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Xi Chen Recognized by Marquis Who's Who for Achievements in Data Science

Dr. Chen is an assistant professor in the department of information, operations, and management sciences at the Stern School of Business and is an affiliated professor at New York University's Center for Data Science

In January of 2017, he was featured in Forbes Magazine's 30 under 30 in Science given his contribution to data science.

    NEW YORK, NY, November 02, 2017 /24-7PressRelease/ -- Xi Chen has been included in Marquis Who's Who. As in all Marquis Who's Who biographical volumes, individuals profiled are selected on the basis of current reference value. Factors such as position, noteworthy accomplishments, visibility, and prominence in a field are all taken into account during the selection process.

Receiving a PhD from Carnegie Mellon University only four years ago, Dr. Chen is a quickly rising star in the fields of statistical machine learning, high-dimensional statistics, and operations research. Dr. Chen has been working as an assistant professor at New York University's Stern School of Business since 2014, and is also serving as an affiliated professor at NYU's Center for Data Science. In January of 2017, he was featured in Forbes Magazine's 30 under 30 in Science given his contribution to data science - the annual list recognizes the most influential people under the age of 30 in Science. In addition to that, Dr. Chen has won several prestigious awards, including the Adobe Data Science Research Award, Google Faculty Research Award, and Simons-Berkeley Research Fellowship.

Before joining NYU, Dr. Chen was a postdoctoral researcher with Professor Michael I. Jordan at the University of California, Berkeley. He completed his Ph.D. in Machine Learning at Carnegie Mellon University in 2013, which is the first Ph.D. program in Machine Learning in the U.S. While pursuing his Ph.D., he completed several industrial internships with Microsoft Research, the IBM Thomas J. Watson Research Center, and the NEC Lab America; he filed two patents with these companies. He was awarded the IBM Ph.D. Fellowship, NIPS and Uncertainty in AI Travel Awards. In 2009, Dr. Chen received an MS in Industrial Administration from the Tepper School of Business at Carnegie Mellon. He finished his undergraduate study by the age of 20 from the Special Class of Gifted Youth Program in Xi'an Jiaotong University in China.

Dr. Chen has already collaborated on numerous journal/conference publications and research projects addressing key challenges in big data analytics and the applications of statistical machine learning to business problems. His work has appeared in more than ten publications on the top-tier statistics journals (e.g., Annals of Statistics, Annals of Applied Statistics), machine learning journals (e.g., Journal of Machine Learning Research), and operations research journals (e.g., Operations Research). Moreover, he has also published more than twenty conference proceedings on top machine learning and artificial intelligence conferences such as NIPS, ICML, AISTATS, UAI, SODA, AAAI, ICDM, and SDM. Due to the impact of his work, several papers have received prestigious best paper awards. For example, the work "Dynamic Recommendation at Checkout under Inventory Constraint " by the team of Will Ma, David Simchi-Levi, Linwei Xin, and Dr. Chen, was honored with a Chinese Scholars Association for Management Science and Engineering Annual Conference Best Paper Award. The work "Statistical Inference for Model Parameters in Stochastic Gradient Descent" by the team of Jason Lee, Xin Tong, Yichen Zhang, and Dr. Chen received the 2017 Joint Statistical Meeting (JSM) Best Student Paper Award in Statistical Learning and Data Science. The work "Wasserstein Distributional Robustness and Regularization in Statistical Learning" by the team of Rui Gao, Anton Kleywegt, and Dr. Chen was honored as the winner for Data Mining Best Paper in Inform in 2017.

Dr. Chen has launched a number of interdisciplinary researches and is committed to solving the challenges in the era of big data and bringing data science into business. As a young research leader in the fields of high-dimensional statistics, and data-driven operations research/management, he has been making important contributions, especially on statistical inference under computational constraints, sequential analysis, and bandit learning. He has developed a series of new techniques to build the bridges between statistical inference and machine learning as well as between statistical learning and online decision-making. The techniques that he developed have been used in a wide range of big-data applications such as high-dimensional structured data analysis, crowdsourcing, ranking aggregation, and data-driven revenue management. He is a prolific speaker, reviewer, and workshop organizer as well.

About Marquis Who's Who :
Since 1899, when A. N. Marquis printed the First Edition of Who's Who in America , Marquis Who's Who has chronicled the lives of the most accomplished individuals and innovators from every significant field of endeavor, including politics, business, medicine, law, education, art, religion and entertainment. Today, Who's Who in America remains an essential biographical source for thousands of researchers, journalists, librarians and executive search firms around the world. Marquis publications may be visited at the official Marquis Who's Who website at www.marquiswhoswho.com.


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