PhD Consortium

KDD 2025 PhD Consortium Program

Agenda

Wednesday, August 6th

Time and Location	Activity
1:00 PM – 1:05 PM (Room 605)	Introduction
1:05 PM – 2:00 PM (Room 605)	Oral Presentations “TimeDiT: Diffusion Transformers Foundation Model for Time Series Forecasting”, Defu Cao, University of Southern California “Extracting and Utilizing Interpretation in Large Language Models”, Xuansheng Wu (University of Georgia) “Fairness and Sparse Link Prediction through Ranking-Aware Metrics and Attribute Enhancement”, João Mattos, Rice University “Deep Interactions for Multimodal Molecular Property Prediction”, Patrick Soga, University of Virginia “ReMol: LLM-guided Molecular Optimization with Reinforcement Learning”, Ziqing Wang (Northwestern University)
2:00 PM – 2:30 PM (Room 605)	Keynote talk by Prof. Evangelos Papalexakis, University of California, Riverside, “My Journey Through Data Mining Research and Lessons Learned”
2:30 PM – 3:00 PM (Room 605)	Keynote talk by Prof. Rex Ying, Yale University, “Important Aspects for Navigating Your Research Journey”
3:00 PM – 3:30 PM	Coffee Break
3:30 PM – 4:00 PM (Room 716 A)	Keynote talk by Prof. Ebrahim Bagheri, University of Toronto, “Who Counts, Who Decides, Who Matters: Human Values in the Age of Algorithms”
4:00 PM – 5:30 PM (Room 716 A)	Joint Panel with Undergraduate & Master’s Consortium on the Topic of “AI-Augmented Academia: Research Innovations and Career Planning for Tomorrow’s Workforce” Panelists: Rex Ying, Yale University Ebrahim Bagheri, University of Toronto Xuan Luo, York University Sudeep Das, DoorDash Neil Shah, Snapchat

Accepted Papers

• 2: Large-scale Unaligned Multi-View Graph Clustering, Yi Wen (City University of Hong Kong)
• 3: Decoding Cardiomyocyte Ageing Through AI: Tracking Functional Decline via Temporal Motion Signatures, Md Abu Sufian, University of Leicester
• 4: TimeDiT: Diffusion Transformers Foundation Model for Time Series Forecasting, Defu Cao (University of Southern California) *
• 5: Knowledge Informed Time Series Forecasting, Zijie Pan, University of Connecticut
• 6: AI Explainability and Fairness with Guarantees, Jiří Němeček (Czech Technical University in Prague)
• 7: Edge Prompt Tuning for Graph Neural Networks, Xingbo Fu (University of Virginia)
• 8: Extracting and Utilizing Interpretation in Large Language Models, Xuansheng Wu (University of Georgia) *
• 9: Building a Global-Scale Trajectory Dataset from OpenStreetMap, Yuanshao Zhu (City University of Hong Kong)
• 10: LSRP: A Leader–Subordinate Retrieval Framework for Privacy-Preserving Cloud–Device Collaboration, Yingyi Zhang (Dalian University of Technology)
• 11: CuFun: Revolutionizing Temporal Point Process Modeling with Cumulative Distribution Functions, Maolin Wang (City University of Hong Kong)
• 12: Density-Aware Phase Segmentation in Sparse 3D Atomistic Data, Abin Shakya (Louisiana State University)
• 13: Designing Equivariant Graph Neural Network Models for Protein Structure Modeling, Animesh Animesh (IIT Kharagpur)
• 14: Fairness and Sparse Link Prediction through Ranking-Aware Metrics and Attribute Enhancement, João Mattos (Rice University) *
• 15: Literature Review and Gap Analysis on the Application of Blockchain Technology in Supply Chain Challenges, Opportunities, and Innovations, Ahmed Abdelmoula (Eötvös Loránd University)
• 16: Multi-Source Spatiotemporal Deep Learning Framework for Non-Recurring Traffic Congestion Forecasting, Jing Li (McMaster University)
• 17: Towards a Unified Evaluation Protocol for Link Prediction Interpretation over Knowledge Graphs, Beshani Weralupitiya (Rochester Institute of Technology)
• 18: Learning under Evolving, Unstable, and Modality Diverse Input Feature Spaces, Heng Lian (William & Mary)
• 19: Phantom Edges: Expanding Functionality in Graph Databases, Adam Pazdor (University of Manitoba)
• 20: ReMol: LLM-guided Molecular Optimization with Reinforcement Learning, Ziqing Wang (Northwestern University)
• 21: Event-Driven Cascade Updates in Knowledge Graphs, Bo Ni (Vanderbilt University)
• 22: Deep Interactions for Multimodal Molecular Property Prediction, Patrick Soga (University of Virginia) *
• 23: Unfair Unlearning? Accounting for Fairness in Machine Unlearning, George Barbulescu (University of Warwick) *

* accepted for oral presentation during the PhD consortium
** all papers will be displayed during Monday evening’s (August 4th) poster reception

PhD Consortium Co-Chairs

Email: KDD25-PhD-Chairs@acm.org

• Arijit Khan (Aalborg University)
• Jundong Li (University of Virginia)

Program Committee

• Abdulkadir Celikkanat, Aalborg University
• Agoritsa Polyzou, Florida International University
• Akhil Arora, Aarhus University
• Aritra Konar, KU Leuven
• Chen Chen, University of Virginia
• Cuneyt Akcora, University of Central Florida
• Dongjin Song, University of Connecticut
• Eliana Pastor, Politecnico di Torino
• Fenglong Ma, Pennsylvania State University
• Filip Ilievski, Vrije Universiteit Amsterdam
• Hua Wei, Arizona State University
• Jaemin Yoo, KAIST
• James Caverlee, Texas A&M University
• Jian Kang, University of Rochester
• Jing Ma, Case Western Reserve University
• Joydeep Chandra, Indian Institute of Technology Patna
• Lorenzo Severini, UniCredit
• Lu Lin, Pennsylvania State University
• Mehwish Alam, Institut Polytechnique de Paris
• Michelangelo Ceci, University of Bari
• Paulo Shakarian, Arizona State University
• Qi Li, Iowa State University
• Ronghua Li, Beijing Institute of Technology
• Sanjukta Bhowmick, University of North Texas
• Sara Hajian, Trustpilot
• Tyler Derr, Vanderbilt University
• Xin Wang, Tsinghua University
• Yao Ma, Rensselaer Polytechnic Institute
• Yiqun Xie, University of Maryland
• Yllka Velaj, University of Vienna
• Yushun Dong, Florida State University