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KDD 2017 Workshops


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KDD 2017 Workshops

6th International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications

KDD BigMine 2017 aims to bring together people from both academia and industry to present their most recent work related to these issues, and exchange ideas and thoughts in order to advance this big data challenge.

The 2017 ACM SIGKDD Workshop on Causal Discovery

Inspired by such achievements and following the success of CD 2016, CD 2017 continues to serve as a forum for researchers and practitioners in data mining and other disciplines to share their recent research in causal discovery in their respective fields and to explore the possibility of interdisciplinary collaborations in the study of causality. Based on the platform of KDD, this workshop is especially interested in attracting contributions that link data mining/machine learning research with causal discovery, and solutions to causal discovery in large scale data sets.

KDD 2017 Workshop on Anomaly Detection in Finance

Detecting anomalies and novel events is vital to the financial industry. These events may often be indicative of illegal activities such as credit card fraud, identity theft, network intrusion, and money laundering. A number of new ideas are emerging to tackle this problem, including semi-supervised learning methods, deep learning based approaches and network/graph based solutions. These approaches must often be able to work in real-time and be able handle large volumes of data. The purpose of this workshop is to bring together researchers and practitioners to discuss these new approaches and solutions.

Workshop on Advancing Education With Data

The KDD 2017 Workshop on Advancing Education with Data brings together data scientists and educators together to stimulate research in the interdisciplinary field of data science for education. At this year’s workshop, we are highlighting the following areas of interest: (1) Lifelong learning, (2) Assessments (3) Learning Analytics and Personalization and (4) Infrastructure.

The 6th International Workshop on Urban Computing

The objective of UrbComp 2017 workshop is to provide professionals, researchers, and technologists with a single forum where they can discuss and share the state-of-the-art of the development and applications related to urban computing, present their ideas and contributions, and set future directions in innovative research for urban computing. Particularly, at KDD 2017 this workshop targets people who are interesting in sensing/mining/understanding urban data so as to tackle challenges in cities and help better formulate the future of cities. This workshop also well aligns with the topic of KDD 2017, data mining for social good.

Workshop on Interactive Data Exploration and Analytics (IDEA)

The Interactive Data Exploration and Analytics (IDEA) workshop addresses the development of data mining techniques that allow users to interactively explore their data. We focus and emphasize on interactivity and effective integration of techniques from data mining, visualization and human-computer interaction (HCI). In other words, we explore how the best of these different but related domains can be combined such that the sum is greater than the parts.

KDD Data Science + Journalism Workshop 2017

Data-driven journalism promises to bring a more objective perspective on reported issues. Methods of data science are applied to find empirically sound stories from the vast amounts of data that are by now collected from every aspect of our lives. Data scientists are experts on empirical evaluation, data investigation, and visualization, methods that can help shed light on many topics of public interest. On the other hand, journalists are typically great storytellers and talented communicators, skills that are valued in data science. Thus, data scientists and journalists can profit a lot from each other’s skills and talents.

This is why we want to bridge the gap between these two communities and bring them closer together at the workshop for Data Science + Journalism (DS+J). We believe the current media situation provides us with an opportunity for attempts at synthesis, forming a common core of problems and ideas, and cross-pollinating across subareas.

lockedflagDiscussion of emerging challenges and interesting problems to solveDiscussion of emerging challenges and interesting problems to solve
Clay Eltzroth, Hannes Munzinger, Nick Diakopoulos Clay Eltzroth, Hannes Munzinger, Nick Diakopoulos

13th International Workshop on Mining and Learning with Graphs

This workshop is a forum for exchanging ideas and methods for mining and learning with graphs, developing new common understandings of the problems at hand, sharing of data sets where applicable, and leveraging existing knowledge from different disciplines. The goal is to bring together researchers from academia, industry, and government, to create a forum for discussing recent advances graph analysis. In doing so we aim to better understand the overarching principles and the limitations of our current methods, and to inspire research on new algorithms and techniques for mining and learning with graphs.

4th Workshop on Fairness, Accountability, Transparency in Machine Learning (FATML)

The FATML workshop aims to bring together a growing community of researchers, practitioners, and policymakers concerned with fairness, accountability, and transparency in machine learning. The past few years have seen growing recognition that machine learning raises novel ethical, policy, and legal challenges. In particular, policymakers, regulators, and advocates have expressed fears about the potentially discriminatory impact of machine learning and data-driven systems, with many calling for further technical research into the dangers of inadvertently encoding bias into automated decisions. At the same time, there is increasing alarm that the complexity of machine learning and opaqueness of data mining processes may reduce the justification for consequential decisions to "the algorithm made me do it" or "this is what the model says." The goal of this workshop is to provide researchers with a venue to explore how to characterize and address these issues with computationally rigorous methods. We seek contributions that attempt to measure and mitigate bias in machine learning, to audit and evaluate machine learning models, and to render such models more interpretable and their decisions more explainable.

Workshop on the Data Science for Intelligent Food, Energy, and Water

In 2016, we used 1.6 times the global resources that the earth can sustainably regenerate. By 2050, we’ll add 2 billion people to the world’s population. It’s a zero-sum game, and we’re currently losing. The future availability of safe and sustainable food, energy, and water is a global concern, and one that must be dealt with swiftly. 1.2 billion people currently live without electricity, climate change threatens global crops, and fresh ground water is being depleted at an ever faster rate. How can Data Science and Artificial Intelligence help?

53:12   Panel
lockedflagBringing Intelligence to Global Sustainable DevelopmentBringing Intelligence to Global Sustainable Development
James Hodson, Vipin Kumar, et al. James Hodson, Vipin Kumar, Marko Grobelnik, Rayid Ghani, Dan Dyer, Melissa Cragin, Malcolm Campbell

1st ACM Workshop on Medical Informatics and Healthcare 2017

The ACM Medical Informatics and Healthcare workshop is on medical data mining to improve healthcare. It aims to provide a forum for data miners, informaticians, data scientists, and clinical researchers to share their latest investigations in applying data mining techniques to healthcare data residing in electronic health records (EHR). The increasing availability of large and complex medical data sets to the research community triggers the need to develop more advanced and sophisticated big data analytical techniques to exploit and manage these big data. The broader context of the workshop comprehends artificial intelligence, information retrieval, machine learning, natural language processing. Submissions are invited to address the need for developing new methods to mine, summarize and integrate the huge volume and diverse modalities of the structured and unstructured biomedical and healthcare data that can potentially lead to significant advances in the field. Accepted papers will be published in the Proceedings of Machine Learning Research (PMLR) and will be posted on the workshop website. We plan to organize a journal special issue and invite extended versions of the accepted papers for that.

1st Workshop on Big data analytics-as-a-Service: Architecture, Algorithms, and Applications in Health Informatics

ophisticated big data analytics-as-a-Service platforms for efficient data analyses is becoming more valuable as the amount of data generated daily in the health care literature exceeds the boundaries of normal processing capabilities. The objective of the bigdas@KDD2017 is to provide a professional forum for data scientists, researchers, and engineers across the world to present their latest research findings, innovations, and developments in turning big data health care analytics into fast, easy-to-use, scalable, and highly available services over the Internet. This workshop is aimed at data science practitioners working at the intersection of big data machine learning, Software as a Service (SaaS) platforms, Internet of Things (IoT), and health informatics. It will highlight current trends and insights for the future of health data analytics, which is bigger and smarter.

1 view, 37:38   Panel
flagChallenges and Future Directions in Big Data Analytics and its Application in Health InformaticsChallenges and Future Directions in Big Data Analytics and its ...
Peggy Peissig, David C. Page, et al. Peggy Peissig, David C. Page, Juan B. Gutierrez, Richard Segall, Vagelis Hristidis, Yuanyuan Tian

2nd International Workshop on Machine Learning Meets Fashion

The goal of this workshop is to gather people from academia, industry, and startups working at the intersection of fashion and data mining and knowledge discovery to further the technology and its adoption.

2nd ACM SIGKDD Workshop on Machine Learning for Prognostics and Health Management (ML for PHM 2017)

Prognostics and Health Management (PHM) is the study of system behaviors to detect anomalies, determine root causes and when possible predict future system behavior. Common applications include large mechanical systems such as aircraft, electronics and increasingly complex cyber-physical systems with humans in the loop. The dramatic increase of sensors, data rates and communication capabilities continue to drive the complexity of PHM applications to new levels. At KDD, data mining for PHM systems has been a long-standing application area. The application of deep learning methods to these large multi-model sensor data sets can be viewed as a disruptive extension of this traditional domain for the KDD community. This workshop brings together academic and industrial researchers in the fields of data mining, machine learning, systems engineering, mechanical engineering, and the broader prognostics communities, in the collaborative effort of identifying and discussing major technical challenges and recent results related to machine learning-based approaches in PHM.

3rd SIGKDD Workshop on Mining and Learning from Time Series (MiLeTS)

The focus of MiLeTS workshop is to synergize the research in this area and discuss both new and open problems in time series analysis and mining. The solutions to these problems may be algorithmic, theoretical, statistical, or systems-based in nature. Further, MiLeTS emphasizes applications to high impact or relatively new domains, including but not limited to biology, health and medicine, climate and weather, road traffic, astronomy, and energy.

flagPoster Highlights (Short Presentations)Poster Highlights (Short Presentations)
Pu Wang, Rui Li, Anna Mándli Pu Wang, Rui Li, Anna Mándli

16th International Workshop on Data Mining in Bioinformatics

Data Mining approaches seem ideally suited for Bioinformatics, since it is data-rich, but lacks a comprehensive theory of life's organization at the molecular level. The extensive databases of biological information create both challenges and opportunities for developing novel KDD methods. To highlight these avenues we organized the Workshop on Data Mining in Bioinformatics, held annually in conjunction with the ACM SIGKDD Conference.

Workshop on Issues of Sentiment Discovery and Opinion Mining (WISDOM)

WISDOM (Workshop on Issues of Sentiment Discovery and Opinion Mining) aims to explore how the wisdom of the crowds is affecting (and will affect) the evolution of the Web and of businesses gravitating around it. In particular, the workshop series explores two different stages of sentiment analysis: the former focusing on the identification of opinionated text over the Web, the latter focusing on the classification of such text either in terms of polarity detection or emotion recognition.

2017 Edition of AdKDD and TargetAd

The workshop focuses on three main aspects of computational advertising.

33:55   Invited Talk
lockedflagUsers and TimeUsers and Time
Alexander J. Smola Alexander J. Smola

The Broadening Participation in Data Mining Workshop (BPDM at KDD 2017)

The goal of this workshop is to foster mentorship, guidance, and connections of minority and underrepresented groups in Data Mining, while also enriching technical aptitude and exposure. We provide venues in which to encourage students from such groups to connect with junior and senior researchers in industry, academia, and government. We hope to create and help grow meaningful lasting connections between researchers, thereby strengthening the Data Mining Community.

flagMentoring Session PanelMentoring Session Panel
Annie En-Shiun Lee, Anthony D. Joseph, et al. Annie En-Shiun Lee, Anthony D. Joseph, Isabelle Moulinier, Funda Günes, Jennifer Wortman Vaughan, Mario Guajardo-Cespedes
flagManaging Research Team PanelManaging Research Team Panel
Christan Grant, Monica Anderson, et al. Christan Grant, Monica Anderson, Tim Weninger, Ivan Brugere

Workshop on Machine Learning for Creativity

All of us must have dreamt of having our own JARVIS (refer, Marvel comics) which can help us write poetry, paint a mural, compose a melody, choreograph a dance, or even write a research paper for this workshop! It is true that machine learning has not only solved challenging problems in the areas of speech, vision, natural language etc. but also hit the headlines by winning against humans in grand challenges such as Jeopardy, Go, and more recently Poker. Yet one of the elusive goals of artificial intelligence remains human-level creativity. All attempts to emulate creativity artificially fall under the umbrella of an emerging field called computational creativity. The goal of this workshop is to generate interest among the machine learning and data science community in this upcoming field by concentrating on applications of machine learning in creative domains. This workshop creates a forum for researchers and practitioners to exchange ideas and decide on the future roadmap of this field.

Workshop on Data-Driven Discovery

This workshop aims to explore this timely topic with the audience from KDD because the Web has become the essential infrastructure to acquire, disseminate, and create data, information, and knowledge. It has also become the unique locations of many informal intuitions (e.g., patient experiences), which can productively constrain existing models. KDD has a broad audience from both the technical and the social side of science. To better understand this topic, it is critical to explore a wide range of perspectives. In this way, KDD represents an ideal forum for this workshop.

lockedflagData Driven Discovery PanelData Driven Discovery Panel
Hyejin Youn, Jie Tang, et al. Hyejin Youn, Jie Tang, Jure Leskovec, W. Scott Spangler

The 2017 KDD Cup on “Highway Tollgates Traffic Flow Prediction”

A team from Alibaba Cloud, the cloud computing arm of Alibaba Group, has been selected to organize the KDD Cup 2017 among a number of teams that submitted promising and strong proposals. This year’s competition titled “Highway Tollgates Traffic Flow Prediction” seeks to empower traffic management authorities with data-driven preemptive measures and to pave the way towards holistic and realistic solution to traffic bottlenecks.

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