Achievement and Awards
Achievement
Teaching at Michigan Technological University
At Michigan Technological University (MTU), teaching excellence is a priority in Mathematical Sciences. Graduate instructors complete the Teaching College Mathematics course before teaching; 3000-level courses are typically assigned to top GTIs with chair approval.
MTU evaluates instruction using the average of seven dimensions:
- Enthusiasm — instructor enthusiasm for the subject
- Clarity — clear communication of material
- Class Engagement — in-class participation/engagement
- Preparation Engagement — support for out-of-class preparation/reflection
- Timely Feedback — prompt, high-quality feedback
- Student Interest — interest in students’ learning and development
- Technology Usage — effective use of technology
Summary of Evaluation Scores (All Semesters)
I instructed MA 3710: Engineering Statistics in multiple modalities: in-person, synchronous online, and asynchronous online. The figures below summarize average evaluation scores by semester and by dimension.
Interpretation. The table reports average student evaluation scores for each semester across the seven dimensions. The line plot shows consistently strong evaluations at or above 4.21, the overall MTU average over the past seven years. The heatmap breaks scores out by dimension (e.g., technology use, participation), highlighting strengths and opportunities for improvement.
Teaching at Macalester College
Macalester College is one of the nation’s leading liberal arts institutions, with a highly diverse student body of about 2,000 undergraduates representing 45 states, the District of Columbia, and more than 70 countries.The college places the highest priority on excellence in teaching and learning. In Fall 2025, I have instructed STAT 155: Introduction to Statistical Modeling. The course is designed using a flipped classroom format, marking my first experience with this pedagogy. The course survey included five dimensions of teaching effectiveness.
Macalester College evaluates instruction using the average of five dimensions:
- Communication — clear communication of course material
- Organization and Engagement — effective course organization and use of class time
- Availability — Instructor availability when support is needed
- Feedback — quality of feedback on assignments and exams
- Inclusive Learning — facilitating an inclusive learning environment where students feel welcomed and respected
Summary of Evaluation Scores (Fall 2025)
I instructed STAT 155: Introduction of Statistical Modeling in the fall 2025. The figures below summarize average evaluation scores by each dimension.
Interpretation. Overall performance was strong, with all dimensions scoring at or above 4.00 on a 5-point scale. The highest ratings were observed for Inclusive Learning and Student Feedback (both 4.41), indicating that students felt welcomed, respected, and well supported through timely and constructive feedback. Communication also received a high mean score (4.35), reflecting clarity in presenting course material. Scores for Student Interest (4.24) and Organization and Engagement (4.00) further suggest consistent student engagement and effective use of class time, with room for continued refinement in course organization.
Awards
Teaching
- Recognition Award for exceptional teaching (7-dimension average), Fall 2020
- Provost’s Office for Academic Affairs, Michigan Technological University
- Among 73 instructors (86 sections) university-wide rated this highly out of 1,000+ evaluated sections
- Provost’s Office for Academic Affairs, Michigan Technological University
- Recognition Award for outstanding teaching, Spring 2020
- Provost’s Office for Academic Affairs, Michigan Technological University
Research
- Finishing Fellowship, Michigan Technological University — Summer 2025
Top Student Awards
Graduate Course Excellence Awards, Department of Mathematical Sciences, Michigan Technological University
- Top performance in Graduate and Ph.D.-level coursework: - Probability II [Spring ’22] - Probability I [Fall ’21] - Predictive Modeling [Fall ’21] - Applied Generalized Linear Model [Fall ’21] - Computational Statistics [Spring ’21] - Mathematical Statistics I [4000 level- Fall ’19, 5000 level- Fall ’20] - Mathematical Statistics II [4000 level- Spring ’20, 5000 level- Spring ’21] - Categorical Data Analysis [Spring ’20] - Teaching College Mathematics [Fall ’19]