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Ross Shachter joined Stanford University's faculty after earning his Ph.D. His doctoral dissertation focused on developing methods for purchasing expert forecasts to encourage accurate revelation of expert beliefs and probabilities. His research primarily investigates the representation, manipulation, and analysis of uncertainty in probabilistic reasoning and decision systems. He has developed fundamental methods for analyzing Bayesian belief networks and influence diagrams, which demonstrated how people and machines can communicate complex relationships involving uncertain quantities, decisions, and objectives. Currently, his research interests include modeling uncertain processes in decision-making, medical policy, meta-analysis, and intelligent systems, with specific applications such as cancer screening processes and vaccination strategies. Shachter actively engages in the academic community, holding multiple administrative roles in professional organizations such as the Association for Uncertainty in Artificial Intelligence.
The Computer Science department emphasizes research potential. GRE General is currently optional but recommended for some tracks.