Sarah Keren
Senior lecturer (assistant professor)
The Taub Computer Science Faculty
Technion - Israel Institute of Technology
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This website is now longer maintained. My new websites:
The CLAIR lab at the Technion: https://clair.cs.technion.ac.il/
My personal website: https://sarahk.cs.technion.ac.il/
Thank you for your interest in my work.
Check out my new websites:
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The CLAIR lab at the Technion: https://clair.cs.technion.ac.il/
My personal website: https://sarahk.cs.technion.ac.il/
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I am a senior lecturer (assistant professor) at the Computer Science Faculty at the Technion, Israel Institute of Technology
Before joining the Technion, I was postdoctoral fellow at the Harvard School of Engineering and Applied Sciences and the Hebrew University School of Computer Science and Engineering.
The overall objective of my research in the area of artificial intelligence (AI) is multi-agent environment design which takes into account the constraints, limitations, and capabilities of the different agents in an AI system and finds the best way to design their environment so that it complements the agents’ capabilities and compensates for their limitations.
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My work brings the power of model-based reasoning and decision making under uncertainty to solve the automated design problem, as well as to interpret the behaviors of the AI agents. I am also using methods from game theory, multi-agent systems, and multi-agent reinforcement learning. My current focus is on applying these methods of automated design to promote effective multi-robot and human-robot collaborations. You can see my robots Stella-1 and Stella-2 on the right, together with some of projects I am working on.
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My long-term goal is to provide the theoretical foundations for designing AI systems that are capable of effective partnership in sustainable and efficient collaborations of automated agents as well as of automated agents and people.
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​The main focus of my doctoral thesis at the Technion - Israel Institute of Technology is a new and exciting research topic we call Goal Recognition Design, which facilitates the ability to infer the goals of acting agents through the analysis and redesign of goal recognition environments.