Work experience
- Research Scientist in AI safety, DeepMind (2016-present)
- theoretical and experimental research on keeping advanced AI systems robust and beneficial
- Software Engineering Intern, Google (2015)
- developed and implemented machine learning algorithms for the Knowledge Graph
- Decision Support Engineering Intern, Google (2013)
- built statistical models of the impact of ads quality on click-through rate in R
- Teaching Fellow in Statistics, Harvard University (2012-2013)
- Quantitative Analyst Intern, D.E.Shaw & Co (2012)
- developed and tested risk modeling algorithms using statistical and numerical optimization methods in Python
- Summer Research Analyst in Computer Science, University of Toronto (2009)
- Teaching Assistant in Mathematics, University of Toronto (2007-2011)
Education
- Harvard University, PhD, Statistics / Machine Learning (2016)
- University of Toronto, MS, Statistics (2011)
- University of Toronto, Honors BS with High Distinction (GPA 3.76/4.00), Statistics / Mathematics (2010)
Service
- CIFAR International Scientific Advisory Committee for Pan-Canadian AI Strategy (2018-present)
- NIPS conference reviewer, top 30% of reviewers (2018)
- ICML conference reviewer (2018)
- AI, Ethics & Society (AIES) conference reviewer (2018)
- Asilomar Beneficial AI (BAI) conference co-organizer (2017)
- Women in ML (WiML) workshop reviewer (2016-2017)
- Puerto Rico Future of AI Conference co-organizer (2015)
- Deputy Leader of the Canadian IMO team (2010)
Competitions
- IMO Silver medal, International Mathematical Olympiad (2006)
- Elizabeth Lowell Putnam Prize (2008)
- highest ranking woman in the Putnam mathematics competition in North America
- University of Toronto Putnam mathematics competition team (2006-2009)
- 3-person team consistently ranked in top 10 in North America
- ACM programming competition team (2007-2008)
- 3-person team competed on the regional level in North America
Advertisements
