Daniel Baumgartner

I am a first-year PhD student in the CS Theory Group at Rutgers University, advised by Professor Jie Gao. I am broadly interested in the theory and practical performance of algorithms for data, with an emphasis on privacy and robustness constraints. My current work focuses on network science, particularly root inference and sequential social learning.

Education

• Ph.D. in Computer Science at Rutgers (2025-present)
• B.S. in Statistics, Mathematics, and Computer Science at Rutgers (2021-2025)
• Study Abroad at Budapest Semesters in Mathematics (Summer 2023)

Experience

• Research Intern at the Max Planck Institute for Informatics (Summer 2025)

Publications

1. Power considerations for Kolmogorov–Smirnov and Anderson–Darling two-sample tests. Daniel Baumgartner and John Kolassa. Communications in Statistics - Simulation and Computation (2023)
2. Continuity Correction and Standard Error Calculation for Testing in Proportional Hazards Models. Daniel Baumgartner and John Kolassa. Stats (2025)

Miscellaneous

I've been involved in competitive programming over the years.

• Some slides I made: Indicator Random Variables
• A project of mine organizes a user's solved Codeforces problems in a table