Dr. Alex Shleyfman

Automated Planning and Artificial Intelligence

In many systems—from medicine to logistics—it is necessary to plan a sequence of actions under numerous and often changing constraints. The challenge is not only to find a feasible solution but to select the optimal one from a vast array of possibilities.

A prime example is a patient required to take multiple medications at various times, often coordinated with food intake or physical activity. Managing this schedule becomes a complex task, where a timing error can impair treatment efficacy or even endanger the patient. Dr. Alex Shleyfman develops algorithms capable of tackling such challenges. For example, the systems he builds weigh multiple constraints—dosage times, drug interactions, and changing conditions—to generate an optimal action plan. Through Automated Planning, these systems not only ensure no conflicts exist but also calculate the best sequence of actions in real time.

Primary Research Areas:

• AI Planning: Algorithms for finding optimal action sequences in complex environments.

• Heuristic Search and Optimization: Solving complex problems within vast search spaces.

• LLMs and Autonomous Agents: Utilizing generative AI to enhance planning capabilities and group coordination.

Research Nature:

 Theoretical-Applied: Developing algorithmic methods alongside their real-world implementation.

Career Path:

Graduates of the program acquire skills in algorithm design and optimization problems, which are highly sought after in:

• High-Tech, Robotics, and Defense: Developing smart logistics and aviation systems.

• Finance and Energy: Optimization of distribution networks and decision-making models.

• Research & Development (R&D): Core teams developing the next generation of Artificial Intelligence.