Andreas Maggiori

I am a Software Engineer at Google NYC.

Prior to that, I was a Postdoctoral Researcher at the Data Science Institute at Columbia University, hosted by Eric Balkanski and Will Ma. I earned my PhD at EPFL, advised by Rudiger Urbanke and Ola Svensson. Before joining EPFL, I completed my undergrad in Greece, in the Electrical and Computer Engineering department of the National Technical University of Athens.

I am interested in data-driven decision-making under uncertainty. My work combines techniques from combinatorial optimization and machine learning with the ultimate goal of designing algorithms that outperform classical algorithms when accurate predictions about the future are available while maintaining robustness against adversarial and/or biased predictions.

Email  /  Google Scholar

Fair Secretaries with Unfair Predictions
with Eric Balkanski and Will Ma
NeurIPS 2024

The Primal-Dual method for Learning Augmented Algorithms
with Etienne Bamas and Ola Svensson
Oral talk (1% acceptance rate) at NeurIPS 2020

Cost-Free Fairness in Online Correlation Clustering
with Eric Balkanski, and Iason Chatzitheodorou
ALT 2025

Data-Driven Solution Portfolios
with Marina Drygala, Silvio Lattanzi, Miltiadis Stouras, Ola Svensson, and Sergei Vassilvitskii
ITCS 2025

Fair Secretaries with Unfair Predictions
with Eric Balkanski and Will Ma
NeurIPS 2024

Dynamic Correlation Clustering in Sublinear Update Time
with Vincent Cohen-Addad, Silvio Lattanzi and Nikos Parotsidis
ICML 2024, Spotlight (3% acceptance rate)

Online and Consistent Correlation Clustering
with Vincent Cohen-Addad, Silvio Lattanzi and Nikos Parotsidis
ICML 2022
slides/ talk

An Improved Analysis of Greedy for Online Steiner Forest
with Etienne Bamas and Marina Drygala
SODA 2022
arxiv/ slides

The Primal-Dual method for Learning Augmented Algorithms
with Etienne Bamas and Ola Svensson
NeurIPS 2020, Oral talk (1% acceptance rate)
arxiv/ talk/ code

Learning Augmented Energy Minimization via Speed Scaling
with Etienne Bamas, Lars Rohwedder and Ola Svensson
NeurIPS 2020, Spotlight (3% acceptance rate)
arxiv/ slides/ code

Online Matching with General Arrivals
with Buddhima Gamlath, Michael Kapralov, Ola Svensson and David Wajc
FOCS 2019
arxiv/ David's talk

• Fair Secretaries with Unfair Predictions
  • 11/2024 University of Massachusetts, Amherst, USA
  • 11/2024 Drexel University, Philadelphia, USA
  • 10/2024 Yale SOM Operations Seminar, Yale University, USA
  • 09/2024 Rutgers/DIMACS Theory of Computing Seminar, Rutgers University, USA
  • 07/2024 INFORMS Revenue Management and Pricing Section Conference, UCLA, USA
  • 07/2024 Workshop on Algorithms with Predictions, Columbia University, USA
  • 06/2024 INFORMS Workshop on Market Design, Yale University, USA
• Data-Driven Solution Portfolios
  • 10/2024 NYU Theory Seminar, New York University, USA
• Online and Consistent Correlation Clustering
  • 06/2023 INFORMS Applied Probability Society Conference, Nancy, France
  • 09/2022 University of Massachusetts, Amherst, USA
• The Primal-Dual method for Learning Augmented Algorithms
  • 09/2022 Simons Institute for the Theory of Computing, UC Berkeley, USA
  • 09/2022 University of Massachusetts, Amherst, USA
  • 06/2021 Google Zurich, Switzerland