About the Husky Seed Fund

The Husky Seed Fund is an award that brings to life innovative ideas by students that are inclusive, impactful, and inventive to the UW. The fund launched as a pilot program for students on the Seattle campus in 2016 and is now expanded to also include the Tacoma and Bothell campuses. The fund is managed by students on the Husky Experience Student Advisory Council with funds from the Office of the Provost. Created by students for students, the goal is to bring to life innovative ideas by awarding funds for projects that that will enhance students’ extracurricular experience.

The winners of the Husky Seed Fund are students who are embracing their Husky Experience. They are making their passions come to life, and gaining the skills they need to prepare for rewarding careers in industry, community and life. As you learn more about their projects, we encourage you to do the same – make the most of your Husky Experience.

For questions or more information, contact HESAC members at seedfund@uw.edu, visit our Facebook page at www.facebook.com/huskyseedfund, and read this UW Daily article.

Applying for the Husky Seed Fund

Applications for 2018 are open from February 14 and close on March 12 at 5pm.

Your idea could become reality with a seed funding award of up to $5000. This is your chance to develop leadership and team building while creating something truly unique for your fellow Huskies.

Projects must be Inclusive, Impactful, and Inventive. Overall, HESAC looks for projects that help students feel at home with the UW and engage with the Husky community.

Eligibility Requirements

To be eligible to apply for this program, you (or the group) must be enrolled at either the UW Seattle, Bothell, or Tacoma campus. The applicant(s) must be in good academic standing with a minimum GPA of 2.5 for each group member, and can be of any discipline, major and class standing (including graduate and professional students).

Award Distribution and Accountability

Projects can request up to $5000 (with an average award amount of $2500). Awardees will be held accountable for their progress. Funds are distributed quarterly and awardees will submit a progress report prior to receiving the funds, giving a summary of their achievements, lessons learned, etc.

Hosted by the UW Libraries Research Commons and Core Programs in the Graduate School, Scholars’ Studio is a quarterly event featuring up to 10 short presentations (5 minutes each) by UW graduate students and postdocs. Each quarter we invite proposals for talks on a theme, in order to encourage the cross-disciplinary sharing of research. The event is fast paced and fun. It includes a Q&A with presenters, and a reception. Presenters receive feedback on their presentations through feedback forms distributed to audience members. Scholars’ Studio is an excellent opportunity for grad students looking to make connections and build presentation skills.

Graduate School Mentoring Award for Postdoctoral Trainees

Call for Nominations: Postdoc Mentoring Award 2018!

The Graduate School and the UW Office of Postdoctoral Affairs awards an annual Mentoring Award for Postdoctoral Trainees to recognize and honor the tremendous role postdocs play in student research and training at UW. The patience postdocs demonstrate in spending time investing in students, even while advancing their own research and career, is a model of what we would hope to see among mentors. As we heard over and over again in the letters: postdocs serve as mentors who guide, push and inspire students to become their best selves. The Mentoring Award recipient receives an honorarium of $2,000.

NOMINATE A POSTDOC MENTOR

Nomination Process

Has a postdoc made a difference in your life this year? Whether in the lab, in the field, or in the classroom, the Graduate School wants to acknowledge the essential role postdoctoral trainees play in our research and teaching missions here at University of Washington.

The Graduate School is seeking nomination for an exceptional UW postdoctoral trainee who dedicates time, energy and effort to mentoring graduate students. Nomination is simple. Write a nomination letter that gives an example (or many) of how this postdoc has impacted you as a mentor. Submissions should be in a single letter of 250-500 words, and can be submitted by students, postdocs, faculty or staff. Collaborations on letters are welcome, but multiple letters will not be accepted. If multiple letters are submitted, we will ask you to combine them into a single submission (reviewers are examining quality of impact the postdoc has made, not quantity of nominations).

A review committee will select finalists based on mentoring qualities so specific examples are encouraged. Finalists will be announced by April 1 and recognized at end of year ceremonies. The award winner receives $2,000 in discretionary funds.

In posting your nomination, please include the following elements:

• Postdoc fellow’s name saved in the file name
• Department affiliation, along with program or lab group, as relevant
• Descriptive account of how the postdoc has served as a mentor for graduate students in particular, and other mentoring examples as appropriate
• Your relationship to the postdoc (nominations can come from faculty, students, staff and fellow postdocs)

Questions? Contact Associate Dean Kelly Edwards, with the Office of Postdoctoral Affairs and the Core Programs in the Graduate School at uwopa@uw.edu.

Timeline for Nomination and Final Announcement

Nomination Process	Timeline
Accepting Nominations	Now–Feb. 28, 2018
Reviewing Nominations	March 1–March 15, 2018
Decision and Announcement	March 15, 2018
Award Ceremony	May 2018

Network Modeling for Epidemics (NME) is a 5-day short course at the University of Washington that provides an introduction to stochastic network models for infectious disease transmission dynamics, with a focus on empirically based modeling of HIV transmission. It is a ”hands-on” course, using the EpiModel software package in R (www.epimodel.org). EpiModel provides a unified framework for statistically based modeling of dynamic networks from empirical data, and simulation of epidemic dynamics on these networks. It has a flexible open-source platform for learning and building several types of epidemic models: deterministic compartmental, stochastic individual-based, and stochastic network models. Resources include simple models that run in a browser window, built-in generic models that provide basic control over population contact patterns, pathogen properties and demographics, and templates for user-programmed modules that allow EpiModel to be extended to the full range of pathogens, hosts, and disease dynamics for advanced research. This course will touch on the deterministic and individual-based models, but its primary focus is on the theory, methods and application of network models.

The course uses mornings for lectures, and afternoons for labs with students working in small groups. On the final day, students have the option of developing an EpiModel prototype for their own research projects, with input from the instructors, which includes the lead EpiModel software developer, Dr. Samuel Jenness.

Returning students: We encourage previous attendees with active modeling projects to apply to return for a refresher course. The EpiModel package has been significantly enhanced over the last few years. Returning students with active projects will have the opportunity to work with course instructors to address key challenges in the design of their network model code.

Dates and location
The course will be taught from Monday, August 20 to Friday, August 24 on the University of Washington campus in Seattle.

Costs
Course fee is $600. Travel and accommodation costs are the responsibility of the participant, although discounted hotel rates are available. We offer a limited number of fee waivers for pre-doctoral students or for attendees from low income countries.  These cover waiver of the registration fee only; travel and accommodation are still the responsibility of the fee waiver recipient.

Application dates and decision dates
*    Apr 1: Fee waiver application deadline. Decisions will be made by Apr 15, and response required by May 1.

*    May 1:  General application deadline. Decisions will be made by May 15 and a response required by June 1. A waitlist will be established with rolling admission through June 30.

Application
Apply online at https://catalyst.uw.edu/webq/survey/morrism/347069

Course website and more information: http://statnet.github.io/nme

Mark Long, CSDE Affiliate, Associate Dean for Research and a Professor at Evans School of Public Policy & Governance, Adjunct Professor of Economics 

Cities and states in the U.S. are increasingly implementing large increases in local minimum wages as a means of combating income inequality. Such policies are prompted by sluggish changes in the federal minimum wage, which has not been increased since 2009 and which is not indexed to account for inflation. The City of Seattle passed a local minimum wage ordinance in 2014, which raised the minimum wage for work done in the city from $9.47 to $11 per hour in 2015, to $13 per hour in 2016, and to $15 per hour in 2017. Using rich administrative data, this paper evaluates the extent to which the 2015 and 2016 increases in the minimum wage had effects on local areas surrounding the city. In particular, we evaluate whether it caused a change in average hourly wages and hours worked by low-wage workers in areas outside of the city limits.

Economists have long recognized that labor, economic, and social policies are often implemented at the local level (e.g., city, county, or state), and have used variation of policy regimes across space and time to study the impact of these policies. Spatial and temporal variation in policy regimes provide a natural experiment in which regions that implement a policy are assigned as the treatment group, and regions that did not implement it are used as controls. A common empirical approach compares the temporal difference in outcomes in the treated region to the temporal difference in outcomes in an immediately adjacent region. Advocates for this “boundary-discontinuity” method contend that it would be reasonable to expect the treated region and immediately adjacent region to have parallel trends in outcomes in the absence of the policy innovation. The parallel trends assumption is key to all difference-in-differences estimations. However, the boundary-discontinuity method can fail if spillover effects occur. When a city establishes a minimum wage, it is likely that low-wage workers currently employed outside of the city boundaries will look for jobs inside the city boundaries to take advantage of the higher wages. These workers might displace some workers employed in the city before the minimum wage was implemented. In reaction, employers outside of city boundaries might raise wages to low-wage workers to retain their workforce. Conversely, if displaced city workers or persons unable to find jobs in the city at the higher minimum seek jobs in nearby jurisdictions, the resulting increase in supply may exert downward pressure on wages. As a result, spillover effects may lead to an increase or decrease in wages and, hence, employment in the adjacent region, which in turn would bias difference-in-differences estimates of the policy’s impact.

In our current working paper, we find evidence of a substantial spillover effect of Seattle’s minimum wage on adjacent regions. We estimate that regions within a 40-minute drive to Seattle increased wage rates for low-wage workers by 30% of the increase that we estimate to have occurred in Seattle, though this effect is not precisely estimated. The same regions also experienced employment decreases on the order of 70% of Seattle’s employment decrease. This impact is large and significantly different from zero. Furthermore, we show that ignoring this spillover effect leads to an underestimate of the effect of the minimum wage hike on wage rates and employment within Seattle. When we re-estimate the impact of the minimum wage in Seattle using the popular adjacent county approach, we find a wage effect of about 58% and an employment effect of about 20% of the ones obtained in the specification which allows for spillovers. As a result, the impact on earnings (i.e. a key policy outcome) is misestimated, leading to incorrect conclusions about the welfare implications of a local minimum wage.