{"id":20481,"date":"2018-10-31T15:02:33","date_gmt":"2018-10-31T19:02:33","guid":{"rendered":"https:\/\/www.bloomberg.com\/company\/stories\/announcing-bloomberg-data-science-ph-d-fellowship-winners-2018-2019\/"},"modified":"2022-02-17T10:11:44","modified_gmt":"2022-02-17T15:11:44","slug":"announcing-bloomberg-data-science-ph-d-fellowship-winners-2018-2019","status":"publish","type":"post","link":"https:\/\/www.bloomberg.com\/company\/stories\/announcing-bloomberg-data-science-ph-d-fellowship-winners-2018-2019\/","title":{"rendered":"Announcing the Bloomberg Data Science Ph.D. Fellowship Winners for 2018-2019"},"content":{"rendered":"
\n \n\t\n\t\n\t
\n\t\t\n\t\t\t
\n\t

\n \"Bloomberg's\"Bloomberg's\n
Bloomberg’s Data Science Ph.D. Fellows for 2018-2019 (L-R): Yuval Pinter, Huazheng Wang, Hongyuan Mei, Tianze Shi and Shruti Rijhwani (Photographer: Lori Hoffman\/Bloomberg)<\/figcaption>\n<\/figure>\n
\n \n

Bloomberg recently welcomed five exceptional doctoral students who are working in broadly-construed data science, including natural language processing (NLP), machine learning, and artificial intelligence, to its Global Headquarters in New York City as part of the company’s inaugural Bloomberg Data Science Ph.D. Fellowship<\/a>. Bloomberg has benefited significantly from its Data Science Research Grant Program<\/a>, which builds relationships with academic researchers around the globe, and created the Fellowship program to engage professionals who may be early in their careers. The goal of this fellowship is to provide support and encouragement of groundbreaking publications in both academic journals and conference proceedings.<\/p>\n

Today, we are very pleased to announce the inaugural class of Bloomberg Data Science Ph.D. Fellows.<\/p>\n

A committee of Bloomberg\u2019s data scientists from across the organization selected the Fellows because of their proposals’ technical resiliency and strengths, and recommendation letters from their academic advisors, who also accompanied the Fellows on their visit to Bloomberg. Each Fellow\u2019s research interests are strongly aligned with the innovative work being done at Bloomberg to leverage data science and machine learning in the company’s products and services<\/a>. During the next year, the Fellows will work to advance their research and explore applications integral to Bloomberg. They will also have the opportunity to participate in an internship during the summer of 2019, during which they will collaborate with a Bloomberg team under the guidance of their research advisor to implement their research into one of the company’s applications.<\/p>\n\n<\/div>\n

\n \"Bloomberg's\"Bloomberg's\n
Bloomberg’s Ph.D. Fellows for 2018-2019 accompanied by their academic advisors and members of Bloomberg’s Data Science team (Photographer: Lori Hoffman\/Bloomberg)<\/figcaption>\n<\/figure>\n
\n \n

\u201cIt\u2019s important to expose people to real-world business problems\u00a0because it provides better context for how you can impact people\u2019s work,\u201d said Gillian Chin, a data scientist with the Office of the CTO at Bloomberg and a member of the Fellowship committee. \u201cMany people traditionally do so via summer internships, but at Bloomberg, we wish to invest in the applications of ideas borrowed from academic research on a much larger scale. In academia, you tend to have simple applications that are short-term in nature; the more difficult questions we’d like to investigate are complex, with multiple dependencies and constraints, and with a much longer time horizon in terms of execution and impact on our users.\u201d<\/p>\n

As an introduction to Bloomberg, the Fellows traveled to New York City for three days to meet with the company’s data science teams and the mentors they will be working with during their upcoming internship. In addition to learning about the variety of work and depth of data science research being done at Bloomberg, the goals of Bloomberg\u2019s data science group, and how Bloomberg operates, the Fellows also had the opportunity to meet the company’s founder, Michael Bloomberg.<\/p>\n\n<\/div>\n

\n \"Bloomberg\"Bloomberg\n
Bloomberg Founder &amp; CEO Michael Bloomberg &amp; CTO, Shawn Edwards meeting with Bloomberg PhD Fellows photographed at Bloomberg World Headquarters in New York on August 28, 2018. Photographer: Lori Hoffman\/Bloomberg<\/figcaption>\n<\/figure>\n
\n \n

\u201cThere are a lot of new, interesting challenges that I haven\u2019t thought about in academia \u2013 solving these challenges could have a real impact on Bloomberg\u2019s customers,\u201d said Huazheng Wang, a Ph.D. candidate in the Department of Computer Science<\/a> of University of Virginia<\/a>. \u201cThe most interesting thing is I get to work with Bloomberg researchers on these problems, which are rarely seen in academia.\u201d<\/p>\n

The fellowship provides exposure to the finance industry and Bloomberg\u2019s broad, high-quality data. Most research in academia is conducted on public data sets, but being able to utilize real-world data presents new challenges and issues that the Fellows will address in their work.<\/p>\n

For example, privacy has a different meaning in finance than it does in other industries, and not all data can be shared in collaborations. \u201cIn finance, investors are worried that someone can get ahold of their perspective, algorithms, and ideas,\u201d said Yuval Pinter<\/a>, a Computer Science Ph.D. student in the School of Interactive Computing<\/a> at Georgia Institute of Technology<\/a>. \u201cYou don\u2019t want people to know what you\u2019re looking at when you\u2019re investing.\u201d<\/p>\n

Markets move fast, and data needs to move at the same speed, while maintaining high accuracy. Other industries care about speed and accuracy, but not to the same degree as the finance sector. \u201cWe don\u2019t generally talk about efficiency at the same scale as Bloomberg does,\u201d said Tianze Shi<\/a>, a Ph.D. candidate in Cornell University’s Department of Computer Science<\/a>. \u201cWe care whether the algorithm can finish in a second, but I didn\u2019t imagine that things worked at that pace. Plus, you have to maintain accuracy at the same time, which is a big challenge.\u201d<\/p>\n\n<\/div>\n

\n \n

Out of about 60 applications from doctorate students at universities around the world, a committee of Bloomberg\u2019s data scientists from across the organization selected the following five Fellows for the 2018-2019 academic year:<\/p>\n\n<\/div>\n\n<\/div>\n\n\n\t\t\n\t<\/div>\n<\/div>\n

\n \n\t\n\t\n\t
\n\t\t\n\t\t\t
\n\t
\n \"Bloomberg\"Bloomberg\n
Bloomberg Ph.D. Fellow Hongyuan Mei of Johns Hopkins University with his advisor, Professor Jason Eisner (Photographer: Lori Hoffman\/Bloomberg)<\/figcaption>\n<\/figure>\n\n\n<\/div>\n
\n\t
\n \n

Hongyuan Mei<\/a><\/h3>\n

Johns Hopkins University<\/p>\n

Modeling Market Events with a Neural Hawkes Process<\/em><\/h4>\n

Market events such as earning reports, news articles and stock movements can influence one another. Modeling the complicated dependencies among them can help us probabilistically predict future events and impute missing events. The neural Hawkes process is a new machine learning model that is good at capturing such dependencies. It can be scaled up to model real-world arenas where various players in multiple sectors stochastically generate many events with detailed properties, according to the roles of these players and their incomplete knowledge of the previous events.<\/p>\n\n<\/div>\n\n\n<\/div>\n\n\t\t\n\t<\/div>\n<\/div>\n

\n \n\t\n\t\n\t
\n\t\t\n\t\t\t
\n\t
\n \"Bloomberg\"Bloomberg\n
Bloomberg Ph.D. Fellow Yuval Pinter (Photographer: Lori Hoffman\/Bloomberg)<\/figcaption>\n<\/figure>\n\n\n<\/div>\n
\n\t
\n \n

Yuval Pinter<\/a><\/h3>\n

Georgia Institute of Technology<\/p>\n

Integrating Distributional, Compositional, and Relational Representations of Language<\/em><\/h4>\n

To understand words from a computing perspective means to represent them using mathematics. Pinter is combining three different approaches to obtain these representations and understand what words are based on different linguistic insights. The first is determining which words are used in the same context. For example, the word \u201cdog\u201d is often used with \u201cbark,\u201d \u201cdoghouse\u201d and \u201cbone.\u201d The second approach views a word as the sum of its parts. Breaking words into their characters and finer grains creates sub-word components that can be added up to a representation, as the word \u201cdoghouse\u201d is composed of the words \u201cdog\u201d and \u201chouse.\u201d The third approach utilizes semantic networks that feature explicit knowledge. Linguists might say dogs are animals, which can be used to represent the word \u201cdog\u201d in a similar way to other animals. \u201cDoghouse\u201d is a type of structure. These can then be compared using a dictionary, leveraging the knowledge of how words are related to other words.<\/p>\n\n<\/div>\n\n\n<\/div>\n\n\t\t\n\t<\/div>\n<\/div>\n

\n \n\t\n\t\n\t
\n\t\t\n\t\t\t
\n\t
\n \"Bloomberg\"Bloomberg\n
Bloomberg Ph.D. Fellow Shruti Rijhwani (Photographer: Lori Hoffman\/Bloomberg)<\/figcaption>\n<\/figure>\n\n\n<\/div>\n
\n\t
\n \n

Shruti Rijhwani<\/a><\/h3>\n

Carnegie Mellon University<\/p>\n

Unsupervised Transfer for Entity Discovery and Linking<\/em><\/h4>\n

This standard natural language processing task puts words into a structure like an encyclopedia to help understand documents without having a human read them. Low-resource languages are those spoken by small populations that may not have access to social media or there is little data available to understand them. Entity linking is able to process documents in these languages automatically. The methods can be used on any language, including Spanish and Chinese, but this research focuses on languages such as Oromo and Tigrinya, which are spoken in Ethiopia; Sinhalese, which is spoken in Sri Lanka; and Kinyarwanda, which is spoken in Rwanda. Since there is a lack of training data available, information from high resource languages is used to try to develop NLP tools for these low resource languages.<\/p>\n\n<\/div>\n\n\n<\/div>\n\n\t\t\n\t<\/div>\n<\/div>\n

\n \n\t\n\t\n\t
\n\t\t\n\t\t\t
\n\t
\n \"Bloomberg\"Bloomberg\n
Bloomberg Ph.D. Fellow Tianze Shi (Photographer: Lori Hoffman\/Bloomberg)<\/figcaption>\n<\/figure>\n\n\n<\/div>\n
\n\t
\n \n

Tianze Shi<\/a><\/h3>\n

Cornell University<\/p>\n

Simple, Efficient, and Accurate Multi-lingual Parsing<\/em><\/h4>\n

Syntactic parsing analyzes sentence structures as a first step to understanding natural language text. For example, with the sentence \u201cI like to swim,\u201d knowing the structure that ‘I’ is the subject to the verbs \u201clike\u201d and \u201cswim\u201d can help an algorithm understand what people are talking about in their natural language text. This research is about designing simple, robust and fast algorithms for extracting these syntactic structures across many languages. It helps provide a deeper understanding of documents or other texts and can have a significant impact on downstream tasks.<\/p>\n\n<\/div>\n\n\n<\/div>\n\n\t\t\n\t<\/div>\n<\/div>\n

\n \n\t\n\t\n\t
\n\t\t\n\t\t\t
\n\t
\n \"Bloomberg\"Bloomberg\n
Bloomberg Ph.D. Fellow Huazheng Wang with his advisor, Professor Hongning Wang (Photographer: Lori Hoffman\/Bloomberg)<\/figcaption>\n<\/figure>\n\n\n<\/div>\n
\n\t
\n \n

Huazheng Wang<\/a><\/h3>\n

University of Virginia<\/p>\n

Collaborative Online Recommendation with Provable Guarantees<\/em><\/h4>\n

Different arms on slot machines give different payouts to players. The goal of this multi-armed bandit problem is to learn while playing which arm is most profitable, and applying this to news stories, understanding one person\u2019s preferences to help refine which stories are recommended to a whole group of readers. Online recommendation systems learn from behavior feedback to estimate one person\u2019s preferences, while a collaborative recommendation system learns from the human interactions within a person\u2019s network and provides real-time feedback so that the system learns with humans in a loop without compromising their privacy. When two people are connected within a network, if one person engages frequently about a particular topic, then there\u2019s a high likelihood of user collaboration and that the other person is interested in the same topic. This learning algorithm leverages connections between different users and their interests to more quickly identify a user\u2019s interests. While the system may provide someone with random recommendations at first, the system will constantly learn and refine itself through human interactions to provide more personalized recommendations over time based on the group’s preferences.<\/p>\n\n<\/div>\n\n\n<\/div>\n\n\t\t\n\t<\/div>\n<\/div>\n

\n \n\t\n\t\n\t
\n\t\t\n\t\t\t
\n\t
\n \n

\u201cWe received many great applications and saw many worthwhile candidates, and we had a very hard time narrowing this down to five Fellows,\u201d said Chin. \u201cWe hope to grow this program in the future, because it\u2019s an important investment in these young students and their work, in addition to the broader data science community.\u201d<\/p>\n\n<\/div>\n\n\n<\/div>\n\n\n\t\t\n\t<\/div>\n<\/div>\n

\n \n\t\n\t\n\t
\n\t\t\n\t\t\t
\n\t\n<\/div>\n\n\n\t\t\n\t<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

Early in their careers, Bloomberg is investing in young data scientists and their research<\/p>\n","protected":false},"author":313,"featured_media":19455,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1466],"tags":[1498,1578,1607,1472,1615,1535,1486,1580,1614],"class_list":["post-20481","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-tech-at-bloomberg","tag-ai","tag-artificial-intelligence","tag-computer-science","tag-data-science","tag-entity-discovery","tag-fellowship","tag-natural-language-processing","tag-nlp","tag-parsing"],"yoast_head":"\nAnnouncing the Bloomberg Data Science Ph.D. Fellowship Winners for 2018-2019 | Bloomberg LP<\/title>\n<meta name=\"description\" content=\"Bloomberg announces the five Bloomberg Data Science Ph.D. Fellowship Winners for 2018-2019, making an early investment in their careers\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.bloomberg.com\/company\/stories\/announcing-bloomberg-data-science-ph-d-fellowship-winners-2018-2019\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Announcing the Bloomberg Data Science Ph.D. Fellowship Winners for 2018-2019 | Bloomberg LP\" \/>\n<meta property=\"og:description\" content=\"Bloomberg announces the five Bloomberg Data Science Ph.D. Fellowship Winners for 2018-2019, making an early investment in their careers\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.bloomberg.com\/company\/stories\/announcing-bloomberg-data-science-ph-d-fellowship-winners-2018-2019\/\" \/>\n<meta property=\"og:site_name\" content=\"Bloomberg L.P.\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/bloomberglp\/\" \/>\n<meta property=\"article:published_time\" content=\"2018-10-31T19:02:33+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2022-02-17T15:11:44+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/assets.bbhub.io\/company\/sites\/51\/2018\/10\/IMG_0346-preferred.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"1024\" \/>\n\t<meta property=\"og:image:height\" content=\"683\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"author\" content=\"akelber5\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:image\" content=\"https:\/\/assets.bbhub.io\/company\/sites\/51\/2018\/10\/IMG_0346-preferred.jpg\" \/>\n<meta name=\"twitter:creator\" content=\"@bloomberg\" \/>\n<meta name=\"twitter:site\" content=\"@bloomberg\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"akelber5\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"10 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/www.bloomberg.com\/company\/stories\/announcing-bloomberg-data-science-ph-d-fellowship-winners-2018-2019\/\",\"url\":\"https:\/\/www.bloomberg.com\/company\/stories\/announcing-bloomberg-data-science-ph-d-fellowship-winners-2018-2019\/\",\"name\":\"Announcing the Bloomberg Data Science Ph.D. Fellowship Winners for 2018-2019 | Bloomberg LP\",\"isPartOf\":{\"@id\":\"https:\/\/www.bloomberg.com\/company\/#website\"},\"datePublished\":\"2018-10-31T19:02:33+00:00\",\"dateModified\":\"2022-02-17T15:11:44+00:00\",\"author\":{\"@id\":\"https:\/\/www.bloomberg.com\/company\/#\/schema\/person\/09c9e1a38b7f345ce5c0b4bbde1656a6\"},\"description\":\"Bloomberg announces the five Bloomberg Data Science Ph.D. Fellowship Winners for 2018-2019, making an early investment in their careers\",\"breadcrumb\":{\"@id\":\"https:\/\/www.bloomberg.com\/company\/stories\/announcing-bloomberg-data-science-ph-d-fellowship-winners-2018-2019\/#breadcrumb\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/www.bloomberg.com\/company\/stories\/announcing-bloomberg-data-science-ph-d-fellowship-winners-2018-2019\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/www.bloomberg.com\/company\/stories\/announcing-bloomberg-data-science-ph-d-fellowship-winners-2018-2019\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":\"1\",\"name\":\"Home\",\"item\":\"https:\/\/www.bloomberg.com\/company\/\"},{\"@type\":\"ListItem\",\"position\":\"2\",\"name\":\"Announcing the Bloomberg Data Science Ph.D. Fellowship Winners for 2018-2019\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/www.bloomberg.com\/company\/#website\",\"url\":\"https:\/\/www.bloomberg.com\/company\/\",\"name\":\"Bloomberg L.P.\",\"description\":\"Bloomberg L.P. is the leader in global business and financial information, enabling customers to make smarter, faster, more informed business decisions.\",\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/www.bloomberg.com\/company\/?s={search_term_string}\"},\"query-input\":\"required name=search_term_string\"}],\"inLanguage\":\"en-US\"},{\"@type\":\"Person\",\"@id\":\"https:\/\/www.bloomberg.com\/company\/#\/schema\/person\/09c9e1a38b7f345ce5c0b4bbde1656a6\",\"name\":\"Bloomberg L.P.\",\"url\":\"https:\/\/www.bloomberg.com\/company\"}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"Announcing the Bloomberg Data Science Ph.D. Fellowship Winners for 2018-2019 | Bloomberg LP","description":"Bloomberg announces the five Bloomberg Data Science Ph.D. Fellowship Winners for 2018-2019, making an early investment in their careers","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/www.bloomberg.com\/company\/stories\/announcing-bloomberg-data-science-ph-d-fellowship-winners-2018-2019\/","og_locale":"en_US","og_type":"article","og_title":"Announcing the Bloomberg Data Science Ph.D. Fellowship Winners for 2018-2019 | Bloomberg LP","og_description":"Bloomberg announces the five Bloomberg Data Science Ph.D. Fellowship Winners for 2018-2019, making an early investment in their careers","og_url":"https:\/\/www.bloomberg.com\/company\/stories\/announcing-bloomberg-data-science-ph-d-fellowship-winners-2018-2019\/","og_site_name":"Bloomberg L.P.","article_publisher":"https:\/\/www.facebook.com\/bloomberglp\/","article_published_time":"2018-10-31T19:02:33+00:00","article_modified_time":"2022-02-17T15:11:44+00:00","og_image":[{"width":1024,"height":683,"url":"https:\/\/assets.bbhub.io\/company\/sites\/51\/2018\/10\/IMG_0346-preferred.jpg","type":"image\/jpeg"}],"author":"akelber5","twitter_card":"summary_large_image","twitter_image":"https:\/\/assets.bbhub.io\/company\/sites\/51\/2018\/10\/IMG_0346-preferred.jpg","twitter_creator":"@bloomberg","twitter_site":"@bloomberg","twitter_misc":{"Written by":"akelber5","Est. reading time":"10 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"WebPage","@id":"https:\/\/www.bloomberg.com\/company\/stories\/announcing-bloomberg-data-science-ph-d-fellowship-winners-2018-2019\/","url":"https:\/\/www.bloomberg.com\/company\/stories\/announcing-bloomberg-data-science-ph-d-fellowship-winners-2018-2019\/","name":"Announcing the Bloomberg Data Science Ph.D. Fellowship Winners for 2018-2019 | Bloomberg LP","isPartOf":{"@id":"https:\/\/www.bloomberg.com\/company\/#website"},"datePublished":"2018-10-31T19:02:33+00:00","dateModified":"2022-02-17T15:11:44+00:00","author":{"@id":"https:\/\/www.bloomberg.com\/company\/#\/schema\/person\/09c9e1a38b7f345ce5c0b4bbde1656a6"},"description":"Bloomberg announces the five Bloomberg Data Science Ph.D. Fellowship Winners for 2018-2019, making an early investment in their careers","breadcrumb":{"@id":"https:\/\/www.bloomberg.com\/company\/stories\/announcing-bloomberg-data-science-ph-d-fellowship-winners-2018-2019\/#breadcrumb"},"inLanguage":"en-US","potentialAction":[{"@type":"ReadAction","target":["https:\/\/www.bloomberg.com\/company\/stories\/announcing-bloomberg-data-science-ph-d-fellowship-winners-2018-2019\/"]}]},{"@type":"BreadcrumbList","@id":"https:\/\/www.bloomberg.com\/company\/stories\/announcing-bloomberg-data-science-ph-d-fellowship-winners-2018-2019\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":"1","name":"Home","item":"https:\/\/www.bloomberg.com\/company\/"},{"@type":"ListItem","position":"2","name":"Announcing the Bloomberg Data Science Ph.D. Fellowship Winners for 2018-2019"}]},{"@type":"WebSite","@id":"https:\/\/www.bloomberg.com\/company\/#website","url":"https:\/\/www.bloomberg.com\/company\/","name":"Bloomberg L.P.","description":"Bloomberg L.P. is the leader in global business and financial information, enabling customers to make smarter, faster, more informed business decisions.","potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/www.bloomberg.com\/company\/?s={search_term_string}"},"query-input":"required name=search_term_string"}],"inLanguage":"en-US"},{"@type":"Person","@id":"https:\/\/www.bloomberg.com\/company\/#\/schema\/person\/09c9e1a38b7f345ce5c0b4bbde1656a6","name":"Bloomberg L.P.","url":"https:\/\/www.bloomberg.com\/company"}]}},"featured_image_rendered":"<img srcset='https:\/\/assets.bbhub.io\/image\/v1\/resize?width=auto&type=webp&url=https:\/\/assets.bbhub.io\/company\/sites\/51\/2018\/10\/IMG_0346-preferred.jpg 5760w, https:\/\/assets.bbhub.io\/image\/v1\/resize?width=auto&type=webp&url=https:\/\/assets.bbhub.io\/company\/sites\/51\/2018\/10\/IMG_0346-preferred.jpg 300w, https:\/\/assets.bbhub.io\/image\/v1\/resize?width=auto&type=webp&url=https:\/\/assets.bbhub.io\/company\/sites\/51\/2018\/10\/IMG_0346-preferred.jpg 768w, https:\/\/assets.bbhub.io\/image\/v1\/resize?width=auto&type=webp&url=https:\/\/assets.bbhub.io\/company\/sites\/51\/2018\/10\/IMG_0346-preferred.jpg 1024w, https:\/\/assets.bbhub.io\/image\/v1\/resize?width=auto&type=webp&url=https:\/\/assets.bbhub.io\/company\/sites\/51\/2018\/10\/IMG_0346-preferred.jpg 170w, https:\/\/assets.bbhub.io\/image\/v1\/resize?width=auto&type=webp&url=https:\/\/assets.bbhub.io\/company\/sites\/51\/2018\/10\/IMG_0346-preferred.jpg 140w' src='https:\/\/assets.bbhub.io\/image\/v1\/resize?width=auto&type=webp&url=https:\/\/assets.bbhub.io\/company\/sites\/51\/2018\/10\/IMG_0346-preferred.jpg' alt='Bloomberg's Data Science Ph.D. Fellows for 2018-2019 (L-R): Yuval Pinter, Huazheng Wang, Hongyuan Mei, Tianze Shi and Shruti Rijhwani (Photographer: Lori Hoffman\/Bloomberg)' \/>","category_info":{"name":"Tech At Bloomberg","blog_landing_name":"Tech At Bloomberg"},"_links":{"self":[{"href":"https:\/\/www.bloomberg.com\/company\/wp-json\/wp\/v2\/posts\/20481","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.bloomberg.com\/company\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.bloomberg.com\/company\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.bloomberg.com\/company\/wp-json\/wp\/v2\/users\/313"}],"replies":[{"embeddable":true,"href":"https:\/\/www.bloomberg.com\/company\/wp-json\/wp\/v2\/comments?post=20481"}],"version-history":[{"count":2,"href":"https:\/\/www.bloomberg.com\/company\/wp-json\/wp\/v2\/posts\/20481\/revisions"}],"predecessor-version":[{"id":20970,"href":"https:\/\/www.bloomberg.com\/company\/wp-json\/wp\/v2\/posts\/20481\/revisions\/20970"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.bloomberg.com\/company\/wp-json\/wp\/v2\/media\/19455"}],"wp:attachment":[{"href":"https:\/\/www.bloomberg.com\/company\/wp-json\/wp\/v2\/media?parent=20481"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.bloomberg.com\/company\/wp-json\/wp\/v2\/categories?post=20481"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.bloomberg.com\/company\/wp-json\/wp\/v2\/tags?post=20481"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}