Max Springer

I am an algorithms researcher, science communicator, and mathematics educator. 

Currently, I serve as a research fellow at Princeton University, where my work focuses on designing efficient algorithms that enable data-driven innovations while preserving fairness. I also study the societal impacts of algorithms, machine learning and AI systems, developing solutions to mitigate their potential harms.

I obtained my PhD in Applied Mathematics from the University of Maryland, where I was advised by MohammadTaghi Hajiaghayi and supported by an NSF Graduate Research Fellowship. My dissertation, "The Price of Fairness in Algorithmic Decision Making," explored theoretical guarantees and trade-offs in equitable algorithm design. Previously, I held research roles at Google Research, Nokia Bell Labs, Yale School of Medicine, and the National Institutes of Health. I completed my undergraduate studies at Cornell University with a B.A. in Mathematics and minors in Cognitive Science and Biological Sciences.

In addition to research, I frequently contribute to Scientific American and was selected as a 2024 AAAS Mass Media Fellow.

Selected Publications

Bi-criteria Metric Distortion

K. Banihashem, D. Chakraborty, S.C. Jahan, I. Gholami, M.T. Hajiaghayi, M. Mahdavi, M. Springer

in review

Fair Polylog Approximate Low-Cost Hierarchical Clustering

J.P. Dickerson, M.T. Hajiaghayi, M. Knittel, M. Springer

NeurIPS 2023

Online Algorithms for the Santa Claus Problem

M.T. Hajiaghayi, M.R. Khani, D. Panigrahi, M. Springer

NeurIPS 2022

Service

I serve on the inaugural AAAI Student Committee, which was formed to increase involvement from student researchers on matters pertaining to the responsible use of Artificial Intelligence. I also volunteer as an editor for Street Sense Media, Washington DC's weekly street newspaper sold by self-employed homeless distributors.

In my free time, I like to take mediocre photos on my Nikon FE or write about any and everything on my substack.