2022 Applied Machine Learning Intensive (AMLI) at the University of Kentucky

  • Baker, Corey (PI)

Grants and Contracts Details


Diversity & Inclusivity The Department of Computer Science at the University of Kentucky (UK) proposes to host the second year of AMLI Bootcamp. The efforts will be led by Assistant Professor Corey E. Baker. As a selected campus, UK will provide state-of-the-art training facilities along with a rich and diverse learning environment for the 18 visiting students. In particular, the AMLI students will have full access to Professor Baker as well as the underrepresented minority doctoral students in his research lab. In addition, in terms of underrepresentation at the doctoral level, the Department of Computer Science is made up 20% women (slightly under the 22% national average), 6.7% Black or African American (significantly above the 1.5% national average), and 2.7% Hispanic or Latino (slightly above the 1.9% national average). The inclusive environment the AMLI students will be trained in is imperative to acclimating them to the rigorous Google Education open-source machine learning material. Student Recruitment To attract the cohort of AMLI students for the summer of 2022, Professor Baker will leverage his recruiting and planning experience along with any provided resources from NACME and Google. To find students, we will leverage our contacts with various college of engineering and computer science departments in the state of Kentucky. In addition, we will encourage students to apply through UK’s College of Engineering social media, reach out to underrepresented organizations and list-serves, as well as to relevant colleagues and contacts. Students will receive standard information via amli.engr.uky.edu which will provide links to NACME’s application system. The goal is to have approximately 12 students currently attending a four year college in the state of Kentucky and 12 students from other institutions. Facilities The AMLI will be held June 6, 2022 - July 29, 2022 in the LEED Gold Certified Davis Marksbury Building (Department of Computer Science) and when needed in the Ralph G. Anderson Building (College of Engineering). The students will be staying in newly renovated dorms that are located on UK’s main campus. The dorms are two (2) bedroom suites with a private bedroom, semi-private bathroom, full XL beds, kitchenette, mini refrigerator and freezer, and a microwave. The university is located within walking distance of downtown Lexington, and students will receive breakfast, lunch, and dinner access to our brand newly opened (2018) Champions Kitchen which is located in our new (2018) student center. Teacher Recruitment The teaching of the material will be conducted by Professor Baker along with two other faculty or post-docs from various Computer Science or Engineering departments. In addition, the program will have at least 4 teaching assistants. The goal is to attract faculty and teaching assistants who are from underrepresented backgrounds to demonstrate to the AMLI students that there are people who look like them in the field. A major hurdle in the recruiting process is attracting competent teachers who fit the aforementioned criteria who can successfully engage the AMLI students; particularly in a time where graduate students with machine learning backgrounds are typically offered well compensated summer internship offers or are bound to Applied Machine Learning Intensive (AMLI) at the University of Kentucky their doctoral research on their respective campuses for the summer. To circumvent the aforementioned issues, we propose to: ? Offer faculty comparable salaries with typical faculty summer salary for the allotted amount of time spent teaching and preparing lectures ? Offer TA’s a comparable amount of salary to their typical summer pay ? Cover round-trip travel for the faculty and graduate teaching assistants ? To further attract strong TA’s along with convincing their research advisors to allow them to participate Proposed Program Structure Students in the AMLI program will be in class for 5 days a week, 8 hours a day. In weeks 3-8 of the curriculum, the AMLI student groups will have their team project idea guided by a graduate teaching assistant and supervised by a faculty member conducting ML based research. In addition, students will earn 8-week summer university credit that they can transfer to their respective universities. For at least four of the weekends during the AMLI program, students UK will use the AMLI provided funds for students to have recreational team building activities such as bowling, laser tag, and visiting local Kentucky sites. The recreational team building is essential since students will be visiting from all over the country and will help bring the students together as a cohort. On other days/weekends students will be encouraged to attend events in walking distance such as: Central Bank Thursday Night Live, DowntownLEX Together, Music in the Park, Free Concerts, and much more. Budgeting and Allocation of NACME AMLI Funds ? UK will use AMLI provided funds to provide (~$4,800) to students in the form of a stipend or scholarship ? UK will use AMLI provided funds to cover relocation and lodging of the AMLI students ? UK will use AMLI provided funds to cover admission, tuition, and cover fees while the AMLI students are on campus ? Course credits for students can range from 3-6 credit hours offered for completing the AMLI course ? Current undergraduate students at four year universities will be charged at in in-state rate for tuition, regardless of state residency ? UK will not use any of the funds to cover indirect or overhead costs ? UK will use AMLI provided funds to cover participating faculty and staff salaries, travel, and lodging when teaching AMLI courses ? UK will use AMLI provided funds to pay for Teaching Assistants along with relocation fees if needed ? UK will use AMLI provided funds to pay for recreational team building activities related to the program ? UK will use AMLI provided funds to pay for supplies, snacks, and drinks (water, tea, coffee, etc.) during the program
Effective start/end date6/5/2211/30/22


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