Assistant Professor Qin Li has been awarded an Vilas Early Investigator Award. The award is meant to recognize research and teaching excellence in faculty who are relatively early in their careers. These awards not only provide very generous research funding to those who receive them, but they are among the most prestigious awards granted by the university.
Two Assistant Professors in the Math department, Mihaela Ifrim and Hung Tran, have won National Science Foundation Early Career Awards. A hallmark achievement for young researchers, the CAREER award is described by the NSF as “a Foundation-wide activity that offers the National Science Foundation’s most prestigious awards in support of early-career faculty who have the potential to serve as academic role models in research and education and to lead advances in the mission of their department or organization. Activities pursued by early-career faculty should build a firm foundation for a lifetime of leadership in integrating education and research.”
Jason Turner, a first year graduate student in the Math Department, has been awarded a Department of Energy Computational Science Graduate Fellowship. This prestigious award provides outstanding benefits and opportunities to students pursuing doctoral degrees in fields that use high-performance computing to solve complex science and engineering problems. It covers 4 years of Ph.D. research.
Sam Stechmann, Jason's advisor, notes "This fellowship is a real honor, and Jason is very deserving. He has been an excellent student, both as an undergraduate and now as a graduate student, and this fellowship will help him pursue his interests in high-performance computing (HPC). He is thinking about applications to weather and climate predictions, where one major challenge is that the models are reasonably good but not perfect. Jason is planning to investigate some new methods for making computational predictions, where he will use multiple models, each with its own strengths and weaknesses, and allow them to communicate with each other during a prediction. By sharing information and taking advantage of each model's strengths, this strategy could create improved predictions."