Jasjeet Sekhon, Professor

Open (1) Measuring and Explaining Pediatricians' Vaccine Policies

Open. Apprentices needed for the fall semester. Please do NOT contact faculty before September 11th (the start of the 4th week of classes)! Enter your application on the web beginning August 16th. The deadline to apply is Tuesday, August 29th at 8 AM.

By focusing on the policies that pediatricians adopt, we hope to better understand why some areas (e.g., Marin County in the Bay Area, Utah, parts of Texas, etc.) have significantly lower vaccination rates than the national average and government recommendations. In previous semesters, URAP participants have helped explore the role of parents and voters in explaining these trends. This spring, we now turn to the role of pediatricians.

Helping to conduct a survey of pediatricians; data analysis if students have such skills; reviewing the literature for previous research on pediatricians' vaccine policies; writing reports.

Regular meetings about the project will take place between students, Professor Sekhon (political science and statistics), and Josh Kalla (PhD student in political science). These meetings will give students an opportunity to learn about research design and data science (from Sekhon and Kalla).

Day-to-day supervisor for this project: Josh Kalla, Graduate Student

Qualifications: n/a

Weekly Hours: 3-6 hrs

Related website: http://sekhon.berkeley.edu

Open (2) Developing Machine Learning Algorithms For Causal Inference In Big Data

Open. Apprentices needed for the fall semester. Please do NOT contact faculty before September 11th (the start of the 4th week of classes)! Enter your application on the web beginning August 16th. The deadline to apply is Tuesday, August 29th at 8 AM.

With the rise of big data, there is growing interest in targeting treatments, programs, and policies to the individuals they would help the most and harm the least. Statistical algorithms are already being used to assign people to online ads, credit, and even prison. But also in medicine, physicians try to determine the individual treatment effect of a single patient to choose the best treatment.

In this project, we will develop and implement a new algorithm to estimate the treatment effect of a single unit (e.g. a patient). We will start with an idea based on the machine learning algorithm, Random Forest. The goal is to implement and publish a version of our algorithm suited for big data.

Regular meetings about the project will take place between students, Professor Sekhon (political science and statistics), and Sören Künzel (Ph.D. candidate in statistics). These meetings will give students an opportunity to learn about causal inference, machine learning, statistical inference for big data, and parallel computing.

Day-to-day supervisor for this project: Sören Künzel, Ph.D. candidate

Qualifications: Implementation will happen in C/C++ with bindings to R. Familiarity with C/C++, and basic knowledge of R is required. An understanding of machine learning techniques, such as Random Forest, Gradient Boosting, and Neural Networks, is appreciated but not required.

Weekly Hours: 9-12 hrs

Related website: http://sekhon.berkeley.edu