{"id":768472,"date":"2021-08-22T23:12:20","date_gmt":"2021-08-23T06:12:20","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-group&#038;p=768472"},"modified":"2021-11-20T00:36:07","modified_gmt":"2021-11-20T08:36:07","slug":"machine-learning-solutions-services","status":"publish","type":"msr-group","link":"https:\/\/www.microsoft.com\/en-us\/research\/group\/machine-learning-solutions-services\/","title":{"rendered":"Machine Learning Solutions & Services Group"},"content":{"rendered":"<section class=\"mb-3 moray-highlight\">\n\t<div class=\"card-img-overlay mx-lg-0\">\n\t\t<div class=\"card-background  has-background-grey card-background--full-bleed\">\n\t\t\t\t\t<\/div>\n\t\t<!-- Foreground -->\n\t\t<div class=\"card-foreground d-flex mt-md-n5 my-lg-5 px-g px-lg-0\">\n\t\t\t<!-- Container -->\n\t\t\t<div class=\"container d-flex mt-md-n5 my-lg-5 align-self-center\">\n\t\t\t\t<!-- Card wrapper -->\n\t\t\t\t<div class=\"w-100 w-lg-col-5\">\n\t\t\t\t\t<!-- Card -->\n\t\t\t\t\t<div class=\"card material-md-card py-5 px-md-5\">\n\t\t\t\t\t\t<div class=\"card-body \">\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\n<h1 id=\"machine-learning-solutions-services-group\" class=\"h2\">Machine Learning Solutions & Services Group<\/h1>\n\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t<\/div>\n\t\t<\/div>\n\t<\/div>\n<\/section>\n\n\n\n\n\n<p><\/p>\n\n\n\n<p>The Machine Learning Solutions & Services (MLSS) Group was born with the rapidly increasing demand on AI-driven digital transformation from a wide range of industrial domains. Inspired by the mission of innovating disruptive AI technologies and even becoming the thought leader in critical industrial verticals, MLSS group has been diving into the following industrial domains through close collaboration with some key partners, i.e., leading companies within respective domains, and identified a couple of crucial research directions:<\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>Sustainability<ul><li>Multi-scale neural physical engine<\/li><li>Computational PDE<\/li><li>Neural DFT<\/li><li>Computational materials\/catalysis<\/li><li>Carbon capture & storage<\/li><\/ul><\/li><li>Supply-chain<ul><li>Inventory management<\/li><li>Dynamic routing<\/li><li>Resource optimization<\/li><li>Order fulfillment<\/li><li>Demand forecasting<\/li><\/ul><\/li><li>Medical<ul><li>Intelligent diagnosis\/prognosis<\/li><li>Dynamic treatment<\/li><li>Counter-factual analysis<\/li><li>Patient risk stratification<\/li><li>Epidemic trend forecasting<\/li><\/ul><\/li><li>Financials<ul><li>Intelligent investment<\/li><li>RegTech: anti-money-laundering, anti-fraud<\/li><li>Financial sentiment analysis<\/li><li>Financial simulation<\/li><li>ESG<\/li><\/ul><\/li><\/ul>\n\n\n\n<p><\/p>\n\n\n\n\n\n<p>Lin, Hengxu, et al. &#8220;<a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"https:\/\/arxiv.org\/abs\/2106.12950\">Learning Multiple Stock Trading Patterns with Temporal Routing Adaptor and Optimal Transport<span class=\"sr-only\"> (opens in new tab)<\/span><\/a>.&#8221;&nbsp;<em>Proceedings of the 27th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining<\/em>. 2021.<\/p>\n\n\n\n<p>Wu, Xueqing, et al. &#8220;<a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"https:\/\/proceedings.mlr.press\/v139\/wu21e.html\">Temporally Correlated Task Scheduling for Sequence Learning.&#8221;&nbsp;<span class=\"sr-only\"> (opens in new tab)<\/span><\/a><em>International Conference on Machine Learning<\/em>. PMLR, 2021.<\/p>\n\n\n\n<p>Fang, Yuchen, et al. &#8220;<a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"https:\/\/www.aaai.org\/AAAI21Papers\/AAAI-3650.FangY.pdf\">Universal Trading for Order Execution with Oracle Policy Distillation<span class=\"sr-only\"> (opens in new tab)<\/span><\/a>.&#8221;&nbsp;<em>arXiv preprint arXiv:2103.10860<\/em>&nbsp;(2021).<\/p>\n\n\n\n<p>Xu, Wentao, et al. &#8220;<a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"https:\/\/dl.acm.org\/doi\/abs\/10.1145\/3442381.3450032\">REST: Relational Event-driven Stock Trend Forecasting<span class=\"sr-only\"> (opens in new tab)<\/span><\/a>.&#8221;&nbsp;<em>Proceedings of the Web Conference 2021<\/em>. 2021.<\/p>\n\n\n\n<p>Yang, Xiao, et al. &#8220;<a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"https:\/\/arxiv.org\/abs\/2009.11189\">Qlib: An AI-oriented Quantitative Investment Platform<span class=\"sr-only\"> (opens in new tab)<\/span><\/a>.&#8221;&nbsp;<em>arXiv preprint arXiv:2009.11189<\/em>&nbsp;(2020).<\/p>\n\n\n\n<p>Yang, Xiao, et al. &#8220;<a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"https:\/\/ieeexplore.ieee.org\/abstract\/document\/8969091\/\">A divide-and-conquer framework for attention-based combination of multiple investment strategies<span class=\"sr-only\"> (opens in new tab)<\/span><\/a>.&#8221;&nbsp;<em>2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP)<\/em>. IEEE, 2019.<\/p>\n\n\n\n<p>Wang, Lewen, et al. &#8220;<a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"https:\/\/ieeexplore.ieee.org\/abstract\/document\/8969173\/\">Conservative or Aggressive? Confidence-Aware Dynamic Portfolio Construction<span class=\"sr-only\"> (opens in new tab)<\/span><\/a>.&#8221;&nbsp;<em>2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP)<\/em>. IEEE, 2019.<\/p>\n\n\n\n<p>Chen, Chi, et al. &#8220;<a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"https:\/\/dl.acm.org\/doi\/abs\/10.1145\/3292500.3330663\">Investment behaviors can tell what inside: Exploring stock intrinsic properties for stock trend prediction<span class=\"sr-only\"> (opens in new tab)<\/span><\/a>.&#8221;&nbsp;<em>Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining<\/em>. 2019.<\/p>\n\n\n\n<p>Li, Zhige, et al. &#8220;<a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"https:\/\/dl.acm.org\/doi\/abs\/10.1145\/3292500.3330833\">Individualized indicator for all: Stock-wise technical indicator optimization with stock embedding<span class=\"sr-only\"> (opens in new tab)<\/span><\/a>.&#8221;&nbsp;<em>Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining<\/em>. 2019.<\/p>\n\n\n\n<p>Ding, Yi, et al. &#8220;<a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"https:\/\/dl.acm.org\/doi\/abs\/10.1145\/3219819.3220113\">Investor-imitator: A framework for trading knowledge extraction<span class=\"sr-only\"> (opens in new tab)<\/span><\/a>.&#8221;&nbsp;<em>Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining<\/em>. 2018.<\/p>\n\n\n\n<p>Hu, Ziniu, et al. &#8220;<a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"https:\/\/dl.acm.org\/doi\/abs\/10.1145\/3159652.3159690\">Listening to chaotic whispers: A deep learning framework for news-oriented stock trend prediction<span class=\"sr-only\"> (opens in new tab)<\/span><\/a>.&#8221;&nbsp;<em>Proceedings of the eleventh ACM international conference on web search and data mining<\/em>. 2018.<\/p>\n\n\n\n<p>Lin, Hengxu, et al. &#8220;<a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"https:\/\/arxiv.org\/abs\/2107.05201\">Deep Risk Model: A Deep Learning Solution for Mining Latent Risk Factors to Improve Covariance Matrix Estimation<span class=\"sr-only\"> (opens in new tab)<\/span><\/a>.&#8221;&nbsp;<em>arXiv preprint arXiv:2107.05201<\/em>&nbsp;(2021).<\/p>\n\n\n\n<p>Tang, Hongshun, et al. &#8220;<a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"https:\/\/arxiv.org\/abs\/2012.06289\">ADD: Augmented Disentanglement Distillation Framework for Improving Stock Trend Forecasting<span class=\"sr-only\"> (opens in new tab)<\/span><\/a>.&#8221;&nbsp;<em>arXiv preprint arXiv:2012.06289<\/em>&nbsp;(2020).<\/p>\n\n\n\n<p>Chen, Chi, et al. &#8220;<a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"https:\/\/arxiv.org\/abs\/2002.06878\">Trimming the Sail: A Second-order Learning Paradigm for Stock Prediction<span class=\"sr-only\"> (opens in new tab)<\/span><\/a>.&#8221;&nbsp;<em>arXiv preprint arXiv:2002.06878<\/em>&nbsp;(2020).<\/p>\n\n\n\n<p>Xing J, Zheng S, Li S, L Huang et al. Mimicking atmospheric photochemical modeling with a deep neural network[J]. Atmospheric Research, 2021: 105919.<\/p>\n\n\n\n<p>Huang L, Liu S, Yang Z, et al. Exploring Deep Learning for Air Pollutant Emission Estimation[J]. Geoscientific Model Development Discussions, 2021: 1-22.<\/p>\n\n\n","protected":false},"excerpt":{"rendered":"<p>The Machine Learning Solutions & Services (MLSS) Group was born with the rapidly increasing demand on AI-driven digital transformation from a wide range of industrial domains. Inspired by the mission of innovating disruptive AI technologies and even becoming the thought leader in critical industrial verticals, MLSS group has been diving into the following industrial domains [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr_group_start":"","footnotes":""},"research-area":[13561,13556,13553,13568],"msr-group-type":[243694],"msr-locale":[268875],"msr-impact-theme":[],"class_list":["post-768472","msr-group","type-msr-group","status-publish","hentry","msr-research-area-algorithms","msr-research-area-artificial-intelligence","msr-research-area-medical-health-genomics","msr-research-area-technology-for-emerging-markets","msr-group-type-group","msr-locale-en_us"],"msr_group_start":"","msr_detailed_description":"","msr_further_details":"","msr_hero_images":[],"msr_research_lab":[199560],"related-researchers":[{"type":"user_nicename","display_name":"Jiang Bian","user_id":38481,"people_section":"Section name 0","alias":"jiabia"},{"type":"user_nicename","display_name":"Lin Huang","user_id":40456,"people_section":"Section name 0","alias":"hul"},{"type":"user_nicename","display_name":"Tie-Yan Liu","user_id":34431,"people_section":"Section name 0","alias":"tyliu"},{"type":"user_nicename","display_name":"Weiqing Liu","user_id":39300,"people_section":"Section name 0","alias":"weiqiliu"}],"related-publications":[],"related-downloads":[],"related-videos":[],"related-projects":[],"related-events":[],"related-opportunities":[],"related-posts":[],"tab-content":[],"msr_impact_theme":[],"_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-group\/768472"}],"collection":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-group"}],"about":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/types\/msr-group"}],"version-history":[{"count":11,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-group\/768472\/revisions"}],"predecessor-version":[{"id":934068,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-group\/768472\/revisions\/934068"}],"wp:attachment":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media?parent=768472"}],"wp:term":[{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=768472"},{"taxonomy":"msr-group-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-group-type?post=768472"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=768472"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=768472"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}