Best Research Paper Award
Computation of the Empirical Frechet Mean for Sets of Large Graphs with Applications to Regression
Daniel Ferguson
University of Colorado at Boulder, U.S.
François G. Meyer
University of Colorado at Boulder, U.S.
Best Application Paper Award
Unified Forward/Inverse Framework for Lake Temperature Modeling with Invertibility aware Integration of Static and Time-series data
Kshitij Tayal
University of Minnesota, U.S.
Xiaowei Jia
University of Pittsburgh, U.S.
Rahul Ghosh
University of Minnesota, U.S.
Jared Willard
U.S. Geological Survey, U.S.
Jordan Read
University of Minnesota, U.S.
Vipin Kumar
University of Minnesota, U.S.
Best Student Poster Award
Algorithmic Solutions for Socially Responsible AI
Lu Cheng
Arizona State University, U.S.
IBM Early Career Data Mining Research Award
Prize Recipient
Jiliang Tang
Michigan State University, U.S.
About the Award: The SDM/IBM Early Career Data Mining Researcher Award for Excellence in Data Analytics seeks to recognize one individual (no runner up/ honorable mention) who has made outstanding, influential, and lasting contributions in the field of data analysis and who will be within 10 years of having received their PhD degree as of the calendar year prior to the year of the award. For example, an award winner in 2021 should have received their PhD no earlier than 2010. An award winner in 2022 should have received their PhD no earlier than 2011.
Applications for 2022 should include achievements dated no later than March 22, 2022.
Nominations: A candidate may be nominated by any member of the community except members of the Award Selection Committee. An individual may nominate at most one candidate for this award. Nominations must be submitted to the Award Selection Committee Co-Chairs by email [email protected] and [email protected] with “SDM/IBM Research Award [2022]” as the subject.
A nomination application (a single PDF file) should contain the following:
1. Name/email of nominator (self-nominations are not permitted).
2. Name/email of candidate being nominated.
3. A statement by the nominator (maximum of 500 words) as to why the nominee is highly deserving of the award.Note that since the award is for outstanding contributions, the statement and supporting letters should address what the contributions are and why they are both outstanding and significant. The nomination should also list the names and email addresses of up to 3 persons who will provide letters supporting the nomination.
4. CV of the nominee (the CV must clearly indicate date of degree received.
5. Up to three support letters. The letters should be collected by the nominator and included in the nomination. The letter writers may not be the nominator nor Award Selection Committee members.
Important Dates:
Nomination Deadline: March 22, 2022
Results Notification: April 15, 2022
Award Selection Committee:
C. Seshadhri (Co-Chair)
Arindam Banerjee (Co-Chair)
Leman Akoglu
Stratis Ioannidis
S. S. Ravi
Hanghang Tong
Tony Wirth
Xifeng Yan
Note that members of the selection committee cannot be nominators or provide support letters. Also note that the intent is to select exactly one awardee – re-nominations for subsequent years are permissible as long as the individuals continue to maintain eligibility. The committee at its discretion may elect not to make an award in a particular year.
Doctoral Student Forum Participants and Student Travel Scholarship Applications
The SDM Doctoral Forum is a unique opportunity for PhD students in data science (including data mining, machine learning, databases, and pattern recognition) to present their doctoral dissertation in poster format and get feedback from SDM participants and senior leaders in the field. The SDM doctoral forum will be held in a plenary poster session alongside posters from the main conference, allowing for an interesting cross fertilization of ideas. Past participants have benefited significantly from this plenary session.
Applications to the Doctoral Forum at the 2022 SIAM International Conference on Data Mining (SDM’22)
We invite doctoral students in data science (including data mining, machine learning, databases, and pattern recognition) to present their doctoral dissertation (including ongoing and future work) in poster format at the Doctoral Forum of the 2022 SIAM International Conference on Data Mining (SDM’22), which will be held in Alexandria, Virginia, U.S. April 28-30, 2022.
The forum is held in a plenary poster session alongside posters from the main conference, allowing for an interesting cross fertilization of ideas.
The forum is a unique opportunity for PhD students to get feedback from SDM participants and senior leaders in the field. It is also an exciting opportunity for networking and creating future collaborations. Past participants have benefited significantly from this plenary session. An award will be given to the best poster presentation.
In addition to the poster session, there will be a mentorship panel with senior leaders in the field, which will provide perspectives and tips for graduate studies and what comes after graduation.
Key Dates
• Application deadline: March 15, 2022
• Notification deadline: March 28, 2022
• Doctoral Forum: as part of SDM’22, April 28-30, 2022 (exact day TBA).
Application
Please apply by filling out the application form at https://forms.gle/1akYWvqdSwTFBdfo6.
We welcome submissions both from senior doctoral students who have a more concrete idea of their dissertation, as well as junior doctoral students who may not have a full plan for their dissertation yet but have a direction and can benefit from the feedback by the forum participants.
Travel support
Limited support for students at U.S.-based institutions who are accepted to the Doctoral Forum is available thanks to an NSF grant.
Additional travel support for students to attend SDM’22, independent from their location and whether they attend the Doctoral forum, is available from SIAM, in the form of Student Travel Awards.
Contacts
For additional information, please write to the Doctoral Forum Co-chairs:
Jiliang Tang, Michigan State University ([email protected]) and Han-Jia Ye, Nanjing University ([email protected])
Doctoral Forum Posters
The Doctoral Forum Poster Session will take place on Friday, April 29 at 6:00 PM Eastern Time.
1. Hongliang Chi, New Jersey Institute of Technology
A general graph contrastive learning boosting framework
2. Lu Cheng, Arizona State University
Algorithmic Solutions for Socially Responsible AI
3. Da-Wei Zhou, Nanjing University
Class-Incremental Learning in the Open-World
4. Yushun Dong, University of Virginia
EDITS: Modeling and Mitigating Data Bias for Graph Neural Networks
5. Yuying Zhao, Vanderbilt University
Fair and Explainable Machine Learning for Social Good
6. Song Wang, University of Virginia
Few-shot Learning Problems on Graphs
7. William Shiao, University of California, Riverside
Fusing Deep Learning and Tensor Methods for Graph Mining
8. Michael Rawson, University of Maryland at College Park
Geometric and Topological Reconstruction
9. Kaize Ding, Arizona State University
Graph Minimally-Supervised Learning
10. Congyu Qiao, Southeast University
Instance-Dependent Partial Label Learning
11. Yongbiao Gao, Southeast University
Label Enhancement for Label Distribution Learning via Prior Knowledge
12. Jing Ma, University of Virginia
Learning Fair Node Representations with Graph Counterfactual Fairness
13. Yu Wang, Vanderbilt University
Overcoming Data Quality Issues for Graph Neural Networks
14. Wen Gu, Huazhong University of Science and Technology
Positive-unlabeled Learning via Optimal Transport
15. Lue Tao, Nanjing University of Aeronautics and Astronautics
Research on Hypocritical Attack and Defense in Machine Learning
16. Hui Hu, University of Wyoming
Robust Privacy-Preserving Deep Learning under Side Channel Power Attacks
17. Lan-Zhe Guo, Nanjing University
Semi-Supervised Learning for Open Environments
18. Işıl Baysal Erez, University of Twente
The mutually reinforcing of sparse data and sparse training models
19. Ying-Peng Tang, Nanjing University of Aeronautics and Astronautics
Towards pragmatic active learning design