The Nevada DRIVE Program

The Nevada DRIVE Program is a two-year graduate research assistantship (GRA) program made possible through the combined efforts of President Brian Sandoval, Vice President for Research and Innovation, Mridul Gautam, and Executive Vice President & Provost, Jeff Thompson.

Applications for this program are now closed.

Dr. Snow and her graduate student performing a neuro science experiment

Purpose

To provide one- or two-year Graduate Research Assistantships to promote the recruitment/retention of doctoral students enrolled at the University of Nevada, Reno.

Program description

The Nevada DRIVE program is designed to promote Doctoral Research in Innovation, Vision and Excellence at the University of Nevada, Reno. All University doctoral programs are eligible to request funding for one or more full- or half- time GRA positions for the 2021-2022 and 2022-2023 academic years. For each year, we anticipate that funding will be available to support approximately 20 full-time (20-hour/week) or 40 half-time (10-hour/week) research assistantships with a stipend of $25,000, or $12,500, respectively. Funding includes non-resident tuition waivers and the standard grant-in-aid to cover course registration fees and student health insurance.

Please refer to the University’s Graduate Assistant Handbook for information on policies, benefits, and definitions regarding graduate assistants. For the purposes of this program, a graduate research assistant is broadly defined below.

Graduate Research Assistant (GRA): GRAs are graduate assistants who work in research-related positions existing primarily in academic departments, research centers and institutes. GRAs gain experience of working alongside faculty members on cutting-edge research.

GRA responsibilities vary greatly and may include, but are not limited to:

  • Conducting research or performing creative activities towards the completion of their doctoral degree
  • Collecting, coding, and/or analyzing data
  • Conducting literature reviews or library research
  • Preparing materials for submission to funding agencies and foundations
  • Writing reports
  • Preparing materials for IRB review

Program requirements

The following program requirements must be met by the graduate program, faculty member, and/or graduate student during the funding year(s).

Graduate student presenting to peers

Incoming Nevada DRIVE Scholars

  • The incoming Nevada DRIVE scholar must submit an individual development plan (IDP) by the end of Fall 2021 (for Fall 2021 admits) or the end of Fall 2022 (for Fall 2022 admits).
  • If appropriate, the incoming Nevada DRIVE scholar must apply to the NSF GAIN Scholars Program (refer to NSF GAIN Scholars webpage for eligibility)

All Nevada DRIVE Scholars

  • The faculty advisor must submit an annual evaluation of progress for the Nevada DRIVE Scholar to the Graduate School by May 2022 and/or May 2023.
  • The Nevada DRIVE Scholar must attend a mandatory orientation session at beginning of Fall 2021 or Fall 2022 semesters, as appropriate.
  • The graduate program must assign a peer mentor to the Nevada DRIVE Scholar. The peer mentor should be an established doctoral student in the Nevada DRIVE Scholar’s graduate program.
  • The Nevada DRIVE Scholar must attend structured seminars and professional development modules to investigate and prepare for careers.
  • The Nevada DRIVE Scholar must receive additional mentorship with a University faculty member other than their doctoral advisor.
  • The Nevada DRIVE Scholar must participate in at least one of the following programs: GradFIT, Gradventure and 3MT
  • The Nevada DRIVE Scholar’s doctoral advisor must complete or have completed the Graduate School’s Mentoring Mentor’s Workshop.
 

Highlighted awardees from 2021

Amber Marshowsky

Amber MarshowskyAmber's research, titled "Increasing the Cultural Competency and Responsiveness of Early Childhood Education Professionals through Professional Development: An Exploratory Study," examines the impact of professional development (PD) training experience on individuals' development of cultural proficiency and/or culturally responsive practices. Her research is motivated by the understanding that the creation of equitable and culturally responsive early learning environments for all children requires a change to both institutional and interpersonal systems.

The ongoing PD training focused on diversity, equity, inclusion, and justice (DEIJ) and the sample in this study included 46 early childhood professionals (administrators, center-based teachers, and home visitors) from one university-affiliated early childhood program in the Western United States. During the PD's cultural proficiency and culturally responsive practice assessment were administered with approval from IRB to collect and analyze de-identified responses to these measures from all consenting PD training participants.  The research tracks trends that aim to increase pre-service and in-service training for early childhood education (ECE) professionals as it focuses on issues of DEIJ. Evidenced-based guidelines for the content and duration of such training are noticeably absent from the current research literature. To Amber's knowledge, there have not been any published, peer-reviewed studies examining relations between features of DEIJ trainings and changes in ECE practitioners’ beliefs or behaviors. Unfortunately, delays caused by the COVID-19 pandemic and high teacher turnover led to numerous interruptions of this study. Thus, it is too soon to know whether participation in the PD program impacts the cultural competency of ECE professionals. View her poster.

Parvaneh Aliniya

A little girl covered in paint sits in front of a painted rainbow with her hands in a bowl.Parvaneh's research focuses on enabling machines to understand the visual world. To this end, she is working on the intersection of computer vision and natural language processing. Generally, for a given image, we mostly rely on using the information in the picture, pixel values, to perform a specific task, such as finding the locations of all the objects in the image; however, it would be more efficient if we leverage textual description of a scene. She intends to test using a large set of pictures paired with the descriptions to train a model that could describe a new given image with sentences.

As the first step toward this goal, she hopes to find an effective way to match the image to the text. Images have pixel values in different locations, and the text contains words. Her first question is how to find the relation between words in the text and parts of the image. To answer this question, she is working on the image-to-text matching problem in which we first convert images and texts to the same modality, and then can measure how close images (image patches) and text (words) are.

Finally, for a new image, the approach can find the words that describe images the best in terms of objects in the scene and their relation.