I am a Michael B. Yuen and Sandra A. Tsai Assistant Professor in the Department of Computer Science at Rice University.

Prior to joining Rice, I worked as a postdoctoral researcher jointly at Boston University and Northeastern University, where I had the privilege of working with Prof. Adam Smith and Prof. Jonathan Ullman. Before that, I was a postdoctoral research associate at UMass Amherst, under the very valuable mentorship of Prof. Andrew McGregor. In Fall 2020, I had the opportunity to participate as a visiting participant in the Probability, Geometry, and Computation in High Dimensions Program at the prestigious Simons Institute at Berkeley.

I receive my Ph.D. and M.S. from MIT, where I had the privilege of being advised by Prof. Ronitt Rubinfeld. Before my time at MIT, I pursued my undergraduate studies at Sharif University of Technology, where I earned a B.S. in Computer Engineering (Software).

I am very passionate about theoretical questions in theoretical computer science and statistical learning theory. These days, I am working on algorithm with computational constraints and differential privacy.

• Metalearning with Very Few Samples Per Task
  Maryam Aliakbarpour, Konstantina Bairaktari, Gavin Brown, Adam Smith, Jonathan Ullman
  Manuscript

• Differentially Private Medians and Interior Points for Non-Pathological Data
  Maryam Aliakbarpour, Rose Silver, Thomas Steinke, Jonathan Ullman
  To appear in ITCS 2024
  Presented in Theory and Practice of Differential Privacy, TPDP 2023

• Hypothesis Selection with Memory Constraints
  Maryam Aliakbarpour, Mark Bun, Adam Smith
  To appear in NeurIPS 2023

• Testing Tail Weight of a Distribution Via Hazard Rate
  Maryam Aliakbarpour, Amartya Shankha Biswas, Kavya Ravichandran, Ronitt Rubinfeld
  34-th International Conference on Algorithmic Learning Theory, ALT 2023

• Estimation of Entropy in Constant Space with Improved Sample Complexity
  Maryam Aliakbarpour, Andrew McGregor, Jelani Nelson, Erik Waingarten
  36-th Conference on Neural Information Processing Systems, NeurIPS 2022

• Local Differential Privacy Is Equivalent to Contraction of an f-Divergence
  Shahab Asoodeh, Maryam Aliakbarpour, Flavio P. Calmon
  2021 IEEE International Symposium on Information Theory, ISIT 2021

• Rapid Approximate Aggregation with Distribution-Sensitive Interval Guarantees
  Stephen Macke, Maryam Aliakbarpour, Ilias Diakonikolas, Aditya Parameswaran, Ronitt Rubinfeld
  37th IEEE International Conference on Data Engineering, ICDE 2021

• Testing Determinantal Point Processes
  Khashayar Gatmiry, Maryam Aliakbarpour, Stefanie Jegelka
  34-th Conference on Neural Information Processing Systems, NeurIPS 2020 (spotlight talk)

• Testing Properties of Multiple Distributions with Few Samples
  Maryam Aliakbarpour, Sandeep Silwal
  11th Innovations in Theoretical Computer Science Conference, ITCS2020

• Private Testing of Distributions via Sample Permutations
  Maryam Aliakbarpour, Ilias Diakonikolas, Daniel Kane, Ronitt Rubinfeld
  33-th Conference on Neural Information Processing Systems, NeurIPS 2019

• Testing Mixtures of Discrete Distributions
  Maryam Aliakbarpour, Ravi Kumar, Ronitt Rubinfeld
  32nd Annual Conference on Learning Theory, COLT 2019
  Full version, Video of the talk at COLT 2019
  

• Towards Testing Monotonicity of Distributions Over General Posets
  Maryam Aliakbarpour, Themistoklis Gouleakis, John Peebles, Ronitt Rubinfeld, Anak Yodpinyanee
  32nd Annual Conference on Learning Theory, COLT 2019
  Full version Video of the talk at COLT 2019
  

• Differentially Private Identity and Equivalence Testing of Discrete Distributions
  Maryam Aliakbarpour, Ilias Diakonikolas, Ronitt Rubinfeld
  35th International Conference on Machine Learning, ICML 2018
  Video of the talk
  

• Sublinear-Time Algorithms for Counting Star Subgraphs via Edge Sampling
  Maryam Aliakbarpour, Amartya Shankha Biswas, Themistoklis Gouleakis, John Peebles, Ronitt Rubinfeld, Anak Yodpinyanee
  Algorithmica 80(2), pp 668-697, 2018.
  ArXiv version

• I've Seen Enough: Incrementally Improving Visualizations to Support Rapid Decision Making.
  Sajjadur Rahman, Maryam Aliakbarpour, Ha Kyung Kong, Eric Blais, Karrie Karahalios, Aditya Parameswaran, Ronitt Rubinfeld
  43rd International Conference on Very Large Data Bases, VLDB 2017
  Full version
  

• Learning and Testing Junta Distributions
  Maryam Aliakbarpour, Eric Blais, Ronitt Rubinfeld
  29th Annual Conference on Learning Theory, COLT 2016
  Video of the talk at COLT 2016

• Join of two graphs admits a nowhere-zero 3-flow
  Saieed Akbari, Maryam Aliakbarpour, Niloofar Ghanbari, Emisa Nategh, Hossein Shahmohamad
  Czechoslovak Mathematical Journal, Volume 64, Issue 2, pp 433-446, June 2014.

• Minimum flow number of complete multipartite graphs
  Saieed Akbari, Maryam Aliakbarpour, Niloofar Ghanbari, Emisa Nategh, Hossein Shahmohamad
  Bulletin of the Institute of Combinatorics and its Applications, Volume 66, pp 57-64, September 2012.

Theses

• PhD Thesis: Distribution Testing: Classical and New Paradigms
  MIT, September 2020.

• Master Thesis: Learning and Testing Junta Distributions over Hypercubes
  MIT, September 2015.

Teaching

• COMP 677: Graduate Seminar in Learning Theory
  Fall 2023.

• COMP 585: Probabilistic Toolkit for Learning and Computing
  Spring 2024.