Day 1 - Professional Development (August 12, 2017)

World Trade and Convention Centre
1800 Argyle Street
Halifax, Nova Scotia NS B3J 2V9
08:00 - 08:30 AM (PDT)
(15:00 GMT)

Welcome (Chairs)

Sarah M Brown and Christan Grant (BPDM General Chairs)

8:30 - 10:00 AM (PDT)
(15:30 GMT)

Keynote: The Human Components of Machine Learning

Jenn Wortman Vaughan - Microsoft Research, New York City

Jenn Wortman

Abstract: Machine learning is usually viewed as an automated process. Data is fed to a learning algorithm that outputs a trained model which then makes predictions. In practice, however, it is common for every step of this process to rely on humans in the loop. Training data is often generated through human activity, either as passive observations of social processes or actively crowdsourced annotations. Humans prepare this data for use by the algorithm, engineer features, and tweak the algorithm’s parameters to fit their needs. And in many applications to medicine, criminal justice, and other critical domains, humans must interpret the learned model’s predictions to determine how to best make use of these predictions in their own decision making process. In this talk, I’ll argue for the importance of considering the humans in the loop. As one example, I’ll describe some of my own research on crowdsourcing and why it’s necessary to understand who the crowd is, what motivates them, and how they communicate. I’ll also discuss a new direction of research that I’m particularly excited about, studying how to make machine learning human-interpretable, and what human interpretability even means. Read More... »

Bio: Jenn Wortman Vaughan is a Senior Researcher at Microsoft Research, New York City. She studies algorithmic economics, machine learning, and social computing, often in the context of prediction markets, crowdsourcing, and other human-in-the-loop systems. Jenn came to MSR in 2012 from UCLA, where she was an assistant professor in the computer science department. She completed her Ph.D. at the University of Pennsylvania in 2009, and subsequently spent a year as a Computing Innovation Fellow at Harvard. She is the recipient of Penn's 2009 Rubinoff dissertation award for innovative applications of computer technology, a National Science Foundation CAREER award, a Presidential Early Career Award for Scientists and Engineers (PECASE), and a handful of best paper or best student paper awards. In her "spare" time, Jenn is involved in a variety of efforts to provide support for women in computer science; most notably, she co-founded the Annual Workshop for Women in Machine Learning, which has been held each year since 2006. Read More... »

10:00 - 10:30 AM (PDT)
(17:00 GMT)

Break

10:30 - 12:30 PM (PDT)
(17:30 GMT)

Mentoring Session

Panelist and Group mentoring where students are broken down by seniority. The mentor will introduce themselves and prompted with questions to facilitate. There are general research-focused and career-focused mentoring.

12:30 - 1:30 PM (PDT)
(19:30 GMT)

Lunch

1:30 - 3:00 PM (PDT)
(20:30 GMT)

Elevator Pitches

To be announced

3:00 - 3:30 PM (PDT)
(22:00 GMT)

Break

3:30 - 5:00 PM (PDT)
(22:30 GMT)

Mentoring Session

Small group matched by topic. The participants are instructed to have a research statement to introduce themselves to the group (including mentor). Mentors are instructed to focus on research feedback

5:00 - 7:30 PM (PDT)
(00:00 GMT -next day-)

BPDM Meet & Greet


Day 2 - Technical Learning and Application (August 13, 2017)

World Trade and Convention Centre
1800 Argyle Street
Halifax, Nova Scotia NS B3J 2V9
08:00 - 09:30 AM (PDT)
(15:00 GMT)

Keynote: Engineering Mindset for Research and Career Development

Tao Xie - University of Illinois Urbana Champaign

Tao Xie

Abstract: Developing a successful career, such as producing a high-impact research portfolio or agenda, is a common goal for researchers. Gaining the skill set to accomplish such goal is also very important for personal development. In this talk, I will use my own experiences on research development as examples to reflect on how engineering mindset can help researchers develop a successful career, such as selecting what research problems to tackle, executing a research project with risk management, managing a research group and engaging academic or industrial collaborators to produce research portfolios, and serving and leading research communities to together realize research visions. Read More... »

Bio: Tao Xie is an Associate Professor and Willett Faculty Scholar in the Department of Computer Science at the University of Illinois at Urbana-Champaign, USA. He worked as a visiting researcher at Microsoft Research. His research interests are in software engineering, focusing on software testing, program analysis, software analytics, software security, and educational software engineering. He received a 2016 Microsoft Research Outstanding Collaborators Award, a 2014 Google Faculty Research Award, 2008, 2009, and 2010 IBM Faculty Awards. He is an ACM Distinguished Speaker and an IEEE Computer Society Distinguished Visitor. He is an ACM Distinguished Scientist. He is the Program Committee Chair of the ACM Richard Tapia Celebration of Diversity in Computing, in 2017. Read More... »

09:30 - 10:00 AM (PDT)
(16:30 GMT)

Poster Set-up

10:00 - 12:00 PM (PDT)
(17:00 GMT)

"Ethics and Fairness" speakers from FATML

The past few years have seen growing recognition that machine learning raises novel challenges for ensuring non-discrimination, due process, and understandability in decision-making. In particular, policymakers, regulators, and advocates have expressed fears about the potentially discriminatory impact of machine learning, with many calling for further technical research into the dangers of inadvertently encoding bias into automated decisions. At the same time, there is increasing alarm that the complexity of machine learning may reduce the justification for consequential decisions to “the algorithm made me do it.” Researchers explore how to characterize and address these issues with computationally rigorous methods.

12:00 - 1:30 PM (PDT)
(19:00 GMT)

Lunch

1:30 - 3:00 PM (PDT)
(20:30 GMT)

In the Lab - Part 1

3:00 - 3:30 PM (PDT)
(22:00 GMT)

Break(optional)

3:30 - 5:00 PM (PDT)
(22:30 GMT)

In the Lab - Part 2

5:00 - 6:00 PM (PDT)
(00:00 GMT -next day-)

Wrap-Up/Close Out