平特五不中

Biostatistics

Seminars - Fall 2023/Winter 2024

Some of the purposes of these sessions are: to promote biostatistics and biostatistical methodology; serve as a learning opportunity for both students and faculty; foster communication, collaboration, professionalism, career development.听The format will be varied: seminar presentations, journal club, discussions of work in progress, interact with guest speakers, etc.

Who is invited: biostatisticians and biostatisticians in training; all other hyphenated-, unhyphenated- and soon-to-be-statisticians with interests in applied statistics.

PLEASE NOTE: The Fall 2023/Winter 2024 Seminar Series will be held in hybrid format (in-person/Zoom) on Wednesdays from 3:30 to 4:30 PM, at the SPGH, 2001 平特五不中 College, Room 1140. Please refer to announcement titles below for details.

Date Speaker Title Recording

WINTER 2024

Jan 10, 2024 Anne-Laure Boulesteix (U of Munich) Towards reliable empirical evidence in methodological biostatistical research: recent developments and remaining challenges
Jan 17, 2024 Jingyi Jessica Li (UCLA)

ClusterDE: a post-clustering differential expression (DE) method robust to false-positive inflation caused by double dipping

Jan 24, 2024 Caleb Miles (Columbia U) Leveraging multi-study, multi-outcome data to improve external validity and efficiency of clinical trials for managing schizophrenia
Jan 31, 2024 Lucy Gao (UBC) Data thinning to avoid double dipping
Feb 7, 2024 Kelly Ramsay (York U) Nonparametric and Robust Inference for Covariance Operators
Feb 14, 2024 Xu Shi (U of Michigan) Double Negative Control Inference in Test-Negative Design Studies of Vaccine Effectiveness
Feb 21, 2024 (Rm 1201) Farouk Nathoo (U of Victoria) Neural Network Feature Extraction and Bayesian Group Sparse Multitask Regression for Imaging Genetics
Feb 28, 2024 Xin He (U of Chicago) Causal-TWAS: a new method for integrative analysis of expression QTLs and GWAS detects causal genes of complex traits
Mar 6, 2024 NO SEMINAR - MARCH BREAK
Mar 13, 2024 Lara Maleyeff (平特五不中) An Adaptive Enrichment Design Using Bayesian Model Averaging for the Identification of Tailoring Variables
Mar 20, 2024 Shariq Mohammed (Boston U) Quantifying Imaging Heterogeneity Via Density Functions with Aapplications in Brain and Pancreatic Cancer Imaging N/A
Mar 27, 2024 In-Person Eric Laber (Duke U) Reinforcement Learning for Respondent-Driven Sampling
Apr 3, 2024 Junwei Lu (Harvard) Knowledge Graph Embedding with ElectronicHealth Records Data

FALL 2023

Sept 6, 2023 Zhihua Su (U of Florida) Envelope-Based Partial Least Squares
Sept 13, 2023 Rui Duan (Harvard T.H. Chan SPH) Federated and Transfer Learning for Healthcare Data Integration
Sept 20, 2023 Michael Wallace听(U of Waterloo) To Find Out More, Press Play:听Creating Accessible Statistics Videos
Sept 27,听2023 Kevin Lin (U of Washington) Tilted-CCA: Quantifying Common and Distinct Information in听Multi-Modal Single-Cell Data via Matrix Factorization
Oct 4, 2023 Paul Gustafson (U of British Columbia) Statistical modelling of threats to validity: Inference, sensitivity analysis, or stuck in the middle with Bayes?
Oct 11, 2023 NO SEMINAR - FALL BREAK
Oct 18, 2023 Silvia Calderazzo听(DKFZ-Heildelberg) External information borrowing in clinical trial hypothesis testing: a frequentist-Bayesian view
Oct 25, 2023 Jessie X. Jeng (NC State U) Transfer learning with false negative control improves polygenic risk prediction
Nov 1, 2023 Aya Mitani (U of Toronto) Analysis of complex multilevel dental data with informative tooth loss
Nov 8, 2023
In-Person (JOINT EBOH/CORE SEMINAR)
Sebastien Haneuse (HSPH Harvard) Double sampling for informatively missing data in EHR-based comparative effectiveness research
Nov 15, 2023 Linbo Wang (U of Toronto) The synthetic instrument: From sparse association to sparse causation N/A
Nov听22, 2023 James Hanley (平特五不中) & Supratik Roy (U College Cork - Ireland)

Prob [Down Syndrome | Parents' Ages]: Statistical Sudoku and Analyses of Penrose's Data (J. Genetics 1933)

Nov 29, 2023
In-Person
Jessica Gronsbell (U of Toronto)
Life after machine learning in health and medicine

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