Biography
Dr. Konstantin Bauman joined the Fox School on a tenure track appointment within the Department of Management Information Systems on January 1, 2018.
He arrives at Fox from the Stern School of Business at New York University, where he served as a postdoctoral research fellow. Bauman’s research interests lie in the areas of technical information systems, with focus on the fields of quantitative modeling and data science. In particular, he works on developing novel machine learning methods for predicting customer preferences, and designing novel approaches to recommender systems that provide personalized advice to customers.
Before joining NYU, Bauman worked as the head of a machine-learning group within the research department of Yandex, where he dealt with large-scale machine learning and data science problems on a daily basis. He also served as a software engineer at Yandex and the Russian Academy of Foreign Trade.
Bauman received his PhD in Mathematics (Geometry and Topology) from Russia’s Moscow State University, where he also earned a Master of Science degree in Mathematics. He also obtained a Master of Science degree in Machine Learning from a joint program between the Moscow Institute of Physics and Technology and the Yandex School of Data Analysis in Russia.
Research Interests
- Data science
- Analytics
- Machine learning
- Data and text mining
- Recommender systems
- Technology enhanced learning
Courses Taught
Number | Name | Level |
|---|---|---|
MIS 2402 | Web Application Development | Undergraduate |
MIS 2502 | Database Management | Undergraduate |
SBM 3585 | Diamond Peer Teachers - Internship I | Undergraduate |
SBM 3586 | Diamond Peer Teachers - Internship II | Undergraduate |
STAT 5606 | Data: Care, Feeding, and Cleaning in Python | Graduate |
STAT 8982 | Independent Study | Graduate |
Selected Publications
Recent
Adomavicius, G., Bauman, K., Tuzhilin, A., & Unger, M. (2022). Context-Aware Recommender Systems: From Foundations to Recent Developments. In Recommender Systems Handbook (pp. 211-250). Springer.
Bauman, K. (2019). Discovering the Graph Structure in Clustering Results. In Advances in Information and Communication Networks Proceedings of the 2018 Future of Information and Communication Conference (FICC). Springer.
Bauman, K. & Bauman, E. (2018). One-Class Semi-supervised Learning. In Array, Braverman Readings in Machine Learning. Key Ideas from Inception to Current State. Lecture Notes in Computer Science, 11100. Springer.
Bauman, K. & Tuzhilin, A. (2018). Recommending remedial learning materials to students by filling their knowledge gaps. MIS Quarterly: Management Information Systems, 42(1), 313-332. doi: 10.25300/MISQ/2018/13770.