Washington, D.C.
KDD 2022
August 14 - 18, 2022
Washington, DC

Overview

The Knowledge Discovery and Data Mining Conference brings together researchers and practitioners from data science, data mining, knowledge discovery, large-scale data analytics, and big data.

Amazon organizing committee members

  • Keynote Speaker
    Amazon Scholar
    Plenary keynote speaker
  • Keynote Speaker
    Amazon Scholar
    Plenary keynote speaker
  • Accepted publications

    Workshops

    KDD 2022 Workshop on Automation in Machine Learning
    August 14 - August 17
    Amazon workshop chairs: Jun (Luke) Huan, Tao Wang

    Website: https://sites.google.com/view/automl2022-workshop
    KDD 2022 Workshop on Mining and Learning with Graphs
    August 15
    The workshop serves as a forum for researchers from a variety of fields working on mining and learning from graphs to share and discuss their latest findings.

    Amazon organizer: Shobeir Fakhraei

    Website: http://www.mlgworkshop.org/2022
    KDD 2022 Workshop on AI for Climate Mitigation, Adaptation, and Environmental Justice
    August 15 - May 15
    Since 2016, the Fragile Earth Workshop has brought together the research community to find and explore how data science can measure and progress climate and social issues, following the framework of the United Nations Sustainable Development Goals (SDGs).

    Amazon organizer: Emre Eftelioglu

    Website: https://ai4good.org/fragile-earth-2022
    KDD 2022 Workshop on Misinformation and Misbehavior Mining on the Web & Making a Credible Web for Tomorrow
    August 15
    MIS2-TrueFact@KDD 2022 provides a venue for researchers and practitioners from academia, government and industry can share insights and identify new challenges and opportunities in adjacent to these diverse areas to coalesce around central and timely topics in online misinformation and misbehavior, resolving conflicts, fact-checking and ascertaining credibility of claims and present recent advances in research.

    Amazon organizer: Mehran Kafai

    Website: http://claws.cc.gatech.edu/mis2-truefact-kdd2022.html
    KDD 2022 Workshop on Online and Adaptive Recommender Systems
    August 15
    The KDD workshop on online and adaptive recommender systems (OARS) will serve as a platform for publication and discussion of OARS. This workshop will bring together practitioners and researchers from academia and industry to discuss the challenges and approaches to implement OARS algorithms and systems, and improve user experiences by better modeling and responding to user intent.

    Amazon organizer: Tao Ye

    Website: https://oars-workshop.github.io/
    KDD 2022 Workshop on First Content Understanding and Generation for e-Commerce
    August 15 - May 2
    KDD 2022 Workshop on Mining and Learning from Time Series – Deep Forecasting: Models, Interpretability, and Applications
    August 15, 8:00 AM - May 15, 5:00 PM EDT
    KDD 2022 Cup ESCI Challenge for Improving Product Search
    August 15
    In this challenge, we introduce the “Shopping Queries Data Set”, a large dataset of difficult search queries, published with the aim of fostering research in the area of semantic matching of queries and products. The primary objective is to build new ranking strategies and, simultaneously, identify interesting categories of results (i.e., substitutes) that can be used to improve the customer experience when searching for products.

    Amazon chair: Karthik Subbian

    Amazon organizers: Lluis Marquez, Fran Valero, Nikhil Rao, Hugo Zaragoza, Sambaran Bandyopadhyay, Arnab Biswas, Anlu Xing, Chandan K Reddy

    Website: https://www.aicrowd.com/challenges/esci-challenge-for-improving-product-search
    KDD 2022 Workshop on Applied Machine Learning Management
    August 15
    The workshop on Applied Machine Learning Management brings together applied research managers from various fields to share methodologies and case-studies on management of ML teams, products, and projects, achieving business impact with advanced AI-methods.

    Amazon organizer: Elena Sokolova

    Amazon speaker: Shiv Vitaladevuni

    Website: https://wamlm-kdd.github.io/2022
    KDD 2022 Workshop on Anomaly and Novelty Detection, Explanation, and Accommodation (ANDEA)
    August 15
    This workshop will gather researchers and practitioners from diverse communities and knowledge background to promote the development of fundamental theories, effective algorithms, and novel applications of anomaly and novelty detection, characterization, and adaptation.

    Amazon organizer: Anton van den Hengel

    Website: https://sites.google.com/view/andea2022
    KDD 2022 Workshop on Artificial Intelligence for Computational Advertising (AdKDD)
    August 15
    AdKDD 2022 is the leading workshop on artificial intelligence for computational advertising in conjunction with KDD 2022.

    Amazon organizer: Kun Liu

    Website: https://www.adkdd.org
    KDD 2022 Workshop on AI-enabled Cybersecurity Analytics and Deployable Defense
    August 15
    This workshop aims to convene academics and practitioners (from industry and government) to share, disseminate, and communicate completed research papers, work in progress, and review articles about AI-enabled cybersecurity analytics and deployable AI-based security defenses.

    Website: https://ai4cyber-kdd.com
    KDD 2022 Workshop on Decision Intelligence and Analytics for Online Marketplaces
    August 15
    The goal of this workshop is to offer an opportunity to appreciate the diversity in applications, to draw connections to inform decision optimization across different industries, and to discover new problems that are fundamental to marketplaces of different domains.

    Amazon speaker: Rui Song

    Website: https://sites.google.com/view/kdd22onlinemarketplaces

    Tutorials

    KDD 2022 Tutorial on Hyperbolic Neural Networks: Theory, Architectures and Applications
    August 14, 1:00 PM - 5:00 PM EDT
    The goal of this tutorial is to introduce researchers/practitioners to the fundamentals of hyperbolic geometry and explain the basic implementations of Hyperbolic models. We will describe the hyperbolic variants of the widely used deep learning architectures (such as GNNs, RNNs, and CNNs) and demonstrate their advantages over the standard Euclidean counterparts.

    Amazon organizers: Nurendra Choudhary, Nikhil Rao, Karthik Subbian, Srinivasan H. Sengamedu, and Chandan K. Reddy

    Website: https://www.nurendra.me/hyperbolic-networks-tutorial
    KDD 2022 Tutorial on Graph Neural Networks in Life Sciences
    August 14, 1:00 PM - 5:00 PM EDT
    Graphs are ubiquitous representation in life sciences and medicine. Recent advance in graph machine learning (ML) approaches such as graph neural networks (GNNs) has transformed a diverse set of problems relying on biomedical networks. The objective of this tutorial is twofold. First, it will provide a comprehensive overview of the types of biomedical graphs/networks, the underlying biological and medical problems, and the applications of graph ML algorithms for solving those problems. Second, it will showcase four concrete GNN solutions in life sciences with hands-on experience for the attendees.

    Amazon organizers: Zichen Wang, Vassilis N. Ioannidis, Huzefa Rangwala, Tatsuya Arai, Ryan Brand, Mufei Li, Yohei Nakayama

    Website: https://github.com/dglai/Graph-Neural-Networks-in-Life-Sciences
    KDD 2022 Tutorial on Anomaly Detection for Spatiotemporal Data in Action
    Unknown date
    Amazon authors: Guang Yang, Ninad D Kulkarni, Paavani Dua, Dipika Khullar, Alex Anto Chirayath
    KDD 2022 Tutorial on Multimodal AutoML for Image, Text and Tabular Data
    Unknown date
    KDD 2022 Tutorial on Model Monitoring in Practice: Lessons Learned and Open Challenges
    Unknown date
    Amazon author: Pradeep Natarajan
    KDD 2022 Tutorial on Prediction for Enormous and Correlated Output Spaces (PECOS)
    Unknown date
    Amazon authors: Hsiang-Fu Yu, Wei-Cheng Chang, Jyun-Yu Jiang, Wei Li

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    To ensure a great internship experience, please keep these things in mind. This is a full time internship and requires an individual to work 40 hours a week for the duration of the internship. Amazon requires an intern to be located where their assigned team is. Amazon is happy to provide relocation and housing assistance if you are located 50 miles or further from the office location. Are you a Masters or PhD student interested in machine learning? We are looking for skilled scientists capable of putting Machine Learning theory into practice through experimentation and invention, leveraging machine learning techniques (such as random forest, Bayesian networks, ensemble learning, clustering, etc.), and implementing learning systems to work on massive datasets in an effort to tackle never-before-solved problems. A successful candidate will be a self-starter comfortable with ambiguity, strong attention to detail, and the ability to work in a fast-paced, ever-changing environment. As an Applied Science Intern, you will own the design and development of end-to-end systems. You’ll have the opportunity to create technical roadmaps, and drive production level projects that will support Amazon Science. You will work closely with Amazon scientists, and other science interns to develop solutions and deploy them into production. The ideal scientist must have the ability to work with diverse groups of people and cross-functional teams to solve complex business problems. Amazon Science gives insight into the company’s approach to customer-obsessed scientific innovation. Amazon fundamentally believes that scientific innovation is essential to being the most customer-centric company in the world. It’s the company’s ability to have an impact at scale that allows us to attract some of the brightest minds in artificial intelligence and related fields. Our scientists use our working backwards method to enrich the way we live and work. For more information on the Amazon Science community please visit https://www.amazon.science.