Alumni

Thursday, January 14, 2021

Coding the future of finance

Chao Qian is preparing for the trading environment of the future. More than 60 percent of stock volume is attributed to algorithmic trading, but older generations of traders have never learned to code. “In the future, I expect that everyone will need to be able to code and be comfortable with AI and machine learning,” he affirmed. “Thanks to the Masters of Quantitative Finance (MQF) program, I will be ready.”

Internet users from Canadian rural and remote communities suffer from frequent Internet interruptions, which generally result from various network issues. The lack of human resources, expertise and support make these issues difficult to identifyand fix. Remote areas lack responsive and cost-effective operations or maintenance efforts.

Thursday, September 10, 2020 4:00 pm - 4:00 pm EDT (GMT -04:00)

Department seminar by Emma Jingfei Zhang, Miami University

Network Response Regression for Modeling Population of Networks with Covariates


Multiple-network data are fast emerging in recent years, where a separate network over a common set of nodes is measured for each individual subject, along with rich subject covariates information. Existing network analysis methods have primarily focused on modeling a single network, and are not directly applicable to multiple networks with subject covariates.

In this talk, we present a new network response regression model, where the observed networks are treated as matrix-valued responses, and the individual covariates as predictors. The new model characterizes the population-level connectivity pattern through a low-rank intercept matrix, and the parsimonious effects of subject covariates on the network through a sparse slope tensor. We formulate the parameter estimation as a non-convex optimization problem, and develop an efficient alternating gradient descent algorithm. We establish the non-asymptotic error bound for the actual estimator from our optimization algorithm. Built upon this error bound, we derive the strong consistency for network community recovery, as well as the edge selection consistency. We demonstrate the efficacy of our method through intensive simulations and two brain connectivity studies.

Join Zoom Meeting

Meeting ID: 844 283 6948
Passcode: 318995

Statistics and Actuarial Science PhD candidate Yilin Chen is one of two students to claim the 2020 Huawei Prize for Best Research paper by a Mathematics Graduate Student. The $4,000 prize affirms the value of Chen’s efforts to establish a framework for analyzing nonprobability survey samples in her winning paper: Doubly Robust Interference with Nonprobability Survey Samples.

Four graduate students were awarded a departmental research presentation award by the Department of Statistics and Actuarial Science, but that's not all they have in common. They all came to Waterloo because they knew of the excellence of the Statistics programs, research, and professors. Their backgrounds vary, as do their research areas, but they have all had a great experience.