{"id":20690,"date":"2020-01-30T09:15:19","date_gmt":"2020-01-30T14:15:19","guid":{"rendered":"https:\/\/www.bloomberg.com\/company\/stories\/announcing-bloomberg-data-science-ph-d-fellowship-winners-2019-2020\/"},"modified":"2022-02-23T09:47:30","modified_gmt":"2022-02-23T14:47:30","slug":"announcing-bloomberg-data-science-ph-d-fellowship-winners-2019-2020","status":"publish","type":"post","link":"https:\/\/www.bloomberg.com\/company\/stories\/announcing-bloomberg-data-science-ph-d-fellowship-winners-2019-2020\/","title":{"rendered":"Announcing the Bloomberg Data Science Ph.D. Fellowship Winners for 2019-2020"},"content":{"rendered":"
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Bloomberg’s Data Science Ph.D. Fellows for 2019-2020 (L-R): Hao Tan, Ariah Klages-Mundt, and Katherine Keith (Photographer: Lori Hoffman\/Bloomberg)<\/figcaption>\n<\/figure>\n
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Four exceptional doctoral students, who are working in broadly-construed data science, including natural language processing (NLP), vision-and-language tasks, machine learning, and artificial intelligence, visited Bloomberg\u2019s Global Headquarters in New York City in 2019 as part of the Bloomberg Data Science Ph.D. Fellowship<\/a>. Bloomberg has benefited significantly from its first class of Fellows in 2018-2019, as well as its Data Science Research Grant Program<\/a>, which builds relationships with academic researchers around the globe.<\/p>\n

The goal of this fellowship is to engage professionals early in their careers and to provide support and encouragement for groundbreaking publications in both academic journals and conference proceedings. Today, we are pleased to announce the second class of Bloomberg Data Science Ph.D. Fellows.<\/p>\n

A committee of Bloomberg\u2019s data scientists from across the organization selected the Fellows based on their proposals\u2019 technical resiliency and strengths, and recommendation letters from their academic advisors, some of whom accompanied the Fellows on their visit to Bloomberg. The committee\u2019s decisions were based in part on the candidate\u2019s diverse academic focus, with priority given to machine learning, NLP, information retrieval, knowledge graph, and quantitative finance; the quality of the ideas presented in the proposal; the candidate\u2019s achievements and experience; and the idea\u2019s potential business impact.<\/p>\n\n<\/div>\n

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\u201cEach project will advance the state of the art in their respective academic areas,\u201d explained Songyun Duan, Head of Machine Learning Incubation in Bloomberg\u2019s Office of the CTO. \u201cThe results will be published in top-tier conferences.\u201d<\/p>\n

During their Fellowship, each of the Fellows will work to advance their research and explore real-world applications that contribute to the innovative work leveraging data science and machine learning in Bloomberg\u2019s products and services<\/a>. The Fellows will also participate in an internship during the summer of 2020, 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\u2019s applications to solve real-world problems and refine workflows.<\/p>\n

As an introduction to Bloomberg, the Fellows traveled to New York City for three days to meet with their mentors and the company\u2019s data science and AI engineering teams. During their visit, they learned more about the variety and depth of the company\u2019s data science research and how the organization operates.<\/p>\n\n<\/div>\n

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Katherine Keith<\/a>, a Ph.D. student in Computer Science in the College of Information and Computer Sciences at the University of Massachusetts Amherst<\/a>, was already familiar with Bloomberg through an internship during the summer of 2019 with Bloomberg\u2019s Data Science team in the Office of the CTO, which offered a hands-on introduction to Bloomberg\u2019s research. Through the relationships she developed during her internship, she published a paper at ACL 2019<\/a> titled “Modeling financial analysts\u2019 decision making via the pragmatics and semantics of earnings calls<\/a>” together with Amanda Stent, NLP Architect in Bloomberg\u2019s Office of the CTO.<\/p>\n

During her fellowship, Keith will further her research focused on improving natural language processing methods for computational social science applications. \u201cI’m interested in improving social measurements of text, such as event extraction and extracting semantic and pragmatic signals from text, and improving methods that use these measurements in descriptive and causal inferences,\u201d Keith said.<\/p>\n

Contagion risk exists within the financial system when market participants utilize contracts to interact with each other, and the complex financial products that bind these networks create new ‘spiral’ risks that can potentially be measured and controlled with algorithms. Applying financial network analysis to real-world problems has been a challenge though, and Ariah Klages-Mundt<\/a>, a Ph.D. candidate in applied math at Cornell University’s Center for Applied Mathematics<\/a>, hopes to overcome hurdles by developing numerical methods and machine learning tools for sensitivity analysis and probabilistic measure of network risks. \u201cI look forward to working with Bloomberg to see first-hand how these tools could realistically be used within the finance industry,\u201d said Klages-Mundt.<\/p>\n

Hao Tan<\/a>, a fourth-year Ph.D. student in the University of North Carolina at Chapel Hill\u2019s NLP Research Group<\/a> (which is led by Assistant Professor Mohit Bansal<\/a>), has worked on building the mapping from words and phrases to visual concepts, like objects and relationships, by designing tasks, building neural models, and developing training methods to learn this connection. At Bloomberg, where billions of data points are processed daily, Tan plans to explore the possibility of including visual information like images, videos and data plots by adapting methods developed for natural images to structural figures.<\/p>\n

Machine learning algorithms can be used to solve sequential decision-making problems, as most interactive systems \u2013 like search engines \u2013 are improved through a recurrent loop with various stages involving learning from new data, improving the features and the model, and then testing the new system.<\/p>\n

\u201cMy goal is to fundamentally speed up this process through new counterfactual inference techniques that move both learning and evaluation from \u2018online\u2019 to \u2018offline\u2019,\u201d said\u00a0Yi Su<\/a>, a Ph.D. student in the Department of Statistics and Data Science (DSDS) at Cornell University<\/a>, where she is advised by Professor Thorsten Joachims<\/a>. \u201cSince logged historical data is both biased and partial, machine learning algorithms on this partial information data can be highly sub-optimal. I\u2019m interested in developing new estimators and algorithms to work on this partial-information setting.\u201d<\/p>\n

The 2018-2019 Ph.D. Fellows<\/a> have all completed their internships and have had their fellowships renewed. Bloomberg has already benefited from the academic collaboration with strong Ph.D. candidates with interests similar to the company\u2019s Data Science and AI Engineering teams. This included facilitating publications in top-tier conferences and improving the quality of the company\u2019s products, where applicable, said Duan.<\/p>\n

Notably, 2018-2019 Fellow Huazheng Wang, a Ph.D. candidate in the Department of Computer Science<\/a> of University of Virginia School of Engineering and Applied Science<\/a>, won the prestigious Best Paper Award at SIGIR 2019<\/a> for his work on “Variance Reduction in Gradient Exploration for Online Learning to Rank<\/a>.” This research, which was supported by Bloomberg, was performed together with his three other students and Professor Hongning Wang<\/a>, his faculty advisor at the University of Virginia.<\/p>\n\n<\/div>\n\n<\/div>\n\n\n\t\t\n\t<\/div>\n<\/div>\n

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2019-2020 Data Science Ph.D. Fellows<\/h2>\n

Out of 79 applications from doctorate students at universities in the United States, European Union and United Kingdom, a committee of Bloomberg\u2019s data scientists from across the organization selected these four Fellows for the 2019-2020 academic year:<\/p>\n\n<\/div>\n\n\n<\/div>\n\n\n\t\t\n\t<\/div>\n<\/div>\n

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Yi Su is a Data Science Ph.D. Fellow at Bloomberg<\/figcaption>\n<\/figure>\n\n\n<\/div>\n
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Yi Su<\/a><\/h3>\n

Cornell University<\/p>\n

Off-Policy Evaluation and Learning for Interactive Systems<\/em><\/h4>\n

Search engines, recommender systems, and most other user interactive systems go through a recurrent loop of improvement. This loop typically involves learning from newly collected data, making improvements to the features and the model, and then testing the new system in an online A\/B test. My goal is to fundamentally speed up this process through new counterfactual inference techniques that move both learning and evaluation from \u201conline\u201d to \u201coffline.\u201d<\/p>\n\n<\/div>\n\n\n<\/div>\n\n\t\t\n\t<\/div>\n<\/div>\n

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Katherine Keith is a Ph.D. Fellow at Bloomberg. (Photographer: Lori Hoffman\/Bloomberg)<\/figcaption>\n<\/figure>\n\n\n<\/div>\n
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Katherine Keith<\/a><\/h3>\n

University of Massachusetts Amherst<\/p>\n

Constructing Subjective Knowledge Bases<\/em><\/h4>\n

The field of information extraction (IE) has made great strides in constructing knowledge bases by extracting facts from large collections of unstructured text. IE methods have been used in many applied settings, including my recent work building a database of police fatality victims. However, extracting facts implies discerning between different realities in order to determine what is \u201ctrue\u201d. Social scientists, journalists, policy makers, and financial investors may be more interested in understanding a populations\u2019 shared and conflicting subjective beliefs and how they vary temporally and spatially. This leads to the following research questions:<\/p>\n