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From Monday, June 14 to Friday, June 18, 2021, \_**the Russel Sage foundation and the SNF**_ will sponsor the Summer Institute in Computational Social Science, to be held virtually at ETH Zürich. The purpose of the Summer Institute is to bring together graduate students, postdoctoral researchers, and beginning faculty interested in computational social science. The Summer Institute is for both social scientists and data scientists (both broadly conceived).

The program will involve lectures, group problem sets, and participant-led research projects mentored by SICSS staff. There will also be outside speakers who conduct computational social science research in a variety of settings, such as academia, industry, and government. Topics covered include text as data, web scraping, digital field experiments, machine learning, and ethics. There will be ample opportunities for students to discuss their ideas and research with the organizers, other participants, and visiting speakers. We are committed to open and reproducible research. All materials created by faculty and students for the Summer Institute will thus be released open source.

Participation is primarily geared towards advanced Master's students, Ph.D. students, and postdoctoral researchers with an economics or political economy focus.
We welcome applicants from all backgrounds and fields of study, especially applicants from groups currently under-represented in computational social science. About 20 participants will be invited, and participants are expected to fully attend and participate in the entire program.

[Application materials](https://compsocialscience.github.io/summer-institute/2021/ethzurich/apply) are due Monday, March 22, 2021.

Because of the COVID-19 pandemic, all events will take place online.

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# Apply Page

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# Apply

## Eligibility

Participation is primarily geared towards advanced Master's students, Ph.D. students and postdoctoral researchers with an interest in economics or political economy.
However, there are no restrictions based on citizenship, country of study, or country of employment. About 20 participants will be invited.

The Summer Institute aims to bring together computational social scientists across all levels of technical experience. Participants with less experience with social science research are expected to complete additional readings in advance of the Institute, and participants with less coding experience are expected to complete a set of online learning modules on the R programming language. Students doing this preparatory work will be supported by a teaching assistant who will hold online office hours during the two months before the Institute.

We welcome applicants from all backgrounds and fields of study, especially applicants from groups currently under-represented in computational social science. We evaluate applicants along a number of dimensions:
1) research and teaching in the area of computational social science
2) contributions to public goods, such as creating open source software, curating public datasets, and creating educational opportunities for others
3) focus on causal identification
4) originality/creativity of the research ideas
6) likelihood to contribute to the educational experience of other participants
7) potential to spread computational social science to new intellectual communities and areas of research

Further, when making our evaluations, we attempt to account for an applicant’s career stage and previous educational opportunities.

## How To Apply

Applicants must submit the following documents:
- a curriculum vitae (including the name and contact of 2 references)
- a statement (maximum three pages) describing both any current research and your interest in computational social science. We ask that the statement includes a research idea that you would be willing to realize using skills acquired during the Summer Institute
- one writing sample (no more than 35 pages). Co-authored work is acceptable for the writing sample, but if you submit co-authored work, we recommend that you include a few sentences describing the contributions of each individual author
- All applications must include an e-mail and an alternative means of contact (e.g., phone number).

In order to be guaranteed full consideration, all application materials must be submitted by **Monday, March 22, 2021**. All application materials must be submitted through[ Google Form](https://forms.gle/YmM55gcmfAjZxGwN7). Applications that are not complete by the deadline may not receive full consideration. We will notify applicants solely through e-mail by **April 22, 2022**, and will ask participants to confirm their participation by the end of April.

Inquiries can be sent to **[e-mail]**.
> [name=Malka] Do we still have access to this email adress ? otherwise we need a new one
**[e-mail]**

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# Pre-arrival page
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# Pre-arrival

The Summer Institute will bring together people from many fields and backgrounds. In order to use our time together efficiently, there are a number of things that you should do before participating in SICSS-Zürich 2021.

- Complete the pre-read
- Complete coding bootcamp (if needed)
- Watch lecture videos
- Prepare your computing environment

Teaching assistants will host office hours through Slack to support you as you work through these pre-arrival materials.

## Reading

In order to prepare for SICSS-Zürich 2021, you should read Matt Salganik’s book, *[Bit by Bit: Social Research in the Digital Age](http://www.bitbybitbook.com)* ([Read online](https://www.bitbybitbook.com/en/1st-ed/preface/) or purchase from [Amazon](https://www.amazon.com/Bit-Social-Research-Digital-Age/dp/0691158649), [Barnes & Noble](https://www.barnesandnoble.com/w/bit-by-bit-matthew-salganik/1125483924), [IndieBound](https://www.indiebound.org/book/9780691158648), or [Princeton University Press](https://press.princeton.edu/books/paperback/9780691196107/bit-by-bit)). Parts of this book, which is a broad introduction to computational social science, will be review for most of you, but if we all read this book ahead of time, then we can use our time together for more advanced topics.

## Coding Boot Camp

The [SICSS Boot Camp](https://sicss.io/boot_camp) is an online training program created by Chris Bail to provide you with beginner level skills in coding so that you can follow the more advanced curriculum we teach at SICSS. The videos and materials are designed for complete beginners and are best viewed as a sequence since each video builds upon content introduced in previous tutorials. If you are already familiar with the topics in these videos, you do not need to complete them.

If you would like more practice after completing the Boot Camp videos, some other materials that we can recommend are:
- [RStudio Primers](https://rstudio.cloud/learn/primers), which can be supplemented by the open access book _[R for Data Science](https://r4ds.had.co.nz/)_ by Garrett Grolemund and Hadley Wickham
- [R for Social Science](https://datacarpentry.org/r-socialsci/), Data Carpentry.
- [Introduction to R for Social Scientists](https://clanfear.github.io/CSSS508/), Taught by Charles Lanfear at University of Washington. This course includes videos of lectures, slides, and assignments.
- [Learn R](https://www.codecademy.com/learn/learn-r), Code Academy.

Please note that the majority of the coding work presented at SICSS-Zürich 2021 will employ R. You are welcome to employ a language of your choice, such as Python, Julia, or other languages that are commonly used by computational social scientists. However, we cannot support those languages.

## Lecture videos

SICSS-Zürich 2021 will be using a [flipped classroom model](https://en.wikipedia.org/wiki/Flipped_classroom). Therefore, you should watch [videos of lectures](https://sicss.io/curriculum) before our meetings, and then we will use our time together for discussion and group activities.

## Computing environment

### R

Some of the activities will require coding, and we will support R. You are welcome to use other languages, but we cannot guarantee that we can support them. Before SICSS you should install a modern, stable-release version of [R](https://www.r-project.org/) and [RStudio](https://rstudio.com/products/rstudio/download/).

### Zoom

SICSS-Zürich 2021 is a virtual event, and we will use Zoom. You can join Zoom meetings using a phone or computer with a microphone and (ideally) a webcam. This [article](https://thewirecutter.com/blog/professional-video-call-from-home/) contains some helpful advice on how to set up your device and space to improve your Zoom experience. (Note: though the article recommends external microphones and webcams, we DO NOT recommend or require that you purchase any equipment to participate in SICSS. In our experience, the microphones and webcams built into most modern laptops and phones are perfectly sufficient).

### Slack

Before participating at SICSS-Zürich 2021, you should have an account in the SICSS 2021 Slack workspace. If you have not used Slack before, you should review these [getting started](https://slack.com/help/categories/360000049043-Getting-started) materials. Slack can be hard to use at first, but we've found that it is the best way to enable everyone to collaborate.

### GitHub

Many participants at SICSS use GitHub to collaborate. If you do not yet have one, you should [create a GitHub account](https://github.com/join). If you are a student, we recommend that you apply for a [GitHub Student Developer Pack](https://education.github.com/pack).

## Office hours

The SICSS-Princeton [teaching assistants](https://sicss.io/2021/princeton/people#teaching_assistants) will host weekly office hours in the SICSS 2021 Slack. You can find information about the office hours in the SICSS 2021 Slack channel #pre-office-hours. If you are not able to attend during the regularly scheduled office hours or have any questions about office hours, please contact one of the [teaching assitants](https://sicss.io/2021/princeton/people#teaching_assistants).

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# People page
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## Faculty

### [Elliott Ash](https://elliottash.com/)
Elliott Ash is Assistant Professor at ETH Zurich Department of Social Sciences, where he chairs the Law, Economics, and Data Science Group. Professor Ash's research undertakes empirical analysis of law and political economy, with methods drawn from applied microeconometrics, natural language processing, and machine learning. Professor Ash was previously Assistant Professor of Economics at University of Warwick, and before that a Postdoctoral Research Associate at Princeton University. He received a PhD in economics and JD from Columbia University, a BA in economics, government, and philosophy from University of Texas at Austin, and an LLM in international criminal law from University of Amsterdam.

### [Malka Guillot](http://malkaguillot.weebly.com/)
Malka Guillot is a postdoctoral associate in the Law, Economics, and Data Science Group in the Lab of Law & Economics at ETH Zurich. Malka teaches introduction to machine learning applied to policy questions at ETH. She has a background in public economics, where she investigates income inequality and taxation. She is interested in application of machine learning and natural language processing in public economics. In 2018, she received her Ph.D. in economics at the Paris School of Economics (France).

### Philine Widmer
Philine Widmer is a Ph.D. student in Economics at the University of St.Gallen. She works on topics in media economics and development economics. She is enthusiastic about bringing machine learning and natural language processing to politico-economic questions. She has been a teaching assistant for various subjects (including introductory data science, political economics, and microeconomics).

## Guest Speakers

More guest speakers will be announced soon.

### [Michael Knaus](https://mcknaus.github.io/)

Michael Knaus is an Assistant Professor of Econometrics at the Swiss Institute for Empirical Economic Research of the University of St.Gallen. His research interests are at the intersection of causal inference and machine learning to answer questions in empirical, mostly labor, economics. For SICSS Zurich, he will be giving a lecture on causal machine learning.

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# Schedule page
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# Schedule