IBM Early Career Data Mining Research Award
Prize Recipient
Yizhou Sun
UCLA, U.S.
This annual award recognizes one individual in the field of data science who has made outstanding, influential, and lasting contributions to the field within 10 years of having received a PhD.
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 2022 should have received their PhD no earlier than 2011. An award winner in 2023 should have received their PhD no earlier than 2012.
Applications for 2023 should include achievements dated no later than March 22, 2023.
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 Chair by email [email protected] with “SDM/IBM Research Award [2023]” as the subject.
A nomination application (a single PDF file) should contain the following:
- Name/email of nominator (self-nominations are
not permitted).
- Name/email of candidate being nominated.
- 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.
- CV of the nominee (the CV must clearly indicate date of degree received)
- DEI (diversity, equity, and inclusion) statement of the nominee
- 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, 2023
- Results Notification: April 15, 2023
Award Selection Committee:
- Hanghang Tong, UIUC (chair)
- Ricardo Baeza-Yates
- Jing Gao
- U Kang
- Neil Shah
- Myra Spiliopoulou
- Petar Veličković
- Wei Wang
- Raymond Chi-Wing Wong
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 2023 SIAM International Conference on Data Mining (SDM23)
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 2023 SIAM International Conference on Data Mining (SDM23), which will be held in Minneapolis, Minnesota, U.S. April 27-29, 2023.
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 10, 2023
- Notification deadline: March 24, 2023
- Doctoral Forum: as part of SDM23, tentatively April 28, 2023
Application
Please apply by filling out the application form at https://forms.gle/wz1zFcUxnhTeok2k7.
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. Information about applying for the Doctoral Forum travel award is included on the application form.
Additional travel support for students to attend SDM23, independent of whether they are at a US-based Institution and whether they attend the Doctoral forum, is available from SIAM, in the form of Student Travel Awards (due on Feb 17th).
Support is also available for Early Career Professionals, such as PostDocs. See this link for more information.
Eligible applicants may apply for both SIAM Travel Support and SDM Doctoral Forum Support, however, applicants are only eligible to receive funding from one of these funding sources.
Contacts
For additional information, please email the Doctoral Forum Co-chairs:
Dr. Liyue Fan, University of North Carolina at Charlotte ([email protected])
Dr. Miao Xu, University of Queensland ([email protected])
Doctoral Forum Posters
Khandakar Tanvir Ahmed, University of Central Florida
Exploiting Multi-omics Data in Computational Biology
Sarwan Ali, Georgia State University
Efficient Sequence Analysis Using Machine Learning
Defu Cao, University of Southern California
Causality Awareness time series applications in the wild.
Shengyu Chen, University of Pittsburgh
Physics-guided machine learning method using in scientific problems solving
Matthew Eagon, University of Minnesota Twin Cities
Data-Driven Approaches to Intelligent Energy Management for Commercial Electric Vehicles and Supporting Infrastructure
SeyedAmirmasoud Ghiassi, Delft University of Technology, The Netherlands
Towards robust learning systems
Zhichun Guo, University of Notre Dame
Generalizing GNNs for Multi-applications with Effective Training Strategies
Jayant Gupta, University of Minnesota Twin Cities
Responsible spatial data science
Wei Jin, Michigan State University
Empowering Graph Neural Networks from a Data-Centric View
Jina Kim, University of Minnesota Twin Cities
Information Extraction From Large-Scale Spatial Data Using Multimodal Representations
Yuxuan Li, Oklahoma State University
Adversarial Learning-assisted Data Analytics in Manufacturing and Healthcare Systems
Zekun Li, University of Minnesota Twin Cities
Geospatial Data Understanding: A Peek into Historical Maps and Contemporary Structured Databases
Zhuoqun Li, Louisiana State University
Predictive Modeling of Temporal event sequences
David Liu, Northeastern University
A complex-systems approach to algorithmic fairness
Jing Ma, University of Virginia
CLEAR: Generative Counterfactual Explanations on Graphs
Raha Moraffah, Arizona State University
Black-box adversarial attacks through the lens of surrogates
Saed Rezayi, University of Georgia
Learning better representations using auxiliary knowledge
Muhammad Usama Saleem, University of North Carolina at Charlotte
Privacy-Preserving Data Synthesis
Somya Sharma, University of Minnesota Twin Cities
Towards Explainable Physics-Guided Machine Learning
Kimia Shayestehfard, Northeastern University
Permutation Invariant Graph Representation Learning
Mingzhou Yang, University of Minnesota Twin Cities
Spatial Data Mining in Transportation Decarbonization
Xin Zhang, Worcester Polytechnic Institute
Enabling Urban Intelligence via Harnessing Human Generated Spatial Temporal Data
Yuying Zhao, Vanderbilt University
Learning Beyond Utility for Social Good: Fairness, Explainability, and Diversity
Guangtao Zheng, University of Virginia
Exploiting Spurious Correlation for Efficient Model Generalization