纽约大学研究生Katie Rosman获得国家科学基金会奖学金！-新东方前途出国

　　发布时间：2019年10月21日

　　Graduate student Katie Rosman won an NSF Fellowship for her work using data science to make a social impact 

　　研究生Katie Rosman因其利用数据科学产生社会影响的工作，获得了NSF奖学金。

　　Master’s degree candidate Katie Rosman is fond of a line of advice from Teddy Roosevelt: “Do what you can, with what you have, where you are.” Her mother, a social worker, had posted the quote on a mirror, so that Rosman would be reminded while growing up that at any moment of any day, she could use her skills and talents to make the world a better place. 

　　硕士学位候选人Katie Rosman喜欢Teddy Roosevelt的一句忠告：“尽你所能，用你所拥有的，在你所处的地方。”她的母亲是一名社会工作者，她把这句话贴在镜子上，这样Katie Rosman在成长过程中就会被提醒，在任何时候，她都可以运用自己的技能和才能，让世界成为一个更好的地方。

 

　　Rosman relayed that anecdote in her application for a National Science Foundation (NSF) Graduate Research Fellowship — the oldest fellowship program of its kind and one with a remarkable track record of accurately identifying recipients who go on to achieve high levels of academic and professional success. (Past fellows include numerous Nobel Prize winners, Google founder Sergey Brin, and Freakonomics co-author Steven Levitt.) 

　　Katie Rosman在申请国家科学基金会（NSF）研究生研究奖学金时转述了这一轶事——这是同类项目中最古老的奖学金项目，在准确确定获得高水平学术和专业成就的接受者方面有着卓越的记录（过去的研究员包括众多诺贝尔奖获得者、谷歌创始人Sergey Brin和畸形经济学（Freakonomics）联合作者Steven Levitt

 

　　While the honor typically goes to doctoral candidates, NSF officials were sufficiently impressed by Rosman’s fierce determination to make a social impact and her work as a data scientist to grant her the fellowship, which comes with a generous three-year stipend, cost-of-education allowance, and plethora of opportunities for international research and professional development. 

　　虽然荣誉通常授予博士生，但Katie Rosman对社会产生影响的坚定决心和她作为数据科学家的工作足以打动NSF的官员，使她获得奖学金，该奖学金有丰厚的三年津贴和教育津贴，以及大量的国际研究和专业发展机会。

 

　　Not too long ago, the idea that she could win an award meant for scientists and technologists would have seemed farfetched to Rosman. Her bachelor’s degree, which she earned from Stanford University in 2013, was in Public Policy and African-American Studies, and she had focused her attention on such topics as racial inequalities in urban school districts and barriers to employment faced by people with criminal records. Her award-winning honors thesis was one of the first academic explorations of job search challenges among adults living in transitional shelters. In working on those and other projects, she began realizing the vital role that technology could play in both informing policy and strengthening the effectiveness of public institutions. 

　　不久前，她能赢得一个科学家和技术专家奖的想法对Katie Rosman来说似乎有些牵强。2013年，她从斯坦福大学获得学士学位，主修公共政策和非裔美国人研究，重点关注城市学区的种族不平等和有犯罪记录者面临的就业障碍等问题。她的获奖论文是学术界首次对居住在过渡收容所的成年人进行求职挑战的探索之一在从事这些项目和其他项目时，她开始认识到技术在宣传政策和加强公共机构效力方面可以发挥的重要作用。

 

　　“I had always conceptualized technology as an important tool for start-ups and businesses, but as I gained more exposure to pressing challenges facing our society, I began to understand that technological innovation could also serve as a powerful force for social good,” says Rosman. “And I wanted to become part of that movement. The only problem was, I had essentially zero technical experience.” 

　　Katie Rosman说：“我一直认为技术是初创企业和企业的重要工具，但随着我更多地接触到我们社会面临的紧迫挑战，我开始明白技术创新也可以成为促进社会福祉的强大力量。”“我想成为这场运动的一部分唯 一的问题是，我基本上没有技术经验。”

 

　　Undeterred, Rosman vowed to develop the expertise necessary to work in data science, an emerging field at the intersection of computer science and statistics. By day, she joined the New York City mayoral administration, helping to create and implement a $10 million portfolio of economic development programs. At night and on weekends, however, she dedicated herself to learning how to code and trained to become a data scientist. Rosman pursued a variety of approaches to build up her technical skills, including attending multiple coding boot camps, engaging in rigorous self-study, and taking part in hackathons and machine learning competitions. 

　　Katie Rosman毫不犹豫地发誓要发展数据科学所需的专业知识，这是计算机科学和统计学交叉的一个新兴领域。白天，她加入了纽约市市长管理局，帮助创建和实施了一个1000万美元的经济发展项目组合。然而，在晚上和周末，她致力于学习如何编码，并接受培训成为一名数据科学家。Katie Rosman追求各种方法来提高她的技术技能，包括参加多个编码训练营，从事严格的自学，参加黑客运动和机器学习比赛。

 

　　In 2016 she was hired as a data scientist by the New York State Attorney General’s office as part of the Research and Analytics Department. There she worked on cases in a variety of fields, including civil rights, consumer fraud, and healthcare, and specialized in novel applications of machine learning techniques. Her technical analysis played a key role in the agency’s settlement with a Brooklyn-based auto dealership for discrimination against non-English speaking customers, as well as the agency’s $65 million settlement with Wells Fargo for fraudulent statements the company made to investors. She also worked on the agency’s case against Insys Therapeutics, helping to uncover the drug company’s practice of bribing health care providers into writing opioid prescriptions. More recently, she served as the lead data scientist for the agency’s landmark lawsuit against 10 opioid manufacturers and distributors, designing an analytical framework that exposed the defendants’ failure to prevent illicit diversion of prescription opioids for years on end. 

　　2016年，她被纽约州总检察长办公室聘为数据科学家，作为研究和分析部门的一员。在那里，她从事各种领域的案件，包括民权、消费者欺诈和医疗保健，并专门研究机器学习技术的新应用。她的技术分析在该机构与总部位于布鲁克林的一家汽车经销商就歧视非英语客户达成和解，以及该机构与富国银行就富国银行对投资者的欺诈性陈述达成6500万美元和解中发挥了关键作用。她还参与了该机构对Insys Therapeutics的诉讼，帮助揭露了这家制药公司贿赂医疗保健提供者撰写阿片类处方的做法。最近，她担任该机构针对10家阿片类药物制造商和经销商的里程碑式诉讼的首 席数据科学家，设计了一个分析框架，揭露被告多年来未能防止处方阿片的非法转移。

 

　　Rosman took great pride in her work, but because of the confidential and ongoing nature of the Attorney General’s investigations, her technical advancements were generally restricted to applications within the agency. She longed to collaborate with other government groups and share her code publicly, and realized that returning to academia could provide a pathway to achieving that vision. 

　　Katie Rosman对自己的工作感到非常自豪，但由于总检察长调查的保密性和持续性，她的技术进步一般仅限于在该机构内部的申请她渴望与其他政府团体合作，公开分享自己的准则，并意识到重返学术界可以提供实现这一愿景的途径。

 

　　She is now studying under NYU Tandon Associate Professor of Urban Analytics Daniel B. Neill, whose lab is devoted to developing innovative machine learning methods that are directly applicable to critical real-world problems, including preventing opioid overdoses, mitigating health disparities caused by environmental factors, and ensuring that the algorithmic systems used to make bail and parole decisions are just and equitable. 

　　她现在在纽约大学坦顿分校城市分析学副教授Daniel B.Neill的带领下学习，他的实验室致力于开发创新的机器学习方法，这些方法直接适用于现实世界的关键问题，包括防止阿片类药物过量，缓解环境因素造成的健康差异，以及确保用于作出保释和假释决定的算法系统是公正和公平的。 

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