DDDI’s 2024 Summer Hangouts program offers students the opportunity to participate in informal, hands-on tutorials led by our team of DDDI postdoctoral research fellows. These tutorials are open to students from all backgrounds and skill levels and cover various data science methods and topics. Hangouts will be held twice a week on Tuesdays and Thursdays, from June 11th through June 25th. A pizza lunch will be provided. All talks and tutorials will take place in the RDDSX space, conveniently located near the Collaborative Classroom in Van Pelt-Dietrich Library.

Our Hangouts series this year will explore causal inference, machine learning, and generative AI for scientific discovery across fields. Students are welcome to attend tutorials as best fit their time and interests, bur please RSVP using this form so we can get the right amount of pizza for everyone!



All tutorials will take place in the RDDSX space just outside the Collaborative Classroom in Van Pelt-Dietrich Library.

Note: To access the Van Pelt-Dietrich Library Center from Walnut Street, enter the gate at Van Pelt Walk. Walk straight ahead, then turn right to enter through the Rosengarten Undergraduate Study Center on the ground floor. The Van Pelt main entrance (by the button on the map below) will be open!

Location of the RDDSX space in Van Pelt Library

Live Stream

The Tuesday sessions live stream links can be found here.

The Thursday sessions live stream links can be found here.


Recordings of the sessions will be made available here.


Hangouts will run twice a week from noon to 1pm on Tuesdays and Thursdays, June 11th through June 25th. A pizza lunch will be provided.


Date Speaker Title + Description
Tuesday 6/11
noon – 1pm
Lyle Ungar +
Louis Hickman
Assessing Interpersonal Judgments using Explainable AI

We use deep learning models to predict ratings of the warmth, competence, and morality of people introducing themselves in short videos. By showing which multimodal features drive these predictions, we provide insight into first impression formation. Explaining such models and their predictions is important both for training workers and for evaluating computer-based assessments of candidates.

Thursday 6/13
noon – 1pm
Carlos Schmidt-Padilla Introduction to Causal Inference for Data Science

This tutorial will explain how causal inference can help reveal cause-and-effect relationships between variables or events, and explore its applications in technology, health sciences, and the social sciences. Slides here.

Tuesday 6/18
noon – 1pm
Emerson Arehart Time series forecasting with models and data

Science often involves forecasting future behavior based on past observations, but this can be very challenging when you have limited observations of a system. We will discuss methods for augmenting limited datasets with theoretical models to improve forecasting success. Slides here. Seeking paid undergrad RA! Contact Emerson for details.

Thursday 6/20
noon – 1pm
Sam Dillavou +
Kieran Murphy
Introduction to machine learning

What is machine learning, how is it used, what does it do well, and where does it go wrong?

Tuesday 6/25
noon – 1pm
Russell Richie Simulation as a tool for the (cognitive) modeler

How/when should the (cognitive) modeler use simulation? Special attention will be paid to parameter/model recovery simulations. Slides and a Google colab notebook.