Esen Yel

Esen Yel

Postdoctoral Scholar | Stanford University | esenyel@stanford.edu

Reliable Intelligent Systems Lab (RISL)

Reliable Intelligent Systems Lab (RISL) at Rensselaer Polytechnic Institute (RPI) will research methods for improving the safety, trustworthiness, and generalizability of intelligent systems starting in January 2024. The topics of interest include reachability analysis, decision-making under uncertainty, runtime monitoring, behavior modeling, and transfer learning. The lab’s application areas include aerial robotics, mobile robot navigation, and autonomous driving.

How to Join

RISL will take multiple Ph.D. students for Fall 2024. If you are interested in joining RISL and doing research in the areas below, you need to (and you are encouraged to) submit a Ph.D. application to ECSE at RPI (Deadline: January 1, 2024). If you have specific questions or research ideas, please reach out to Esen Yel by email (esenyel@stanford.edu) with the subject line “Interested in joining RISL”.

Research Areas

Adaptation to Unforeseen Situations

Trajectory adaptation

AV transfer

Autonomous systems may need to operate under different conditions than they have been designed for (e.g., unforeseen faults, different environment settings, etc.). To deal with this problem, the systems need to be able to adapt their models to new situations without the need for large amounts of data. Our work has focused on leveraging meta-learning and Bayesian techniques to provide adaptation to unforeseen conditions.

Relevant Publications:

Assured Autonomy

Assured planning

Autonomous systems can be subject to uncertainties due to external disturbances or due to the use of internal learning-based components. Since these systems are mainly used in safety-critical settings, it is important to assess the safety of the system before and during its deployment. Our research uses reachability analysis and neural network verification techniques to provide safety-critical autonomous systems with such capabilities.

Relevant Publications:

Predictive Autonomy

Assured planning For safe navigation and planning, it is important for autonomous systems to accurately predict how other agents in their environments are going to move over time. Our research uses various machine-learning techniques ranging from Gaussian Processes to deep neural networks to enable systems to predict the future states of their environment so that they can proactively plan safe trajectories. These techniques have been applied in various application areas such as aerial robotics, autonomous driving, and underwater robotics.

Relevant Publications

Reachability-based Self/Event-triggered Scheduling and Replanning

Self-triggered scheduling

High-frequency periodic sensor measurements are usually expected for a traditional system to safely navigate in environments occupied with obstacles. However, monitoring the sensors and processing the sensor measurements at high frequency are computationally consuming, and often not necessary especially when the vehicle is operating in obstacle-free environments. Our work uses reachability analysis together with self-triggered scheduling to minimize the computation associated with sensor monitoring while guaranteeing safety.

Relevant Publications