{"id":20644,"date":"2020-10-15T08:52:02","date_gmt":"2020-10-15T12:52:02","guid":{"rendered":"https:\/\/www.bloomberg.com\/company\/stories\/announcing-the-bloomberg-data-science-ph-d-fellowship-winners-for-2020-2021\/"},"modified":"2022-02-18T16:47:30","modified_gmt":"2022-02-18T21:47:30","slug":"announcing-the-bloomberg-data-science-ph-d-fellowship-winners-for-2020-2021","status":"publish","type":"post","link":"https:\/\/www.bloomberg.com\/company\/stories\/announcing-the-bloomberg-data-science-ph-d-fellowship-winners-for-2020-2021\/","title":{"rendered":"Introducing the Third Class of Bloomberg Data Science Ph.D. Fellows (2020-2021)"},"content":{"rendered":"
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Bloomberg proudly announces its newest class of early-career researchers to receive the Bloomberg Data Science <\/a>Ph.D<\/a>. Fellowship<\/a> for 2020-2021. These three doctoral students are engaged in research that complements ongoing artificial intelligence projects at Bloomberg. They will receive financial and professional support to pursue their research interests over the course of the academic year as they work towards the completion of their doctoral degrees.<\/p>\n

The fellowship program is an extension of Bloomberg\u2019s Data Science Research Grant Program<\/a>, which has promoted relationship building between the company and academic researchers around the world in the fields broadly construed as AI, such as machine learning (ML), natural language processing (NLP), information retrieval (IR), recommender systems, time series analyses, and optimization. Working with the company’s CTO Office, more than 50 data scientists, machine learning engineers, and AI researchers participated in the review of more than 100 fellowship applications this year. They selected three recipients on the basis of their outstanding proposals and academic references. Each of this year’s Fellows displayed exceptional novelty in their fields and delivered proposals of relevance to Bloomberg with significant business potential.<\/p>\n

Now in its third year, the competitive fellowship has consistently attracted motivated talent. \u201cIt is exciting to see the program evolve with more and more exceptional and innovative projects from new cohorts,\u201d said Jing Wang, a senior researcher in Bloomberg’s AI Engineering group who works on enrichment of news and social media content using novel natural language processing methods.<\/p>\n

Minjie Xu, a senior researcher in the AI Engineering group who works on clustering and summarization, notes that each year\u2019s crop of Fellows has demonstrated the desire to make a \u201ctangible impact\u201d on the world. Fellows work closely with Bloomberg\u2019s team of AI scientists and engineers, who provide support over the course of the academic year as mentors or co-authors.<\/p>\n

\u201cI self-identify more as a collaborator than a mentor when working with them,\u201d Xu said of the Fellows. \u201cOn the technical side, we often identify similar problems proactively and even come up with similar solutions. Discussions feel like a breeze and we constantly get inspired by each other’s ideas.\u201d<\/p>\n

The Fellows also bring new perspectives and fresh eyes to problems that Bloomberg\u2019s researchers are working on, according to Chen-Tse Tsai, a senior researcher who works on pattern recognition in the AI Engineering group.<\/p>\n

\u201cThe reviewing process itself is very fulfilling, as we get to use our experience to gauge the soundness of different proposals from the top Ph.D. students and their advisors in the field. We then get to help guide the selected proposals with the experience we\u2019ve gained at Bloomberg working on real products.\u201d<\/p>\n

Data Science Fellows receive the opportunity to renew their fellowship annually for up to three years. In the summer of 2021, the three winners will participate in an internship program to leverage their research aims in Bloomberg services or products.<\/p>\n\n<\/div>\n\n\n<\/div>\n\n\n\t\t\n\t<\/div>\n<\/div>\n

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\n \"Alexander\"Alexander\n
Alexander Spangher is a Data Science Ph.D. Fellow at Bloomberg.<\/figcaption>\n<\/figure>\n\n\n<\/div>\n
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Alexander Spangher<\/h2>\n

Alexander Spangher, a Ph.D. candidate in the Department of Computer Science<\/a> at the USC Viterbi School of Engineering<\/a>, notes that the Fellowship offered a unique opportunity to further his work in NLP and computational journalism. \u201cThere is no other journalism company that has 2,800 reporters on staff, deep engineering expertise and senior AI\/NLP researchers, and vast and reliable high-quality data resources,\u201d he says. \u201cReal-world data is readily available, product pipelines are swift and robust, and the opportunity to have an impact is real.\u201d<\/p>\n\n<\/div>\n

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Spangher\u2019s interest in journalism dates back to his high-school days, but his current research interests emerged from his work as a data scientist at The New York Times, where he received an inside look at the challenges traditional newsrooms face in sourcing and gathering material under tight budgets and in an evolving media landscape. Spangher\u2019s research involves building content models for stories that highlight gaps in information, identify potential sources, and retrieve relevant background information. As a Data Science Fellow, Spangher will develop human-in-the-loop pipelines to aid journalists in finding new stories, sources, and information throughout the writing process.<\/p>\n\n<\/div>\n\n<\/div>\n\n\t\t\n\t<\/div>\n<\/div>\n

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\n \"Sheena\"Sheena\n
Sheena Panthaplackel is a Data Science Ph.D. Fellow at Bloomberg.<\/figcaption>\n<\/figure>\n\n\n<\/div>\n
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Sheena Panthaplackel<\/h2>\n

With the support of Bloomberg\u2019s real-world financial data, Sheena Panthaplackel of the University of Texas at Austin\u2019s<\/a> Department of Computer Science<\/a> seeks to use her fellowship to probe the interplay between software and natural language. For instance, engineers invest significant time and resources to write source code comments to document crucial data about software usage and error. As Panthaplackel notes, this task can be streamlined by developing AI-driven tools that harness natural language elements which emerge from human-generated search queries, comments, and error reports.<\/p>\n\n<\/div>\n

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Panthaplackel\u2019s research is intended to facilitate software development with tasks including code comprehension, code maintenance, and issue resolution through natural language elements. This will reduce confusion, minimize software vulnerability, and ultimately save time in the software development process. \u201cWith Bloomberg technology being used across the financial markets, Bloomberg engineers and researchers take on an extremely important role, which requires delivering efficient and reliable software products and also quickly adapting software for evolving market conditions,\u201d Panthaplackel says. \u201cI hope to evaluate how my research can be applied to facilitate the workflow for developing this financial software.\u201d<\/p>\n\n<\/div>\n\n<\/div>\n\n\t\t\n\t<\/div>\n<\/div>\n

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\n \"Difan\"Difan\n
Difan Zou is a Data Science Ph.D. Fellow at Bloomberg.<\/figcaption>\n<\/figure>\n\n\n<\/div>\n
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Difan Zou<\/h2>\n

As a Data Science Fellow, Difan Zou, a grad student in the UCLA Statistical Machine Learning Lab (UCLA-ML)<\/a>, which is part of the Department of Computer Science<\/a> at the UCLA Samueli School of Engineering<\/a>, intends to employ machine learning tools to enhance Bloomberg\u2019s financial products and to collaborate with other researchers to solve real-world problems of import to the company via machine learning. Zou\u2019s research focuses on Graph Convolutional Networks, or GCNs, which produce representations that support recommender systems. However, GCNs typically do not scale to large graphs. Over the course of the fellowship, Zou hopes to build efficient training systems to bring GCNs to scale, with the intention of surpassing the current leading models.<\/p>\n\n<\/div>\n\n\n<\/div>\n\n\t\t\n\t<\/div>\n<\/div>\n

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Each of these three projects offers a unique contribution to its field and demonstrates significant business viability. Further information about each project can be found below.<\/p>\n\n<\/div>\n

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Looking back at our earlier Ph.D. Fellows’ work<\/h2>\n

The four 2019-2020 Ph.D. Fellows<\/a> all completed their internships. They and the five 2018-2019 Ph.D. Fellows<\/a> have all had their fellowships renewed. To date, the Fellows have published more than 20 papers during their fellowship, with many of them including Bloomberg mentors as co-authors. Some of this past year\u2019s examples include:<\/p>\n