Join us for our Spring Biostatistics and Epidemiology Career Panel! We’re thrilled to bring you a career panel featuring leading figures in biostatistics and epidemiology. Dive into their career stories, explore their research, and their unique perspectives on the field.
Date: Wednesday, April 8th
Time: 12:00pm - 1:30pm (This event will begin at 12pm sharp)
Be part of the conversation! On Wednesday, April 1, the CTML Seminar Series welcomes CTML GSR Kirsten Landsiedel, who will present her talk, “Longitudinal Targeted Maximum Likelihood Estimation for Two-Stage Designs.” The seminar will take place at 12:00 PM in Berkeley Way West, 5th Floor, Room 5401.
Abstract: We consider longitudinal survival studies where individuals are followed until failure or random end-of-study and some are lost to follow-up (LTFU) before either event is recorded. When LTFU is...
Don't miss the next session of the CTML Seminar Series on Wednesday, March 18th, where CTML Postdoc Marie Charpignon will discuss "Emulation of the ACORN RCT Using Observational Data from a Large Integrated Health System in California." This talk will take place from 12:00PM-1:00PM at Berkeley Way West, 5th Floor, Room 5401.
Abstract: This study employs a target trial emulation framework to replicate the design of the ACORN randomized controlled trial (RCT) conducted in 2021–2022 at Vanderbilt...
We’re excited to host two insightful talks next week as part of our CTML Seminar Series on Wednesday, March 11th from 12:00PM-1:00PM at Berkeley Way West, 5th Floor, Room 5401.
Tianyue Zhou, CTML Graduate Student Researcher Title: "Generating High-Quality Real-World Evidence at Scale with a Causal Roadmap Copilot" Abstract: In this talk, I will discuss why we are at an unprecedented moment for generating rigorous real-world evidence at scale. I will then introduce our vision for what we aim...
Hear from a leading expert in the field! Join us next Wednesday, February 25th for a seminar with Andrew Mertens, Ph.D., presenting "Spatial Superlearner for Subnational Micronutrient Deficiency Prediction and Proxy Identification in Data-sparse Settings." The seminar will take place at 12:00 PM in Berkeley Way West, 5th Floor, Room 5401. The speaker will present remotely.
Abstract: Vitamin and mineral deficiencies remain a major contributor to anemia, impaired child development, adverse pregnancy...
Design better simulations—and understand why they matter! Join CTML on Wednesday, February 18th for “Simulations Done Right,” a workshop with Alejandro Schuler, CTML Faculty. This workshop will take place at 12:00 PM in Berkeley Way West, 5th Floor, Room 5401.
Abstract: Simulation studies are ubiquitous in statistical research and practice, but students are rarely given formal training in how to design them. Since they are not glamorous, many...
This week’s seminar brings another thought-provoking discussion! Join us on February 11th to hear from CTML GSR Mingxun (Michael) Wang presenting his talk on "Highly Adaptive Principal Component Regression: Fast HAL/HAR via Outcome-Blind Kernel PCA." The seminar will take place at 12:00 PM in Berkeley Way West, 5th Floor, Room 5401.
Abstract: The Highly Adaptive Lasso (HAL) has strong rate guarantees under minimal smoothness assumptions, but can be computationally prohibitive in moderate to high dimensions due to its...
Join us for our Spring Biostatistics Career Panel! We’re thrilled to bring you a career panel featuring leading figures in biostatistics. Dive into their career stories, explore their research, and their unique perspectives on the field. This is your chance to engage in lively discussions and discover the many exciting paths within biostatistics!
Date: Wednesday, March 19th
Time: 12:00pm - 1:30pm (This event will begin at 12pm sharp)
We’re pleased to welcome Toru Shirakawa, CTML GSR and CPH PhD Student, to next week’s CTML Seminar on Wednesday, February 4th, presenting on “A Conformalized Inference on Unobservable Variables.” The seminar will take place at 12:00 PM in Berkeley Way West, 5th Floor, Room 5401.
Abstract: Quantifying uncertainty in predicted unobservable variables is a critical area of research in statistics, artificial intelligence, and empirical science. Most scientific studies assume a specific structure involving unobservable...
