Brown Bag Seminar (DoSS, University of Toronto)

About the seminar series:

Welcome to our casual research seminar organized in the Department of Statistical Sciences at the University of Toronto. Our aim is to explore the diverse research conducted by our faculty, students, and postdocs. Talks usually last around 30 minutes, followed by discussions. We cover current research, overviews of emerging topics, and more. Some pizzas will be offered before the seminar around 12:20pm.

Seminar organizers: Austin Brown & Archer Gong Zhang & Piotr Zwiernik

Schedule of Talks for 2023/2024

Meetings are on Tuesdays, 12:30pm (30 min talk + discussion), room: 9014 (pizza will be served around 12:20pm)

To sign up to give a talk use our spreadsheet.

Upcoming talks

The Summer seminar has concluded, and we will resume in the Fall!

Past talks

Date Speaker Title Remarks
11/6/24 Nathan Kirk What can machine learning do for quasi-Monte Carlo methods? External speaker
4/6/24 Skip due to SSC    
28/5/24 Selvakkadunko Selvaratnam Applications of robust methods in spatial analysis Internal speaker
21/5/24 Michaela Drouillard and Inessa De Angelis Rebuilding Spotify’s audio features + Modelling harassment of female politicians Internal speakers
14/5/24 Yaoming Zhen Network-based neighborhood regression Internal speaker
7/5/24 Hengchao Chen Estimation theory for manifold data analysis Internal speaker
30/4/24 Piotr Zwiernik Entropic covariance models Internal speaker
23/4/24 (11:30am-12:30pm) Wenlong Mou On Bellman equations for continuous-time policy evaluation Internal speaker
16/4/24 Thibault Randrianarisoa Deep Gaussian Processes External speaker
9/4/24 Riccardo Passeggeri A universal robustification procedure External speaker invited by Nancy Reid
2/4/24 Ziteng Cheng Learning conditional distribution on continuous spaces Internal speaker
26/3/24 Cancelled    
19/3/24 Skipped due to job talk    
12/3/24 Sebastian Jaimungal An Intro to Mean Field Game Theory and Step-Wise Regret Internal speaker
5/3/24 Leonard Wong How finance, geometry and transport came together Internal speaker
27/2/24 Xin Bing Linear Discriminant Regularized Regression Internal speaker
20/2/24 Jun Young Park Spatial-extent inference for neuroimaging data Internal speaker
13/2/24 Skipped due to job talk    
6/2/24 Austin Brown Error analysis for a parallel Monte Carlo estimator from many short Markov chains Internal speaker
30/1/24 Break    
23/1/24 Skipped due to room conflict    
16/1/24 Marlon Moresco Uncertainty Propagation and Dynamic Robust Risk Measures Internal speaker
9/1/24 Yaoming Zhen Some examples of tensor decompositions Internal speaker
12/12/23 Paolo Onorati An Extension of the Unified Skew-Normal Family of Distributions and Application to Bayesian Binary Regression Visiting PDF from Rome
05/12/23 Xin Bing Optimal vintage factor analysis with deflation varimax Internal speaker
28/11/23 (starts 12:00pm) Rohan Alexander Evaluating the Decency and Consistency of Data Validation Tests Generated by LLMs ( Internal speaker, Information and DoSS
21/11/23 (starts 1:15pm) Josh Speagle Nested Sampling and its Potential Role in Computational Bayesian Education Internal speaker
14/11/23 Krishna Balasubramanian From Stability to Chaos: Analyzing Gradient Descent Dynamics in Quadratic Regression A guest from UC Davis
7/11/23 Liam Welsh Nash Equilibria in Greenhouse Gas Offset Credit Markets PhD student at DoSS
31/10/23 Emma Kroell Optimal Robust Reinsurance with Multiple Insurers PhD student at DoSS
24/10/23 Wenlong Mou A decorrelation method for general regression adjustment Internal speaker
17/10/23 Vedant Choudhary Generative modeling of financial time series data PhD student at DoSS
10/10/23 Luis Nieto-Barajas Spatio-temporal Pareto modelling of heavy-tail data Luis is a visiting professor from ITAM-Mexico
3/10/23 Qiang Sun From asymptotics to finite samples, and back again Internal speaker
25/9/23 (M) Jing Dong General Transformation for Consistent Online Approximation Algorithms External speaker invited by Qiang Sun
18/9/23 (M) Austin Brown How to utilize lower bounds on the convergence rates to tune Metropolis-Hastings algorithms to avoid poor empirical performance. Internal speaker
- We would like to acknowledge funding from the Department of Statistical Sciences.