Professor of Computer Science at Gettysburg College
Previous courses:
CS 103: Introduction to Computing (Home Page, Online Text),
CS 107: Introduction to Scientific Computation,
CS 111: Computer Science I,
CS 216: Data Structures,
CS 341: Principles of Programming Languages,
CS 371: Artificial Intelligence,
CS 374: Compilers,
CS 391: Selected Topics: Kaggle Competition,
CS 391: Selected Topics: Game Artificial Intelligence,
CS 391: Selected Topics: Data Mining,
CS 391: Selected Topics: Machine Learning,
CS 392: Selected Topics: Game Artificial Intelligence,
DS 256: Data Science Programming,
FYS 187-4: Games and Computation.
Todd W. Neller is a Professor of Computer Science at Gettysburg College, and was the recipient of the 2018 AAAI/EAAI Outstanding Educator Award for "longstanding dedication and service to the AI education community at large, for curating shared resources, and for advancing and energizing the field of AI education." In 2010, he co-founded the Educational Advances in Artificial Intelligence (EAAI) symposium, where he chairs the Model AI Assignments track.
A Cornell University Merrill Presidential Scholar, he received a B.S. in Computer Science with distinction in 1993. In 2000, he received his Ph.D. with distinction in teaching at Stanford University, where he was awarded a Stanford University Lieberman Fellowship, and the George E. Forsythe Memorial Award for excellence in teaching. His dissertation concerned extensions of artificial intelligence (AI) search algorithms to hybrid dynamical systems, and the refutation of hybrid system properties through simulation and information-based optimization.
A game enthusiast, Neller has enjoyed pursuing game AI challenges, computing optimal play for jeopardy dice games such as Pass the Pigs and bluffing dice games such as Dudo, creating new reasoning algorithms for Clue/Cluedo, analyzing optimal Risk attack and defense policies, and designing games and puzzles.
We apply such techniques to the
metalevel control of search and optimization algorithms. For
example, we have successfully applied RL techniques to the control
of simulated annealing, dynamically adjusting the temperature and
deciding when to terminate optimization.
