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Operations

Associate Professor of Operations

Portrait of Maria Ibanez, Faculty at the Kellogg School of Management

 

Maria Ibanez is an Associate Professor of Operations Management at the Kellogg School of Management at Northwestern University. She received her doctorate in Technology and Operations Management from Harvard Business School.

She specializes in worker discretion—freedom to decide which tasks to work on, when, and how. Her research investigates how to create the conditions that promote desirable exercise of discretion. From a practical perspective, her research focuses on improving performance by designing data-driven systems that lead individuals to exercise discretion in ways that increase their productivity and work quality. Her work spans archival big data and field experiments in contexts ranging from radiology to restaurant inspections and emergency departments. With a primary focus on healthcare, she collaborates with organizations to understand their work and develop implementable solutions for relevant challenges. Combining operations management with economic theory and the psychology of decision-making, she analyzes large-scale field data to identify causal relationships that generate new insights regarding the connections between operational factors, decision-making, and performance.

Professor Ibanez's research has been published in leading journals such as Management Science and has also been featured in popular press outlets, including The Economist, Forbes, and the Harvard Business Review.

 

See Research and Publications

 

  • DBA, 2018, Technology and Operations Management, Harvard Business School
  • Assistant Professor of Operations, Operations, Kellogg School of Management, Northwestern University, 2018-present
  • Chairs' Core Course Teaching Award
    Chairs' Core Course Teaching Award

Emerging Areas in Operations Managements (OPNS-525-0)

This course studies novel, emerging topics and methods used in academic research of operations management. Content will depend on the expertise and interests of the instructor. Past content included statistical (machine) learning and sequential decision-making, such as bandit learning, balancing exploration/exploitation, and reinforcement learning, including methods for value function approximation and algorithms for efficient exploration.

Empirical Methods in Operations Management (OPNS-524-0)

This course examines: (1) how to critically read empirical studies, (2) how to ask questions that are interesting and worthwhile studying empirically, (3) what each method of causal inference (e.g. instrumental variables, panel data methods, regression discontinuity, etc.) does and why, when, and how to use each method, and (4) how an empirical researcher goes from an idea to a finished paper.