VLDB 2026 Shadow PC

We are introducing a Shadow PC to VLDB, which will follow the reviewing cycles of VLDB 2026 (Vol 19) submissions.

What is Shadow PC?

VLDB is organizing a shadow program committee for the VLDB 2026 conference. Shadow PC reviews will have no effect on the decisions of the VLDB PC, but Shadow PC members will be able to see and review the submitted VLDB papers if the authors of those papers opt in to be part of the Shadow PC paper pool at submission time. Shadow PC is educational. The goal is to give reviewing experience on real submissions to a top data management conference to the new members of the data management community.

The regular VLDB PC reviews / comments will remain invisible to the Shadow PC. The reviews written by the Shadow PC will only be visible to the shadow PC members and eventually to the authors.

The VLDB conflict-of-interest rules will apply to shadow PC as well, and Shadow PC members will be required to comply with ethical code of conduct.

Why join a shadow PC?

The VLDB Shadow PC is a great opportunity for everyone who are new to the data management community (from junior researchers to senior ones who may have started working across communities) to gain experience with the review and program committee process.

Besides the recognition of being selected to Shadow PC, serving in VLDB Shadow PC is worthwhile for a number of reasons:

How does the shadow PC work?

Shadow PC members must commit themselves to writing their own detailed and rigorous reviews for papers assigned to them by the allotted deadlines. This timely review commitment is essential to the good functioning of the Shadow PC. Candidates who might be unable to fulfil their reviewing duties should refrain from applying.

To keep the load feasible, the Shadow PC will review one paper submitted to VLDB every other month starting from June. The paper pools and review timelines will mimic the VLDB 2026 review cycles and timelines, and we will be using CMT as the reviewing platform like VLDB.

The Shadow PC will receive feedback on their reviews by the Shadow PC chairs and have a chance to learn from the reviews of other Shadow PC members.

Here is the more detailed timeline:

(Tentative) Timeline for reviewing months:

How to become a shadow PC member?

For this instance of VLDB Shadow PC, our target audience are the people who are involved in the data management community through their research and wish to get some reviewing experience in this community but haven't had a data management conference reviewing experience yet. Therefore, we are looking for candidates that (1) have already published at a data management venue or an affiliated workshop and (2) haven't yet served in the program committee of any data management venue.

You will need to submit your application at forms.office.com/e/XYihKj4UKq by March 1st, 2025 (AoE). The required information for the application can be found on the form.

Contact

For any further information, please contact the shadow PC chairs: vldb2026_shadowpc_chairs@o365team.itu.dk.

Shadow PC Members

Adeel Aslam, University of Southampton England
Bharath Vissapragada, Redpanda Data Inc
Charlotte Felius, CWI
Christos Tsapelas, National and Kapodistrian University of Athens
Chuangtao Ma, Aalborg University
Dalsu Choi, SAP
Daniel Lindner, Hasso Plattner Institute
Daniel ten Wolde, CWI
Danrui Qi, Simon Fraser University
David Justen, BIFOLD & TU Berlin
Dippy Aggarwal, Microsoft
Fangzhu Shen, Duke University
Francesco Pugnaloni, Hasso Plattner Institute
Franziska Neuhof, L3S Research Center & Leibniz University Hannover
Gang Liao, Meta
Gaurav Saxena, Amazon
Gourab Mitra, Datometry
Guanli Liu, The University of Melbourne
Guopeng Li, University of Science and Technology of China
Haoxiang Zhang, Uber
Ilaria Battiston, CWI
Ilias Azizi, UM6P
Jees Augustine, Microsoft
Jiacheng Wu, University of Washington
Jianwei Wang, University of New South Wales
Jiayao Zhang, Zhejiang University
Jonghyeok Park, Korea University
Kishy Kumar, Oracle
Lam-Duy Nguyen, Technical University of Munich
Leonhard Spiegelberg, Snowflake
Letong Wang, University of California, Riverside
Manos Chatzakis, University of Paris Cite
Maximilian K. Egger, Aarhus University
Michael Jungmair, Technical University of Munich
Mijin An, SAP Labs Korea
Navid Eslami, University of Toronto
Niko Tsikoudis, Datometry
Olga Ovcharenko, BIFOLD & Technische Universität Berlin
Peizhi Wu, University of Pennsylvania
Qilong Li, Southern University of Science and Technology
Rodrigo Nunes Laigner, University of Copenhagen
Sean Bin Yang, Aalborg University
Shubham Kaushik, Brandeis University
Shubham Vashisth, McGill University
Siyuan Dong, University of Michigan, Ann Arbor
Stavros Maroulis, Athena Research Center
Sumedh Sakdeo, LinkedIn
Ted Shaowang, University of Chicago
Ties Robroek, IT University of Copenhagen
Vinay Banakar, University of Wisconsin-Madison
Vineet Garg, Snowflake
Wentao Huang, National University of Singapore
Xiangpeng Hao, University of Wisconsin-Madison
Xiangyun Ding, University of California, Riverside
Xiao Li, IT University of Copenhagen
Xiaojun Dong, University of California, Riverside
Xinle Cao, Zhejiang University
Yangshen Deng, Southern University of Science and Technology
Yichao Yuan, University of Michigan
Yihao Hu, Duke University
Ying Zheng, National University of Singapore
Yongye Su, Purdue University
Yue Gong, University of Chicago
Zeyu Wang, Fudan University & University of Paris Cite
Zheng Zheng, Northeastern University (Toronto Campus)
Ziyang Men, University of California, Riverside
Ziyi Yan, Simon Fraser University