{"id":1116780,"date":"2025-01-17T09:37:58","date_gmt":"2025-01-17T17:37:58","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?p=1116780"},"modified":"2025-01-17T09:38:05","modified_gmt":"2025-01-17T17:38:05","slug":"research-focus-week-of-january-13-2025","status":"publish","type":"post","link":"https:\/\/www.microsoft.com\/en-us\/research\/blog\/research-focus-week-of-january-13-2025\/","title":{"rendered":"Research Focus: Week of January 13, 2025"},"content":{"rendered":"\n<p class=\"has-text-align-center\"><strong>In this edition:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>We introduce privacy enhancements for multiparty deep learning, a framework using smaller, open-source models to provide relevance judgments, and other notable new research.<\/li>\n\n\n\n<li>We congratulate Yasuyuki Matsushita, who was named an IEEE Computer Society Fellow.<\/li>\n\n\n\n<li>We\u2019ve included a recap of the extraordinary, far-reaching work done by researchers at Microsoft in 2024.&nbsp;&nbsp;<\/li>\n<\/ul>\n\n\n\n<figure class=\"wp-block-image aligncenter size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"1400\" height=\"788\" src=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2025\/01\/NEW_RF56-BlogHeroFeature-1400x788-1.jpg\" alt=\"Decorative graphic with wavy shapes in the background in blues and purples. Text overlay in center left reads: \u201cResearch Focus: January 17, 2024\u201d\" class=\"wp-image-1122399\" srcset=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2025\/01\/NEW_RF56-BlogHeroFeature-1400x788-1.jpg 1400w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2025\/01\/NEW_RF56-BlogHeroFeature-1400x788-1-300x169.jpg 300w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2025\/01\/NEW_RF56-BlogHeroFeature-1400x788-1-1024x576.jpg 1024w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2025\/01\/NEW_RF56-BlogHeroFeature-1400x788-1-768x432.jpg 768w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2025\/01\/NEW_RF56-BlogHeroFeature-1400x788-1-1066x600.jpg 1066w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2025\/01\/NEW_RF56-BlogHeroFeature-1400x788-1-655x368.jpg 655w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2025\/01\/NEW_RF56-BlogHeroFeature-1400x788-1-240x135.jpg 240w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2025\/01\/NEW_RF56-BlogHeroFeature-1400x788-1-640x360.jpg 640w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2025\/01\/NEW_RF56-BlogHeroFeature-1400x788-1-960x540.jpg 960w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2025\/01\/NEW_RF56-BlogHeroFeature-1400x788-1-1280x720.jpg 1280w\" sizes=\"auto, (max-width: 1400px) 100vw, 1400px\" \/><\/figure>\n\n\n\n<div class=\"wp-block-group is-layout-constrained wp-block-group-is-layout-constrained\">\n<p class=\"has-blue-color has-text-color has-link-color wp-elements-8d9d7d4143e56f533f8c36e4237c1c8d\">NEW RESEARCH<\/p>\n\n\n\n<h3 class=\"wp-block-heading h2\" id=\"ai-meets-materials-discovery\">AI meets materials discovery<\/h3>\n\n\n\n<p>Two of the transformative tools that play a central role in Microsoft\u2019s work on AI for science are MatterGen and MatterSim. In the world of materials discovery, each plays a distinct yet complementary role in reshaping how researchers design and validate new materials.<\/p>\n\n\n\n<div class=\"wp-block-buttons is-content-justification-center is-content-justification-center is-layout-flex wp-container-core-buttons-is-layout-1 wp-block-buttons-is-layout-flex\">\n<div class=\"wp-block-button\"><a data-bi-type=\"button\" class=\"wp-block-button__link wp-element-button\" href=\"https:\/\/www.microsoft.com\/en-us\/research\/story\/ai-meets-materials-discovery\/\">Read the story<\/a><\/div>\n<\/div>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity is-style-dots\"\/>\n\n\n\n<h2 class=\"wp-block-heading h6 has-blue-color has-text-color has-link-color wp-elements-e734c6e9609233ab051742bb3beeed63\" id=\"new-research\">NEW RESEARCH<\/h2>\n\n\n\n<h3 class=\"wp-block-heading h2\" id=\"heading\">Communication Efficient Secure and Private Multi-Party Deep Learning<\/h3>\n\n\n\n<p>Distributed training enables multiple parties to jointly train a machine learning model on their respective datasets, which can help address the challenges posed by requirements in modern machine learning for large volumes of diverse data. However, this can raise security and privacy issues \u2013 protecting each party\u2019s data <em>during<\/em> training and preventing leakage of private information from the model <em>after<\/em> training through various inference attacks.&nbsp;&nbsp;<\/p>\n\n\n\n<p>In a recent paper, <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/communication-efficient-secure-and-private-multi-party-deep-learning\/\" target=\"_blank\" rel=\"noreferrer noopener\">Communication Efficient Secure and Private Multi-Party Deep Learning<\/a>, researchers from Microsoft address these concerns simultaneously by designing efficient Differentially Private, secure Multiparty Computation (DP-MPC) protocols for jointly training a model on data distributed among multiple parties. This DP-MPC protocol in the two-party setting is 56-to-794 times more communication-efficient and 16-to-182 times faster than previous such protocols. This work simplifies and improves on previous attempts to combine techniques from secure multiparty computation and differential privacy, especially in the context of training machine learning models.&nbsp;<\/p>\n\n\n\n<div class=\"wp-block-buttons is-content-justification-center is-content-justification-center is-layout-flex wp-container-core-buttons-is-layout-2 wp-block-buttons-is-layout-flex\">\n<div class=\"wp-block-button is-style-outline is-style-outline--1\"><a data-bi-type=\"button\" class=\"wp-block-button__link wp-element-button\" href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/communication-efficient-secure-and-private-multi-party-deep-learning\/\">Read the paper<\/a><\/div>\n<\/div>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity is-style-dots\"\/>\n<\/div>\n\n\n\n<div class=\"wp-block-group is-layout-constrained wp-block-group-is-layout-constrained\">\n<h2 class=\"wp-block-heading h6 has-blue-color has-text-color has-link-color wp-elements-e734c6e9609233ab051742bb3beeed63\" id=\"new-research\">NEW RESEARCH<\/h2>\n\n\n\n<h3 class=\"wp-block-heading h2\" id=\"heading\">JudgeBlender: Ensembling Judgments for Automatic Relevance Assessment<\/h3>\n\n\n\n<p>Training and evaluating retrieval systems requires significant relevance judgments, which are traditionally collected from human assessors. This process is both costly and time-consuming. Large language models (LLMs) have shown promise in generating relevance labels for search tasks, offering a potential alternative to manual assessments. Current approaches often rely on a single LLM. While effective, this approach can be expensive and prone to intra-model biases that can favor systems leveraging similar models.<\/p>\n\n\n\n<p>In a recent paper: <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/judgeblender-ensembling-judgments-for-automatic-relevance-assessment\/\">JudgeBlender: Ensembling Judgments for Automatic Relevance Assessment<\/a>, researchers from Microsoft we introduce a framework that employs smaller, open-source models to provide relevance judgments by combining evaluations across multiple LLMs (LLMBlender) or multiple prompts (PromptBlender). By leveraging the LLMJudge benchmark, they compare JudgeBlender with state-of-the-art methods and the top performers in the LLMJudge challenge. This research shows that JudgeBlender achieves competitive performance, demonstrating that very large models are often unnecessary for reliable relevance assessments.<\/p>\n\n\n\n<div class=\"wp-block-buttons is-content-justification-center is-content-justification-center is-layout-flex wp-container-core-buttons-is-layout-3 wp-block-buttons-is-layout-flex\">\n<div class=\"wp-block-button is-style-outline is-style-outline--2\"><a data-bi-type=\"button\" class=\"wp-block-button__link wp-element-button\" href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/judgeblender-ensembling-judgments-for-automatic-relevance-assessment\/\">Read the paper<\/a><\/div>\n<\/div>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity is-style-dots\"\/>\n<\/div>\n\n\n\n<div class=\"wp-block-group is-layout-constrained wp-block-group-is-layout-constrained\">\n<h2 class=\"wp-block-heading h6 has-blue-color has-text-color has-link-color wp-elements-e734c6e9609233ab051742bb3beeed63\" id=\"new-research\">NEW RESEARCH<\/h2>\n\n\n\n<h3 class=\"wp-block-heading h2\" id=\"heading\">Convergence to Equilibrium of No-regret Dynamics in Congestion Games<\/h3>\n\n\n\n<p>Congestion games are used to describe the behavior of agents who share a set of resources. Each player chooses a combination of resources, which may become congested, decreasing utility for the players who choose them. Players can avoid congestion by choosing combinations that are less popular. This is useful for modeling a range of real-world scenarios, such as traffic flow, data routing, and wireless communication networks.<\/p>\n\n\n\n<p>In a recent paper: <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/convergence-to-equilibrium-of-no-regret-dynamics-in-congestion-games\/\">Convergence to Equilibrium of No-regret Dynamics in Congestion Games<\/a>; researchers from Microsoft and external colleagues propose CongestEXP, a decentralized algorithm based on the classic exponential weights method. They evaluate CongestEXP in a traffic congestion game setting. As more drivers use a particular route, congestion increases, leading to higher travel times and lower utility. Players can choose a different route every day to optimize their utility, but the observed utility by each player may be subject to randomness due to uncertainty (e.g., bad weather). The researchers show that this approach provides both regret guarantees and convergence to Nash Equilibrium, where no player can unilaterally improve their outcome by changing their strategy.<\/p>\n\n\n\n<div class=\"wp-block-buttons is-content-justification-center is-content-justification-center is-layout-flex wp-container-core-buttons-is-layout-4 wp-block-buttons-is-layout-flex\">\n<div class=\"wp-block-button is-style-outline is-style-outline--3\"><a data-bi-type=\"button\" class=\"wp-block-button__link wp-element-button\" href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/convergence-to-equilibrium-of-no-regret-dynamics-in-congestion-games\/\">Read the paper<\/a><\/div>\n<\/div>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity is-style-dots\"\/>\n<\/div>\n\n\n\n<div class=\"wp-block-group is-layout-constrained wp-block-group-is-layout-constrained\">\n<h2 class=\"wp-block-heading h6 has-blue-color has-text-color has-link-color wp-elements-e734c6e9609233ab051742bb3beeed63\" id=\"new-research\">NEW RESEARCH<\/h2>\n\n\n\n<h3 class=\"wp-block-heading h2\" id=\"heading\">RD-Agent: An open-source solution for smarter R&D<\/h3>\n\n\n\n<p>Research and development (R&D) plays a pivotal role in boosting industrial productivity. However, the rapid advance of AI has exposed the limitations of traditional R&D automation. Current methods often lack the intelligence needed to support innovative research and complex development tasks, underperforming human experts with deep knowledge.<\/p>\n\n\n\n<p>LLMs trained on vast datasets spanning many subjects are equipped with extensive knowledge and reasoning capabilities that support complex decision-making in diverse workflows. By autonomously performing tasks and analyzing data, LLMs can significantly increase the efficiency and precision of R&D processes.<\/p>\n\n\n\n<p>In a <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/articles\/rd-agent-an-open-source-solution-for-smarter-rd\/\">recent article<\/a>, researchers from Microsoft introduce RD-Agent, a tool that integrates data-driven R&D systems and harnesses advanced AI to automate innovation and development.<\/p>\n\n\n\n<p>At the heart of RD-Agent is an autonomous agent framework with two key components: a) Research and b) Development. Research focuses on actively exploring and generating new ideas, while Development implements these ideas. Both components improve through an iterative process, illustrated in Figure 1 of the article, ensures the system becomes increasingly effective over time.<\/p>\n\n\n\n<div class=\"wp-block-buttons is-content-justification-center is-content-justification-center is-layout-flex wp-container-core-buttons-is-layout-5 wp-block-buttons-is-layout-flex\">\n<div class=\"wp-block-button is-style-outline is-style-outline--4\"><a data-bi-type=\"button\" class=\"wp-block-button__link wp-element-button\" href=\"https:\/\/www.microsoft.com\/en-us\/research\/articles\/rd-agent-an-open-source-solution-for-smarter-rd\/\">Read the article<\/a><\/div>\n<\/div>\n<\/div>\n\n\n\n\t<div class=\"border-bottom border-top border-gray-300 mt-5 mb-5 msr-promo text-center text-md-left alignwide\" data-bi-aN=\"promo\" data-bi-id=\"1002645\">\n\t\t\n\n\t\t<p class=\"msr-promo__label text-gray-800 text-center text-uppercase\">\n\t\t<span class=\"px-4 bg-white display-inline-block font-weight-semibold small\">Spotlight: AI-POWERED EXPERIENCE<\/span>\n\t<\/p>\n\t\n\t<div class=\"row pt-3 pb-4 align-items-center\">\n\t\t\t\t\t\t<div class=\"msr-promo__media col-12 col-md-5\">\n\t\t\t\t<a class=\"bg-gray-300\" href=\"https:\/\/aka.ms\/research-copilot\/?OCID=msr_researchforum_Copilot_MCR_Blog_Promo\" aria-label=\"Microsoft research copilot experience\" data-bi-cN=\"Microsoft research copilot experience\" target=\"_blank\">\n\t\t\t\t\t<img decoding=\"async\" class=\"w-100 display-block\" src=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2024\/01\/MSR-Chat-Promo.png\" alt=\"\" \/>\n\t\t\t\t<\/a>\n\t\t\t<\/div>\n\t\t\t\n\t\t\t<div class=\"msr-promo__content p-3 px-5 col-12 col-md\">\n\n\t\t\t\t\t\t\t\t\t<h2 class=\"h4\">Microsoft research copilot experience<\/h2>\n\t\t\t\t\n\t\t\t\t\t\t\t\t<p class=\"large\">Discover more about research at Microsoft through our AI-powered experience<\/p>\n\t\t\t\t\n\t\t\t\t\t\t\t\t<div class=\"wp-block-buttons justify-content-center justify-content-md-start\">\n\t\t\t\t\t<div class=\"wp-block-button\">\n\t\t\t\t\t\t<a href=\"https:\/\/aka.ms\/research-copilot\/?OCID=msr_researchforum_Copilot_MCR_Blog_Promo\" class=\"btn btn-brand glyph-append glyph-append-chevron-right\" aria-label=\"Microsoft research copilot experience\" data-bi-cN=\"Microsoft research copilot experience\" target=\"_blank\">\n\t\t\t\t\t\t\tStart now\t\t\t\t\t\t<\/a>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div><!--\/.msr-promo__content-->\n\t<\/div><!--\/.msr-promo__inner-wrap-->\n\t<\/div><!--\/.msr-promo-->\n\t\n\n\n<div style=\"padding-bottom:64px; padding-top:64px\" class=\"wp-block-msr-immersive-section alignfull row wp-block-msr-immersive-section\">\n\t\n\t<div class=\"container\">\n\t\t<div class=\"wp-block-msr-immersive-section__inner\">\n\t\t\t<div class=\"msr-cards__card msr-cards__card--default col\">\n\t<div class=\"card has-spectrum-border-top__hover material-card h-100 p-0\" data-mount=\"click-group\">\n\n\t\t\n\t\t<div class=\"card-body bg-white p-4 pt-3\">\n\t\t\t\t\t\t\n\t\t\t\t\t<\/div>\n\t<\/div>\n<\/div>\t\t<\/div>\n\t<\/div>\n\n\t<\/div>\n","protected":false},"excerpt":{"rendered":"<p>In this edition: Privacy enhancements for multiparty deep learning; using smaller, open-source models to provide relevance judgments; new tool uses AI, data to automate innovation and development; Yasuyuki Matsushita named IEEE 2025 Computer Society Fellow.<\/p>\n","protected":false},"author":38004,"featured_media":1122399,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","footnotes":""},"categories":[1],"tags":[],"research-area":[13561,13556,13548,13560,13555,13558],"msr-region":[],"msr-event-type":[],"msr-locale":[268875],"msr-post-option":[269148,243984,269142],"msr-impact-theme":[],"msr-promo-type":[],"msr-podcast-series":[],"class_list":["post-1116780","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-research-blog","msr-research-area-algorithms","msr-research-area-artificial-intelligence","msr-research-area-economics","msr-research-area-programming-languages-software-engineering","msr-research-area-search-information-retrieval","msr-research-area-security-privacy-cryptography","msr-locale-en_us","msr-post-option-approved-for-river","msr-post-option-blog-homepage-featured","msr-post-option-include-in-river"],"msr_event_details":{"start":"","end":"","location":""},"podcast_url":"","podcast_episode":"","msr_research_lab":[199560,199561,199565,437514,1012650],"msr_impact_theme":[],"related-publications":[],"related-downloads":[],"related-videos":[],"related-academic-programs":[],"related-groups":[267093,269241,696544,793670,802999,811276],"related-projects":[855579,507611],"related-events":[],"related-researchers":[],"msr_type":"Post","featured_image_thumbnail":"<img width=\"960\" height=\"540\" src=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2025\/01\/NEW_RF56-BlogHeroFeature-1400x788-1-960x540.jpg\" class=\"img-object-cover\" alt=\"Research Focus: January 17, 2025\" decoding=\"async\" loading=\"lazy\" srcset=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2025\/01\/NEW_RF56-BlogHeroFeature-1400x788-1-960x540.jpg 960w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2025\/01\/NEW_RF56-BlogHeroFeature-1400x788-1-300x169.jpg 300w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2025\/01\/NEW_RF56-BlogHeroFeature-1400x788-1-1024x576.jpg 1024w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2025\/01\/NEW_RF56-BlogHeroFeature-1400x788-1-768x432.jpg 768w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2025\/01\/NEW_RF56-BlogHeroFeature-1400x788-1-1066x600.jpg 1066w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2025\/01\/NEW_RF56-BlogHeroFeature-1400x788-1-655x368.jpg 655w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2025\/01\/NEW_RF56-BlogHeroFeature-1400x788-1-240x135.jpg 240w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2025\/01\/NEW_RF56-BlogHeroFeature-1400x788-1-640x360.jpg 640w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2025\/01\/NEW_RF56-BlogHeroFeature-1400x788-1-1280x720.jpg 1280w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2025\/01\/NEW_RF56-BlogHeroFeature-1400x788-1.jpg 1400w\" sizes=\"auto, (max-width: 960px) 100vw, 960px\" \/>","byline":"","formattedDate":"January 17, 2025","formattedExcerpt":"In this edition: Privacy enhancements for multiparty deep learning; using smaller, open-source models to provide relevance judgments; new tool uses AI, data to automate innovation and development; Yasuyuki Matsushita named IEEE 2025 Computer Society Fellow.","locale":{"slug":"en_us","name":"English","native":"","english":"English"},"_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/posts\/1116780","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/users\/38004"}],"replies":[{"embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/comments?post=1116780"}],"version-history":[{"count":26,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/posts\/1116780\/revisions"}],"predecessor-version":[{"id":1122438,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/posts\/1116780\/revisions\/1122438"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media\/1122399"}],"wp:attachment":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media?parent=1116780"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/categories?post=1116780"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/tags?post=1116780"},{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=1116780"},{"taxonomy":"msr-region","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-region?post=1116780"},{"taxonomy":"msr-event-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-event-type?post=1116780"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=1116780"},{"taxonomy":"msr-post-option","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-post-option?post=1116780"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=1116780"},{"taxonomy":"msr-promo-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-promo-type?post=1116780"},{"taxonomy":"msr-podcast-series","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-podcast-series?post=1116780"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}