Professor Jacobson’s research interests are in the field of Operations Research, with a particular focus on applied probability, discrete event computer simulation (analysis and modeling), and discrete optimization (analysis and heuristics). One current research focus looks at how tools and techniques in applied probability and discrete optimization can be combined to gain insights into difficult problems within each of these areas. This has resulted, for example, into new insights on the finite-time performance of the simulated annealing algorithm. This research has also lead to the development of the generalized hill climbing algorithm framework for addressing intractable discrete optimization problems. Generalized hill climbing algorithms provide a well-defined framework for modeling a large body of local search algorithms, including simulated annealing, threshold accepting, tabu search, and the noising method, among others. Generalized hill climbing algorithms have been used to address hard, large-scale discrete optimization problems. Extensions to this framework include ordinal hill climbing algorithms and simultaneous generalized hill climbing algorithms.