Two U of T Department of Statistical Sciences researchers are among 72 Vector Institute Faculty Affiliates that make up the 2020 cohort. Vianey Leos Barajas and Radu Craiu are joining faculty members Jessica Gronsbell, Jeffrey Rosenthal, and Linbo Wang in receiving this honour.
The appointment of our faculty not only acknowledges and celebrates our faculty’s outstanding research but also recognizes the important role of statistical methods and principles in advancing machine learning. Statistical techniques are essential in building strong machine learning models, understanding the biases and limitations of an algorithm, and interpreting data output correctly.
Since the beginning of the program, Vector Faculty Affiliates have played a key role in developing, growing and diversifying knowledge and research within the AI community, including both researchers and industry.
“We are very excited to be part of a dynamic and effective research environment that spurs collaborations with industry partners and promotes innovation,” says Radu Craiu, chair of the department and one of this year’s Vector Faculty Affiliates.
Faculty Affiliates are appointed for two years with new nominations considered annually. Affiliates are selected based on recommendations from a committee of Vector Faculty who hold appointments in a variety of institutions.
Congratulations to all 2020 Vector Institute Faculty Affiliates, and meet the bright minds who hold this distinction at our department:
Vianey Leos Barajas
Dr. Vianey Leos Barajas is an assistant professor at the University of Toronto. She is cross-appointed between statistical sciences and the School of the Environment. She received her PhD in statistics from Iowa State University in 2019 and was a postdoctoral researcher at North Carolina State University from 2019-2020. The focus of her research is on Markov-switching processes, time series analysis and Bayesian inference applied to sensor and video data collected from animals and the environment.
The amount of data now being collected via sensors and cameras from animals and the environment is outpacing the tools that exist to analyze it. As a Faculty Affiliate at the Vector Institute, I hope to spur collaborations between researchers across multiple fields (computer science, ecology, environment, statistics, and more), to develop methods that can answer important questions like how and why animals move in the way they do (e.g. sharks, eagles, sheep), and how our environment is changing and adapting under rapid climate change. Statistics and machine learning for conservation is coming to the Vector Institute!
Dr. Radu V. Craiu is professor and chair of statistical sciences at the University of Toronto. He studied mathematics at the University of Bucharest (BS 1995, MS 1996) and received a PhD from the Department of Statistics at The University of Chicago in 2001. His main research interests are in computational methods in statistics, especially, Markov chain Monte Carlo algorithms (MCMC), Bayesian inference, copula models, model selection procedures and statistical genetics. He is currently associate editor for the Harvard Data Science Review, Journal of Computational and Graphical Statistics, The Canadian Journal of Statistics and STAT - The ISI's Journal for the Rapid Dissemination of Statistics Research. He received the 2016 CRM-SSC prize, is a Fellow of the Institute of Mathematical Statistics and an elected member of the International Statistical Institute.
I am excited to be part of a dynamic and effective research environment that spurs collaborations with industry partners and promotes innovation. It also sends a useful message to the world: just like machine learning research has generated a number of rich directions for statistical exploration, statistical methods and principles are instrumental to machine learning developments. I am looking forward to learning about and contributing to new research at the intersection of machine learning and statistics.
Dr. Jesse Gronsbell is an assistant professor at the University of Toronto in the Department of Statistical Sciences. She received her PhD in biostatistics under the supervision of Dr. Tianxi Cai at Harvard University and was a postdoctoral researcher with Dr. Lu Tian at Stanford University. She also spent a few years as a data scientist at Alphabet's Verily Life Sciences prior to joining U of T. The focus of her research is on the development of statistical learning methods for modern health data sets such as electronic health records and mobile health data.
There is a pressing need for statistical and machine learning methods that can accommodate massive amounts of complex health data. The Vector Institute fosters and encourages the collaborations and innovation necessary to bridge statistics and machine learning in health-related applications. I am eager to learn from and engage in the numerous AI in healthcare efforts at Vector.
Jeffrey Rosenthal's primary research area is the theoretical analysis of Markov chain Monte Carlo algorithms, which can be viewed as a "cousin" of machine learning. He is always looking for new interdisciplinary directions, and wants tomove towards the interface of statistics and machine learning. He recently published (with his PhD student Cedric Beaulac) a paper in the journal Applied Artificial Intelligence, and gave a research talk to the Vector Institute in November.
I am very pleased to be named a Vector Institute Faculty Affiliate. The Vector Institute provides a very welcoming environment for leading-edge machine learning research, and will help me to move further in that direction. I look forward to many more collaborations with them in the years ahead.
Linbo Wang is an assistant professor in the Department of Statistical Sciences, University of Toronto. He is also an affiliate assistant professor in the Department of Statistics, University of Washington. Prior to these roles, he was a postdoc at Harvard T.H. Chan School of Public Health. He obtained his Ph.D. from the University of Washington. His research interest is centered around causality and its interaction with statistics and machine learning.
In recent years, artificial intelligence researchers have actively engaged in developing tools that help understand the causal relationships in data. Researchers at Vector have been at the forefront of these efforts. I am excited to join this dynamic team with diverse backgrounds, and look forward to fruitful collaborations leading to new AI solutions that not only know how, but also know why.