Friday, March 24, 2017

Research Assistant Tony Wang Asses the Impact of the Mason Innovation Exchange

I'm Tony Wang, currently a sophomore majoring in Mechanical Engineering with a minor in Computer Science here at George Mason University. My current OSCAR project focuses on assessing the impact of the newly created Mason Innovation Exchange with Dr. Patrick Vora and Dr. Colin Reagle. The Mason Innovation Exchange, better known as the MIX, is a place where students can work together on projects spanning science, engineering, art, and entrepreneurship. Staff members help guide students in their projects and work to gain access to resources, equipment, and space.

I work with the MIX staff members (mostly undergraduates) to coordinate the best ways to make the MIX achieve its full potential. My research focuses on data-driven assessment of current demand for the MIX, the types of projects students work on here, operational areas that could be improved, and the overall impact on the GMU student body. Using this data, I hope to quantify the positive impact the MIX has had thus far and emphasize that establishing additional facilities like the MIX is very important for helping students realize what they can achieve with their own creativity and hard work.

Usually before staff meetings or towards the end of each week, I will sift through the collected data and analyze it, finding issues and making remarks on how the MIX is doing. During the weekdays, I help staff the mix and work on improving our data collection. On the weekends, I will take the data collected and analyze it. If there are any issues that need to be addressed I bring them up at the next staff meeting. Two or three times a month, I meet up with a large group of faculty and present my findings to better help them understand the direction of the MIX and future MIXs.

Working with Dr. Vora on the MIX has allowed me to expand my horizons. It has allowed me to work towards bringing STEAM to the public and hone my personal skill so that I can be better prepared for the professional world and future research opportunities. From learning how to 3D print to figuring out how to organize and analyze varying types of data sets, to getting first-hand experience on how difficult it is to start a new program and keep it running. The MIX is a great opportunity for all students to expand their horizons and this OSCAR research position has allowed me to be a part of this amazing opportunity to help make that happen.

Thursday, March 23, 2017

Research Assistant Candace Nelson Explores Peoples Motivation for Using Lyft and Uber for Transportation after Drinking

As a Community Health major, I became interested in the current project I am working on because I wanted to learn more about research in the field of public health. In certain classes I have taken, I learned about research specific to public health and the impact it can have, however, acquiring real experience puts it all into perspective. I work with Dr. Rossheim, a faculty member of the Department of Global and Community Health. The research he conducts pertains to alcohol and how it affects people. The current project I have been working on explores peoples’ motivation to use ride-sharing applications such as Uber and Lyft as transportation after drinking at bars. Research about the effects of alcohol relates to my long-term goal of potentially applying to a graduate program for public health. Initially, applying to such a program was not one of my long-term goals, however, this project has inspired me to consider doing so.

On a weekly basis, I complete several tasks that contribute to the projects Dr. Rossheim is working on. Examples of these tasks include collaborating with other researchers to advance projects, literature reviews, editing surveys, and making suggestions for potential research questions that could be answered in a study. I was able to learn ample new information about how alcohol affects individuals’ behavior and cognitions while working with Dr. Rossheim. However, the most interesting discovery I have made is that I truly have a passion for research. Before participating in the research process, I did not know that it was something I would enjoy. The fact that the study I contribute to may be able to help people in the future is motivational and it inspires me to conduct research to the best of my ability. 

Wednesday, March 22, 2017

Research Assistant Muzhdah Karimi Conducts Government Research

My name is Muzhdah Karimi. I am a junior studying Government International Relations. I work as an undergraduate research assistant in the Schar School of Policy and Government for Office of Scholarship, Creative Activities, and Research (OSCAR). I really enjoy working as a research assistant. I am learning about research, reports and presentations. In January 2017, I began research with professor Sita Slavov Ph.D., director of Public Policy. It is a great opportunity for me to work with Dr. Slavov. I am learning about government research, which is related to Social Security, retirement and health expenses of older people. In addition, we are using Zotero with Chrome and Safari to keep research references in one place. I have different weekly tasks. In one week, I work on references and add them into Zotero and during the next week I work on a presentation about Social Security.

Last year, I worked with Dr. Benjamin Gatling, a folklorist specializing in the folklore of Central Asia and the Middle East and an Assistant Professor in the English Department. I assisted Dr. Gatling with transcriptions of south Asian languages, Tajiki, Dari, Farsi. I transcribed interviews, folktales, and religious Islamic sermons from Tajikistan and Afghanistan. I coded short stories about Sufi saints and stories from the Quran. I also summarized a short Islamic Persian textbook into English. It was a great time to work with Dr. Gatling and I learned about Tajikistan people, culture, and politics. Every week, I had different tasks to do and it was never the same work. One day I would transcribe and the next day I read Tajikistan short texts, which was written in Russian letters. Thus, I learned some Russian language as well.

These projects have given me research experience, and it is a great learning opportunity that will help me with research projects in the future.

Tuesday, March 21, 2017

Research Assistant Alina Moody Scans Picture Negatives from Former On-Campus Newspaper Broadside

My name is Alina Moody and I’m a senior Creative Writing major with a Multimedia minor working with Special Collections and Archives in Fenwick. I am working on the Broadside project, in which we scan in picture negatives taken by members of the old on-campus newspaper, Broadside. I actually began working on this project last year and became invested in it.

Since I first began working on this project, I have gained a lot of knowledge in keeping metadata, working with excel spreadsheets (which is always a useful skill), using Adobe photoshop, and working with scanners. As I am working toward a Multimedia minor, gaining skills with scanners—both regular skills and troubleshooting any problems—is quite helpful. I also hope to work with a publishing company one day, possibly a magazine, where I will need some sort of experience with all of these programs. I’m gaining a large base knowledge in programs that I will probably need no matter what I end up doing.

Every week, I work at my station flipping through pages of old 35mm picture negatives and scan them in on an Epson printer. As I scan, I look for any minor flaws that I can correct using photoshop and log in a detailed description of every frame to be used later in the project. Right now, we’re working on our last two boxes of picture negatives. Since I began work, I’ve scanned in pictures spanning the years 1998-2000.

Friday, March 10, 2017

URSP Student Jordan Shimer Studies Economic and Political Predictors of Revolution

Together with Jamie Wheeler, I am studying economic and political predictors of revolution.  I am taking various measures of well-being and applying them within the framework of a model of revolution I developed during a previous semester. My hope is to better predict instability in developing countries, which would have applications in fields from diplomacy to finance.

I became interested in this project while studying at Trinity College in Oxford, during a tutorial in comparative politics. I studied both revolution and regime failure, and noticed that many theories were more explanatory than predictive.  These studies often were based on case studies, and I believe a large-n study (even with a large error coefficient) can help guide future studies.

I am planning on pursuing a PhD in Political Science.  Obviously, there is no better way to prepare for graduate school than bolstering my resume with successful research, and hopefully conference presentations or a paper publication. After grad school, I hope to either teach at a university, work in a think-tank, or work as a policy advisor.  Any of these career paths would also be aided by having published works and research experience.

I spend most my research time poring over databases and looking for data that most closely resembles the variables within the theoretical model.  I hold weekly meetings with my mentor, where we cover what I have learned or want to focus on for the next week.  We often spend large portions of this time debating the scope and structure of the project.  Coming from different academic fields, we often look to apply vastly different tools to solve the problems in front of us. These differences can lively discussions, but also allow us to take a unique approach to a large, oft-studied issue in political science. 

This week, I discovered several statistical methods to deal with the real-world challenges of measuring well-being in states that are, by definition, politically unstable. While data is often incomplete or must be estimated in many cases, careful selection of sources can provide results that are precise, but inaccurate.  However, the direction of these inaccuracies is often similar, allowing this variability to become baked into the error term of the model.  While this reduces the predictive power of the model, it allows the model to hopefully apply to a larger number of cases.