KDD AIDSH 2024 aims to foster discussions and developments that push the boundaries of Artificial Intelligence (AI) and Data Science (DS) in healthcare, enhance diagnostic accuracy and promote human-centric approaches to healthcare, thus stimulating future interdisciplinary collaborations. This year's symposium will focus on expanding the application of AI/DS in healthcare/medicine and bridging existing gaps. The workshop invites submissions of full papers as well as work-in-progress on the application of AI/DS in healthcare. The workshop will feature three invited talks from eminent speakers, spanning academia, industry, and clinical researchers. In addition, selected papers will be invited to publish in Health Data Science, a Science Partner Journal. This summary provides a brief description of the half-day workshop to be held on August 26th, 2024. The webpage for the workshop can be found at https://aimel.ai/kdd2024aidsh.
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Deep Learning has facilitated various high-stakes applications such as crime detection, urban planning, drug discovery, and healthcare. Its continuous success hinges on learning from massive data in miscellaneous sources, ranging from data with independent distributions to graph-structured data capturing intricate inter-sample relationships. Scaling up the data access requires global collaboration from distributed data owners. Yet, centralizing all data sources to an untrustworthy centralized server will put users' data at risk of privacy leakage or regulation violation. Federated Learning (FL) is a de facto decentralized learning framework that enables knowledge aggregation from distributed users without exposing private data. Though promising advances are witnessed for FL, new challenges are emerging when integrating FL with the rising needs and opportunities in data mining, graph analytics, foundation models, generative AI, and new interdisciplinary applications in science. By hosting this workshop, we aim to attract a broad range of audiences, including researchers and practitioners from academia and industry interested in the emergent challenges in FL. As an effort to advance the fundamental development of FL, this workshop will encourage ideas exchange on the trustworthiness, scalability, and robustness of distributed data mining and graph analytics and their emergent challenges.
In recent years, two-sided marketplaces have emerged as viable business models in many real-world applications. In particular, we have moved from the social network paradigm to a network with two distinct types of participants representing the supply and demand of a specific good. Examples of industries include but are not limited to accommodation (Airbnb, Booking.com), video content (YouTube, Instagram, TikTok), ridesharing (Uber, Lyft), online shops (Etsy, Ebay, Facebook Marketplace), music (Spotify, Amazon), app stores (Apple App Store, Google App Store) or job sites (LinkedIn). The traditional research in most of these industries focused on satisfying the demand. OTAs would sell hotel accommodation, TV networks would broadcast their own content, or taxi companies would own their own vehicle fleet. In modern examples like Airbnb, YouTube, Instagram, or Uber, the platforms operate by outsourcing the service they provide to their users, whether they are hosts, content creators or drivers, and have to develop their models considering their needs and goals.
Machine learning applications are rapidly adopted by industry leaders in any field. The growth of investment in AI-driven solutions,including the emerging field of General AI (GenAI), has 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 Third KDD 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.
The Knowledge-infused Learning Workshop is a recurring event in ACM's KDD Conference that gathers the research community on knowledge graphs and knowledge-enabled learning, grounded neurosymbolic AI, explainable and safe AI, and applications in high-stakes decision-making problems. This year, the workshop aligned with Biden's vision of Responsible AI Development.
The Fragile Earth Workshop is a recurring event in ACM's KDD Conference on research in knowledge discovery and data mining that gathers the research community to find and explore how data science can measure and progress climate and social issues, following the United Nations Sustainable Development Goals (SDGs) framework.
This workshop aims to spearhead research on Human-Interpretable Artificial Intelligence (HI-AI) by providing: (i) a general overview of the key aspects of HI-AI, in order to equip all researchers with the necessary background and set of definitions; (ii) novel and interesting ideas coming from both invited talks and top paper contributions; (iii) the chance to engage in dialogue with prominent scientists during poster presentations and coffee breaks. The workshop welcomes contributions covering novel interpretable-by-design or post-hoc approaches, as well as theoretical analysis of existing works. Additionally, we accept visionary contributions speculating on the future potential of this field. Finally, we welcome contributions from related fields such as Ethical AI, Knowledge-driven Machine learning, Human-machine Interaction, but also applications in Medicine and Industry, and analyses from Regulatory experts.
The digital advertising field has always had challenging ML problems, learning from petabytes of data that is highly imbalanced, reactivity times in the milliseconds, and more recently compounded with the complex user's path to purchase across devices, across platforms, and even online/real-world behavior. The AdKDD workshop continues to be a forum for researchers in advertising, during and after KDD. Our website which hosts slides and abstracts receives approximately 2,000 monthly visits and 1,800 active users during the KDD 2021. In surveys during AdKDD 2019 and 2020, over 60% agreed that AdKDD is the reason they attended KDD, and over 90% indicated they would attend next year. The 2024 edition is particularly timely because of the increasing application of Graph-based NN and Generative AI models in advertising. Coupled with privacy-preserving initiatives enforced by GDPR, CCPA the future of computational advertising is at an interesting crossroads. For this edition, we plan to solicit papers that span the spectrum of deep user understanding while remaining privacy-preserving. In addition, we will seek papers that discuss fairness in the context of advertising, to what extent does hyper-personalization work, and whether the ad industry as a whole needs to think through more effective business models such as incrementality. We have hosted several academic and industry luminaries as keynote speakers and have found our invited speaker series hosting expert practitioners to be an audience favorite. We will continue fielding a diverse set of keynote speakers and invited talks for this edition as well. As with past editions, we hope to motivate researchers in this space to think not only about the ML aspects but also to spark conversations about the societal impact of online advertising.
The use of Knowledge Graphs (KGs) which constitute large networks of real-world entities and their interrelationships, has grown rapidly. A substantial body of research has emerged, exploring the integration of deep learning (DL) and large language models (LLMs) with KGs. This workshop aims to bring together leading researchers in the field to discuss and foster collaborations on the intersection of KG and DL/LLMs.
This workshop aims to explore the intersection of Generative AI with the rich tapestry of financial data types, seeking to uncover new methodologies and techniques that can enhance predictive analytics, fraud detection, and customer insights across the sector. By harnessing these advancements in AI, we can pave the way to not only understand customer behavior but also anticipate their needs more effectively, leading to superior customer outcomes and more personalized services. Our objective is to shed light on the challenges and opportunities presented by the diverse data formats in finance. We aim to bridge the gap between the dominance of traditional models for tabular data analysis and the emerging potential of Generative AI to revolutionize the treatment of time series, click streams, and other unstructured data forms.
The Finance Day at KDD 2024 will take place on August 26th in Barcelona, Spain. Following the success of the inaugural event last year, the second edition highlights the significant role of AI in transforming the financial industry. This special day serves as a forum for discussion of innovations at the intersection of AI and finance. An exciting lineup of 12 influential speakers from nine different countries will be featured, representing a mix of government organizations, leading banks, innovative hedge funds, and top academic institutions. These experts will delve into a range of topics, from cutting-edge FinTech innovations to ethical considerations in machine learning, providing a comprehensive overview of the finance and AI. The distinguished speakers include Avanidhar Subrahmanyam from UCLA, Henrike Mueller from the Financial Conduct Authority, Claudia Perlich from Two Sigma, Eyke Hüllermeier from Ludwig-Maximilians-Universität München, Senthil Kumar from Capital One, Stefan Zohren from the University of Oxford, Dumitru Roman from SINTEF ICT, Kubilay Atasu from TU Delft, Xiao-Ming Wu from Hong Kong Polytechnic University, Yongjae Lee from UNIST, Jundong Li from the University of Virginia, and Milos Blagojevic from BlackRock.
The Generative AI (AIGC) Day at KDD'24 is a dedicated full-day event for generative AI at KDD. This is an opportunity to bring together researchers, practitioners, and startups to share the insights about the cutting-edge advancements and to discuss the potential societal impacts of LLMs and AIGC. It is exciting that this year, we have invited speakers from both industry (e.g., Amazon, Zhipu AI) and academia (e.g., USC, UCLA). The topics cover various perspectives of generative AI including foundation models, streaming LLMs, LLM training and inference. As demonstrated, data plays a crucial role in developing cutting-edge generative AI models. For example, the Gemini Team has found that "data quality is an important factor for highly-performing models...''. To date, there is still significant room to define design principles and develop methods for improved data collection, selection, and synthetic data generation for the pre-training and alignment of language, vision, and multi-modal models. Therefore, the Day will invite the speakers and KDD audience to discuss the challenges and opportunities for data mining researchers in the era of generative AI.
The European Data Science Day offers a full day focused exclusively on innovative KDD-relevant research and development projects from national and regional funding programs, as well as corporate, start-up, and nonprofit channels. The idea is to bring together a diverse community of researchers in Data Science, Machine Learning, Language Technologies, and Knowledge Discovery, as well as partnerships in the social and physical sciences/arts, to showcase the state-of-the-art in research and applications.
Environmental problems such as air pollution monitoring and prevention, flood detection and prevention, land use, forest management, river water quality, wastewater treatment supervision, etc. are more complex than typical real-world problems usually AI faces to. This added complexity rises from several aspects, such as the randomness shown by most of environmental processes involved, the 2D/3D nature of involved problems, the temporal aspects, the spatial aspects, the inexactness of the information, etc. In fact, environmental problems belong to the most difficult problems with a lot of inexactness and uncertainty, and possibly conflicting objectives to be solved according to several classifications such as the one by Funtowicz & Ravetz (Funtowicz & Ravetz, 1999), which states that there are 3 kinds of problems. Also, they are non-structured problems in the classification proposed by H. Simon (Simon, 1966). All this complexity means that to effectively solve those problems a lot of knowledge is needed. This knowledge can be theoretical knowledge expressed in mechanistic models, such as the Gravidity Newton's Theory, or it can be empirical knowledge that can be expressed by means of empirical models, originated by some data and observations (data-driven knowledge) or by the expertise gathered by people when coping with such problems (model-driven knowledge, particularly expert-based knowledge). The KDD 2024 Special Day for AI for environment brings together researchers and practitioners to present their perspective on this very timely topic on how AI can be used for good, and improving the environment where we all live in.
This paper provides an overview of the ACM SIGKDD 2024 AI4Science4AI special day. It includes information about the organizers, invited speakers, keynote speakers, the event agenda, and insights from related workshops. The AI4Science4AI special day aims to bring together experts in artificial intelligence (AI) and science to discuss the latest developments, challenges, and future directions.
The ACM KDD 2024 Health Day theme, "Building Health AI Ecosystem: From Data Harmonization to Knowledge Discovery," highlights the transformative potential of AI-driven ecosystems in healthcare, translational biomedical research, and basic biological research. This extended abstract discusses recent advancements, challenges, and future directions, focusing on integrating AI-ready data sets, interdisciplinary collaborations, and ethical AI practices. It aims to catalyze discussions on the potential of AI ecosystems in revolutionizing healthcare and related fields.
The Equity, Diversity & Inclusion event is a special day organized in conjunction with KDD '24, the 30 ^th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, which will take place from Sunday, August 25 to Thursday, August 29, 2024 at the Center de Convencions Internacional de Barcelona in Barcelona, Spain. This special day, scheduled for August 28, 2024, promotes equity, diversity, and inclusion (EDI) in data science, artificial intelligence, and beyond. It will bring together academics, researchers, practitioners, and human resources professionals (i) to present algorithms, techniques, methodologies, and projects in data science that enable responsible data processing and modeling; (ii) to discuss policies, best practices, and guidelines to promote an inclusive work environment and effective collaboration; (iii) to share personal stories to encourage young researchers, including those from groups unrepresented in the research community, to develop strong careers in data science; and (iv) to collaboratively develop and discuss an EDI Manifesto to promote an inclusive workplace environment and guiding principles in the development of research activities.
We summarize the goals of the Responsible AI day, giving a glimpse on the program as well as a short biography of the organizers.
Causality lays the foundation for the trajectory of our world. Causal inference (CI), which aims to infer intrinsic causal relations among variables of interest, has emerged as a crucial research topic. Nevertheless, the lack of observation of important variables (e.g., confounders, mediators, exogenous variables, etc.) severely compromises the reliability of CI methods. The issue may arise from the inherent difficulty in measuring the variables. Additionally, in observational studies where variables are passively recorded, certain covariates might be inadvertently omitted by the experimenter. Depending on the type of unobserved variables and the specific CI task, various consequences can be incurred if these latent variables are carelessly handled, such as biased estimation of causal effects, incomplete understanding of causal mechanisms, lack of individual-level causal consideration, etc. In this survey, we provide a comprehensive review of recent developments in CI with latent variables. We start by discussing traditional CI techniques when variables of interest are assumed to be fully observed. Afterward, under the taxonomy of circumvention and inference-based methods, we provide an in-depth discussion of various CI strategies to handle latent variables, covering the tasks of causal effect estimation, mediation analysis, counterfactual reasoning, and causal discovery. Furthermore, we generalize the discussion to graph data where interference among units may exist. Finally, we offer fresh aspects for further advancement of CI with latent variables, especially new opportunities in the era of large language models (LLMs).
In the era of big data and Artificial Intelligence, an emerging paradigm is to utilize contrastive self-supervised learning to model large-scale heterogeneous data. Many existing foundation models benefit from the generalization capability of contrastive self-supervised learning by learning compact and high-quality representations without relying on any label information. Amidst the explosive advancements in foundation models across multiple domains, including natural language processing and computer vision, a thorough survey on heterogeneous contrastive learning for the foundation model is urgently needed. In response, this survey critically evaluates the current landscape of heterogeneous contrastive learning for foundation models, highlighting the open challenges and future trends of contrastive learning. In particular, we first present how the recent advanced contrastive learning-based methods deal with view heterogeneity and how contrastive learning is applied to train and fine-tune the multi-view foundation models. Then, we move to contrastive learning methods for task heterogeneity, including pretraining tasks and downstream tasks, and show how different tasks are combined with contrastive learning loss for different purposes. Finally, we conclude this survey by discussing the open challenges and shedding light on the future directions of contrastive learning.