The Undergraduate Consortium at KDD 2023 (KDD-UC) is an initiative that endeavors to expand and enhance the participation of undergraduate students of diverse backgrounds in research pertaining to knowledge discovery from data. Towards that goal, the KDD-UC will:
- Provide an undergraduate research paper submission track which will be used to both provide critical feedback to undergraduate students on their ongoing research projects and select a subset of students that will be provided financial assistance to attend and present their work at KDD 2023.
- Organize student paper and poster presentations at KDD 2023, along with participating reviewers that will provide additional feedback on talks.
- Match participating students with academic and industry mentors that can provide them feedback on current or future research project ideas, along with overall career advice.
- Organize a panel focused on rewards and potential challenges of different research career pathways, including graduate school/PhD, research labs, and industry.
The KDD-UC will accept paper submissions from only undergraduate students (they should be the primary authors of the paper, and other authors and their advisor can be co-authors). Students exploring a career in data science research are encouraged to apply. Preference will be given to students who identify with groups traditionally underrepresented in the field of computing and/or students who have limited resources related to graduate school at their home institutions.
The aim of the KDD-UC is to broaden the participation of undergraduate students from different backgrounds in research pertaining to knowledge discovery from data by providing mentorship and support for the conference experience. We especially invite students who are women and minorities in computing, students from “primarily undergraduate” institutions, and students who have limited resources for research and graduate school at their home institutions.
The target audience for the KDD-UC is current undergraduate students who:
- Have worked on a data science research project in one aspect of the data science lifecycle including but not limited to: data cleaning and preparation, data transformation, mining, inference, learning, explainability, data privacy, and dissemination of results.
- Have genuine interest in pursuing graduate studies involving data science research.
- Are in need of support and feedback from a mentor and data science community for their interest in pursuing data science research after graduation.
Students who were not enrolled in an undergraduate program in the 2022-2023 academic year are not eligible for the Undergraduate Consortium.
- May 26, 2023 – Submission deadline
- June 20, 2023 – Decisions announced
- August 7, 2022 – Undergraduate Consortium (tentative date)
Applications must be submitted in full via the submission portal by 11:59:59 pm UTC-12 (Anywhere on Earth) on the stated deadline date. Application materials should not be anonymized.
Submit the following materials using the following Web site:
1. Research Paper
The research paper MUST be 4-6 pages, excluding references, using the ACM Conference Proceeding templates (two column format). References are limited to 1 page. Template guidelines are available here: https://www.acm.org/publications/proceedings-template. In addition, authors can provide an optional one (1) page supplement at the end of their submitted paper (it needs to be in the same PDF file and start at page 8) focused on reproducibility (include details for how someone can reproduce your work). An undergraduate student must be the leading author of the paper. Once the paper has been submitted, the set of authors cannot be changed.
2. Personal Statement
The student’s personal statement will be submitted at the same time as the research paper using the CMT submission system. It should help readers understand the student’s interests, research experiences and contributions, and future goals for data science research. In their statement, they should
I. Answer the following questions:
- How did they start data science research, and how did they join the presented research project in particular?
- Did they have difficulties in their pursuit of data science research and how did they overcome these?
- What excites them about their research area that would drive them to continue working on those problems after graduation?
- What is their expectation from participating in the UC and receiving mentorship?
II. Discuss their specific role and contributions in the data science project presented in their submitted research paper. They should,
- Write a short summary (2-3 sentences) describing the data science project presented in the submitted research paper in a way that can be understood by a broad audience within data science.
- Discuss whether project work was done as part of a team and/or independently.
- Discuss their specific contributions to the submitted research project, including:
- Did they design or implement algorithms?
- Did they design or implement the evaluation protocol?
- Did they contribute to the analysis of results?
- Did they contribute any other specific ideas of the project?
III. Provide contact information for the student’s advisor. The advisor will be asked to submit a reference letter for the student (see Item 3 below). The advisor’s name, institution, position, email, and phone number should be included.
Personal statements must be written using the NSF GRFP statement formatting guidelines, which require standard 8.5″x 11″ page size, Times New Roman font for all text, no smaller than 11-point (except text that is part of an image), 1″ margins on all sides, and no less than single spacing (approximately 6 lines per inch). Please use the provided template of the personal statement as follows.
3. Reference Letter from Advisor
A request will be emailed to an advisor of the student’s choice (not necessarily a co-author of the paper) who can speak towards the student’s data science research interests and abilities. The advisor will be asked to provide some details about the student’s contribution to the submitted research project; the student’s progress through their current undergraduate program; and how they believe the student can contribute to, and benefit from, participating in the UC. The advisor should be a faculty member, post-doc, or professional researcher with a graduate degree who can speak to all of these points.
Advisor questionnaires will be sent out shortly after the application submission deadline, and they are expected to be completed within a week. No letters of recommendation will be accepted.
Applications will be reviewed according to the following criteria:
- Clarity and completeness of the submission packet;
- Level of progress in the student’s undergraduate degree program;
- Significance of the student’s research contribution in the submitted research paper;
- Overall participation of the student in research projects;
- Utility of the UC participation and mentorship in support of the student’s continuing in post-graduate data science research;
- And assessment of how the student can contribute to others participating in the UC.
Accepted applicants who also attend the UC will have their papers published by KDD (online only). Additionally, a subset of the students will be invited to present their papers orally during the UC, while the rest will be invited to present their work in the form of a poster. All accepted applicants will also receive some financial support towards attending the conference, in exchange for volunteering a few hours of their time at the conference. The maximum amount of support provided to each grantee is set by the sponsors (ACM SIGKDD, NSF), and they are intended to partially cover the grantee’s expenses. Conference registration will be waived for all student travel award grantees. Travel may or may not be partially covered depending on the total availability of funds and the number of awards given.
Support for the 2023 Undergraduate Consortium is graciously provided by ACM SIGKDD and the National Science Foundation.
Undergraduate Consortium Chairs
Yifeng Gao, UT Rio Grande Valley
Hajar Homayouni, San Diego State University
David Anastasiu, Santa Clara University
Virginia de Sa, University of California San Diego