2025 PAKDD Distinguished Research Contributions Award Nomination
2025 PAKDD Distinguished Service Award Nomination
2025 PAKDD Early Career Research Award Application
2025 PAKDD Student Travel Award Application
PAKDD-2025 will provide the Distinguished Research Contributions Award to recognize an individual in our community who has made significant research contributions to the advancement of knowledge discovery and data mining areas. The recipient will receive the award at the annual PAKDD conference during the award session.
Eligibility
Please use the nomination form to nominate and describe clearly and substantively the research contributions of the nominee, using publications, products, awards, or other facts (e.g., citations, H-Index, major lasting impacts to the field) as evidences if so applicable.
Submission
The nominator should email the nomination form (in PDF) by December 31, 2024 (23:59:59 PST) to the PAKDD Senior Award Selection Committee Chair, Ming-Syan Chen (email: mschen@ntu.edu.tw), with the email title “PAKDD25: Distinguished Research Contributions Award Nomination”. The selection result will be announced in the annual PAKDD conference.
Download Nomination Form HerePAKDD-2025 will provide the Distinguished Service Award to recognize an individual who has made outstanding service to the community of Knowledge discovery and data mining. The recipient will receive the award at the annual PAKDD conference during the award session.
Eligibility
Please use the nomination form to nominate and describe clearly and substantively the value and the degree of the service the nominee has made, with an emphasis on how the service made is important to the evolvement of the PAKDD community.
Submission
The nominator should email the nomination form (in PDF) by December 31, 2024 (23:59:59 PST) to the PAKDD Senior Award Selection Committee Chair, Ming-Syan Chen (email: mschen@ntu.edu.tw), with the email title “PAKDD25: Distinguished Service Award Nomination”. The selection result will be announced in the annual PAKDD conference.
Download Nomination Form HereThanks to PAKDD Steering Committee, PAKDD will provide Early Career Research Award to an individual in the first 10 years after PhD. The award aims to promote young researchers in KDD fields when they create their career. It consists of a plaque (and potentially cash) to the awardee. The recipient will receive the award at the annual PAKDD Conference during the awards session.
For any questions, please contact the PAKDD Junior Award Selection Committee Chair, Raymond Wong (email: raywong@cse.ust.hk).
Eligibility
Selection Criteria
The application is evaluated based on the nominee's overall research contributions, leadership, and service in KDD fields since the awarding of the PhD. The awardee should commit to attend the PAKDD 2025 ceremony on-site to receive the award.
The Award Selection Committee will evaluate all nominations and decide on zero or more winners.
Submission Details
The nominator should send a single PDF file containing all the following documents by Dec 31, 2024 (23:59:59 PST) to the current PAKDD Award Selection Committee Chair, Raymond Wong (email: raywong@cse.ust.hk ). The email title is “PAKDD: Application of 2025 PAKDD Early Career Research Award”. In the email, please include the nominee in the CC list.
Only PDF files are accepted. No other file formats are accepted. Submissions violating the above guideline will be disregarded.
Notifications will be sent to the nominators and the nominees by Jan 25, 2025 (23:59:59 PST).
Nominations that did not result in an award in the current year can be re-submitted or updated in subsequent years as long as the eligibility conditions for the award still hold.
Download Nomination Form HereThanks to PAKDD Steering Committee, PAKDD will provide a number of Student Travel Awards to student authors of accepted papers to help them cover part of the cost for attending the conference to present their work. The amount of award for different winners may be different according to the needs of the applicants and the capacity of the fund. Typically, the amount for each award should be at most USD $500.
Applicants receiving the travel awards are required to
For any questions or doubts, please contact the PAKDD Junior Award Selection Committee Chair, Raymond Wong (email: raywong@cse.ust.hk ).
Eligibility
Only the applicants who are full‐time students (including undergraduate students and graduate students) (i) in the conference period or (ii) on the conference paper submission deadline are eligible for this award.
Selection Criteria
The following shows the selection criteria.
We will give priorities to the following applicants in this order.
PAKDD has historically drawn participants from specific groups (e.g., some countries). It has also been predominantly male. We recognize that we must change our approach and explicitly facilitate involvement from underrepresented groups in order to move the needle. If the applicant feels that his/her presence at PAKDD would help us address these representational and inclusion disparities, please include a 1 paragraph section "Representation" with a brief explanation as part of his/her submission. Higher priority will be given to applicants from underrepresented groups.
Submission Details
Please submit a single PDF file containing all the following documents to Track “2025 PAKDD Student Travel Award” of https://cmt3.research.microsoft.com/PAKDD2025 by Feb 15, 2025 (23:59:59 PST).
Only PDF files are accepted. No other file formats are accepted. Submissions violating the above guideline will be disregarded.
Notifications will be sent to the applicants by March 15, 2025 (23:59:59 PST).
Professor Hongjun Lu
Professor Hiroshi Motoda
Clustering Large Data Sets with Mixed Numeric and Categorical Values by Zhexue Huang. Proceedings of the 1st Pacific-Asia Conference on Knowledge Discovery and Data Mining, (PAKDD 1997), Singapore, World Scientific, 1997. Pages 21-35
The 2007 award for most distinguished contribution went to Professor Ramamohanarao Kotagiri, FIEAust, FTSE, FAA, Professor, Department of Computer Science and Software Engineering, The University of Melbourne. Professor Kotagiri has a long and distinguished academic research career. He is well known for his pioneering work on hashing and information retrieval in database management, new techniques for intelligent, efficient and expressive database queries, the theory and practice of emerging patterns, intrusion detection and text mining. His publications have appeared in many highly regarded conferences and journals, including PAKDD, KDD, ICDM and PKDD, and IEEE TKDE, Data Mining and Knowledge Discovery, Machine Learning and Knowledge and Information Systems. He is a member of the Australian Academy of Science and also the Australian Academy of Technological Science and Engineering. He has also served on the Prime Minister’s Science and Engineering Innovation Council.
Enhancing Effectiveness of Outlier Detections for Low Density Patterns, by Jian Tang, Zhixiang Chen, Ada Wai-Chee Fu, and David Wai-Lok Cheung. In Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, (PAKDD 2002), Lecture Notes In Computer Science, Vol 2035, Pages 247-259.
An Analysis of Quantitative Measures Associated with Rules by Yiyu Yao and Ning Zhong. Proceedings of the 3rd Pacific-Asia Conference on Knowledge Discovery and Data Mining, (PAKDD 1999), Lecture Notes In Computer Science, Vol 1574, Springer, 1999
The 2009 award for most distinguished contribution went to Professor David Cheung, Head of the Department of Computer Science and Director of the Center for E-Commerce Infrastructure Development, of the University of Hong Kong for his persistent outstanding contributions to PAKDD and eminent academic record. The citation for the award notes that Professor Cheung has been associated with the PAKDD Conference since it inception, serving on the steering committee since 2001 including a period as the chair of the steering committee. He was chair of the program committee of the fifth and ninth PAKDD conferences, in 2001 and 2005 and conference chair of PAKDD 2007. Professor Cheung has actively promoted the conference series in the Asia Pacific region and beyond and continues to encourage his own students and colleagues to contribute to the PAKDD series of conferences. Professor Cheung has a distinguished academic research career, introducing many new students to data mining and pushing forward the boundaries of research in data mining. His publications appear in many highly regarded conferences and journals. He and his students continue to support PAKKD as a conference of choice for presentation of esearch.
Mining Access Patterns Efficiently from Web Logs by Jian Pei, Jiawei Han, Behzad Mortazavi-Asl, Hua Zhu. Proceedings of the 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, (PAKDD 2000), Lecture Notes In Computer Science, Vol 1805, Springer, 2000
The 2010 award for Most Distinguished Contribution went to Professor Masaru Kitsuregawa of the University of Tokyo. Masaru has been very active in his support and contribution to PAKDD over many years. He has served various roles in a number of PAKDD conferences, including as conference chair for 2000, 2009 and 2010, and program co-chair for 2006. He continues to provide guidance as a life member of the PAKDD Steering Committee. He is also a highly respected researcher in data mining and databases. He was a member of the VLDB Trustee during 1996-2002. He served as the co-general chair of IEEE ICDE 2005, held in Tokyo. He is serving as an Asian Coordinator of the IEEE Technical Committee on Data Engineering as well as chair of several well known database and data mining conferences. He is leading a large project named ‘Info-plosion’ in Japan. In 2009 he received the ACM SIGMOD E. F. Codd Innovation Award.
Feature Selection for Clustering by Manoranjan Dash and Huan Liu. Proceedings of the 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2000), Lecture Notes In Computer Science, Vol 1805, Springer, 2000. Pages 110 - 121.
Best Paper Leman Akoglu, Mary McGlohon and Christos Faloutsos. OddBall: Spotting Anomalies in Weighted Graphs
Best Paper Runner Up Tao Yang, Longbing Cao and Chengqi Zhang. A Novel Prototype Reduction Method for the K-Nearest Neighbor Algorithm with K>=1
Best Student Paper Wanhong Xu. Supervising Latent Topic Model for Maximum-Margin Text Classification and Regression
Best Student Paper Runner Up Pallika Kanani, Andrew McCallum and Shaohan Hu. Resource-bounded Information Extraction: Acquiring Missing Feature Values On Demand
The 2011 award for Most Distinguished Contribution went to Professor Graham Williams, Director and Senior Data Miner of the Australian Taxation Office, and Adjunct Professor, Australian National University, University of Canberra, and Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences. Graham is a key supporter of PAKDD and has made significant contribution to PAKDD since the inauguration of the conference. He has served in many roles in the PAKDD conference series. He has been the Steering Committee Co-Chair and Treasurer since 2001. He was the Organization Committee Chair of PAKDD 1998, Program Co-Chair of PAKDD 2001, Industrial Chair of PAKDD 2004 and the Tutorial Chair of PAKDD 2007. He is also the founder and Steering Committee Co-Chair of the Australasian Data Mining Conference series. Graham has made significant technical contributions with considerable success in the application of data mining technology to real world problems. His pivotal role in deploying data mining in industry was recognised by the Australian Taxation Office with an Innovation Award in 2006 and an Australia Day award in 2007. Graham has published extensively in data mining in many important conferences and journals. He is the author of the immensely popular open source Rattle data mining software and a keen advocate of freely sharing research software. His Internet book Data Mining Desktop Survival Guide is a highly popular tool book for data mining practitioners and this will be published shortly under the title Data Mining with Rattle and R: The Art of Excavating Data for Knowledge Discover, by Springer.
Evaluation of Interestingness Measures for Ranking Discovered Knowledge by Robert J. Hilderman and Howard J. Hamilton. Lecture Notes In Computer Science, Vol 2035. Proceedings of the 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining, 2001. Pages 247 - 259. This paper introduced a new framework for considering how we measure the interestingness of discoveries, and has been adopted widely by other researchers.
The 2012 award for Most Distinguished Contribution went to Professor Huan Liu of the Arizona State University, USA. Professor Liu has been involved in PAKDD from its beginnings in Singapore 16 years ago. He has been an active participant and, importantly, has helped guide PAKDD to ensure its ongoing quality and relevance to the international data mining community. The award also recognises his major research contributions over many years to knowledge discovery and data mining.
The 2013 award for PAKDD Distinguished Contribution was awarded to Professor Jaideep Srivastava of the University of Minnesota, USA. This award recognises significant and ongoing contributions in research and services to the advancement of the PAKDD community and series of conferences. Professor Srivastava has been involved in PAKDD since PAKDD 2006 where he was the conference General Co-Chair. He has been an active supporter, contributing to the steering committee since then and has guided PAKDD administratively, and supported ongoing participation in the conference series. The award also recognises his major research contributions over many years to knowledge discovery and data mining. Congratulations to Professor Srivastava.
The Distinguished Contributions Award is awarded to a member of our community to recognise and honour an individual who has made significant and continued contributions in research and services to the advancement of the PAKDD conferences. The selection committee for 2014 was chaired by Professor Jaideep Srivastava of the University of Minnesota, supported by Professors David Cheung of the University of Hong Kong and Masaru Kitsuregawa of Tokyo University.
The 2014 PAKDD Distinguished Contribution is awarded to Professor Kyu-Young Whang, a Distinguished Professor of the Korean Advanced Institute of Science and Technology. This award recognises significant and ongoing contributions in research and services to the advancement of the PAKDD community and series of conferences. Professor Whang has been involved in PAKDD since PAKDD 2003. He has been an active supporter, contributing to the steering committee since then and has guided PAKDD over the years to maintain a strong conference supporting data mining across the region. The award also recognises his major research contributions over many years to data engineering and data mining. Congratulations to Professor Whang.
The Most Influential Paper Award is an award for a paper published at PAKDD tens years ago. The award recognises a paper that has had significant influence over the past decade. Google Scholar is used to identify a candidate pool of papers and these papers are then reviewed by the awards committee to consider the quality of citations. An important criteria is that the paper should present novel and big ideas which change our way of thinking. A challenger/champion approach is used by the awards committee to identify the most influential paper. The awards committee for 2014 was chaired by Professor Huan Liu, Arizona State University, USA, supported by Professor Joshua Huang of Shenzhen University, China, and Professor Thanaruk Theeramunkong, Thammasat University, Thailand.
The 2014 award for Most Influential Paper from PAKDD 2004 goes to Shantanu Godbole now of IBM India and Sunita Sarawagi of the Indian Institute of Technology, for their paper title Discriminative Methods for Multi-labeled Classification. The paper proposes to exploit the co-occurrence relationships of classes to label sets of documents, developing a new support vector machine ensemble to tackle the problem of multi-labelled text classification.
The 2014 PAKDD Distinguished Contribution is awarded to Professor Kyu-Young Whang, a Distinguished Professor of the Korean Advanced Institute of Science and Technology. This award recognises significant and ongoing contributions in research and services to the advancement of the PAKDD community and series of conferences. Professor Whang has been involved in PAKDD since PAKDD 2003. He has been an active supporter, contributing to the steering committee since then and has guided PAKDD over the years to maintain a strong conference supporting data mining across the region. The award also recognises his major research contributions over many years to data engineering and data mining. Congratulations to Professor Whang.
The Distinguished Contributions Award is awarded to a member of our community to recognise and honour an individual who has made significant and continued contributions in research and services to the advancement of the PAKDD conferences. The selection committee for 2016 was chaired by Professor David Cheung of the University of Hong Kong and assisted by Professors Kyu-Young Whang of the Korean Advanced Institute of Science & Technology and Chengqi Zhang of the University of Technology Sydney.
The 2016 PAKDD Distinguished Contribution is awarded to Zhi-Hua Zhou.
Distinguished Contributions: Professor Zhou’s distinctive research contributions in Machine Learning and Data Mining areas can be witnessed, e.g., by his many papers published in premium journals/conferences such as the AIJ (8), IEEE PAMI (6), IEEE TKDE (9), AAAI (30), IJCAI (18), ICML (9), NIPS (4), COLT (2) and KDD (6). According to GoogleScholar, his publications have received 20,000+ citations, with H-index 71, and 19 papers are with 200+ citations.
His research has been highly motivated by the core challenge of predictive modeling, i.e., generalization. Aiming to improve generalization ability of learning systems, Zhi-Hua spans his research in two wings: On one wing, he tries to construct stronger models, and thus he emphasizes studies on ensemble methods; on the other wing, he tries to study data encountered in real situations, and thus he focuses on learning from multi-labeled and partially-labeled data. First, Zhi-Hua is one of the leaders in the research of “ensemble learning” in the world. Because ensemble methods are usually with superior learning performance, they have been widely adopted by almost all winners of various data analysis competitions such as Kaggle competitions and KDDCups. Zhi-Hua’s work helped refine the scope of this area. For example, his AIJ 2002 paper received 1,280+ citations, this impressive citation number clearly shows the high impact of this work. His book “Ensemble Methods: Foundations and Algorithms” has been well-recognized as “bible in ensemble methods”. Second, Zhi-Hua is a leading expert in the research of learning from multi-labeled and partially-labeled data; involving multi-label learning, multi-instance learning, semi-supervised learning. For example, his MLkNN algorithm has become a common standard and one of the most often used multi-label learning algorithm; it has been implemented in the online MULAN toolbox by an European group, and applied by many researchers to various tasks such as image annotation, text categorization, gene function selection, etc. The corresponding paper received 900+ citations. In addition to theoretical and algorithmic contributions, he has also made significant contributions to practical applications. He held 14 patents, and developed many application systems.
Service Contributions: Zhi-Hua has distinctive contributions to the community by his outstanding services. He served on editorial boards for more than twenty journals, and served as chairs or program committee members for all major conferences in his research fields. He has served as General Chair of IEEE ICDM 2016, Program Chair of IEEE ICDM 2015, SIAM Data Mining Conference 2013, IJCAI’ 2015 Machine Learning Track, among many other conferences. He is also the founder of the ACML conference series.
PAKDD Contributions: Zhi-Hua has made great contributions to PAKDD. He served as Steering Committee member from 2007, and life member from 2015. He served as General Chair for PAKDD 2014, Program Chair for PAKDD 2007 and 2015, Area Chair/SPC, PC member for many times. He has served as Keynote speaker for PAKDD 2011. His paper won the PAKDD 2008 Best Paper Award, and he was the team leader for PAKDD 2006 Data Mining Competition (Open Category) Grand Champion, and PAKDD 2012 Data Mining Competition (Open Category) Grand Prize Winner. Education contributions: Zhi-Hua is also a talented educator. He has supervised eleven PhD students in completion, among which eight students have won the “CCF Distinguished PhD Thesis Award”, which is a prestigious award presented by the China Computer Federation, each year only 10 PhD theses in computer science in the whole country will be selected. That means, his students achieved 10% of all awards for the whole country! Many of his students have become active data mining researchers in both academia and industry.
Zhi-Hua is an AAAI Fellow, IEEE Fellow, IAPR Fellow, IET/IEE Fellow, CCF Fellow and ACM Distinguished Scientist.
Professor Tu Bao Ho, Japan Advanced Institute of Science and Technology (JAIST), Japan
Chun-Kit Chui, Ben Kao, Edward Hung. Mining Frequent Itemsets from Uncertain Data. Proceedings of the 11th Pacific-Asia Conference on Knowledge Discovery and Data Mining, (PAKDD 2007), Nanjing, China. Pages 47-58.
Harish Yenala, Manoj Chinnakotla, Jay Goyal, Convolutional Bi-Directional LSTM for Detecting Inappropriate Query Suggestions in Web Search.
Hung Vu, Tu Dinh Nguyen, Anthony Travers, Svetha Venkatesh, Dinh Phung. Energy-Based Localized Anomaly Detection in Video Surveillance.
Thanh Dai Nguyen, Sunil Gupta, Santu Rana, Svetha Venkatesh. Stable Bayesian Optimization.
Professor Christos Faloutsos, Carnegie Mellon University, USA
Qiuge Liu, Qing He and Zhongzhi Shi, Extreme Support Vector Machine Classifier. Proceedings of the 12th Pacific-Asia Conference on Knowledge Discovery and Data Mining, (PAKDD 2008), Osaka, Japan. Pages 222-233.
Professor Yang Yu, Nanjing University
Thanh Dai Nguyen, Sunil Gupta, Santu Rana, Svetha Venkatesh. A Privacy Preserving Bayesian Optimization with High Efficiency.
Jinlong Ji, Changqing Luo, Xuhui Chen, Lixing Yu, and Pan Li. Cross-Domain Sentiment Classification via A Bifurcated-LSTM.
Saurav Manchanda and George Karypis. Distributed Representation of Multi-Sense Words: A Loss Driven Approach.
Professor Ee-Peng Lim, Singapore Management University, Singapore
Xiaowei Ying and Xintao Wu. On Link Privacy in Randomizing Social Networks. Proceedings of the 13th Pacific-Asia Conference on Knowledge Discovery and Data Mining, (PAKDD 2009), Bangkok, Thailand. Pages 28-39.
Feida Zhu, Singapore Management University, Singapore
Yinghua Zhang, Yu Zhang, Qiang Yang. Parameter Transfer Unit for Deep Neural Networks.
Heng-Yi Li, Ming Li, Zhi-Hua Zhou. Towards one Reusable Model for Various Software Defect Mining Tasks.
Jianfei Zhang, Shengrui Wang, Lifei Chen, Gongde Guo, Rongbo Chen, Alain Vanasse. Time-dependent Survival Neural Network for Remaining Useful Life Prediction.
Professor Hiroshi Motoda, Osaka University, Japan Professor Graham Williams, Australian National University, Australia
Leman Akoglu, Mary McGlohon and Christos Faloutsos. OddBall: Spotting Anomalies in Weighted Graphs. Proceedings of the 14th Pacific-Asia Conference on Knowledge Discovery and Data Mining, (PAKDD 2010), Hyderabad, India. Pages 410-421.
Hengzhu Tang, Yanan Cao, Zhenyu Zhang, Jiangxia Cao, Fang Fang, Shi Wang, Pengfei Yin. HIN: Hierarchical Inference Network for Document-Level Relation Extraction.
Sonam Damani, Kedhar Nath Narahari, Ankush Chatterjee, Manish Gupta, Puneet Agrawal. Optimized Transformer Models for FAQ Answering.
Bonhun Koo, Hyunsik Jeon, U Kang. Accurate News Recommendation Coalescing Personal and Global Temporal Preferences.
Professor George Karypis, University of Minesota, USA
Professor P. Krishna Reddy, IIIT Hyderabad, India
Class Confidence Weighted kNN Algorithms for Imbalanced Data Sets (published in PAKDD2011) Wei Liu, Sanjay Chawla
Professor Ming Li, Nanjing University, China
Ting Guo, Xingquan Zhu, Yang Wang, Fang Chen. Weak Supervision Network Embedding for Constrained Graph Learning.
Zifeng Wang, Yifan Yang, Rui Wen, Xi Chen, Shao-Lun Huang, Yefeng Zheng. Lifelong Learning based Disease Diagnosis on Clinical Notes.
Jeshuren Chelladurai, Sudarsun Santhiappan, Balaraman Ravindran. GrabQC: Graph based Query Contextualization for automated ICD coding.
Professor Geoff Webb, Monash University, Australia
Professor Tanmoy Chakraborty IIIT Delhi, India
Jierui Xie, Boleslaw K. Szymanski. Towards Linear Time Overlapping Community Detection in Social Networks. Proceedings of the 16th Pacific-Asia Conference on Knowledge Discovery and Data Mining, (PAKDD 2012), Kuala Lumpur, Malaysia. Pages 25-36.
Tong Wei, Jiang-Xin Shi, Yu-Feng Li, Min-Ling Zhang. Prototypical Classifier for Robust Class-Imbalanced Learning.
Siyang Jiang, Wei Ding, Hsi-Wen Chen, Ming-Syan Chen. PGADA: Perturbation-Guided Adversarial Alignment for Few-shot Learning Under the Support-Query Shift。
Jun Zhang, Menqian Cai, Chenyu Zhao, Xiaowei Zhang, Zhiqian Zhang, Haiheng Chen, Sulong Xu. Extreme Multi-Label Classification with Hierarchical Multi-Task for Product Attribute Identification。
Professor Myra Spiliopoulou, Otto-von-Guericke Universitaet Magdeburg, Germany
Professor Jundong Li, University of Virginia, USA
Ricardo J. G. B. Campello, Davoud Moulavi, Joerg Sander. Density-Based Clustering Based on Hierarchical Density Estimates. Proceedings of the 17th Pacific-Asia Conference on Knowledge Discovery and Data Mining, (PAKDD 2013), Gold Coast, Australia. Pages 160-172.
Najeeb Jebreel, Josep Domingo-Ferrer, Yiming Li. Defending Against Backdoor Attacks by Layer-Wise Feature Analysis.
Wei-I Lin, Hsuan-Tien Lin. Reduction from Complementary-Label Learning to Probability Estimates.
Yingze Xie, Jie Xu, Liqiang Qiao, Yun Liu, Feiran Huang, Chaozhuo Li. Generative Sentiment Transfer via Adaptive Masking.