts in detecting intrusions and investigating incidents. I also discuss the importance of simplicity and interpretability of data science practices to cyber defense organizations, and suggest approaches for research and innovation to impact their evolution.

Resume Writing Workshop

Employers receive hundreds of applications for a job posting in today’s market. Knowing how to create a professional resume that catches the reader’s attention while highlighting relevant skills is more important than ever. This webinar will discuss how to increase your chances of securing an interview by creating a targeted resume and properly responding to the job posting.

Matthew Geddes

Career Specialist, University of Calgary

Matthew is a Career Specialist at the University of Calgary. He supports graduate students with achieving their career aspirations through collaborative individual consultations and interactive workshops. As a lifelong learner, he is passionate about bringing new resources and research-based practices to support students, whether it is writing a resume that gets read, standing out in the interview or clarifying ones career goals.

Hackathon

The best way to learn is by doing. Join us for this data science hackathon. Participants will be asked to develop a visualization dashboard using IOTO’s Goverlytics API. The Goverlytics API collects data on the legislative process in Canada and the US: who are the legislators, which bills are being discussed, what is going on in committees. By summarizing and visualizing this data, we hope to better measure legislative performance.

Teams will be judged on the visual quality of their dashboard, on the metrics being displayed, and on the insights generated by the dashboard.

Keynote address from Dr. Raymond Ng

UBC Computer Science Professor | PROOF (Prevention of Organ Failure) Centre Chief Informatics Officer | Canada Research Chair in Data Science and Analytics

Raymond’s main research area for the past two decades is on data mining, with a specific focus on health informatics and text mining. He has published over 210 peer-reviewed publications on data clustering, outlier detection, OLAP processing, health informatics and text mining. He is the recipient of two best paper awards – from the 2001 ACM SIGKDD conference, the premier data mining conference in the world, and the 2005 ACM SIGMOD conference, one of the top database conferences worldwide. For the past decade, he has co-led several large-scale genomic projects funded by Genome Canada, Genome BC and industrial collaborators. Since the inception of the PROOF Centre of Excellence, which focuses on biomarker development for end-stage organ failures, he has held the position of the Chief Informatics Officer of the Centre. From 2009 to 2014, Dr. Ng was the associate director of the NSERC-funded strategic network on business intelligence.

The DSSG program has been offered for the past five years to selected undergraduate and graduate students. In the first half of this presentation, I will summarize the three projects in 2021 – namely, biodiversity vs city planning, housing affordability and traffic camera analysis. In the second half, I will make a few general observations on the ingredients of a successful data science experiential learning program.

hackhub registration

Guest Speakers

Megan Dewar

Megan Dewar is the Head of the Tutte Institute for Mathematics and Computing (TIMC), a Canadian Government research institute whose mission is to deliver fundamental research results in mathematics and computer science that address the most important scientific challenges facing the security and intelligence community. The majority of TIMC’s research is focussed in the fields of cryptography and data science. Megan joined TIMC as a researcher a decade ago, and has taken on several leadership roles before becoming Head in 2018. Megan completed her PhD in Discrete Mathematics at Carleton University in 2010. She is a co-author of Ordering Block Designs: Gray codes, universal cycles and configuration orderings, a book based on her thesis work. Her previous masters and undergraduate studies — completed at Dalhousie University — focused on graph theory and game theory.

Frank Maurer

Frank Maurer is a professor of Computer Science at the University of Calgary. His research focuses on engineering immersive analytics applications, combining machine learning, extended reality, and agile software development. He currently serves as the Associate Dean, Innovation and Strategic Partnerships at the Faculty of Science. Dr. Maurer is a co-founder and CTO of VizworX, a Calgary-based custom solutions company focusing on AR/VR, machine learning and mobile/web technologies

Job Recruiting and Information Session

Thinking about what opportunities are available that will use your mathematical science skills? During this session, educators and employers will be available to answer questions regarding the job opportunities that will be circulated to participants in advance. This is a chance for student participants to ask about the career opportunities available for people who are highly skilled in the mathematical sciences. This session will include information about job and internship position which are accepting applications now!