Data Science Summer Hangouts

Data Science Summer Hangouts

Each summer, DDDI hosts a data science workshop for undergraduates involved in research. Penn faculty members from both the natural and social sciences present work that applies data science to their research. Undergraduates also attend tutorials led by our postdocs and graduate students on data science methods.  This article by Omnia highlights some of the student experiences from the 2019 hangouts. Here are some themes we've explored in the past: 

  • 2025 - Machine learning, statistics, optimization, and generative AI for scientific discovery across fields.
  • 2024 - Causal inference, machine learning, and generative AI for scientific discovery across fields.
  • 2023 - A “choose your own adventure” through data science. 

Hangouts will be held twice a week. 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. Below you'll find our past hangouts with recordings. 

Upcoming Summer Hangouts

No events are currently scheduled.
Please check back soon.

Past Summer Hangouts

Summer Hangouts: Supranta S. Boruah

Diffusion Models in Action: From Toy Problems to Dark Matter Maps

RDDSX space outside the Collaborative Classroom
Van Pelt-Dietrich Library

Diffusion models have emerged as powerful tools in generative modeling, achieving impressive results across different domains. In this tutorial, we’ll explore how these models can be adapted to tackle a cosmological challenge: mapping dark matter in the Universe.

Summer Hangouts: Sourav Dey

Bayesian optimization, with applications in chemical reaction discovery and optimization

RDDSX space outside the Collaborative Classroom
Van Pelt-Dietrich Library

Machine learning in the low data regime. How do you find the optimal solution to a problem when the objective function is expensive to evaluate. We will also discuss how to optimize these types of black box functions in a sparse space.

Summer Hangouts: Coby Viner

Speeding up science: GNU Parallel for bioinformatics and beyond

RDDSX space outside the Collaborative Classroom
Van Pelt-Dietrich Library

This session introduces GNU Parallel—the shell utility that can launch thousands of jobs simultaneously, harnessing every CPU core (or even multiple machines) with a single command.

Summer Hangouts: Tess Cherlin

From Data to Insight: Applying Survival Analysis in Clinical Research

RDDSX space outside the Collaborative Classroom
Van Pelt-Dietrich Library

In this tutorial, we will use simulated clinical data and apply survival analysis methods to explore disease outcomes.

Summer Hangouts: Sam Dillavou and Kieran Murphy

Introduction to Machine Learning

RDDSX space outside the Collaborative Classroom
Van Pelt-Dietrich Library

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

Data Science Summer Hangouts Series 2025

RDDSX space outside the Collaborative Classroom
Van Pelt-Dietrich Library

DDDI's 2025 Summer Hangouts program offers students the opportunity to participate in informal, hands-on tutorials led by our postdoctoral research fellows.

Summer Hangouts: Russell Richie

Simulation as a Tool for the (Cognitive) Modeler

Collaborative Classroom in Van Pelt-Dietrich Library

How/when should the (cognitive) modeler use simulation? Special attention will be paid to parameter/model recovery simulations.

Summer Hangouts: Emerson Arehart

Time series forecasting with models and data

RDDSX space outside the Collaborative Classroom

Van Pelt-Dietrich Library

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…

Summer Hangouts: Carlos Schmidt-Padilla

Introduction to Causal Inference for Data Science

RDDSX space outside the Collaborative Classroom

Van Pelt-Dietrich Library

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.

Summer Hangouts: Lyle Ungar and Louis Hickman

Assessing Interpersonal Judgments using Explainable AI

RDDSX space outside the Collaborative Classroom

Van Pelt-Dietrich Library

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.