* We apologize if you receive multiple copies of this CfP * * For the online version of this Call, visit: https://cikm2024.org/call-for-phd-symposium/
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CIKM 2024: 33rd ACM International Conference on Information and Knowledge Management
Boise, Idaho, USA
October 21–25, 2024
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We are excited to invite Ph.D. students in databases (DB), information retrieval (IR), and knowledge management (KM) to submit their research proposals for the PhD Symposium at the 33rd ACM International Conference on Information and Knowledge Management (CIKM 2024). The conference will take place at the Boise Centre in Boise, Idaho, USA, from October 21 to 25, 2024.
The PhD Symposium is designed to provide a supportive environment where doctoral students can present their ongoing research, receive feedback from experienced researchers, and engage with peers at similar stages of their doctoral journey. This event aims to foster discussions on research questions, methodologies, and preliminary results, contributing to the student’s doctoral research progression.
CIKM 2024 is deeply committed to improving the field by making the research community more diverse, equitable, and inclusive. We highly encourage women and students from other underrepresented demographic groups to submit their work.
-------------------------- Key Dates --------------------------
* Submission Deadline: 17 June 2024 * Acceptance Notification: 16 July 2024 * Camera-ready Version Due: 8 August 2024 * Doctoral Consortium: 25 October 2024
-------------------------- Symposium Objectives -------------------------- * Feedback and Guidance: Offer a platform for doctoral students to present their research and receive constructive feedback from the CIKM community’s senior researchers. * Community Building: Help participants network with other doctoral students and researchers, facilitating knowledge exchange and potential collaborations. * Insight into Career Paths: Through panel discussions and networking sessions, provide insights into career opportunities post-PhD in academia and industry. * Prospective attendees should have written or be close to completing a thesis proposal (or equivalent). It is desirable that students are not so close to completing their Ph.D. that the event would have little impact on their work. Similarly, students should not be so early in their Ph.D. program that a concrete topic has not been chosen yet. We strongly advise students to discuss this criterion with their advisor(s) or supervisor(s) before submitting.
Doctoral students who submit to the Symposium are allowed to have previously published their research. They are encouraged to submit full, short, or demo papers of their work to the CIKM 2024 conference and associated workshops.
-------------------------- Topics of Interest --------------------------
We welcome submissions across the broad spectrum of AI, data science, databases, information retrieval, and knowledge management. Research with real-world social impact is particularly encouraged.
Topics of interest include, but are not limited to, the following areas:
* Data and information acquisition and preprocessing (e.g., data crawling, IoT data, data quality, data privacy, mitigating biases, data wrangling) * Integration and aggregation (e.g., semantic processing, data provenance, data linkage, data fusion, knowledge graphs, data warehousing, privacy and security, modeling, information credibility) * Efficient data processing (e.g., serverless, data-intensive computing, database systems, indexing and compression, architectures, distributed data systems, dataspaces, customized hardware) * Special data processing (e.g., multilingual text, sequential, stream, spatiotemporal, (knowledge) graphs, multimedia, scientific, and social media data) * Analytics and machine learning (e.g., OLAP, data mining, machine learning and AI, scalable analysis algorithms, algorithmic biases, event detection and tracking, understanding, interpretability) * Neural Information and knowledge processing (e.g., graph neural networks, domain adaptation, transfer learning, network architectures, neural ranking, neural recommendation, and neural prediction) * Information access and retrieval (e.g., ad hoc and web search, facets, and entities, question answering and dialogue systems, retrieval models, query processing, personalization, recommender systems) * Users and interfaces for information and data systems (e.g., user behavior analysis, user interface design, perception of biases, personalization, interactive information retrieval, interactive analysis, conversational interfaces) * Evaluation, performance studies, and benchmarks (e.g., online and offline evaluation, best practices, user studies) * Crowdsourcing (e.g., task assignment, worker reliability, optimization, trustworthiness, transparency, best practices) * Understanding multi-modal content (e.g., natural language processing, speech recognition, computer vision, content understanding, knowledge extraction, knowledge graphs, and knowledge representations) * Data presentation (e.g., visualization, summarization, readability, VR, speech input/output) * Applications (e.g., urban systems, biomedical and health informatics, legal informatics, crisis informatics, computational social science, data-enabled discovery, social media) * Fairness, accountability, transparency, and ethics (e.g., sociotechnical nature of information access systems, algorithmic fairness, transparency and explainability, misinformation and disinformation)
-------------------------- Submission Guidelines -------------------------- PhD students interested in participating should submit a paper (up to 4 pages, including references) using the ACM camera-ready two-column template. Submissions are single-blind, should be solely authored by the student, and clearly state the Ph.D. supervisor(s) (“supervised by …”). The submitted paper should be discussed with the PhD supervisor(s) before submission. Submissions should cover the following aspects:
* Problem: What research problem or question does your work address? * State of the Art: How does your work relate to existing research in CIKM-related fields (e.g., information retrieval, databases, machine learning, data mining)? * Approach: Your novel approach to addressing the problem. * Methodology: The methodology you use or plan to use, including evaluation strategies. * Results: Any preliminary results you have obtained. * Conclusion and Future Work: Your conclusions and future research directions so far. * Additionally, include a one-page appendix detailing: - Topics and questions you wish to discuss with mentors and peers. - A statement from your advisor(s) supporting your participation, describing the current status of your research, and providing an anticipated thesis completion date.
-------------------------- Selection Procedure -------------------------- Candidates will be selected based on the potential of their research for future impact and their potential to benefit from participating in the Symposium.
Submissions will be reviewed by the PhD Symposium Program Committee, comprising experienced researchers who will provide feedback and suggest future research directions.
All accepted PhD Symposium papers (excluding the appendix) will be included in the main proceedings and available through the ACM Digital Library. If accepted, presenting the results at the PhD Symposium is mandatory.
-------------------------- Symposium Format -------------------------- The symposium will include presentations by the Ph.D. students, plenary discussions, one-to-one mentorship sessions, and panel discussions focusing on career paths post-PhD.
-------------------------- Student Travel Support -------------------------- Students are highly encouraged to apply for student travel support from CIKM. Application details will be available on the CIKM 2024 website. Students must apply for the support to be considered.
-------------------------- Chairs Contact Information -------------------------- For more information, contact the PhD Symposium chairs at: CIKM2024-phdsymposium [at] easychair [dot] org
Yanfang (Fanny) Ye (University of Notre Dame, US) Jiaxin Mao (Renmin University of China, China)