HumRRO is proud to announce Chen Tang<\/a> as this year\u2019s recipient of the Meredith P. Crawford Fellowship. Tang is a doctoral student in Human Resources and Industrial Relations at the University of Illinois at Urbana-Champaign\u2019s School of Labor and Employment Relations.<\/p>\n Named in honor of HumRRO\u2019s founding director, the Meredith P. Crawford Fellowship is one of the most prestigious awards, granting a non-renewable $12,000 stipend, in the fields of human resources and organizational behavior. Since 1998, HumRRO has selected a doctoral candidate annually in I-O psychology or closely related discipline who demonstrates exceptional research skills, academic achievement, and professional productivity.<\/p>\n In selecting Tang this year, the selection committee noted his \u201cpublished peer-reviewed articles and his impressive slate of manuscripts in advanced stages of the review process with high-impact I-O psychology journals\u2014all of which are well-poised to make meaningful impacts on the field.\u201d In addition, Tang received exceptional recommendations, has won awards for his research from his doctoral program and from the International Personnel Assessment Council (IPAC), and he has already begun to distinguish himself as an excellent instructor in addition to demonstrating great promise as a researcher.<\/p>\n<\/div> \u201cRecognizing upcoming researchers for their talent and potential to impact the field is one of the most important and rewarding things we do at HumRRO,\u201d said Suzanne Tsacoumis<\/a>, Ph.D., president and CEO of HumRRO. \u201cChen encompasses all the attributes we look for when awarding the Fellowship, and we are excited about his research focus on maximizing validity and minimizing adverse impact in the selection process.\u201d<\/p>\n Tang\u2019s dissertation blends his interest in Pareto optimization with his interest in machine learning to delineate the conditions under which applied psychologists would be best advised to develop Pareto solutions based on local evidence, meta-analytic evidence, an empirical-Bayesian blend of local and meta-analytic evidence, or a machine learning-driven estimation procedure.<\/p>\n<\/div>