Upcoming
- June 25, 2026, 1:00 to 2:30 pm Eastern Time. Rahul Satija, New York Genome Center. Integrated analysis of single-cell data across technologies, patients, and perturbations. Registration link.
AI Tools for Data Science & Stats series
UPCOMING WEBINARS
[07] The Data Science Team of One
AI tools can act as a force multiplier for a solo data scientist. The key is being strategic about where you sit in the workflow and what actually gets multiplied. I'll show you what's worked for me.
PAST WEBINARS
[01] The AI Train Has Not Left the Station. (But Get On Board.)
The hardest part of using AI tools is taking the first step. Let's cut through the noise and get you started.
[02] Claude Code, Cowork, & Chat
If I had to pick just one AI tool, it would be Claude. But Claude is actually 3-in-1 - so let's walk through all of them.
[03] Claude Cowork for Data Scientists
Claude Cowork works way better when tuned to data science. I'll hand over the files, prompts, and architecture - and show you how to use them.
[04] Contextapalooza
Like a T-shirt cannon at a sports event, except everyone gets something useful. My favorite context files + the prompts to get AI to tune each one to your situation.
[05] Claude Code for Data Scientists
Claude Code runs a lot better once it knows your project structure, analysis workflow, and what you're trying to do. I'll show you my setup for building analysis, data science, and machine learning projects from scratch.
[06] An AI Setup for Data Science Consulting
Clients are asking for AI tools - and some are openly wondering if they still need you. I'll show you one Goldilocks setup where they get the benefit of your expertise and the ability to interrogate their data on demand.
Archive
- May 28, 2026, 2:00 to 3:30 pm Eastern Time. Hadley Wickham, Posit. y code when ai?
- Apr 23, 2026, 2:00 to 3:30 pm Eastern Time. Anru Zhang, Duke University. When Does Synthetic Data Help Imbalanced Learning?
- Mar 26, 2026, 1:00 to 2:30 pm Eastern Time. Lihua Lei, Stanford University. Compound Selection Decisions: An Almost SURE Approach.
- Feb 26, 2026, 1:00 to 2:30 pm Eastern Time. Zhuoran Yang, Yale University. Unlocking Out-of-Distribution Generalization in Transformers via Recursive Latent Space.
- Jan 28, 2026, 2-3:30pm Eastern Time. Glen Wright Colopy. The AI Train Has Not Left the Station. But You Do Need to Step on Board.
- April 27, 2023, 1-2:30pm Eastern Time: Qiqi Deng, "A Brief introduction for drug development and how biostatistician can contribute".
- October 27th,, 2021, 1-2:30pm Eastern Time: Nesime Tatbul, Topic: anomaly detection with time series data
- August 24th, 2021, 2-3:30pm Eastern Time: Jean Feng, Topic: Deep Learning
- September, 28th, 2021, 1-2:30pm Eastern Time: Byron Jaeger, Topic: Github
- June 23, 2021, 1-2:30pm Eastern Time: Andreas Ziegler (video), Topic: Calibration in Machine Learning. (Passcode: Ja646%VA)
- May 25, 2021, 1-2:30pm Eastern Time: Polo Chau (Video), Topic: Interpretable AI
- April 29, 2021, 2-3:30pm Eastern Time: Brian Lee Yung Rowe (Video), Topic: Automation and Reproducibility.
- March 31, 2021. 2-3:30PM Eastern time: Naomi Brownstein (Video), Topic: Clustering and Clusterability.
- February 25th 2021, 2-3:30pm Eastern Time: Beth Wolf (Video): Topic: Variable Importance in ML.
- January 2021 webinar, Dr. Helen Zhang (Video), Topic: Data Science teams.