Robin Schmucker
rschmuck[at]cs.cmu.edu
8227 Gates Hillman Center
4902 Forbes Ave
Pittsburgh, PA 15213
I am a PhD candidate in the Machine Learning Department at Carnegie Mellon University (CMU), where I am fortunate to be advised by Prof. Tom Mitchell.
My research focuses on machine learning for education, particularly in the context of large-scale online education. I aim to develop technologies that automatically refine their ability to teach as they support individual students and that generate insights that enhance our understanding of human learning. Some questions I am actively pursuing:
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What can we learn about student knowledge acquistion using modern machine learning and robust statistical methods? [1, 2, 3, 4]
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How can reinforcement learning help us understand the effects of instructional materials and refine the abilities of learning systems? [1, 2]
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How can generative-AI facilitate structured conversational learning activities and foster new types of content authoring tools? [1, 2]
We are grateful to collaborate with the CK-12 Foundation, where our algorithms for student knowledge modeling and content selection benefit millions of learners worldwide.
Previously, I studied computer science at KIT in Germany. I was a research assistant at TECO with Prof. Michael Beigl. Supported by the CLICS fellowship, I worked on human-robot interaction advised by Prof. Manuela Veloso. In the industry, I was a research intern at AWS where I designed new algorithms and contributed to AutoGluon.
Research opportunities: I am happy to collaborate, discuss research and answer questions about CMU’s academic programs. If you are interested, please feel free to send me an email.
I am on the academic job market for positions in machine learning, education and related disciplines.
news
Jul 28, 2024 | Our project Artificial Mentors for Student-Driven Projects won a Tools Competiton Catalyst award. We develop LLM-based technologies to support project-based learning activities. |
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Jul 06, 2024 | Looking forward to two weeks of insightful discussions and presentations at AIED and L@S. We are honored to present several of our recent works [1,2,3]. |
May 15, 2024 | Our two papers (Differential Course Difficulty, KC Generation+Tagging) were accepted at L@S. We will also organize an interactive demonstration of Ruffle&Riley. |
Mar 21, 2024 | Come visit our LAK presentation on measuring temporal difficulty variations in university courses! We are honored to have received a best-paper nomination. |
Mar 15, 2024 | Our work our LLM-based conversational tutoring system was accepted to AIED! You can find the open-source implementation of Ruffle&Riley in our public GitHub repository. |