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Sheldon Recognized for Excellence in Teaching with 2023 College Outstanding Teaching Award

Dan Sheldon
Dan Sheldon

Manning College of Information and Computer Sciences Associate Professor Dan Sheldon has been selected to receive the college's 2023 Outstanding Teaching Award, an honor given annually to a faculty member who demonstrates excellence and creativity in teaching, and who has a positive impact on their students and mastery of their subject. 

Candidates for the 2023 award were selected based on faculty nominations and student feedback. Ramesh Sitaraman, distinguished professor and associate dean for educational programs and teaching, commended Sheldon's work, stating, "Dan is an extremely effective teacher who has consistently contributed in major ways to the teaching mission of the college. He teaches in a creative and intuitive way that is impactful, enjoyable, and stress-free for the students."

Student feedback focused on Sheldon's dedicated effort and ability to make advanced concepts accessible. "He always came well-prepared with the material for that day's lecture, and presented the material in an extremely intuitive and easy-to-grasp manner. He would take a topic that seemed incredibly daunting, dynamic programming, for example, and would break it up into smaller and more manageable chunks," wrote one student. "It's clear in the way he runs his courses that he cares deeply about students, and he constantly evaluates whether his teaching is effective in helping each student meet their goals," said another. "His genuine humility and enthusiasm for teaching really inspired me." 

Sheldon's research focuses on machine learning and applied algorithms with applications in large-scale environmental data and dynamic ecological processes. Currently, he is leading the NSF-funded BirdFlow project, which created the first predictive model capable of accurately forecasting the flight patterns of migratory birds.

Sheldon joined the Manning College of Information and Computer Sciences in 2012. He received his PhD in computer science from Cornell University in 2009.