Andreas Maggiori

I am a Postdoctoral Researcher at the Data Science Institute (DSI) at Columbia University, where I am working with Eric Balkanski and Will Ma.

I am interested in data-driven decision-making under uncertainty and its societal implications. 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.

Prior to that, I earned my PhD at EPFL, advised by Rudiger Urbanke and Ola Svensson. During my PhD, I visited the Simons Institute, UC Berkeley for 2 months for the Data-Driven Decision Processes program. I also interned twice at Google Zurich, hosted by Nikos Parotsidis and Ehsan Kazemi accordingly.

Before joining EPFL, I did my undergrad in Greece, in the Electrical and Computer Engineering department of National Technical University of Athens.

Email  /  Google Scholar  /  CV

profile photo
I am on the job market!
News
Work in Progress
(Authors are in alphabetical order)

Fair Secretaries with Unfair Predictions
with Eric Balkanski and Will Ma
Job market paper – preliminary version accepted at NeurIPS 2024

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

Fair and Consistent Correlation Clustering
Submitted
with Eric Balkanski and Iason Chatzitheodorou

Publications
(Authors are in alphabetical order)

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

Fair Secretaries with Unfair Predictions
with Eric Balkanski and Will Ma
to appear at 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

Invited Talks
  • Fair Secretaries with Unfair Predictions
    • 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 (UMass), 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 (UMass), USA
    • 06/2021 Google Zurich, Switzerland
Teaching/Study groups/Workshops

  • I organized a study-group on how continuous optimization methods can be used to tackle combinatorial problems. The website of the study-group with notes and recorded lectures can be found here. (If you do not have an ETH account and you want to have access to the lecture videos, please drop me an email)

  • I am/was teaching assistant for the following courses:
    • NTUA: Algorithms and Complexity, Discrete Mathematics
    • EPFL: Theory of Computation, Machine Learning, Learning Theory, Algorithms, Advanced Probability and Applications, Foundations of data science

More

  • I am from Athens, Greece and enjoy gelato, rod fishing, kayaking, snowboarding and basketball. When I am in Athens, your chances of finding me at Amorgiano listening to Thanassis are pretty high.












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