Levi's current research is focused on the design and the performance analysis of efficient algorithms for fundamental stochastic and deterministic optimization models, arising in the context of supply chains and inventory, revenue management, logistics and healthcare management. These fundamental, multistage stochastic models are typically very hard to solve optimally, both theoretically and in practice. Hence, it is important to develop efficient heuristics that provide provably near-optimal policies for these hard models. Levi has special interest in Cost-Balancing techniques, data-driven (sampling-based) algorithms, and modern Linear-Programming-based approximation techniques applied to models in the above domains. In addition, he is interested in stochastic and combinatorial optimization and mathematical programming in their broad definition, and especially in their intersection with problems that arise in the context of real-life applications.