Journal of Biomedical Semantics, (impact factor: 1.845), 2018, accepted. References will not count towards the page limit. Liang Zhao, Jieping Ye, Feng Chen, Chang-Tien Lu, Naren Ramakrishnan. Aug 11, 2022: Get early access for registration at L Street Bridge, Washington DC Convention Center, from 4-6 pm, Saturday, August 13. Attendance is open to all registered participants. Optimal transport theory, including statistical and geometric aspects; Gromov-Wasserstein distance and its variants; Bayesian inference for/with optimal transport; Gromovization of machine learning methods; Optimal transport-based generative modeling. Please note that the KDD Cup workshop will have no proceedings and the authors retain full rights to submit or post the paper at any other venue. Jos Miguel Hernndez-Lobato, University of CambridgeProf. The 21st IEEE International Conference on Data Mining (ICDM 2021), (Acceptance Rate: 9.9%), accepted. It further combines academia and industry in a quest for well-founded practical solutions. How can we develop solid technical visions and new paradigms about AI Safety? Shiyu Wang, Xiaojie Guo, Xuanyang Lin, Bo Pan, Yuanqi Du, Yinkai Wang, Yanfang Ye, Ashley Ann Petersen, Austin Leitgeb, Saleh AlKhalifa, Kevin Minbiole, Bill Wuest, Amarda Shehu, Liang Zhao. Distributed Self-Paced Learning in Alternating Direction Method of Multipliers. Metagraph Aggregated Heterogeneous Graph Neural Network for Illicit Traded Product Identification in Underground Market. This workshop aims to bring together researchers from AI and diverse science/engineering communities to achieve the following goals: 1) Identify and understand the challenges in applying AI to specific science and engineering problems2) Develop, adapt, and refine AI tools for novel problem settings and challenges3) Community-building and education to encourage collaboration between AI researchers and domain area experts. Ranking, acceptance rate, deadline, and publication tips. Accepted submissions will be notified latest by August 7th, 2022. Half day event featuring a panel, invited and keynote speakers and presentations selected through a CFP. We invite researchers to submit either full-length research papers (8 pages) or extended abstracts (2 pages) describing novel contributions and preliminary results, respectively, to the topics above; a more extensive list of topics is available on the Workshop website. A 2-day workshop to share knowledge and research on five tracks of DSTC-10 and general related technical track. The 9th International Conference on Learning Representations (ICLR 2021), (acceptance rate: 28.7%), accepted. The 30th International World Wide Web Conference, the Web Conference (WWW 2021), (acceptance rate: 20.6%), accepted. Adversarial attacking deep learning systems, Robust architectures against adversarial attacks, Hardware implementation and on-device deployment, Benchmark for evaluating model robustness, New methodologies and architectures for efficient and robust deep learning, December 3, 2021 Acceptance Notification, Applications of privacy-preserving AI systems, Differential privacy: theory and applications, Distributed privacy-preserving algorithms, Privacy preserving optimization and machine learning, Privacy preserving test cases and benchmarks. 689-698, Barcelona, Spain, Dec 2016. "Online and Distributed Robust Regressions under Adversarial Data Corruption", in Proceedings of the IEEE International Conference on Data Mining (ICDM 2017) , regular paper; (acceptance rate: 9.25%), pp. Dynamic Activation of Clients and Parameters for Federated Learning over Heterogeneous Graphs. 4, Roosevelt Rd., Taipei, TaiwanAffiliation: National Taiwan UniversityPhone: +1-412-465-0130Email: yvchen@csie.ntu.edu.tw, Paul CrookAddress: 1 Hacker Way, Menlo Park, CA, USAAffiliation: FacebookPhone: +1-650-885-0094Email: pacrook@fb.com, DSTC 10 home:https://dstc10.dstc.community/homeDSTC 10 CFPs:https://dstc10.dstc.community/calls_1/call-for-workshop-papers. TG-GAN: Continuous-time Temporal Graph Deep Generative Models with Time-Validity Constraints. The goal of ITCI22 is to bring together researchers working at the intersection of information theory, causal inference and machine learning in order to foster new collaborations and provide a venue to brainstorm new ideas, exemplify to the information theory community causal inference and discovery as an application area and highlight important technical challenges motivated by practical ML problems, draw the attention of the wider machine learning community to the problems at the intersection of causal inference and information theory, and demonstrate to the community the utility of information-theoretic tools to tackle causal ML problems. Information theoretic quantities (entropy, mutual information, divergence) estimation, Information theoretic methods for out-of-domain generalization and relevant problems (such as robust transfer learning and lifelong learning), Information theoretic methods for learning from limited labelled data, such as few-shot learning, zero-shot learning, self-supervised learning, and unsupervised learning, Information theoretic methods for the robustness of DNNs in AI systems, The explanation of deep learning models (in AI systems) with information-theoretic methods, Information theoretic methods in different AI applications (e.g., NLP, healthcare, robotics, finance). 639-648, Nov 2015. The review process is double-blind, and we follow the Conflict of Interest Policy for ACM Publications. Naren Ramakrishnan, Patrick Butler, Sathappan Muthiah, Nathan Self, Rupinder Khandpur, Parang Saraf, Wei Wang, Jose Cadena, Anil Vullikanti, Gizem Korkmaz, Chris Kuhlman, Achla Marathe, Liang Zhao, Ting Hua, Feng Chen, et al.. "'Beating the news' with EMBERS:forecasting civil unrest using open source indicators." Thirty-third AAAI Conference on Artificial Intelligence (AAAI 2020), (acceptance rate: 20.6%), accepted. In addition, several invited speakers with distinguished professional background will give talks related the frontier topics of GNN. 2022. Thirty-fourth AAAI Conference on Artificial Intelligence (AAAI 2021), (acceptance rate: 21.0%), accepted. Jan 13, 2022: Notification. What approaches emerge in building fundamentally robust and adaptive AI/ML systems? [paper] This one-day workshop will bring concentrated discussions on self-supervision for the field of speech/audio processing via keynote speech, invited talks, contributed talks and posters based on community-submitted high-quality papers, and the result representation of SUPERB and Zero Speech challenge. What AI safety considerations and experiences are relevant from industry? We allow papers that are concurrently submitted to or currently under review at other conferences or venues. The Thirty-Sixth Annual Conference on Neural Information Processing Systems (NeurIPS 2022), (Acceptance Rate: 25.6%), to appear, 2022. Xuchao Zhang, Liang Zhao, Arnold Boedihardjo, and Chang-Tien Lu. Accepted papers will not be archived but will be hosted on the workshop website. The following paper categories are welcome: Submission site:https://sites.google.com/view/eaai-ws-2022/call, Silvia Tulli (Dept. What safety engineering considerations are required to develop safe human-machine interaction? By the end of this century, the earths population is projected to increase by 45% with available arable land decreasing by 20% coupled with changes in what crops these arable lands can best support; this creates the urgent need to enhance agricultural productivity by 70% before 2050. For program deadlines, click on the Admissions and Regulations tab on the specific page of study. Topics of interest include, but are not limited to: Paper submissions will be in two formats: full paper (8 pages) and position paper (4 pages): The submission website ishttps://easychair.org/conferences/?conf=trase2022. Maria Malik, Hassan Ghasemzadeh, Tinoosh Mohsenin, Rosario Cammarota, Liang Zhao, Avesta Sasan, Houman Homayoun, Setareh Rafatirad. All submissions must be anonymous and conform to AAAI standard for double-blind review. AI is now shaping the way businesses, governments, and educational institutions do things and is making its way into classrooms, schools and districts across many countries. Continuous V&V and predictability of AI safety properties, Runtime monitoring and (self-)adaptation of AI safety, Accountability, responsibility and liability of AI-based systems, Avoiding negative side effects in AI-based systems, Role and effectiveness of oversight: corrigibility and interruptibility, Loss of values and the catastrophic forgetting problem, Confidence, self-esteem and the distributional shift problem, Safety of AGI systems and the role of generality, Self-explanation, self-criticism and the transparency problem, Regulating AI-based systems: safety standards and certification, Human-in-the-loop and the scalable oversight problem, Experiences in AI-based safety-critical systems, including industrial processes, health, automotive systems, robotics, critical infrastructures, among others. Adverse event detection by integrating Twitter data and VAERS. Application fees are not refundable. Industry-wide reports highlight large-scale remediation efforts to fix the failures and performance issues. Typically, we receive around 40~60 submissions to each previous workshop. InProceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD 2013), demo track, pp. Trade-Off between Privacy-Preserving and Explainable Federated Learning Federated Learning Multi-Party Computation, Federated Learning Homomorphic Encryption, Federated Learning Personalization Techniques, Federated Learning Meets Mean-Field Game Theory, Federated Learning-based Corporate Social Responsibility. These models can also generate instant feedback to instructors and help them to improve their teaching effectiveness. Guangji Bai, Chen Ling, Yuyang Gao, Liang Zhao. However, research in the AI field also shows that their performance in the wild is far from practical due to the lack of model efficiency and robustness towards open-world data and scenarios. Examples of the datasets which may be considered are the DBTex Radiology Mammogram dataset and the Johns Hopkins COVID-19 case reports. Babies learn their first language through listening, talking, and interacting with adults. At least three research trends are informing insights in this field. These cookies ensure basic functionalities and security features of the website, anonymously. Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI 2022), (Acceptance Rate: 15%), accepted. For example, failures in IoT can result in infrastructure disruptions, and failures in autonomous cars can lead to congestion and crashes. Xuchao Zhang, Liang Zhao, Arnold Boedihardjo, Chang-Tien Lu. Second, psychological experiments in laboratories and in the field, in partnership with technology companies (e.g., using apps), to measure behavioral outcomes are being increasingly used for informing intervention design. Yiming Zhang, Yujie Fan, Yanfang Ye, Liang Zhao, Jiabin Wang, and Qi Xiong. Novel AI-based techniques to improve modeling of engineering systems. How can we engineer trustable AI software architectures? 2022. The workshop will focus on the application of AI to problems in cyber-security. Neurocomputing (Impact Factor: 5.719), accepted. Online Flu Epidemiological Deep Modeling on Each accepted paper presentation will be allocated between 15 and 20 minutes. Novel mechanisms for eliciting and consuming user feedback, recommender, structured and generative models, concept acquisition, data processing, optimization; HCI and visualization challenges; Analysis of human factors/cognition and user modelling; Design, testing and assessment of IML systems; Studies on risks of interaction mechanisms, e.g., information leakage and bias; Business use cases and applications. Current rates of progress are insufficient, making it impossible to meet this goal without a technological paradigm shift. All these changes require novel solutions, and the AI community is well-positioned to provide both theoretical- and application-based methods and frameworks. We will accept both original papers up to 8 pages in length (including references) as well as position papers and papers covering work in progress up to 4 pages in length (not including references).Submission will be through Easychair at the AAAI-22 Workshop AI4DO submission site, Professor Bistra Dilkina (dilkina@usc.edu), USC and Dr. Segev Wasserkrug, (segevw@il.ibm.com), IBM Research, Prof. Andrea Lodi (andrea.lodi@cornell.edu), Jacobs Technion-Cornell Institute IIT and Dr. Dharmashankar Subrmanian (dharmash@us.ibm.com), IBM Research. The paper submissions must be in pdf format and use the AAAI official templates. Modern surveillance systems employ tools and techniques from artificial intelligence and machine learning to monitor direct and indirect signals and indicators of disease activities for early, automatic detection of emerging outbreaks and other health-relevant patterns. The workshop will be organized as a full day meeting. This is a one-day workshop, planned with a 10-minute opening, 6 invited keynotes, ~6 contributed talks, 2 poster sessions, and 2 panel discussions. Participants in the hack-a-thon will be asked to either register as a team or be randomly assigned to a team after registration. CoRL 2023 97 days 17h 29m 15s November 06-09, 2023. The workshop will focus on both the theoretical and practical challenges related to the design of privacy-preserving AI systems and algorithms and will have strong multidisciplinary components, including soliciting contributions about policy, legal issues, and societal impact of privacy in AI. Deadline: FSE 2023. How do metrics of capability and generality, and the trade-offs with performance affect safety? In some programs, spots may be available after the deadlines. This half day workshop will focus on research into the use of AI techniques to extract knowledge from unstructured data in financial services. ECoST: Energy-Efficient Co-Locating and Self-Tuning MapReduce Applications. The reproducibility papers include a clarification phase: Deadlines refer to 23:59 (11:59pm) in the AoE (Anywhere on Earth) time zone. Workshops are one day unless otherwise noted in the individual descriptions. At least one author of each accepted submission must register and present their paper at the workshop. We collaborate with Saudi Aramco to use machine learning for simulating oil and water flows, . Information extraction and information retrieval for scientific documents; Question answering and question generation for scholarly documents; Word sense disambiguation, acronym identification and expansion, and definition extraction; Document summarization, text mining, document topic classification, and machine reading comprehension for scientific documents; Graph analysis applications including knowledge graph construction and representation, graph reasoning and query knowledge graphs; Biomedical image processing, scientific image plagiarism detection, and data visualization; Code/Pseudo-code generation from text and im-age/diagram captioning, New language understanding resources such as new syn-tactic/semantic parsers, language models or techniques to encode scholarly text; Survey or analysis papers on scientific document under-standing and new tasks and challenges related to each scientific domain; Factuality, data verification, and anti-science detection. Integration of non-differentiable optimization models in learning. An increasing world population, coupled with finite arable land, changing diets, and the growing expense of agricultural inputs, is poised to stretch our agricultural systems to their limits. It is also central for tackling decision-making problems such as reinforcement learning, policy or experimental design. Please submit the papers and system reports toEasyChair, Thien Huu Nguyen (University of Oregon, thien@cs.uoregon.edu), Walter Chang (Adobe Research, wachang@adobe.com), Amir Pouran Ben Veyseh (University of Oregon, apouranb@uoregon.edu), Viet Dac Lai (University of Oregon, viet@uoregon.edu), Franck Dernoncourt (Adobe Research, franck.dernoncourt@adobe.com), Workshop URL:https://sites.google.com/view/sdu-aaai22/home. We are excited to continue promoting innovation in self-supervision for the speech/audio processing fields and inspiring the fields to contribute to the general machine learning community. 1-39, November 2016. Yanfang Ye, Yiming Zhang, Yujie Fan, Chuan Shi and Liang Zhao. 14, 2022: The information of Keynote Speakers is available at, Apr. Incomplete Label Multi-task Deep Learning for Spatio-temporal Event Subtype Forecasting.Thirty-third AAAI Conference on Artificial Intelligence (AAAI 2019), (acceptance rate: 16.2%), Hawaii, USA, Feb 2019, accepted. The official dates for submitting an application are detailed below, but see the exact deadline posted on the Description Page for the program of study. This thread already has a best answer. Motif-guided Heterogeneous Graph Deep Generation. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Such advances would enrich the range of applicability of semi-autonomous systems to real-world tasks, most of which involve cooperation with one or more human partners. Deadline: Fri Jun 09 2023 04:59:00 GMT-0700 Yahoo! In this workshop, we want to explore ways to bridge short-term with long-term issues, idealistic with pragmatic solutions, operational with policy issues, and industry with academia, to build, evaluate, deploy, operate and maintain AI-based systems that are demonstrably safe. The impact of robustness assurance on other AI ethics principles: RAISA will also explore aspects related to ethical AI that overlap and interact with robustness concerns, including security, fairness, privacy, and explainability. This calls for novel methods and new methodologies and tools to address quality and reliability challenges of ML systems. This workshop aims to discuss important topics about adversarial ML to deepen our understanding of ML models in adversarial environments and build reliable ML systems in the real world. to protect data owner privacy in FL. the 33rd Annual Computer Security Applications Conference (ACSAC 2018), (acceptance rate: 20.1%), San Juan, Puerto Rico, USA, Dec 2018, accepted. At least one author of each accepted submission must be present at the workshop. The final schedule will be available in November. Use Compass, the interactive checklist designed exclusively for the Universit de Montral, to carefully prepare your application and to avoid common pitfalls along the way. The workshop will focus on two thrusts: 1) Exploring how we can leverage recent advances in RL methods to improve state-of-the-art technology for ED; 2) Identifying unique challenges in ED that can help nurture technical innovations and next breakthroughs in RL. "Misinformation Propagation in the Age of Twitter." The post-lunch session will feature one long talk, two short talks, and a poster session. We plan to invite 2-4 keynote speakers from prestigious universities and leading industrial companies. 2020. The goal of this workshop is to bring together the optimal transport, artificial intelligence, and structured data modeling, gathering insights from each of these fields to facilitate collaboration and interactions. While most work on XAI has focused on opaque learned models, this workshop also highlights the need for interactive AI-enabled agents to explain their decisions and models. Deadline in . The IEEE International Conference on Data Mining (ICDM 2022), full paper, (Acceptance Rate: 20%=174/870), short paper, to appear, 2022. 625-634, New Orleans, US, Dec 2017. Theoretical understanding of adversarial ML and its connection to other areas. "Forecasting Significant Societal Events Using The Embers Streaming Predictive Analytics System." . Thirty-Second AAAI Conference on Artificial Intelligence (AAAI 2018), Oral presentation (acceptance rate: 11.0%), pp. URL: https://sites.google.com/view/kdd22onlinemarketplaces Call For Papers (Submission deadline: June3, 2022) GeoInformatica (impact factor: 2.392), 24, 443475 (2020). Qingzhe Li, Jessica Lin, Liang Zhao and Huzefa Rangwala. The workshop follows a single-blind reviewing process. Submissions are limited to 4 pages, not including references. Liang Zhao, Qian Sun, Jieping Ye, Feng Chen, Chang-Tien Lu, and Naren Ramakrishnan. This workshop aims to explore and advance the current state of research and practice, including but not limited to the following topics: In addition to the invited talks and the panel discussion on topics related to Document Intelligence, the workshop program will include paper sessions which provides an opportunity to present peer-reviewed work on the topic related to Document Intelligence. Incomplete Label Multi-Task Ordinal Regression for Spatial Event Scale Forecasting. We have the following keynote speakers confirmed: Andreas Holzinger (Medical Univ. For authors who do not wish their papers to be posted online, please mention this in the workshop submission. Given the ever-increasing role of the World Wide Web as a source of information in many domains including healthcare, accessing, managing, and analyzing its content has brought new opportunities and challenges. Zheng Chai, Yujing Chen, Ali Anwar, Liang Zhao, Yue Cheng, Huzefa Rangwala. Some of the key questions to be explored include: The workshop will take place in person and will span over one day. And considering robustness, input data with noises frequently occur in open-world scenarios, which presents critical challenges for the building of robust AI systems in practice. For general inquiries about AI2ASE, please write to the lead organizer aryan.deshwal@wsu.edu or jana.doppa@wsu.edu. The submission website ishttps://easychair.org/conferences/?conf=fl-aaai-22. iDetective: An Intelligent System for Automatic Identification of Key Actors in Online Hack Forums. Attendance is open to all. The PAKDD is one of the longest established and leading international conferences in the areas of data mining and knowledge discovery. By registering, you agree to receive emails from UdeM. https://doi.org/10.1007/s10707-019-00376-9. By clicking Accept All, you consent to the use of ALL the cookies. This cookie is set by GDPR Cookie Consent plugin. First, large data sources, both conventionally used in social sciences (EHRs, health claims, credit card use, college attendance records) and unconventional (social networks, fitness apps), are now available, and are increasingly used to personalize interventions. algorithms applied to the above topics: deep learning, reinforcement learning, multi-armed bandits, causal inference, mathematical programming, and stochastic optimization. Knowledge Discovery and Data Mining is an interdisciplinary area focusing upon methodologies and applications for extracting useful knowledge from data [1] . Extended abstract up to 2 pages are also welcome. Introduction: SIGKDD aims to provide the premier forum for advancement and adoption of the "science" of knowledge discovery and data mining.SIGKDD will encourage: basic research in KDD (through annual research conferences, newsletter and other related activities . Accelerated Gradient-free Neural Network Training by Multi-convex Alternating Optimization. The IEEE International Conference on Data Mining (ICDM 2022), full paper, (Acceptance Rate: 9.77%), to appear, 2022. Alan Yuille (Professor, Johns Hopkins University); Hao Su (Assistant Professor, UC San Diego); Rongrong Ji (Professor, Xiamen University); Xianglong Liu (Professor, Beihang University); Jishen Zhao (Associate Professor, UC San Diego); Tom Goldstein (Associate Professor, University of Maryland); Cihang Xie (Assistant Professor, UC Santa Cruz); Yisen Wang (Assistant Professor, Peking University); Bohan Zhuang (Assistant Professor, Monash University), Haotong Qin (Beihang University), Yingwei Li (Johns Hopkins University), Ruihao Gong (SenseTime Research), Xinyun Chen (UC Berkeley), Aishan Liu (Beihang University), Xin Dong (Harvard University), Jindong Guo (University of Munich), Yuhang Li (Yale University), Yiming Li (Tsinghua University), Yifu Ding (Beihang University), Mingyuan Zhang (Nanyang Technological University), Jiakai Wang (Beihang University), Jinyang Guo (University of Sydney), Renshuai Tao (Beihang University), Workshop site:https://practical-dl.github.io/. The KDD 2022 program promises to be the most robust and diverse to date, with keynote presentations, industry-led sessions, workshops, and tutorials spanning a wide range of topics - from data-driven humanitarian mapping and applied data science in healthcare to the uses of artificial intelligence (AI) for climate mitigation and decision . Submissions should follow the AAAI-2022https://aaai.org/Conferences/AAAI-22/aaai22call/. The workshop plans to invite about 50-75 participants. Universit de MontralOffice of Admissions and RecruitmentC. Out of these, the cookies that are categorized as necessary are stored on your browser as they are essential for the working of basic functionalities of the website. Aug 11, 2022: Get early access for registration at L Street Bridge, Washington DC Convention Center, from 4-6 pm, Saturday, August 13. Disease Contact Network. in Proceedings of the 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2019), research track (acceptance rate: 14.2%), accepted, Alaska, USA, Aug 2019. Scientists and engineers in diverse domains are increasingly relying on using AI tools to accelerate scientific discovery and engineering design. Graph Neural Networks: Foundations, Frontiers, and Applications. How can we make AI-based systems more ethically aligned? Qingzhe Li, Amir A. Fanid, Martin Slawski, Yanfang Ye, Lingfei Wu, Kai Zeng, and Liang Zhao. The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2022) (Acceptance Rate: 14.99%), accepted, 2022. search, ranking, recommendation, and personalization. The 30th International World Wide Web Conference, the Web Conference (WWW 2021), (acceptance rate: 20.6%), accepted. May 8, 2022: Student Travel Awards announcement is, Apr. However, workshop organizers may set up any archived publication mechanism that best suits their workshop. Furthermore, DNNs are data greedy in the context of supervised learning, and not well developed for limited label learning, for instance for semi-supervised learning, self-supervised learning, or unsupervised learning. [materials]. IEEE Transactions on Neural Networks and Learning Systems (TNNLS), (Impact Factor: 14.255), accepted. with other vehicles via vehicular communication systems (e.g., dedicated short range communication (DSRC), vehicular ad hoc networks (VANETs), long term evolution (LTE), and 5G/6G mobile networks) for cooperation. The submission website ishttps://cmt3.research.microsoft.com/PPAI2022. Poster session: One poster session of all accepted papers which leads for interaction and personal feedback to the research. Submission site:https://cmt3.research.microsoft.com/ITCI2022, Murat Kocaoglu, Chair (Purdue University, mkocaoglu@purdue.edu), Negar Kiyavash (EPFL, negar.kiyavash@epfl.ch), Todd Coleman (UCSD, tpcoleman@ucsd.edu), Supplemental workshop site:https://sites.google.com/view/itci22. Qingzhe Li, Liang Zhao, Yi-Ching Lee, Avesta Sassan, and Jessica Lin. These challenges are widely studied in enterprise networks, but there are many gaps in research and practice as well as novel problems in other domains. Taseef Rahman, Yuanqi Du, Liang Zhao, Amarda Shehu. The cookies is used to store the user consent for the cookies in the category "Necessary". A Systematic Survey on Deep Generative Models for Graph Generation. SL-VAE: Variational Autoencoder for Source Localization in Graph Information Diffusion. Representation learning, distributed representations learning and encoding in natural language processing for financial documents; Synthetic or genuine financial datasets and benchmarking baseline models; Transfer learning application on financial data, knowledge distillation as a method for compression of pre-trained models or adaptation to financial datasets; Search and question answering systems designed for financial corpora; Named-entity disambiguation, recognition, relationship discovery, ontology learning and extraction in financial documents; Knowledge alignment and integration from heterogeneous data; Using multi-modal data in knowledge discovery for financial applications; Data acquisition, augmentation, feature engineering, and analysis for investment and risk management; Automatic data extraction from financial fillings and quality verification; Event discovery from alternative data and impact on organization equity price; AI systems for relationship extraction and risk assessment from legal documents; Accounting for Black-Swan events in knowledge discovery methods.
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