Three hundred undergraduate researchers (UGRs) from 38 different states and 75 different institutions have participated in this program, and about 80 have published their projects in high-quality venues. Each year, we solicit applications, and we receive well over 150. After a careful interview, we make offers until our 10 positions are filled. Given our successful streak, we try to shed some perspective over our efforts and experiences; see http://crcv.ucf.edu/REU/
Why UCF Has Kept Winning Renewals
It is instructive to contemplate our success and examine our evolution—there are several factors that appear to have contributed independently to our longevity.
Focus: Computer vision. Our site is focused on exciting and appealing topics in computer vision, which facilitate a condensed short course covering key topics, coordination among faculty and graduate students mentors, and interaction and exchanging ideas among UGRs.
Duration: 12 weeks. While the duration of the program is the most controversial aspect of our site with reviewers (because it makes ineligible those students who have fewer weeks available), it is the channel that gives us capacity for all our activities. We use the first two weeks to train UGRs in background material and then have a week of sufficient deliberation for topic selection, and the following nine weeks are for the UGR to conduct research. In contrast to our 12 weeks, many sites offer REU summers to students for as low as 8 weeks.
Immerse the UGR within the graduate students' lab. Experiencing work in a research laboratory environment with graduate students, has innumerable benefits; the undergraduates see in so many ways the metamorphosis from their current stage to more experienced researcher. We could not have accomplished our goals each year without a large, successful computer vision Ph.D. program. The Ph.D. program offers a scaffolding for the summer REU.
We could not have accomplished our goals each year without a large, successful computer vision Ph.D. program.
We shower the REU students with guidance and caring. Like helicopter parents, we keep the undergraduates feeling attended to, valued, and consequently focused. We expend large amounts of effort each year on our REU activities, and this appears to give each participant so much to take away to the next step of their journey in life.
What we wish our activities will deliver. Our activities during the summer and beyond are intended to provide the UGR with the following quality experiences.
The activities. At the end of each activity, we list the letters associated with the experiences that were previously described in this Viewpoint.
At the core of all these activities lies the UGR's immersion in the graduate environment. The UGR's research team is formed depending on the project topic. UGRs are given a desk proximal to the graduate student on their team. The graduate student meets with the UGR at different times of the day, as the UGR makes progress or has questions to discuss. Informal short meetings with the faculty mentor occur every one to three days. All these activities lead up to the weekly presentation by the UGR. Additionally, the UGR has opportunities to meet the faculty mentors and graduate students at social events, and the weekly research meeting for the larger graduate student group.
The field of computer vision is rapidly evolving and the REU site has kept pace with the changes.
Our progress during the summer is evaluated by a professional assessment team, which provides us mid-summer feedback allowing us to adjust and adapt our strategies.
Changes Over the Years in Structure and Logistics
Our site has seen changes in many ways over the years. Initially, it offered a year-long REU; the summer was full-time research, while the Fall and Spring components involved part-time research due to full class load. The site was shared with another in-state institution, and half the UGRs were local to one institution while the other half were local to the other, so during the summer the UGRs commuted from home to their institution, and during the Fall and Spring semesters, they were able to take continued computer vision academic courses on site. The yearlong duration allowed the training in background computer vision techniques to spill over many weeks and allowed some room for easy accommodation of project topic changes. The first change came with the program becoming a single site. Additional professors from our institution were added to the team as mentors.
The next change was when the site took participants from other states. This necessitated the move to on-campus housing, the transition to focus on the summer months, the need for logistics for managing the processing of the selected out-of-state students, and widespread advertising, recruitment, and interviewing procedures.
The focus on the summer months has led to annual review of the short summer background training, inclusion of and proper scheduling of the vast variety of activities. The pre-summer activities of planning the research topics in advance has also taken greater attention.
The recent change of adding new faculty to the Center for Research in Computer Vision (CRCV) has permitted flexibility in how the 10 students are subgrouped for their weekly reporting meetings, how they are mentored each day, and has opened up new research areas within computer vision and machine learning.
Changes in Content
The field of computer vision is rapidly evolving and the REU site has kept pace with the changes. Machine learning approaches started to appear in computer vision, as they were able to contribute to object recognition solutions during the mid-1990s. Approaches such as neural networks, boosting, and support vector machines were actively competing for ascendance during the early 2000s. The advent of Deep Learning in the 2010s has slowly gained acceptance as the dominant paradigm in computer vision, and today, research in computer vision must start with a quick study of deep learning approaches and novices must acquire competence in running practical experiments with large data sets in deep learning implementation environments. Consequently, our own short course now has a strong emphasis on environments like Keras, Tensorflow, and a shift to teaching Python (away from MatLab).
Sample Topics. Looking at the topics pursued over the past 30 years indicates the student projects have evolved with the growth of computer vision. Over the six five-year periods, two topics per period are listed here.
UCF's REU has a strong commitment to broaden participation among underrepresented groups. Of the 50 participating UGRs in the past 5 years, 23 are female, and 10 of the 27 males are African-American or Hispanic. This diversity in the cohort contributes to increasing the pipeline of students pursuing graduate careers.
After 30 years (and approximately 300 students), some patterns have emerged. Approximately half the students have proceeded to graduate school. Many of the participants have proceeded to leadership positions in their professions: becoming faculty members, starting their own companies, and rising to managerial positions in Fortune 500 Technology companies. Details about student successes are provided in the booklet at http://crcv.ucf.edu/REU/Book-let_071117.pdf
UCF's CRCV has seen many benefits from its cultivated REU strength. UGRs have provided an opportunity to explore research directions, to develop mentoring skills among faculty (older and newer) and graduate students. CRCV-trained UGRs have populated graduate programs around the nation. Our models of evaluation and attentiveness have allowed for best practices to be tested and employed. The commitment of time, effort, and resources is expected to continue into future decades.
Niels Da Vitoria Lobo (email@example.com) is an Associate Professor at the Department of Computer Science, University of Central Florida, Orlando, FL, USA.
Mubarak A. Shah (firstname.lastname@example.org) is the founding Director of the Center for Research in Computer Vision, University of Central Florida, Orlando, FL, USA.
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