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Meltwater Scientific Advisory Board

Fueling Meltwater's AI Investments

The Scientific Advisory Board is a team of distinguished scientific researchers and professionals that offer invaluable guidance to the Meltwater team as we develop industry-leading, AI-based solutions to meet our customers toughest challenges.

The Role of the Scientific Advisory Board

Meltwater’s Scientific Advisory Board provides insights into shifts in the frontier of technological innovation in their areas of expertise, and identifies novel solutions or technologies for some of the hardest technological challenges Meltwater is working on.

As an innovation leader, Meltwater R&D heavily invests in AI & Machine Learning, and the role of the SAB is to support Meltwater leadership and teams in setting this medium and long-term technology strategy. They advise Meltwater new areas of scientific and technology investment and provide invaluable insights into the future of technological innovation in their areas of expertise. Leveraging their research and expertise, they are materially involved in advising individual teams on scientific and technical roadmaps and long-term strategy.

Members of the Scientific Advisory Board

Regina Barzilay, Ph.D.

Areas of expertise: Natural Language Processing (NLP) and medial AI, as well as AI ethics and explainability.

Regina Barzilay is the Distinguished Professor for AI and Health at the MIT School of Engineering, as well as the Lead Researcher of Mirai, a ground-breaking ML algorithm used for predicting breast cancer. Regina has been instrumental in forging the strategy and direction of Meltwater’s NLP offerings, which has seen a significant increase in languages support and improvements across all our NLP enrichments since Regina joined the SAB. Regina’s expertise on NLP, cross-lingual transfer learning, and summarization aligns strongly with our NLP and enrichment roadmap for 2022, where we are planning to significantly increase the number of supported languages and plan to make available to customers a number of new text analysis enrichments including abstractive summarization.

Juliana Freire, Ph.D.

Areas of expertise: Data integration and data analysis with seminal works on data provenance, trust in AI, and crawling.

Freire is a Professor of Computer Science and Data Science and Director of the VIDA Center at NYU Tandon School of Engineering. Freirre was the first woman to be elected chair of the leading professional organization for data management, ACM SIGMOD, in 2017. Juliana’s expertise on data integration and record linking is strongly aligned to Meltwater’s efforts in building the most relevant knowledge graph in our industry and making it available to our 27,000 customers world-wide. Juliana’s expertise in trust and AI is well aligned with our roadmaps and efforts to ensure transparency into trustworthiness and bias of our models for text analysis and graph learning. Her expertise in crawling and web data extraction is supporting our continued improvements to our crawling system both through further automation and through integrating more unsupervised methods and closer alignment with our search and analysis product.

Georg Gottlob, Ph.D.

Areas of expertise: Crawling, reasoning and knowledge graphs.

Georg Gottlob, is a Professor of Informatics at Oxford University’s Department of Computer Science, as well as the lead researcher for both Wrapidity (acquired by Meltwater 2016) and DeepReason (acquired by Meltwater 2021). Georg has been instrumental in supporting Meltwater’s transition from largely manually created web crawlers to fully AI powered crawlers with a significant, automatic self-healing capability. He has also helped direct our knowledge graph efforts, and recently around reasoning capabilities both for improving and validating our knowledge graph. Georg’s expertise on crawlers, knowledge graphs, graph reasoning, and reasoning on large databases helps shaping our technology investments and aligns strongly with our efforts on further automating and scaling out or crawling capabilities and with out efforts on integrating and merging the Owler graph with all its unique community contributions to a foundation for all of our products. We are planning to roll out graph-based entity search as well as our next generation one-click search and insight experience.

Jure Leskovec, Ph.D.

Areas of expertise: Machine learning on large graphs, network analysis, and social media analytics.

Jure Leskovec is Professor of Computer Science at Stanford University and serves as Chief Scientist at Pinterest. Jure has been instrumental in supporting Meltwater’s knowledge graph population and learning efforts that are the foundation of our ability to quickly integrate data from new sources, such as Owler and the CRM’s of our recent acquisitions. Jure’s expertise on network analysis and efficient knowledge graph learning is supporting our efforts to integrate the Linkfluence, Klear data and provide advanced social network analytics. Jure’s expertise on machine learning on large graphs and social media analytics is aligned with our efforts to improve content discovery and analytics capabilities such as community detection, segmentation within audience analytics, viraility detection, and more.

Eric Nyberg, Ph.D.

Areas of expertise: Natural Language Understanding (NLU) with special focus on question answering systems. Leading expert on question answering for IBM Watson.

Eric Nyberg is the Professor and Director of the Master of Computational Data Science Program at Carnegie Mellon University School of Computer Science. Since his appointment to the SAB, Eric has been instrumental in guiding Meltwater’s efforts on detecting business events such as acquisitions or executive appointments that are powering our real-time Smart Alerts. Eric’s expertise on question answering is supporting our efforts to enable customers to get to relevant insights more seamlessly, as showcased at our recent investor conference. These capabilities also form the basis for our next generation one-click search and insight experience. Eric’s expertise on NLU aligns well with our efforts on graph-based entity search, improved entity linking, and entity-level sentiment.