Penn Undergraduate Data Science Hangout

Van Pelt Library
Summer 2019

Data science hangout

The summer 2019 hangout is intended for undergraduate students whose summer research involves quantitative analysis of datasets, including variants of machine learning. The research project could be in the humanities, social sciences or natural sciences. As part of SAS’s initiative in data science, we have set up a resource that we hope will be interactive and stimulating for you. Once a week, from 12-5pm, the Collaborative Classroom in the Weigle Information Commons at the Van Pelt library will be available to you to hang out, work collaboratively, learn about research by students in other departments, and listen to talks on data science from your peers and faculty.

 

We will provide pizza for lunch to start the afternoon, and cookies for a break at 3:30pm. Typically there will be talks from 12:30-1:30pm, and a tutorial on the basics of data science from 1:30-2:30pm. The rest of the time is largely open for you to use as you wish. We can help you set up activities including hack sessions on topics such as data visualization and machine learning tools. 

 

The classroom room has tables for joint work and walls you can use to write on and project from each table. The space around the room has “data diner booths" for informal discussions and study rooms for group work. 

 

Schedule and Speakers

 

June 19th 

  • Introduction to data science hangout - Professor Bhuvnesh Jain, Department of Physics and Astronomy; Professor Masao Sako, Department of Physics and Astronomy; Professor Emily Hannum, Department of Sociology.

  • Brainstorming and goals for future hangouts

June 26th

  • How to Predict and Prevent Mass Shootings in the U.S. - Professor Richard Berk, Department of Criminology.

  • Tutorial: Linear Regression - Professor Masao Sako, Department of Physics and Astronomy.

July 11th 

  • Gang Injunction, Public Transit and Crime, Homeless Shelters and CrimeGreg Ridgeway, Department of Criminology.

  • Tutorial: Linear Regression Pt. II Professor Masao Sako, Department of Physics and Astronomy.

July 18th

  • Data-driven Modeling of Human Color Vision - Professor David Brainard, Department of Psychology.

  • High Dimensional data Reduction and Visualization in Genomics - Professor Junhyong Kim, Department of Biology.

August 1st

  • The Application of Multivariable Prediction Models to Aid Decision-making in Mental Health Contexts - Professor Robert J. DuRubeis, Department of Psychology.

  • Tutorial: Introduction to Machine Learning - Professor Bhuvnesh Jain, Department of Physics and Astronomy.

  • Application of Machine Learning to Galaxy Classification and Introduction to Neural Networks Sebastian Gonzalez and Jacob Nibauer, Department of Physics and Astronomy.

August 8th

  • Big, Publicly Available Data Sets for Cognitive Neuroscience: advantages, challenges, and what we’ve been doing with one of them (the Human Connectome Project) - Professor Martha Farah, Department of Psychology.

  • Student Presentations

 

August 15th

  • Human Happiness in High Resolution: Insights from large-scale experience sampling - Dr. Matt Killingsworth, The Wharton School.

  • Tutorial: Ridge Regression and related topics in regression analysis - Lingqi Zhang, Department of Psychology

August 22nd

  • Urban Analytics in Philadelphia: How can we use data to improve our cities?  - Professor Shane Jensen, Department of Statistics, The Wharton School.

  • Leaf Fingerprinting: applying  persistence homology and data driven discovery to the  network of plant leaf veins - Professor Eleni Katifori, Department of Physics and Astronomy.

Students

65

Topics

11

Sessions

16

Speakers

9