Draft:Susan L. Epstein

Susan L. Epstein is a computer science professor on the faculty at Hunter College and The Graduate Center of The City University of New York. She is known for her contributions to artificial intelligence, machine learning, cognitive science, computational cognitive science, cognitive architecture, and knowledge representation and reasoning. She has held several notable positions within the AI community, including chair of the Cognitive Science Society, executive councilor and ethics chair for the Association for the Advancement of Artificial Intelligence (AAAI), and an officer for SIGAI, the Association for Computing Machinery (ACM) special interest group on artificial intelligence. Epstein's interdisciplinary work has advanced the understanding of how knowledge and learning can be simulated and applied in computer systems.

Early life and education
Epstein was born in New York. She earned a BA in mathematics from Smith College, an MS in mathematics from The Courant Institute at New York University, and an MS and PhD in computer science from Rutgers University in 1983. Her doctoral thesis, "Knowledge Representation in Mathematics: A Case Study In Graph Theory," was supervised by Natesa S. Sridharan, a student of Herbert Gelernter.

Career and research
Epstein joined the City University of New York in 1984 as a computer scientist. Her early work includes the Graph Theorist, the first system that hypothesized mathematical concepts then proved theorems about those hypotheses. She is a professor of computer science and director of CUNY's Problem Solving and Machine Learning Laboratory at Hunter College. Her tenure at Hunter College and The Graduate Center of CUNY has been marked by her leadership in AI research and dedication to advancing the field through education and collaboration.

Epstein is best known for developing FORR, a novel machine-learning cognitive architecture with the ability to problem solve and make expert decisions. Systems based on FORR have autonomously learned to play multiple games expertly, solve challenging constraint satisfaction problems, converse with an expert to achieve a mutual goal, and control a robot as it pragmatically navigates complex, real-world environments. She has also pioneered work in collaborative intelligence and mathematical discovery by computer. Epstein defines collaborative intelligence as humans and intelligent machines working together to achieve human-specified goals. She has challenged the AI community to develop systems that collaborate effectively with humans, adapt to their needs, and leverage human expertise.

Epstein's interdisciplinary collaboration has contributed to a broader understanding of knowledge and learning principles in mathematics, psychology, geography, linguistics, microbiology, architecture, and robotics. Epstein's recent significant contributions to the Combined Knowledge and Competency (CKC) Model for Computer Science Curricula and robotic-assisted adaptive landscaping reflect her ongoing commitment to leveraging AI for practical and innovative applications toward social good.