I am a Postdoctoral Research Associate at University of Massachusetts Amherst. In Fall 2020, I was a visiting participant of Probability, Geometry, and Computation in High Dimensions Program at Simons Institute. I have received my Ph.D. from MIT. I was very fortunate to have Prof. Ronitt Rubinfeld as my advisor.

I am interested in the following topics:

- Statistical Learning Theory
- Property Testing
- Differential Privacy
- Sublinear Algorithms

Email: my first name DOT my last name AT gmail DOT com

### Publications

Testing Tail Weight of a Distribution Via Hazard Rate

**Maryam Aliakbarpour**, Amartya Shankha Biswas, Kavya Ravichandran, Ronitt Rubinfeld

Preprint.Rapid Approximate Aggregation with Distribution-Sensitive Interval Guarantees

Stephen Macke,**Maryam Aliakbarpour**, Ilias Diakonikolas, Aditya Parameswaran, Ronitt Rubinfeld

To appear in ICDE 2021.Testing Determinantal Point Processes

Khashayar Gatmiry,**Maryam Aliakbarpour**, Stefanie Jegelka

34-th Conference on Neural Information Processing Systems (NeurIPS),**spotlight**talk, 2020.Testing Properties of Multiple Distributions with Few Samples

**Maryam Aliakbarpour**, Sandeep Silwal

11th Innovations in Theoretical Computer Science Conference (ITCS). pp. 69:1--69:41, 2020Private Testing of Distributions via Sample Permutations

**Maryam Aliakbarpour**, Ilias Diakonikolas, Daniel Kane, Ronitt Rubinfeld

33-th Conference on Neural Information Processing Systems (NeurIPS), pp. 10878-10889, 2019.Testing Mixtures of Discrete Distributions

**Maryam Aliakbarpour**, Ravi Kumar, Ronitt Rubinfeld

32nd Annual Conference on Learning Theory (COLT), pp. 83-114, 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), pp. 34-82, 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), pp. 169-178, 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 versionI'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), pp. 1262-1273, 2017.

Full version

Learning and Testing Junta Distributions

**Maryam Aliakbarpour**, Eric Blais, Ronitt Rubinfeld

29th Annual Conference on Learning Theory (COLT), pp. 19-46, 2016.

Video of the talk at COLT 2016

Slides (short version), Slides (long version)**Master Thesis:**Learning and Testing Junta distributions over Hypercubes

September 2015.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.