IBM Early Career Data Mining Research Award

Prize Recipient

Danai Koutra
University of Michigan, 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 2024 should have received their PhD no earlier than 2013.

Applications for 2024 should include achievements dated no later than March 22, 2024.

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 [2024]” 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. DEI (diversity, equity, and inclusion) statement of the nominee
6. 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, 2024
• Results Notification: April 15, 2024

Award Selection Committee:

Luna Dong, Meta
Eamonn Keogh, UCR
Neil Shah, SnapChat
Ambuj Singh, UCSB
Jiliang Tang, MSU
Hanghang Tong, UIUC
Wei Wang, UCLA
Xifeng Yan, UCSB

Doctoral Student Forum Participants and Student Travel Scholarship Applications

Best Poster Award Runner-Up
COMBOOD: A Semiparametric Approach for Detecting Out-of-distribution Data for Image Classification

Best Poster Award
H^2ABM: Heterogeneous Agent-based model on Hypergraphs to Capture Group Interactions

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 2024 SIAM International Conference on Data Mining (SDM24)

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 2024 SIAM International Conference on Data Mining (SDM24), which will be held in Houston, Texas, U.S. April 18-20, 2024.

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.

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 2024 SIAM International Conference on Data Mining (SDM24), which will be held in Houston, Texas, U.S. April 18-20, 2024.

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, 2024
• Notification deadline: March 24, 2024
• Doctoral Forum: as part of SDM24, tentatively April 19, 2024

Application
Please apply by filling out the application form at https://forms.gle/A2aCZieHUoGBqa5K6

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 SDM24, 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 Jan 18th).

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. Jelena Gligorijevic, Yahoo! Research, U.S.
Dr. Jundong Li, University of Virginia, U.S.

Doctoral Forum Posters

Ujun Jeong, Arizona State University
Adaptive Models of User Behavior in the Dynamic Landscape of Social Networking Platforms

Bohan Jiang, Arizona State University
Disinformation Detection: An Evolving Challenge in the Age of LLMs

Xuanming Hu, Arizona State University
Automated Urban Planning: Concepts, Algorithm, and Applications

Han Xie, Emory University
Data Heterogeneity in Federated Graph Learning: Problems, Applications, and Methods

Chen Ling, Emory University
Navigating the Unknown: Enhancing Data Mining Applications with Generative AI under Uncertainty

Zhuomin Chen, Florida International University
Explainability in Graph Neural Networks with In-Distributed Proxies

Usman Gohar, Iowa State University
Evaluation of Fairness in Complex ML Systems

Yixin Liu, Lehigh University
Safegurading against Unauthorized Exploitation and Robustness in Explainable AI

Haoyu Han, Michigan State University
Effective and Efficient Graph Representation Learning

Yingqian Cui, Michigan State University
Watermarking Techniques for Copyright Protection against Generative Diffusion Models

Cong Qi, New Jersey Institute of Technology
Overcome of Unseen: Prediction of Binding between TCR and Peptides

Rui Xue, North Carolina State University
Large-scale Graph Representation Learning: Scalability, Efficiency and Applications

Yizhou Wang, Northeastern University
Towards Effective and Explainable Deep Anomaly Detection

Emmanuel Yanguem, Oklahoma State University
Diffusion based Analytics for Smart Engineering and healthcare Systems

Yujia Wang, Pennsylvania State University  
Towards Efficient Federated Learning in Heterogeneous Networks

Jinghan Zhang, Portland State University
Prototypical Reward Network for Data-Efficient RLHF

Hongliang Chi, Rensselaer Polytechnic Institute
Overcome of Unseen: Prediction of Binding between TCR and Peptides

Ge Shi, UC Davis
Advances in Explainable AI and Transparent Deep Learning

Yezi Liu, University of California Irvine
Toward efficient and trustworthy graph neural networks 

Jordan Steinhauser, University of California Riverside
Understanding Fear and Beyond in Neuronal Networks with Tensor and Graph Methods: An Interdisciplinary End-to-End Data Science Approach

Dongjie Wang, University of Central Florida 
Data-Centric AI: Taming AI-ready Feature Space from Decision-Making to Generative-AI Perspectives

Tingsong Xiao, University of Florida
Interactive Graph Temporal Point Process for Clinical Event Data

Fangxin Wang, University of Illinois Chicago
Conformal Prediction for Trustworthy Data Mining

Lihui Liu, University of Illinois Urbana-Champaign
Knowledge graph reasoning and its application

Meghna Singhm, University of Minnesota
Sleep Apnea Diagnosis and Therapy Management using Machine Learning

Majid Farhadloo, University of Minnesota
Explainable GeoAI Techniques for Spatial Pathology 

Zehong Wang, University of Notre Dame

Shengyu Chen, University of Pittsburgh
Physics-enhanced Neural Operator: An Application in Simulating Turbulent Transport

Song Wang, University of Virginia
Generalizability in Machine Learning

Zihan Chen, University of Virginia
Towards federated graph learning

Dongliang Guo, University of Virginia
Trustworthy Multi-Modal Models for Visual Understanding

He Cheng, Utah State University
Interpretable and Robust Deep Sequential Anomaly Detection

Yu Wang, Vanderbilt University
Data-quality-aware Graph Machine Learning

Yao Su, Worcester Polytechnic Institute
Towards End-to-End Knowledge Discovery in Complex Brain Imaging Data

Guanchu Wang, Rice University
Advancing Faithful Explanation Towards Transparent Machine Learning

Yu-Neng Chuang, Rice University
Efficiency for Interpretable Machine Learning: Model Reasoning via Inherent Knowledge to Model Architecture 

Jiayi Yuan, Rice University
Towards efficient and reliable next generation foundation models

Weijieying Ren, Pennsylvania State University
Incremental and adaptive learning for heterogeneous stream data

Zelin Xu, University of Florida
Infusing Spatial Knowledge into Deep Learning for Earth Science

Dongqi Fu, University of Illinois Urbana-Champaign
Empowering Graph Intelligence via Natural and Artificial Dynamics

Somya Sharma, University of Minnesota 
Towards Explainable Knowledge-Guided Machine Learning 

Jiawei Yao, University of Washington Tacoma
User-Preference Guided Representation Learning Towards User-Friendly Deep Multiple Clustering

Mingzhi Hu, Worcester Polytechnic Institute
Exploring Human Behavior: A Spatial-Temporal Foundation Model Approach