KDD-2015, the premier international forum for data science, data mining, knowledge discovery and big data research and practice, will feature plenary presentations, paper presentations, poster sessions, workshops, tutorials, exhibits, and the KDD Cup competition.

21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD-2015)
August 10-13, 2015, Sydney, Australia
http://www.kdd.org/kdd2015/

• Registration: http://www.kdd.org/kdd2015/registration.html
• Accepted Papers: http://www.kdd.org/kdd2015/program.html#accepted-research-papers
• Workshops: http://www.kdd.org/kdd2015/workshop.html
• Tutorials: http://www.kdd.org/kdd2015/tutorial.html
• KDD Cup: https://kddcup2015.com/information.html

Please become a SIGKDD member. Membership is a great way to stay connected and contribute back. Encourage students too, and help support their participation at KDD. Student travel awards are available. At $25 ($15 for students), just the discount (over $100 USD) on this year's KDD conference is worth it!

The conference is yet to start but the conversations are ready to go on LinkedIn Group , Facebook page or Twitter handle (@KDD_News).

The 21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining will be held in Sydney, Australia on August 10-13, 2015. This is the first time for SIGKDD to be held in the southern hemisphere.

KEYNOTES, INDUSTRY & GOVERNMENT INVITED TALKS:

KDD-2015 features an exciting program of 4 keynote addresses by leading authorities: Susan Athey, Hugh Durrant-Whyte, Ronny Kohavy, and Daphne Koller.

Industry and Government invited talks from recognized thought-leaders.

PAPERS

The conference will feature 160 Research Track papers and 68 Industrial and Government Track papers. In addition to oral presentations, all papers will be showcased during evening poster sessions. Full details available at: http://www.kdd.org/kdd2015/program.html

WORKSHOPS

14 exciting workshops featured at KDD-2015 are:

• Workshop 1: Workshop on Outlier Definition, Detection, and Description (ODDx3)
• Workshop 2: The 2nd International Workshop on Data Mining for Brain Science (BrainKDD)
• Workshop 3: Workshop on Learning from Small Sample Sizes
• Workshop 4: Workshop on Connected Health at Big Data Era (BigCHat)
• Workshop 5: Workshop on High Performance Graph Mining
• Workshop 6: Workshop on Mining and Learning from Time Series (MiLeTS)
• Workshop 7: Workshop on Interactive Data Exploration and Analytics (IDEA)
• Workshop 8: Workshop on Issues of Sentiment Discovery and Opinion Mining (WISDOM)
• Workshop 9: Workshop on Large-Scale Sports Analytics
• Workshop 10: The 14th International Workshop on Data Mining in Bioinformatics (BIOKDD)
• Workshop 11: The 1st International Workshop on Population Informatics for Big Data (PopInfo)
• Workshop 12: The 4th International Workshop on Urban Computing
• Workshop 13: Workshop on Social Recommender Systems (SRS)
• Workshop 14: The 4nd International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications (BigMine)

TUTORIALS

12 in-depth tutorial sessions to be conducted at KDD-2015 are:

• Tutorial 1: VC-Dimension and Rademacher Averages: From Statistical Learning Theory to Sampling Algorithms. Matteo Riondato, Eli Upfal
• Tutorial 2: Graph-Based User Behavior Modeling: From Prediction to Fraud Detection. Alex Beutel, Leman Akoglu, Christos Faloutsos
• Tutorial 3: Data-Driven Education. Rakesh Agrawal
• Tutorial 4: Dense subgraph discovery (DSD). Aristides Gionis, Charalampos Tsourakakis
• Tutorial 5: Automatic Entity Recognition and Typing from Massive Text Corpora: A Phrase and Network Mining Approach. Xiang Ren, Ahmed El-Kishky, Chi Wang, Jiawei Han
• Tutorial 6: Big Data Analytics: Optimization and Randomization. Tianbao Yang, Qihang Lin, Rong Jin
• Tutorial 7: Big Data Analytics: Social Media Anomaly Detection: Challenges and Solutions. Sanjay Chawla, Yan Liu
• Tutorial 8: Diffusion in Social and Information Networks: Problems, Models and Machine Learning Methods. Manuel Gomez Rodriguez, Le Song
• Tutorial 9: Medical Mining. Myra Spiliopoulou, Pedro Pereira Rodrigues, Ernestina Menasalvas
• Tutorial10: Large Scale Distributed Data Science using Apache Spark. James G. Shanahan, Liang Dai
• Tutorial 11: Data-Driven Product Innovation. Xin Fu, Hernán Asorey
• Tutorial 12: Web Personalization and Recommender Systems. Shlomo Berkovsky, Jill Freyne

REGISTRATION

KDD 2015 Early Bird Registration is now open. http://www.kdd.org/kdd2015/registration.html

We look forward to hosting you at KDD 2015 in Sydney, Australia!

Paper submission and deadlines

Website:

KDD accepts only electronic submissions in PDF format at https://cmt.research.microsoft.com/KDD2015/.

Deadlines:

The deadline for submission is February 20, 2015, at 11:59PM Pacific Standard Time.

Description of the Research track

We invite submission of papers describing innovative research on all aspects of knowledge discovery and data mining, ranging from theoretical foundations to novel models and algorithms for data mining problems in science, business, medicine, and engineering. Visionary papers on new and emerging topics are also welcome, as are application-oriented papers that make innovative technical contributions to research. Authors are explicitly discouraged from submitting incremental results that do not provide significant advances over existing approaches.

Papers submitted to the Research Track are solicited in all areas of data mining, knowledge discovery, and large-scale data analytics, including, but not limited to:

• Big Data: Efficient and distributed data mining platforms and algorithms, systems for large-scale data analytics of textual and graph data, large-scale machine learning systems, distributed computing (cloud, map-reduce, MPI), large-scale optimization, and novel statistical techniques for big data.
• Data Science: Methods for analyzing scientific data, business data, social network analysis, recommender systems, mining sequences, time series analysis, online advertising, bioinformatics, systems biology, text/web analysis, mining temporal and spatial data, and multimedia processing.
• Foundations of Data Mining: Data mining methodology, data mining model selection, visualization, asymptotic analysis, information theory, security and privacy, graph and link mining, rule and pattern mining, web mining, dimensionality reduction and manifold learning, combinatorial optimization, relational and structured learning, matrix and tensor methods, classification and regression methods, semi-supervised learning, and unsupervised learning and clustering.

Evaluation and decision criteria

As per KDD tradition, reviews are not double-blind, and author names and affiliations should be listed.

Submitted papers will be assessed based on their novelty, technical quality, potential impact, and clarity. For papers that rely heavily on empirical evaluations, the experimental methods and results should be clear, well executed, and repeatable. Authors are strongly encouraged to make data and code publicly available whenever possible.

Papers will be reviewed by members of the KDD program committee and decisions will be emailed to all authors by May 12, 2015. Note that there will be an author response phase between submission and decisions.

Formatting requirements

Papers are limited to 10 pages, including references, diagrams, and appendices, if any. The format is the standard double column ACM Proceedings Template, Tighter Alternate style.

Additional information about formatting and style files are available online at: http://www.acm.org/sigs/publications/proceedings-templates.

As per KDD tradition, reviews are not double-blind, and author names and affiliations should be listed.

All submitted articles must be in PDF format. The maximum file size for submissions is 10MB.

Subject areas

When you submit your paper to CMT, you will be asked to select which terms from a pre-defined list of subjects could best be used to describe the content of your paper. The purpose of this is to help us in assigning reviewers to your paper. Reviewers will also indicate their expertise using the same set of subject area terms.

Authors submitting a paper will be asked to select one primary subject area, and up to 5 secondary subject areas from the sets of terms below. The terms have been grouped to provide a somewhat systematic overview of topics relevant to the KDD conference.

• Applications
• Advertising
• Bioinformatics
• Data mining for social good
• E-commerce
• Education
• Finance
• Healthcare and medicine
• Information retrieval
• Marketing
• Markets and crowds
• Mobile
• Natural language processing
• Physical sciences
• Social media and publishing
• Social sciences
• User modeling
• Web mining

• Big Data
• Big-data infrastructure
• Distributed computing --- cloud, map-reduce, MPI, others
• Hashing
• Large-scale optimization
• Sampling
• Scalable methods
• Stream mining

• Knowledge discovery
• Causal inference
• Exploratory analysis
• Interpretable models
• Sparse models
• Visualization

• Design of experiments
• Active learning
• Adaptive experimentation
• Adaptive models
• Online learning and bandits
• Surveys

• Data mining foundations
• Data ethics
• Data mining methodology

• Mining rich data types
• Graphs and links
• Multimedia
• Relations and structure
• Sequence
• Spatial
• Temporal / time series
• Text
• Unstructured

• Recommender systems
• Cold-start
• Collaborative filtering
• Content-based methods
• Evaluation and metrics

• Security and privacy
• Anonymization
• Fraud detection
• Intrusion detection
• Spam detection

• Semi-supervised learning
• Anomaly/novelty detection
• Learning with partial labels

• Social and graphs
• Community detection
• Graph algorithms
• Link prediction
• Social and information networks

• Supervised learning
• Classification
• Metric learning
• Multi-label
• Ranking
• Regression
• Structured output prediction
• Transfer learning

• Dimensionality reduction and unsupervised learning
• Clustering
• Dimensionality reduction
• Feature selection
• Matrix/tensor factorization
• Rule and pattern mining
• Topic models, embeddings, and latent variable models

• Methods
• Bayesian inference
• Decision trees
• Ensemble methods
• Generalized linear models
• Kernels
• Large-margin methods
• Matrix and tensor methods
• Neural networks and deep learning
• Probabilistic methods and graphical models
• Similarity-based methods

• None of the above

Dual submission policy

Submitted papers must describe work that is substantively different from work that has already been published, or accepted for publication, or submitted in parallel to other conferences or to journals. Such submissions violate our dual submission policy.

There are several exceptions to this rule:

• Submission is permitted of a short version of a paper that has been submitted to a journal, but has not yet been published in that journal. Authors must declare such dual-submissions either through the CMT submission form, or via email to the program chairs (research-chairs2015@kdd.org). It is the authors’ responsibility to make sure that the journal in question allows dual concurrent submissions to conferences.
• Submission is permitted for papers presented or to be presented at conferences or workshops without proceedings, or with only abstracts published.
• Submission is permitted for papers that have previously been made available as a technical report (or similar, e.g., in arXiv).

Conflicts of interest

Enter the email domains of all institutions with which you have an institutional conflict of interest. You have an institutional conflict of interest if

• You are currently employed or have been employed at this institution in the past three years.
• You have extensively collaborated with this institution within the past three years.

Furthermore, all authors are required to identify all members of the program committee with which they have a conflict of interest. Select all PC and SPC members

• who have been your PhD advisor;
• for whom you have been a PhD advisor;
• with whom you have co-authored a paper published in or since 2010;

If there are any other PC or SPC members who you believe have, or may be perceived to have, a conflict of interest not covered above, please notify the PC Chairs by email (research-chairs2015@kdd.org).

Copyright

Accepted papers will be published in the conference proceedings by ACM and also appear in the ACM Digital Library.

The rights retained by authors who transfer copyright to ACM can be found here.

CMT Submission System

https://cmt.research.microsoft.com/KDD2015.

To log into the CMT system:

• If you do not yet have a CMT account for KDD 2015, you can request a new one by following the link “New Users? Sign up here”.
• If you are already registered as PC or SPC Member for the Research Track, you can log in under that account and chose “Author” under “Select your Role”.

Paper submission and deadlines

Website:

KDD accepts only electronic submissions in PDF format at https://cmt.research.microsoft.com/PT2015/.

Deadlines:

The deadline for submission is February 20, 2015, at 11:59PM Pacific Standard Time.

Description of the Industry & Government track

We invite submissions of papers describing research and implementations of data mining/data analytics/big data/data science solutions and systems for practical tasks and practical settings. The application domains of interest include, but are not limited to education, public policy, industry, government, healthcare, e-commerce, telecommunications, law, or non-profit settings. Our primary emphasis is on papers that advance the understanding of, and show how to deal with, practical issues related to deploying analytics technologies. This track also highlights new research challenges motivated by analytics and data mining applications in the real world.

Submitted papers will go through a competitive peer review process. The Industry & Government Track is distinct from the Research Track in that submissions solve real-world problems and focus on systems that are deployed or are in the process of being deployed. Submissions must clearly identify one of the following three areas they fall into: "deployed", "discovery", or "emerging".

The criteria for submissions in each category are as follows:

• Deployed: Must describe deployment of a system that solves a non-trivial real-world problem. The focus should be on describing the problem, its significance, decisions and tradeoffs made when making design choices for the solution, deployment challenges, and lessons learned.
• Discovery: Must include results that are discoveries with demonstrable value to an industry or government organization. This discovered knowledge must be "externally validated" as interesting and useful; it can not simply be a model that has better performance on some traditional evaluation metrics such as accuracy or area under the curve. A new scientific discovery enabled by the use of data mining techniques is an example of what this category will include.
• Emerging: Submissions do not have to be deployed but must have clear applications to Industry/ Government to distinguish them from KDD research papers. They may also provide insight into issues and factors that affect the successful use and deployment of Data Mining and Analytics. Papers that describe enabling infrastructure for large-scale deployment of Data Mining and analytics techniques also fall in this category.

Evaluation and decision criteria

As per KDD tradition, reviews are not double-blind, and author names and affiliations should be listed.

Submitted papers will be assessed based on their novelty, technical quality, potential impact, and clarity. For papers that rely heavily on empirical evaluations, the experimental methods and results should be clear, well executed, and repeatable. Authors are strongly encouraged to make data and code publicly available whenever possible.

Papers will be reviewed by members of the KDD program committee and decisions will be emailed to all authors by May 12, 2015. Note that there will be an author response phase between submission and decisions.

Formatting requirements

Papers are limited to 10 pages, including references, diagrams, and appendices, if any. The format is the standard double column ACM Proceedings Template, Tighter Alternate style.

Additional information about formatting and style files are available online at: http://www.acm.org/sigs/publications/proceedings-templates.

Note: Papers that do not meet the formatting requirements will be rejected without review.

Dual submission policy

Submitted papers must describe work that is substantively different from work that has already been published, or accepted for publication, or submitted in parallel to other conferences or to journals. Such submissions violate our dual submission policy.

There are several exceptions to this rule:

• Submission is permitted of a short version of a paper that has been submitted to a journal, but has not yet been published in that journal. Authors must declare such dual-submissions either through the CMT submission form, or via email to the program chairs ( industry-chairs2015@kdd.org). It is the authors’ responsibility to make sure that the journal in question allows dual concurrent submissions to conferences.
• Submission is permitted for papers presented or to be presented at conferences or workshops without proceedings, or with only abstracts published.
• Submission is permitted for papers that have previously been made available as a technical report (or similar, e.g., in arXiv).

Copyright

The rights retained by authors who transfer copyright to ACM can be found here.

The KDD 2015 organizing committee solicits proposals for full-day and half-day workshops to be held in conjunction with the main conference. The purpose of a workshop is to provide an opportunity for participants from academia, industry, government and other related parties to present and discuss novel ideas on current and emerging topics relevant to knowledge discovery and data mining. Workshops are (tentatively) scheduled for August 10, 2015.

Description

Each workshop should be organized under a well-defined theme focusing on emerging research areas, challenging problems and industrial/governmental applications. Organizers have free controls on the format, style as well as building blocks of the workshop. Possible contents of a workshop include but are not limited to invited talks, regular papers/posters, panels, and other pragmatic alternatives. In case workshop proposers need extra time to prepare their workshop, early decisions may be considered if justified.

The goal of the workshops is to provide an informal forum to discuss important research questions and practical challenges in data mining and related areas. Novel ideas, controversial issues, open problems and comparisons of competing approaches are strongly encouraged as workshop topics. In particular, we would like to encourage organizers to avoid a mini-conference format by (i) encouraging the submission of position papers and extended abstracts, (ii) allowing plenty of time for discussions and debates, and (iii) organizing workshop panels.

Topics of Interest

Possible workshop topics include all areas of data mining and knowledge discovery, machine learning, statistics, and data and information sciences, but are not limited to these. Interdisciplinary workshops with applications of data mining and data sciences to various disciplines (such as health, medicine, biology, sustainability, ecology, social sciences, humanities, or aerospace) are of high interest.

Duties

Organizers of accepted workshops are expected to announce the workshop and disseminate call for papers, maintain the workshop website, gather submissions, conduct the reviewing process and decide upon the final workshop program. They are also required to prepare an informal set of workshop proceedings to be distributed with the registration materials at the main conference, with a proceedings format template provided by KDD 2015. Workshop organizers may choose to form organizing or program committees aiming to accomplish these tasks successfully.

Proposals

The proposal should contain the following information:

• The NAMES, AFFILIATIONS, and SHORT BIOS of all the organizers
• The MAIN CONTACT organizer's e-mail and telephone number
• The TITLE of the workshop
• A maximum of three paragraphs that describe the TOPIC of the workshop, its target AUDIENCE, and its RELEVANCE to SIGKDD
• One paragraph MOTIVATING the workshop (why we should organize it NOW in conjunction with KDD 2015)
• Tentative names of invited speakers, reviewers, and panelists (if a panel will be organized)
• The desired LENGTH of the workshop: full-day (~8 hours) or half-day (~4 hours)
• Tentative PROGRAM SKETCH
  • For workshops previously held at KDD or other conferences, details on venue, attendance and number of submissions/accepted papers from past editions
  • For new workshops, a list of possible attendees/submissions and/or a justification of the expected attendees/submissions
  • Tentative descriptions of all other workshop components (panels, discussion sessions, poster sessions, invited talks,etc.)

Workshop proposals should be emailed to workshops2015@kdd.org by March 6, 2015 at 11:59 PM Pacific Standard Time.

Deadlines and Dates

This year, all workshops will have a uniform deadline for their paper submissions and notifications. In addition, all deadlines are at 11:59 PM Pacific Standard Time.

• Workshop proposal submissions: March 6, 2015
• Workshop proposal notifications: March 30, 2015
• Workshop paper submissions: June 5, 2015
• Workshop paper notifications: June 30, 2015
• Final submission of workshop program and materials: July 15, 2015
• Workshop date: August 10, 2015

Workshop Co-chairs

• Tina Eliassi-Rad (Rutgers University, USA)
• Johannes Fuernkranz (Technische Universitat Darmstadt Darmstadt, Germany)

Description

KDD 2015 will host tutorials covering topics in data mining of interest to the research community as well as application developers. The tutorials will be part of the main conference technical program, and are free of charge to the attendees of the conference.

We invite proposals for tutorials from active researchers and experienced tutors. Ideally, a tutorial will cover the state-of-the-art research, development and applications in a specific data mining direction, and stimulate and facilitate future work. Tutorials on interdisciplinary directions, novel and fast growing directions, and significant applications are highly encouraged. We encourage tutorials in areas that are somewhat different from the usual KDD mainstream, but still very much related to KDD mission and objectives of gaining insight from data.

Proposals

Each tutorial should be about 3 hours in length.

A tutorial proposal should be formatted in the following sections.

• Title
• Abstract (up to 150 words)
• Target audience and prerequisites. Proposals must clearly identify the intended audience for the tutorial (e.g., novice users of statistical techniques, or expert researchers in text mining). What background will be required of the audience? Why is this topic important/interesting to the KDD community? What is the benefit to participants?
• Outline of the tutorial. Enough material should be included to provide a sense of both the scope of material to be covered and the depth to which it will be covered. The more details that can be provided, the better (up to and including links to the actual slides). Note that the tutors should NOT focus exclusively on their own research results. A KDD tutorial is not meant to be a forum for promoting one's research or product.
• A list of forums and their time and locations if the tutorial or a similar/highly related tutorial has been presented by the same author(s) before, and highlight the similarity/difference between those and the one proposed for KDD'15 (up to 100 words for each entry)
• Tutors' short bio and their expertise related to the tutorial (up to 200 words per tutor)
• A list of the most important references that will be covered in the tutorial
• (Optional) URLs of the slides/notes of the previous tutorials given by the authors, and any specific audio/video/computer requirements for the tutorial.

Proposals should be received by Friday, March 20. Please submit by email to: tutorials2015@kdd.org with subject heading: "KDD15 Tutorial Proposal Submission"

Deadlines and Dates

• Tutorial proposal submissions: March 20, 2015
• Tutorial proposal notifications: April 25, 2015

Tutorial Co-chairs

• Jian Pei (Simon Fraser University)
• Zhi-Hua Zhou (Nanjing University)

Description

The Industry and Government invited talks track comprises of technical invited talks and panel discussions / debates by leading experts in the world of applied data mining and knowledge discovery. The track will feature highly influential speakers who have directly contributed to successful data mining applications in their respective fields. The talks and discussions will focus on innovative and leading-edge, large-scale industry or government applications of data mining in areas such as finance, health-care, bio-informatics, public policy, infrastructure (transportation, utilities, etc.), telecommunications, social media, and computational advertising.

The objective of the Industry and Government Invited Talks track is to bring together leading industry and government practitioners to share their insights and experiences will inspire the KDD community and spread awareness of the variety of seminal, innovative, and proven applications of data mining and knowledge discovery in the industry and government. This track will complement the already established Industry and Government track at KDD that focuses on peer reviewed publications.

Information on the previous successful editions of the Industry and Government Invited Talks can be found at:

• KDD 2014 Industry and Government Invited Talks (http://www.kdd.org/kdd2014/industry-gov-talks.html)
• KDD 2013 IPE (http://www.kdd.org/kdd2013/industry-practice-expo)

Co-Chairs

• Rajesh Parekh (Groupon)
• Usama Fayyad (Barclays)

Advisory Committee

• Carolina Barcenas (Visa)
• Paul Bradley (Zirmed)
• Longbin Cao (University of Technology, Sydney)
• Soument Chakrabarti (IIT Bombay)
• Thorsten Joachims (Cornell University)
• Ronny Kohavi (Microsoft)
• Ying Li (Jobaline.com)
• Gabor Melli (Viglink)
• Gregory Piatetsky-Shapiro (KDNuggets)
• Raghu Ramakrishnan (Microsoft)
• Ramasamy Uthuruswamy (GM, Retd.)
• Geoff Webb (Monash University)
• Graham Williams (Australian Taxation Office)

For more information please contact the Industry Practice Expo co-chairs, Rajesh Parekh and Usama Fayyad, at industry-invited2015@kdd.org.

ACM SIGKDD Innovation and Service Awards recognize outstanding technical innovations and outstanding professional contributions to the field of Big Data, Data Mining, Knowledge Discovery, and Predictive Analytics.

2015 ACM SIGKDD Innovation and Service Awards

Nominations due June 5, 2015
ACM SIGKDD invites your nominations for its 2015 Innovation and Service Awards

ACM SIGKDD, ACM's Special Interest Group on Knowledge Discovery and Data Mining (KDD) is the premier global professional organization for researchers and professionals dedicated to the advancement of the science and practice of knowledge discovery and data mining. It established the Innovation and Service Awards to recognize outstanding technical and service contributions to the KDD field.

ACM SIGKDD Innovation Award

The ACM SIGKDD Innovation Award recognizes one individual or one group of collaborators whose outstanding technical innovations in the field of Knowledge Discovery and Data Mining have had a lasting impact in advancing the theory and practice of the field. The contributions must have significantly influenced the direction of research and development of the field or transferred to practice in significant and innovative ways and/or enabled the development of commercial systems.

The previous SIGKDD Innovation Award winners were Rakesh Agrawal, Jerome Friedman, Heikki Mannila, Jiawei Han, Leo Breiman, Ramakrishnan Srikant, Usama M. Fayyad, Raghu Ramakrishnan, Padhraic Smyth, Christos Faloutsos, J. Ross Quinlan, Vipin Kumar, Jon Kleinberg, and Pedro Domingos.

ACM SIGKDD Service Award

The ACM SIGKDD Service Award recognizes one individual or one group for their outstanding professional services contributions to the field of knowledge discovery and data mining. Services recognized include significant contributions to the activities of professional KDD societies and conferences, educating students, researchers and practitioners, funding R&D activities, professional volunteer services in disseminating technical information to the field, and contributions to society at large through applications of KDD concepts to improve global medical care, education, disaster/crisis management, environment, etc.

The previous SIGKDD Service Award winners were Gregory Piatetsky-Shapiro, Ramasamy Uthurusamy, Usama M. Fayyad, Xindong Wu, the Weka team led by Ian Witten and Eibe Frank, Won Kim, Robert Grossman, Sunita Sarawagi, Osmar R. Zaiane, R. Bharat Rao, Ying Li, Gabor Melli, and Ted Senator.

Nomination Process

Nominations should include a 1-2 page summary statement justifying the nomination along with other supporting materials. Each nomination should be co-sponsored by at least 3 people. At most one award will be given each year in each category. All communications will be via email. Nominations will be valid for a period of 3 years.

Please email all nomination and support documents by June 5, 2015 to kdd2015awards@verizon.net with subject line "SIGKDD Award Nomination”

The 2015 awards will be presented at the 21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining in Sydney, Australia, August 10-13, 2015

SIGKDD Chair and members of the SIGKDD Awards Committee are not eligible to be nominated for either Award and are excluded from participating in the nomination process as nominators or as supporters of the nominations.

Ted Senator
Chair, 2015 ACM SIGKDD Awards Committee

Description

Nomination Deadline: April 30, 2015

This annual award was established by ACM SIGKDD in 2008 to recognize excellent research by doctoral candidates in the field of data mining and knowledge discovery. The KDD Doctoral Dissertation Award winner and up to two runners-up will be recognized at the KDD conference, and their dissertations will have the opportunity to be published on the KDD Web site.(http://www.kdd.org) The award winner will receive a plaque, a check for $2,500. The award winner will also receive a free registration to attend the KDD conference. The runners-up will receive a plaque at the conference. The winner and runners-up will be invited to present his or her work in a special session at the KDD conference.

Eligibility

The final dissertation defense should take place at the nominee's host institution before the submission deadline. Furthermore, the final dissertation defense must not have taken place prior to January 1st, 2014. We will use the submission date of the final hard copy of the dissertation to the university's library, or the completion date of a dissertation stated by the head of the faculty as the "final dissertation defense date". Nominations are limited to one doctoral dissertation per department or academic unit. Submissions must be received by the submission deadline.

Each nominated dissertation must also have been successfully defended by the candidate, and the final version of each nominated dissertation must have been accepted by the candidate's academic unit. An English version of the dissertation must be submitted with the nomination. We will consider the final form of the thesis only. If a candidate would like to be considered, the candidate must submit the thesis in its final form, which should also be submitted to the university’s library, or at least certified by the department head as the "final version". A dissertation can be nominated for both the SIGKDD Doctoral Dissertation Award and the ACM Doctoral Dissertation Award.

Important Dates

• Submission Deadline: April 30, 2015
• Notification of Awards: July 1, 2015.
• Award Presentation at KDD 2015: August 10-13, 2015, Sydney, Australia.

Submission Procedure

All nomination materials must be submitted electronically to: kshim [at] snu [dot] ac [dot] kr

Please use "SIGKDD Dissertation Award Nominations" in your subject line.

All nomination materials must be in English. PDF format is preferred for all materials. Late submissions will not be accepted. A nomination must include:

• A nomination letter, written by the dissertation advisor of the candidate. This letter must include full contact information for both the advisor and the nominee as well as a one- or two-page summary of the significance of the dissertation.
• An endorsement letter signed by the department head.
• One PDF copy of the doctoral dissertation.
• A copyright transfer form signed by the candidate is required giving permission for the dissertation to appear on KDD.org Web site if the dissertation is selected as an award recipient (but if the nomination is also being submitted for the ACM Doctoral Dissertation Award, only one form needs to be signed). See: http://www.acm.org/pubs/copyright_form.html
• Optionally, the nomination may include up to two supporting letters from other individuals, discussing the significance of the dissertation.

Please direct questions to the Award Committee Chair: Kyuseok Shim, Seoul National University, kshim [at] snu [dot] ac [dot] kr

Dissertation Award Selection Committee

• Gautam Das (University of Texas at Arlington)
• Christos Faloutsos (Carnegie-Mellon University)
• Wei Fan (Baidu Big Data Lab)
• Joydeep Ghosh (University of Texas at Austin)
• Aris Gionis (Aalto University)
• Bart Goethals (University of Antwerp)
• Dimitrios Gunopulos (University of Athens)
• Jiawei Han (UIUC)
• Irwin King (The Chinese University of Hong Kong)
• Ravi Kumar (Google)
• Vipin Kumar (University of Minesota)
• Raymond Ng (UBC)
• Rajeev Rastogi (Amazon)
• Myra Spiliopoulou (Otto-von-Guericke-University Magdeburg)
• Jimeng Sun (Georgia Institute of Technology)
• Evimaria Terzi (Boston University)
• Shusaku Tsumoto (Shimane University)
• Wei Wang (UCLA)
• Hwanjo Yu (POSTECH)

Massive open online courses (MOOCs) provide students with an unprecedented opportunity to obtain high-quality education. However, the high dropout rate on MOOC platforms has been widely acknowledged. Predicting the likelihood of a dropout would be useful for maintaining the learning progress and encouraging students' professional development.

At KDD Cup 2015, the challenge will be the dropout prediction for XuetangX, one of the largest MOOC platforms in China. The dataset contains students' behavior records for 39 courses on XuetangX - you will be asked to predict whether or not a student will drop out of a course.

Are you ready for the challenge?
http://kddcup2015.com

SIGKDD 2015 will offer travel awards to full-time student attendees. The purpose of the student travel grants is to encourage graduate student participation at the conference by partially funding the costs of students who would otherwise be unable to attend.

Applications are accepted from full-time students at degree granting institutions. The maximum amount and type of support provided to each grantee are set by the sponsors (NSF and ACM SIGKDD), and they are intended to partially cover the grantee's lodging and registration. Travel may or may not be partially covered depending on the total availability of funds and the number of awards given.

All full-time students with evidence established in data mining research are encouraged to apply. Other criteria will include evidence of KDD’15 registration. SIGKDD encourages participation of women and under-represented minorities, as well as participation of students from under-represented institutions.

How to submit application:

Submission deadline: 11:59PM PDT, June 12, 2015.

All applicants need to submit their applications at https://cmt.research.microsoft.com/KDDSTA2015/

• Please write down the applicant’s full name under “Title”.
• Please include a brief statement under “Abstract” that includes
  • The applicant’s current status (including institution, type of graduate study (PhD or MS), number of years entering the current program etc.)
  • the applicant’s research interest and current accomplishments, and their future research plans,
  • a description of how the grant will help them with their research plans,
  • any information that the applicant feels relevant for supporting their application,
  • The total budget for attending the KDD’15, and how the travel award can help close the gap for attending KDD’15 (because the travel award will only partially cover the cost.).
• Please upload a recommendation letter (in PDF) from the applicant’s advisor supporting the application, that:
  • confirms that the applicant was a student in good standing at the time the paper was submitted to KDD,
  • describes how the applicant will benefit from attending KDD, and
  • explains why the student is in need of the travel award and whether there is funding available to cover the expense that is not covered by the student travel award.
• Students receiving the travel awards are required to:
  • Participate the one-day KDD mentoring program.
  • Assist the conference organizers in numerous activities during the event days.
  • Submit the original invoice up to the amount of the travel award for reimbursement after the conference. (Instructions will be sent later.)

Only PDF documents are accepted. Late submissions, or documents in other formats will not be accepted. All questions should be addressed to studentaward2015@kdd.org.