About me

I am a postdoctoral associate at MIT Operations Research Center (ORC) and Sloan School of Management where I'm fortunate to be advised by Swati Gupta. I received my Ph.D. in the multidisciplinary program Algorithms, Combinatorics and Optimization / Computer Science (ACO/CS) at Georgia Tech. I was very fortunate to be advised by Santosh Vempala. I have completed my M.Sc. in Computer Science at the University of British Columbia, under supervision of Bruce Shepherd and Mark Schmidt. Previously, I received my B.Sc. in Computer Engineering Department from Sharif University of Technology.
My research has been recognized by several awards including Georgia Tech's Sigma Xi Best Ph.D. Thesis Award , Georgia Tech's College of Computing Outstanding Doctoral Dissertation Award, and Borealis AI Global Fellowship Award.

Research Interests

• Numerical Linear Algebra
• Combinatorial and Convex Optimization
• Design and Analysis of Algorithms, Approximation and Randomized Algorithms
• Machine Learning, Fairness, Diffrerential Privacy, Data Mining
• Theoretical Machine Learning, Theoretical Aspects of Deep Learning, Tensor Decomposition

Work Experience

• Postdoctoral Associate MIT ORC/Sloan. September 2023 - Present
  Host: Swati Gupta.
• Research Scientist Intern in Real-time Experiences and Algorithms Lab at Adobe. May 2022 - August 2022
  Hosts: Anup Rao, Tung Mai, and David Arbour.
  Worked on casual inference, experimental design, average treatment effect (ATE) and individual treatment effect (ITE) estimation.
• Student researcher in Algorithms and Optimization Team at Google. August 2021 - May 2022
  Hosts: Matthew Fahrbach and Thomas Fu.
  Worked on tensor decomposition methods for deep neural network and feature engineeting applications.
• Research intern in Algorithms and Optimization Team at Google. May 2021 - August 2021
  Hosts: Matthew Fahrbach and Thomas Fu.
  Worked on tensor decomposition methods for deep neural network applications.

Education

• Ph.D. in ACO/CS 2019 - 2023
  Algorithms, Combinatorics, and Optimization / Computer Science, Georgia Institute of Technology
  Advisor: Santosh Vempala
  Minor: Algebraic and Enumerative Combinatorics.
• M.Sc. in Computer Science 2017 - 2019
  Department of Computer Science, University of British Columbia
  Advisors: Bruce Shepherd and Mark Schmidt
  Thesis title: "Beyond Submodular Maximization: One-Sided Smoothness and Meta-Submodularity"
• B.Sc. in Computer Engineering 2011 - 2016
  Department of Computer Engineering, Sharif University of Technology
  Advisor: Hamid Zarrabi-Zadeh
  Thesis title: "Discrete Voronoi Game"

Awards and Honors

• Georgia Tech's Sigma Xi Best Ph.D. Thesis Award 2024
  — This is awarded to only 10 dissertations per year (1-2% of all dissertations awarded annually by Georgia Tech across all disciplines)
• Georgia Tech's College of Computing Outstanding Doctoral Dissertation Award 2024
• ARC-TRIAD Student Fellowship 2022
• The Machine Learning Center at Georgia Tech (ML@GT) Fellowship 2021
• Institute for Data Engineering and Science (IDEaS) and Transdisciplinary Research Institute for Advancing Data Science (TRIAD) Research Scholarship for Ph.D. Students and Postdocs 2020
• Borealis AI Global Fellowship Award 2018
• Silver Medal at Iranian National Mathematical Olympiad 2010

Journal Publications

• A Multiscale Agent-Based Framework Integrated with a Constraint-Based Metabolic Network Model of Cancer for Simulating Tumor Growth Molecular BioSystems 2017
  M. Ghadiri^*, M. Heidari^*, S. H. Mousavi and S. A. Marashi (* equal contribution)
  
  Link

Conference Publications (Peer-Reviewed)

• The Bit Complexity of Dynamic Algebraic Formulas and their Determinants ICALP 2024
  E. Anand, J. Brand, M. Ghadiri, and D. Zhang
  pdf
• Improving the Bit Complexity of Communication for Distributed Convex Optimization STOC 2024
  M. Ghadiri, Y. T. Lee, S. Padmanabhan, W. Swartworth, D. P. Woodruff, G. Ye.
  pdf
• A Parameterized Family of Meta-Submodular Functions SODA 2024
  M. Ghadiri, R. Santiago, and B. Shepherd
  pdf
• Finite Population Regression Adjustment and Non-asymptotic Guarantees for Treatment Effect Estimation NeurIPS 2023
  M. Ghadiri, D. Arbour, T. Mai, C. Musco and A. Rao
  pdf
• The Bit Complexity of Efficient Continuous Optimization FOCS 2023
  M. Ghadiri, R. Peng and S. Vempala
  pdf
• On Symmetric Factorizations of Hankel Matrices FOCS 2023
  M. Ghadiri
  — This is one of the only two papers (out of about 30) accepted in the conjecture track at FOCS.
  pdf
• Approximately Optimal Core Shapes for Tensor Decompositions ICML 2023
  M. Ghadiri, M. Fahrbach, G. Fu, and V. Mirrokni
  pdf
• Subquadratic Kronecker Regression with Applications to Tensor Decomposition NeurIPS 2022
  M. Fahrbach, G. Fu, and M. Ghadiri (Listed Alphabetically)
  pdf
• Amortized rejection sampling in universal probabilistic programming AISTATS 2022
  S. Naderiparizi, A. Scibior, A. Munk, M. Ghadiri, A. G. Baydin, B. G. Hansen, C. S. de Witt, R. Zinkov, P. Torr, T. Rainforth, Y. W. Teh, F. Wood
  [Oral Presentation]
  A preliminary version appeared in PROBPROG 2020.
  pdf
• Socially Fair k-Means Clustering FAccT 2021
  M. Ghadiri, S. Samadi, and S. Vempala
  pdf
• Beyond Submodular Maximization via One-Sided Smoothness SODA 2021
  M. Ghadiri, R. Santiago, and B. Shepherd
  pdf
• Distributed Maximization of Submodular Plus Diversity Functions for Multi-label Feature Selection on Huge Datasets AISTATS 2019
  M. Ghadiri and M. Schmidt
  pdf
• Scalable Feature Selection via Distributed Diversity Maximization AAAI 2017
  M. Ghadiri^*, S. Abbasi Zadeh^*, M. Zadimoghaddam and V. Mirrokni (* equal contribution)
  [Oral Presentation]
  pdf
• Linear Relaxations for Finding Diverse Elements in Metric Spaces NIPS 2016
  A. Bhaskara, M. Ghadiri, V. Mirrokni and O. Svensson
  
  pdf
• Minimizing the Total Movement for Movement to Independence Problem on a Line CCCG 2016
  M. Ghadiri and S. Yazdanbod
  
  pdf
• Active Distance-Based Clustering using K-medoids PAKDD 2016
  M. Ghadiri^*, A. Aghaee^*, M. Soleymani Baghshah (* equal contribution)
  
  pdf

Working Papers

• Constant-Factor Approximation for Socially Fair k-Clustering
  M. Ghadiri, M. Singh and S. Vempala
  pdf

Workshops

• Efficient Bayesian Inference for Nested Simulators AABI 2019
  B. G. Hansen, C. S. de Witt, R. Zinkov, S. Naderiparizi, A. Scibior, A. Munk, F. Wood, M. Ghadiri, P. Torr, Y. W. Teh, A. G. Baydin, T. Rainforth
  
  pdf

Presentations

• Finite Population Regression Adjustment and Non-asymptotic Guarantees for Treatment Effect Estimation
  LIDS Student Conference February 2024
• Scalable Constant-Factor Approximation Algorithms for Socially Fair k-Clustering
  INFORMS Session on Fairness in Operations Research October 2023
• On Symmetric Factorizations of Hankel Matrices
  Carnegie Mellon University (CMU) May 2023
• Bit Complexity of Efficient Optimization
  University of British Columbia (UBC) April 2023
• On Symmetric Factorizations of Hankel Matrices
  American Mathematical Society (AMS) Special Session on Algebraic Methods in Algorithms, II March 2023
• Bit Complexity of Efficient Optimization
  Canadian Mathematical Society (CMS) Special Session on Algorithms and Complexity aspects of Optimization December 2022
• Socially Fair k-Clustering
  INFORMS Special Session on Ethical AI and Optimization - Part II October 2022
• Faster p-Norm Regression Using Sparsity
  University of Washington (UW) May 2022
• Socially Fair k-Means Clustering
  Canadian Discrete and Algorithmic Mathematics Conference (CanaDAM) May 2021
• Beyond Submodular Maximization via One-Sided Smoothness and Meta-Submodularity
  Google Research January 2021
• In Search of Tractable Supermodular Maximization Problems
  Canadian Discrete and Algorithmic Mathematics Conference (CanaDAM) May 2019
• Beyond Submodular Maximization
  Bellairs Workshop on Discrete Optimization April 2019
• Scalable Feature Selection via Distributed Submodular and Diversity Maximization
  Element AI Research Workshop August 2018