CV | Victoria Krakovna

Research Scientist in AI safety, DeepMind (2016-present)
- theoretical and experimental research on designing good incentives for AI systems
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)

Harvard University, PhD, Statistics (2016)
University of Toronto, MS, Statistics (2011)
University of Toronto, Honors BS with High Distinction (GPA 3.76/4.00), Statistics / Mathematics (2010)

NeurIPS conference reviewer (2020)
JMLR reviewer (2020)
ICML conference reviewer (2019)
ICLR Safe ML workshop co-organizer (2019)
Beneficial AGI (BAGI) conference co-organizer and technical safety program chair (2019)
CIFAR International Scientific Advisory Committee for Pan-Canadian AI Strategy (2018)
NeurIPS conference reviewer, top 30% of reviewers (2018)
ICML conference reviewer (2018)
AI, Ethics & Society (AIES) conference reviewer (2018)
Beneficial AI (BAI) conference co-organizer (2017)
Women in ML (WiML) workshop reviewer (2016-2017)
Future of AI conference co-organizer (2015)
Canadian IMO team deputy leader (2010)

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