Available positions
Prospective postdocs: A postdoctoral fellowship is available in my group jointly with Prof. Vova Braverman. For more details and to apply, please visit here.
Prospective PhD students: I am looking for strong PhD students. Students interested in working with me can email me and apply to our PhD program.
Rice undergrad students Please send me your CV and transcript, and indicate your coursework experience related to algorithms (e.g., COMP 182, COMP 382), mathematics, and probability.
High school students: Unfortunately, I do not have any research projects suitable for high school students. However, Rice University offers several programs tailored for high school students. For more information, please visit their webpage.
About Me
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.
Publications
Metalearning with Very Few Samples Per Task
Maryam Aliakbarpour, Konstantina Bairaktari, Gavin Brown, Adam Smith, Jonathan Ullman
To appear in COLT 2024Differentially Private Medians and Interior Points for Non-Pathological Data
Maryam Aliakbarpour, Rose Silver, Thomas Steinke, Jonathan Ullman
15th Innovation s in Theoretical Computer Science Conference, ITCS 2024
Presented in Theory and Practice of Differential Privacy, TPDP 2023Hypothesis Selection with Memory Constraints
Maryam Aliakbarpour, Mark Bun, Adam Smith
36-th Conference on Neural Information Processing Systems, NeurIPS 2023Testing 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 2023Estimation 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 2022Local 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 2021Rapid 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 2021Testing 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, ITCS2020Private Testing of Distributions via Sample Permutations
Maryam Aliakbarpour, Ilias Diakonikolas, Daniel Kane, Ronitt Rubinfeld
33-th Conference on Neural Information Processing Systems, NeurIPS 2019Testing 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 2019Differentially 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 talkSublinear-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 2017
Full versionLearning and Testing Junta Distributions
Maryam Aliakbarpour, Eric Blais, Ronitt Rubinfeld
29th Annual Conference on Learning Theory, COLT 2016
Video of the talk at COLT 2016Join 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
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PhD Thesis: Distribution Testing: Classical and New Paradigms
MIT, September 2020. -
Master Thesis: Learning and Testing Junta Distributions over Hypercubes
MIT, September 2015.