Matthew Groh
Assistant Professor of Management and Organizations
Assistant Professor in the Department of Computer Science (CS), McCormick School of Engineering (Courtesy)
Matt Groh is an Assistant Professor of Management and Organizations. His research examines the dynamics of human-AI collaboration in deepfake detection, medical diagnosis, and empathic communication.
Professor Groh's research has been published in Science, Proceedings on National Academy of Science (PNAS), Nature Medicine, Nature Communications, NeurIPS, Computer Supported Collaborative Work (CSCW), Affective Computing and Intelligence Interactions (ACII), and Communications of the ACM among other journals and conferences. His work has been featured in the popular press including The New York Times, Wall Street Journal, Science, Scientific American, NPR, Le Monde, Aeon, and Fast Company.
Professor Groh received his BA from Middlebury College with a major in economics and minors in mathematics and Arabic and received his MA and PhD in Media Arts and Sciences from MIT.
- Human-AI Collaboration
- Computational Social Science
- Applied Machine Learning
- Affective Computing
- Generative AI
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BA, 2010, Economics with minors in Mathematic and Arabic, Middlebury College
MA, 2019, Media Arts and Science, MIT
PhD, 2023, Media Arts and Science, MIT -
Assistant Professor, Management & Organizations, Kellogg School of Management, Northwestern University, 2023-present
Assistant Professor, Computer Science (by courtesy), McCormick School of Engineering, Northwestern University, 2024-present -
Sidney J. Levy Teaching Award
Human & Machine Intelligence (MORSX-950-0)
This course focuses on the fundamentals of what artificial intelligence is, how it is applied in business, and why it is heralded as the next industrial revolution. This course requires no prior technical knowledge, starting with an examination of what machine learning and artificial intelligence is, how it functions, and where it excels. We progress to building AI models, exploring potential benefits and pitfalls of AI in comparison to traditional decision-making and how the two are complementary, and discussing best-practices on implementation at organizations. We cover both the historical origins of AI---including how IBM's Deep Blue and Google's AlphaGo beat Chess and Go Grandmasters--as well as modern applications across a broad range of business sectors and applications. This course is a lecture-based course with case-based discussions, group activities and projects, and a final individual assignment. The overarching goal for the course is to enable you to confidently lead data science and design teams, know the expansiveness and limits of AI, and be capable of applying human and machine thought partnerships to grow businesses or disrupt Grand Masters in any field.
Human and Machine Intelligence (AIML-950-5)
Human and Machine Intelligence builds upon the foundations of AIML901-MO and dives deeper into how humans and AI interact, what that means for leadership and organizations, and how to successfully lead AI adoption in organizations. This is a real-world applications oriented course that draws on interdisciplinary insights from machine learning, cognitive science, human-computer interaction, organizational behavior, and literature to examine how and why humans and machines differ in problem solving abilities. By understanding the strengths and weaknesses of current and future humans and machines across a breadth of case studies, you will learn to recognize where AI can complement human intelligence and organizational processes, when human judgment is indispensable, what kind of trade-offs emerge when employing humans versus machines, and how to design hybrid teams that outperform either humans or machines working alone. This course is an interactive lecture-based course with four individual assignments and a final group project. The goal of the course is to prepare you to identify and evaluate AI opportunities, uncover human and machine blindspots, navigate ethical dilemmas, and lead teams of hybrid human and machine intelligence to effectively integrate AI into new businesses and adapt old ones.
AI Foundations for Managers - MORS (AIML-901MO-5)
Artificial intelligence (AI) is a transformative technology and a modern day equivalent to fire in the early stages of human civilization. Organizations can use AI to solve complex problems, automate tasks, make predictions, and even boost human creativity. However, like fire, AI also carries significant risks when misused. This is a practical, fundamentals of AI in business course designed for students who want to lead and consult on the deployment of AI systems in organizations, manage data science and software teams, or build and invest in AI companies. You will learn the conceptual foundations for how AI uses machine learning with neural networks to help computers see (computer vision), talk and write (large language models), and learn by trial and error (reinforcement learning). Moreover, you will build intuition for what AI can (and cannot yet) and discover where AI delivers value across industries but also where it fails spectacularly by examining a series of business applications, real-world examples, ethical considerations, and evaluation frameworks. This course is an interactive lecture-based course with four interrelated individual assignments and a final. The course is continually updated with the latest science and industry insights and is based on the first five weeks of Professor Groh's 2024 and 2025 Human and Machine Intelligence MORS950 course. The goal is to set you on your way to leading AI adoption in any organization and being equipped for AIML950, which dives deeper into how humans and AI interact, what that means for leadership and organizations, and how to successfully adopt AI in organizations.
