09/16/2026
We are very excited to announce the graduate students selected for the 2025-2026 academic year awards.
We have three specific annual honors that celebrate the talent, dedication, and contributions of our graduate students, and the faculty recognize excellence in three award categories: Best New Student, Best Teaching Assistant, and Best Overall. The Best New Student is chosen from among the first year cohort and is based on excellent performance in coursework and collegiality. The Best Teaching Assistant is given to a student to recognize high quality teaching and commitment to student success based on departmental evaluation. Finally, the Best Overall award is given to a graduating student for their exceptional research accomplishments and meaningful, sustained service to the department that will leave a lasting impression.
The students selected by the faculty for these awards are as follows:
Best New Student: Matthew Farnsworth
Matt has excelled in his first year classes and on the written preliminary exams. In spite of beginning this program with very little statistics background, his dedication to learning in method, computational, and theoretical statistics has impressed all of the faculty. He not only sets very high standards for himself, but he is also a collegial addition to the department, willing to help others, and participates in departmental activities.
Best TA: Terry Tsai
Terry does an excellent job of not only presenting the class material but involving the students in the lecture so that their understanding of the material is cemented. As one student noted, "I'd taken stats before, but this class helped me to actually comprehend it, and made everything really clear." Another student said, "He is so helpful and truly wants us to succeed which I appreciate."
Best Overall Student: Caleb Fox
Caleb was an indispensable part of the Consulting team, working independently and as lead collaborator on difficult projects, several of which led to publications. He was first choice for the most challenging and high-visibility funded projects. He was also a mentor for incoming graduate students in the consulting center, and he led the departmental Paper Club for two years. Furthermore, his dissertation research was high quality work as he independently developed new methods for measuring uncertainty in parameter estimation for complicated spatial-temporal self exciting models.
Congratulations to each of you and thank you for your contributions to the quality and excellence of our department.