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Joyanta Basak: Ph.D. student in Computer Science and Engineering at the University of Connecticut

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On Counting Mobile Lock Patterns

less than 1 minute read

Published: August 17, 2019

What are the chances Somebody will guess your Mobile pattern lock?

While I was teaching about probability in my Discrete Mathematics class, I came across this problem. I tried to solve it by counting all the simple paths in the graph. My approach was to systematically generate the paths leveraging the symmetry of grids and thus reduce almost 3/4 th of the computation. But I could not find an exact formulae to count them.
If you have any ideas or thoughts, please drop them at joyantabasak13ATgmailDOTcom.
Details of my thoughts on this is in this article.

Lecturer

Undergraduate Course Instructor, Dept of CSE, Eastern University, 2019

I have taught 2 theory courses and 1 lab course which was in total 10.5 credit-hour/week.

CSE211: Discrete Mathematics (Lectured to a single class) (3 Credit)

I had encountered this problem while taking the class.

CSE103: Structured Programming (Lectured to a single class) (3 Credit)

It was an introductory course to programming for freshman year students. Taught the basics of programming with C language

CSE 104: Structured Programming Laboratory (Instructed 3 classes) (1.5*3 = 4.5 Credit)

It was the sessional part of CSE103 course. Here I got a chance to supervise projects of 40 very enthusiastic students.

Software Engineer

Software R&D, Application Development, Samsung Research (Samsung R&D Institute Bangladesh), 2020

(January 2020 – July 2021) I worked on multiple system modules. My main contribution was in developing Samsung Cloud Service (system API) on Tizen 5.5 OS for Galaxy Watch3 models and later development in Android platform.

Graduate Research Assistant

Ph.D. Research, Applied Algorithm Laboratory, University of Connecticut, 2022

(August 2022 – present) Research on scalable record linkage, entity resolution, string algorithms, and high-performance computing for large-scale data integration, supervised by Prof. Sanguthevar Rajasekaran. Research contributor to the U.S. Census Bureau–University of Connecticut cooperative agreement on computational methods for large-scale record linkage (Award No. CB21RMD0160003). See my CV and publications.

Scalable Record Linkage and Entity Resolution

Fast string-similarity algorithms and blocking techniques for linking large, noisy datasets, developed under a U.S. Census Bureau–University of Connecticut cooperative agreement. Includes a linear-time Jaro similarity algorithm, SuperBlocking, and phonetic blocking.

GPU-Accelerated Record Linkage

Bit-parallel and GPU algorithms for record linkage that process millions of records and billions of candidate comparisons.

Record Linkage Quality Without Ground Truth

Cluster-quality metrics and local-search optimization for evaluating and refining record-linkage results when true matches are unknown.

Communication-Efficient Distributed Quantum Computing

Problem-specific communication strategies for distributed quantum algorithms such as the Quantum Fourier Transform and Grover’s search.

Undergraduate Thesis Research (BUET)

Developed an efficient computational tool on CPLM-2.0 database proposing a prediction scheme based on evolutionary conserved information of protein sequences. The study explored the efficacy of protein sequence based features and protein secondary structure for predicting Lysine Succinylation sites using machine learning techniques. (Click on the title for a brief overview)

Multivariate Stock Price Prediction Model (BUET, earlier work)

RNN with LSTM model on DSE data

Campus VM (BUET, earlier work)

On Credit Vending Machine for BUET Students, Teachers & Staffs

School Education Manager (BUET, earlier work)

An easy-to-maintain Information System Design for a School

On Computing the Jaro Similarity Between Two Strings

International Symposium on Bioinformatics Research and Applications (ISBRA), LNCS, 2023

Shows that standard Jaro similarity algorithms take quadratic time in the worst case, and gives a linear-time algorithm that computes the same measure.

Recommended citation: J. Basak, A. T. Soliman, N. Deo, K. B. Haase, A. Mathur, K. Park, R. C. Steorts, D. H. Weinberg, S. Sahni, S. Rajasekaran. (2023). "On Computing the Jaro Similarity Between Two Strings." International Symposium on Bioinformatics Research and Applications (ISBRA), LNCS. https://doi.org/10.1007/978-981-99-7074-2_3

Novel Blocking Techniques and Distance Metrics for Record Linkage

International Conference on Information Integration and Web Intelligence (iiWAS), LNCS, 2023

New blocking techniques and distance metrics for record linkage.

Recommended citation: N. Deo, J. Basak, A. T. Soliman, D. H. Weinberg, R. C. Steorts, S. Rajasekaran. (2023). "Novel Blocking Techniques and Distance Metrics for Record Linkage." International Conference on Information Integration and Web Intelligence (iiWAS), LNCS. https://doi.org/10.1007/978-3-031-48316-5_40

SuperBlocking: An Efficient Blocking Technique for Record Linkage

IEEE International Conference on Big Data (BigData), 2023

Introduces SuperBlocking, which reduces unnecessary record comparisons while keeping likely matches. Includes new record-linkage algorithms and parallel versions with near-linear speedups.

Recommended citation: J. Basak, S. Sahni, S. Rajasekaran. (2023). "SuperBlocking: An Efficient Blocking Technique for Record Linkage." IEEE International Conference on Big Data (BigData). https://doi.org/10.1109/BigData59044.2023.10386738

The Soundex Blocking: A Novel Blocking Approach for Record Linkage

IEEE International Conference on Big Data (BigData), 2024

A Soundex-based phonetic blocking method that reduces the comparisons needed in large-scale record linkage.

Recommended citation: N. Shah, A. T. Soliman, J. Basak, S. Sahni, K. B. Haase, A. Mathur, K. Park, D. H. Weinberg, J. White, S. Rajasekaran. (2024). "The Soundex Blocking: A Novel Blocking Approach for Record Linkage." IEEE International Conference on Big Data (BigData). https://doi.org/10.1109/BigData62323.2024.10825041

Problem Specific Communications in Distributed Quantum Computing

IEEE International Conference on Quantum Communications, Networking, and Computing (QCNC), 2025

Examines problem-specific communication strategies for distributed quantum computing.

Recommended citation: J. Basak, B. Wang, S. Rajasekaran. (2025). "Problem Specific Communications in Distributed Quantum Computing." IEEE International Conference on Quantum Communications, Networking, and Computing (QCNC). https://doi.org/10.1109/QCNC64685.2025.00069

Double Metaphone Blocking: An Innovative Blocking Approach to Record Linkage

International Symposium on Bioinformatics Research and Applications (ISBRA), LNCS, 2025

A phonetic blocking approach for record linkage based on the Double Metaphone encoding.

Recommended citation: N. Shah, J. Basak, S. Sahni, A. Mathur, K. Park, D. H. Weinberg, S. Rajasekaran. (2025). "Double Metaphone Blocking: An Innovative Blocking Approach to Record Linkage." International Symposium on Bioinformatics Research and Applications (ISBRA), LNCS. https://doi.org/10.1007/978-981-95-0695-8_12

Efficient Record Linkage in the Age of Large Language Models: The Critical Role of Blocking

Algorithms, 2025

Studies blocking techniques for LLM-based record linkage, cutting the number of candidate pairs that need expensive LLM comparisons.

Recommended citation: N. Shah, S. Patiyara, J. Basak, S. Sahni, A. Mathur, K. Park, S. Rajasekaran. (2025). "Efficient Record Linkage in the Age of Large Language Models: The Critical Role of Blocking." Algorithms. https://doi.org/10.3390/a18110723

Fast Algorithms for Computing Jaro Similarity

Journal of Computational Biology, 2026

Journal version extending our linear-time Jaro similarity work with faster algorithms for computing Jaro similarity, a string-comparison measure central to record linkage.

Recommended citation: J. Basak, N. Shah, K. B. Haase, A. Mathur, K. Park, R. C. Steorts, D. H. Weinberg, S. Sahni, S. Rajasekaran. (2026). "Fast Algorithms for Computing Jaro Similarity." Journal of Computational Biology. https://doi.org/10.1177/15578666261421965

Using Cluster Quality Metrics for Record Linkage Evaluation

14th International Conference on Computational Advances in Bio and Medical Sciences (ICCABS 2026), 2026

Cluster-quality metrics for evaluating record-linkage results when ground-truth matches are unavailable.

Recommended citation: J. Basak, N. Shah, A. Mathur, J. A. L. Hutnick, K. Park, D. Weinberg, S. Sahni, S. Rajasekaran. (2026). "Using Cluster Quality Metrics for Record Linkage Evaluation." ICCABS 2026. Forthcoming in Springer proceedings.