KDD Cup is an annual data mining and knowledge discovery competition organised by the Association for Computing Machinery's Special Interest Group on Knowledge Discovery and Data Mining (ACM SIGKDD). The competition aims to promote research and development in data mining and knowledge discovery by providing a platform for researchers and practitioners to share their innovative solutions to challenging problems in various domains. The KDD Cup Workshop 2024 will be held in Barcelona, Spain, from Sunday, August 25, 2024, to Thursday, August 29, 2024, in conjunction with ACM SIGKDD 2024.

Website: https://www.aicrowd.com/challenges/amazon-kdd-cup-2024-multi-task-online-shopping-challenge-for-llms

In 2023, the average worldwide internet user spent on average 6.5 hours daily across all devices interacting with online content almost entirely sponsored by advertisements. At almost $700B global market size in 2024, and expected to pass $830B by 2026, digital advertising has already surpassed traditional ads in global spend and continues to grow despite economic headwinds. Digital advertising and in particular computational advertising is perhaps the most visible and ubiquitous application of machine learning and one that interacts directly with consumers. When done right, ads connect us to opportunities to enrich our lives and creep us out when done badly. Recently at the forefront of political battles between governments, large multinational corporations, and consumers, digital advertising remains a dynamic industry and research area.

Amazon co-organizer: Suju Rajan
Website: https://www.adkdd.org/

Personalization is key in understanding user behavior and has been a main focus in the fields of knowledge discovery and information retrieval. Building personalized recommender systems is especially important now due to the vast amount of user-generated textual content, which offers deep insights into user preferences. The recent advancements in Large Language Models (LLMs) have significantly impacted research areas, mainly in Natural Language Processing and Knowledge Discovery, giving these models the ability to handle complex tasks and learn context.

However, the use of generative models and user-generated text for personalized systems and recommendation is relatively new and has shown some promising results. This workshop is designed to bridge the research gap in these fields and explore personalized applications and recommender systems. We aim to fully leverage generative models to develop AI systems that are not only accurate but also focused on meeting individual user needs. Building upon the momentum of previous successful forums, this workshop seeks to engage a diverse audience from academia and industry, fostering a dialogue that incorporates fresh insights from key stakeholders in the field.

Amazon co-organizers: Narges Tabari, Aniket Deshmukh, Rashmi Gangadharaiah
Website: https://genai-personalization.github.io/GenAIRecP2024

This workshop aims to bring together researchers and practitioners from academia and industry to share their experiences and insights on applying causal inference and machine learning techniques to real-world problems in the areas of product, brand, policy, and beyond. The workshop welcomes original research that covers machine learning theory, deep learning, causal inference, and online learning. Additionally, the workshop encourages topics that address scalable system design, algorithm bias, and interpretability.

Amazon co-organizer: Hasta Vanchinathan
Website: https://causal-machine-learning.github.io/kdd2024-workshop/

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).

The Fragile Earth Workshop was one of three workshops associated with the planned Earth Day event at KDD 2019 (organized by our OC members, Shashi Shekhar and James Hodson), provided keynotes and panels for Earth Day in 2020, and has been a recurring workshop at the annual KDD conference for the past seven years.

Amazon co-organizer: Emre Eftelioglu
Website: https://ai4good.org/fragile-earth-2024/

This workshop seeks to expedite efforts at the intersection of Symbolic Knowledge and Statistical Knowledge inherent in LLMs. The objective is to establish quantifiable methods and acceptable metrics for addressing consistency, reliability, and safety in LLMs. Simultaneously, we seek unimodal or multimodal NeuroSymbolic solutions to mitigate LLM issues through context-aware explanations and reasoning. The workshop also focuses on critical applications of LLMs in health informatics, biomedical informatics, crisis informatics, cyber-physical systems, and legal domains. We invite submissions that present novel developments and assessments of informatics methods, including those that showcase the strengths and weaknesses of utilizing LLMs.

Amazon co-organizer: Nikhita Vedula
Website: https://kil-workshop.github.io/

Large language models (LLMs) is an active area of research that has had a significant impact on both academia and industry. Both proprietary and open models, such as Code Llama, have demonstrated significant capability for code development tasks such as code completion, test generation, and code summarization.

However, the next leap will involve reasoning and planning with LLM trained on code. Reasoning is of core importance to code development and future LLM coding capabilities. The inputs to the reasoning process are multifaceted. Common ones include the source code and error logs for code translation and debugging. Additional information could be gained through static analysis of the code, such as abstract syntax tree (AST), a tree representation of the structure of the source code. Yet another source of information is the runtime profiler, where information regarding where the runtime is spent is collected.

Amazon co-organizers: Jun (Luke) Huan, Omer Tripp
Website: https://nl2ql.github.io/#program

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.

Amazon co-organizer: Jun (Luke) Huan
Website: https://kdd-milets.github.io/milets2024/#introduction

The landscape of machine learning and artificial intelligence has been profoundly reshaped by the advent of Generative AI Models and their applications, such as ChatGPT, GPT-4, Sora, and etc. Generative AI includes Large Language Models (LLMs) such as GPT, Claude, Flan-T5, Falcon, Llama, etc., and generative diffusion models. These models have not only showcased unprecedented capabilities but also catalyzed trans- formative shifts across numerous fields. Concurrently, there is a burgeoning interest in the comprehensive evaluation of Generative AI models, as evidenced by pioneering efforts in research bench- marks and frameworks for LLMs like PromptBench, BotChat, OpenCompass, MINT, and others. Despite these advancements, the quest to accurately assess the trustworthiness, safety, and ethical congruence of Generative AI Models continues to pose significant challenges. This underscores an urgent need for developing robust evaluation frameworks that can ensure these technologies are reliable and can be seamlessly integrated into society in a beneficial manner. Our workshop is dedicated to foster- ing interdisciplinary collaboration and innovation in this vital area, focusing on the development of new datasets, metrics, methods, and models that can advance our understanding and application of Generative AI.

Amazon co-organizers: Yuan Ling, Shujing Dong, Yarong Feng, George Karypis, Chandan Reddy
Website: https://genai-evaluation-kdd2024.github.io/genai-evalution-kdd2024/#home

The second edition of this interactive workshop aims to build on this discourse focusing on two aspects: First, bringing together invited AI thought leaders from academia, big tech, and startups to share their perspective on realizing the opportunities of GenAI in various business verticals via use-case themes, challenges, and risks. Second, inviting startup founders (from academia and industry) focused on verticalized GenAI offerings to share their journey in product commercialization and the challenges of the GenAI productization landscape.

Amazon co-organizer: Shenghua Bao
Website: https://ai2sdata.github.io/ai2s/

Machine learning applications are rapidly adopted by industry leaders in any field. The growth of investment in AI-driven solutions created new challenges in managing Data Science and ML resources, people and projects as a whole. The discipline of managing applied machine learning teams, requires a healthy mix between agile product development tool-set and a long term research oriented mindset. The abilities of investing in deep research while at the same time connecting the outcomes to significant business results create a large knowledge based on management methods and best practices in the field. 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 co-organizer: Elena Sokolova
Website: https://wamlm-kdd.github.io/wamlm/index.html

This workshop aims to bring together leading researchers and practitioners to exchange and share their experiences and latest research/application results on all aspects of Talent and Management Computing based on data mining technologies. It will provide a premier interdisciplinary forum to discuss the most recent trends, innovations, applications as well as the real-world challenges encountered and corresponding data-driven solutions in relevant domains.

Website: https://tmc-2024.github.io/