MIT senior Rachel Chae and alumnus Sihao Huang '22 have been selected to join the 2023 class of Marshall Scholars and will begin graduate studies in the U.K. next fall. Funded by the British government, the Marshall Scholarship provides up to 50 scholarships for exceptional American students to pursue advanced study in any field at any university in the U.K. MIT's endorsed Marshall candidates are advised and supported by the distinguished fellowships team, led by Associate Dean Kim Benard in Career Advising and Professional Development. They are also mentored by the MIT Presidential Committee on Distinguished Fellowships, co-chaired by professors Will Broadhead and Tamar Schapiro. "Working with this year's Marshall applicants has been as rewarding and humbling as ever," says Broadhead. "These amazing students engage in a months-long exercise in critical introspection and personal growth, supported by the expert mentorship provided by Kim Benard and her team in the Distinguished Fellowships Office and by the dedicated faculty, staff, and graduate students who serve on the Distinguished Fellowships Committee. We on the committee have been inspired by all of this year's fellowship applicants and are especially pleased to congratulate Rachel and Sihao, whose wisdom, good humor, and future-minded optimism will serve them well as they take their richly deserved places in this year's class of Marshall Scholars."

Story generation has emerged as an interesting yet challenging NLP task in recent years. Some existing studies aim at generating fluent and coherent stories from keywords and outlines; while others attempt to control the global features of the story, such as emotion, style and topic. However, these works focus on coarse-grained control on the story, neglecting control on the details of the story, which is also crucial for the task. To fill the gap, this paper proposes a model for fine-grained control on the story, which allows the generation of customized stories with characters, corresponding actions and emotions arbitrarily assigned. Extensive experimental results on both automatic and human manual evaluations show the superiority of our method. It has strong controllability to generate stories according to the fine-grained personalized guidance, unveiling the effectiveness of our methodology. Our code is available at https://github.com/victorup/CHAE.