Faculty Profile

Sanjukta Bhowmick

Title
Associate Professor
Department
Computer Science and Engineering
College
College of Engineering

    

Education

PhD, Pennsylvania State University, 2004.
Major: Computer Science

Current Scheduled Teaching*

CSCE 6940.742, Individual Research, Fall 2021
CSCE 6900.742, Special Problems, Fall 2021

* Texas Education Code 51.974 (HB 2504) requires each institution of higher education to make available to the public, a syllabus for undergraduate lecture courses offered for credit by the institution.

Previous Scheduled Teaching*

CSCE 4890.742, Directed Study, Summer 10W 2021
CSCE 6933.742, Advanced Topics in Computer Science and Engineering, Spring 2021 SPOT
CSCE 4110.001, Algorithms, Spring 2021 Syllabus SPOT
CSCE 4110.004, Algorithms, Spring 2021 Syllabus SPOT
CSCE 6940.705, Individual Research, Spring 2021
CSCE 4110.001, Algorithms, Fall 2020 Syllabus SPOT
CSCE 4110.004, Algorithms, Fall 2020 Syllabus SPOT
CSCE 6940.742, Individual Research, Fall 2020
CSCE 5950.742, Master's Thesis, Fall 2020
CSCE 4110.001, Algorithms, Spring 2020 Syllabus
CSCE 4110.002, Algorithms, Spring 2020 Syllabus
CSCE 5950.742, Master's Thesis, Spring 2020
CSCE 6933.001, Advanced Topics in Computer Science and Engineering, Fall 2019 Syllabus SPOT
CSCE 4110.001, Algorithms, Fall 2019 Syllabus SPOT
CSCE 4110.002, Algorithms, Fall 2019 Syllabus SPOT
CSCE 6940.742, Individual Research, Fall 8W2 2019
CSCE 4110.001, Algorithms, Spring 2019 Syllabus SPOT
CSCE 4110.002, Algorithms, Spring 2019 Syllabus SPOT
CSCE 4890.805, Directed Study, Spring 2019
CSCE 4110.001, Algorithms, Fall 2018 Syllabus SPOT
CSCE 4110.002, Algorithms, Fall 2018 Syllabus SPOT
CSCE 4110.021, Algorithms, Summer 10W 2018 Syllabus SPOT
CSCE 4110.002, Algorithms, Spring 2018 Syllabus SPOT
CSCE 4110.002, Algorithms, Fall 2017 Syllabus SPOT
CSCE 4110.001, Algorithms, Fall 2016 Syllabus SPOT
CSCE 2110.021, Computing Foundations II, Summer 10W 2016 Syllabus SPOT
CSCE 2110.201, Computing Foundations II, Summer 10W 2016 SPOT
CSCE 2110.202, Computing Foundations II, Summer 10W 2016 SPOT

* Texas Education Code 51.974 (HB 2504) requires each institution of higher education to make available to the public, a syllabus for undergraduate lecture courses offered for credit by the institution.

Published Publications

Published Intellectual Contributions

Book Chapter
Sarkar, S., Kumar, S., Bhowmick, S., Mukherjee, A. (2018). Centrality and Community Scoring Functions in Incomplete Networks: Their Sensitivity, Robustness, and Reliability. Machine Learning Techniques for Online Social Networks. 135--154. Springer, Cham.
Bhowmick, S., Srinivasan, S. (2013). A template for parallelizing the louvain method for modularity maximization. Dynamics On and Of Complex Networks, Volume 2. 111--124. Birkh\"auser, New York, NY.
Chatterjee, A., Bhowmick, S., Raghavan, P. (2013). Improving Classifications Through Graph Embeddings. Graph Embedding for Pattern Analysis. 119--138. Springer, New York, NY.
Bhowmick, S., Eijkhout, V., Freund, Y., Fuentes, E., Keyes, D. (2011). Application of alternating decision trees in selecting sparse linear solvers. Software Automatic Tuning. 153--173. Springer, New York, NY.
Bansal, S., Bhowmick, S., Paymal, P. (2011). Fast community detection for dynamic complex networks. Complex Networks. 196--207. Springer, Berlin, Heidelberg.
Bhowmick, S., Hovland, P. D. (2008). A polynomial-time algorithm for detecting directed axial symmetry in Hessian computational graphs. Advances in Automatic Differentiation. 91--102. Springer, Berlin, Heidelberg.
Norris, B., Bhowmick, S., Kaushik, D., Mclnnes, L. C. (2007). Middleware for dynamic adaptation of component applications. Grid-Based Problem Solving Environments. 127--149. Springer, Boston, MA.
Bhowmick, S., Kaushik, D., McInnes, L., Norris, B., Raghavan, P. (2006). -Parallel Adaptive Solvers in Compressible PETSc-FUN3D Simulations. Parallel Computational Fluid Dynamics 2005. 277--284.
Conference Proceeding
Qian, W., Bhowmick, S., Mikler, A. R., O'Neill II, M., Ramisetty-Mikler, S. (2020). A Probabilistic Infection Model for Efficient Trace-Prediction of Disease Outbreaks in Contact Networks. International Conference on Computational Science 2020.
Gasper, W., Cooper, K., Cornelius, N., Ali, H., Bhowmick, S. (2020). Characterization of S. cerevisiae Protein Complexes by Representative DDI Graph Planarity. Proceedings of the 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics. 1--6.
Komar, K. S., Santra, A., Bhowmick, S., Chakravarthy, S. (2020). EER $$$\backslash$rightarrow $$ MLN: EER Approach for Modeling, Mapping, and Analyzing Complex Data Using Multilayer Networks (MLNs). International Conference on Conceptual Modeling. 555--572.
Cooper, K., Cornelius, N., Gasper, W., Bhowmick, S., Ali, H. (2020). On the Planarity of Validated Complexes of Model Organisms in Protein-Protein Interaction Networks. International Conference on Computational Science. 652--666.
Bhowmick, S., Srinivasan, S., Riazi, S., Das, S., Norris, B. (2018). A Shared-Memory Parallel Algorithm for Updating Single Source Shortest Paths in Weighted Dynamic Graphs. International Conference of High Performance Computing.
Sarkar, S., Bhowmick, S., Mukherjee, A. (2018). On Rich Clubs of Path-Based Centralities in Networks. Proceedings of the 27th ACM International Conference on Information and Knowledge Management. 567--576.
Parsinia, M., Peng, Q., Bhowmick, S., Matyjas, J. D., Kumar, S. (2017). Gender Assignment for Directional Full-Duplex FDD Nodes in a Multihop Wireless Network. Ad Hoc Networks. 390--401. Springer, Cham.
Santra, A., Bhowmick, S. (2017). Holistic Analysis of Multi-source, Multi-feature Data: Modeling and Computation Challenges. International Conference on Big Data Analytics. 59--68.
Santra, A., Bhowmick, S., Chakravarthy, S. (2017). Hubify: efficient estimation of central entities across multiplex layer compositions. Data Mining Workshops (ICDMW), 2017 IEEE International Conference on. 142--149.
Srinivasan, S., Bhowmick, S., Das, S. (2016). Application of Graph Sparsification in Developing Parallel Algorithms for Updating Connected Components. 2016 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW). 885--891.
Sarkar, S., Kumar, S., Bhowmick, S., Mukherjee, A. (2016). Sensitivity and reliability in incomplete networks: Centrality metrics to community scoring functions. 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). 69--72.
Ufimtsev, V., Sarkar, S., Mukherjee, A., Bhowmick, S. (2016). Understanding stability of noisy networks through centrality measures and local connections. Proceedings of the 25th ACM International on Conference on Information and Knowledge Management. 2347--2352.
Ufimtsev, V., Bhowmick, S. (2014). An extremely fast algorithm for identifying high closeness centrality vertices in large-scale networks. Proceedings of the 4th Workshop on Irregular Applications: Architectures and Algorithms. 53--56.
Ufimtsev, V., Bhowmick, S., Rajamanickam, S. (2014). Building blocks for graph based network analysis. High Performance Extreme Computing Conference (HPEC), 2014 IEEE. 1--6.
Ufimtsev, V., Bhowmick, S. (2014). Finding high betweenness centrality vertices in large networks. CSC14: The Sixth SIAM Workshop on Combinatorial Scientific Computing. 45.
Chakraborty, T., Srinivasan, S., Ganguly, N., Mukherjee, A., Bhowmick, S. (2014). On the permanence of vertices in network communities. Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. 1396--1405.
Dempsey, K., Chen, T., Srinivasan, S., Bhowmick, S., Ali, H. (2013). A structure-preserving hybrid-chordal filter for sampling in correlation networks. High Performance Computing and Simulation (HPCS), 2013 International Conference on. 243--250.
West, S., Dempsey, K., Bhowmick, S., Ali, H. (2013). Analysis of incrementally generated clusters in biological networks using graph-theoretic filters and ontology enrichment. Data Mining Workshops (ICDMW), 2013 IEEE 13th International Conference on. 584--591.
Ufimtsev, V., Bhowmick, S. (2013). Application of group testing in identifying high betweenness centrality vertices in complex networks. Eleventh Workshop on Mining and Learning with Graphs. 1--8.
Ufimtsev, V., Bhowmick, S. (2013). Identifying high betweenness centrality vertices in large noisy networks. Parallel and Distributed Processing Symposium Workshops \& PhD Forum (IPDPSW), 2013 IEEE 27th International. 2234--2237.
Dasgupta, P., Bhowmick, S. (2013). Towards Context-Aware, Real Time and Autonomous Decision Making Using Information Aggregation and Network Analytics.. STIDS. 166--169.
Bastola, D. R., McGrath, S., Bhowmick, S., Thapa, I. (2012). A Comparison of Computational Approaches in the Molecular Identification of Pathogenic Organisms. Healthcare Informatics, Imaging and Systems Biology (HISB), 2012 IEEE Second International Conference on. 73--73.
Halappanavar, M., Feo, J., Dempsey, K., Ali, H., Bhowmick, S. (2012). A novel multithreaded algorithm for extracting maximal chordal subgraphs. Parallel Processing (ICPP), 2012 41st International Conference on. 58--67.
Lee, K. L., Stotts, D. (2012). Composition of bioinformatics model federations using communication aspects. Bioinformatics and Biomedicine (BIBM), 2012 IEEE International Conference on. 1--5.
Dempsey, K., Duraisamy, K., Bhowmick, S., Ali, H. (2012). The development of parallel adaptive sampling algorithms for analyzing biological networks. 2012 IEEE 26th International Parallel and Distributed Processing Symposium Workshops \& PhD Forum. 725--734.
Duraisamy, K., Dempsey, K., Ali, H., Bhowmick, S. (2011). A noise reducing sampling approach for uncovering critical properties in large scale biological networks. High Performance Computing and Simulation (HPCS), 2011 International Conference on. 721--728.
Paymal, P., Patil, R., Bhowmick, S., Siy, H. (2011). Measuring disruption from software evolution activities using graph-based metrics. Software Maintenance (ICSM), 2011 27th IEEE International Conference on. 532--535.
Bhowmick, S., Shafiullah, M., Rai, H., Bastola, D. (2010). A Parallel Non-Alignment Based Approach to Efficient Sequence Comparison using Longest Common Subsequences. Journal of Physics: Conference Series. 256(1), 012012.
Chatterjee, A., Bhowmick, S., Raghavan, P. (2010). Feature subspace transformations for enhancing k-means clustering. Proceedings of the 19th ACM international conference on Information and knowledge management. 1801--1804.
Mclnnes, L. C., Kaushik, D., Norris, B., Bhowmick, S. (2010). Middleware for Dynamic Adaptation of Component Applications. IFIP International Federation for Information Processing. 239(1), .
Bhowmick, S., Toth, B., Raghavan, P. (2009). Towards low-cost, high-accuracy classifiers for linear solver selection. International Conference on Computational Science. 463--472.
Chatterjee, A., Bhowmick, S., Raghavan, P. (2008). FAST: Force-directed approximate subspace transformation to improve unsupervised document classification. Proceedings of 6th Text Mining Workshop held in Conjunction with SIAM International Conference on Data Mining, Atlanta, GA, USA.
Bhowmick, S., Hovland, P. D. (2008). Improving the performance of graph coloring algorithms through backtracking. International Conference on Computational Science. 873--882.
Norris, B., McInnes, L. C., Bhowmick, S., Li, L. (2007). Adaptive numerical components for PDE-based simulations. PAMM: Proceedings in Applied Mathematics and Mechanics. 7(1), 1140509--1140510.
Eijkhout, V., Fuentes, E., Ramakrishnan, N., Kang, P., Bhowmick, S., Keyes, D., Freund, Y. (2006). A self-adapting system for linear solver selection. Proc. 1st int’l workshop on automatic performance tuning (iWAPT2006). 44--53.
Bhowmick, S., Boman, E. G., Devine, K., Gebremedhin, A., Hendrickson, B., Hovland, P., Munson, T., Pothen, A. (2006). Combinatorial algorithms enabling computational science: tales from the front. Journal of Physics: Conference Series. 46(1), 453.
Bhowmick, S., Hovland, P. (2005). A backtracking correction heuristic for improving performance of graph coloring algorithms. Proceedings of the Second International Workshop on Combinatorial Scientific Computing.
Hovland, P. D., Norris, B., Strout, M. M., Bhowmick, S., Utke, J. (2005). Sensitivity analysis and design optimization through automatic differentiation. Journal of Physics: Conference Series. 16(1), 466.
Bhowmick, S., McInnes, L., Norris, B., Raghavan, P. (2004). Robust algorithms and software for parallel PDE-based simulations. Proceedings of the Advanced Simulation Technologies Conference, ASTC. 4, 18--22.
McInnes, L., Norris, B., Bhowmick, S., Raghavan, P. (2003). Adaptive sparse linear solvers for implicit CFD using Newton-Krylov algorithms. Proceedings of the Second MIT Conference on Computational Fluid and Solid Mechanics. 2, 1024--1028.
Bhowmick, S., McInnes, L., Norris, B., Raghavan, P. (2003). The role of multi-method linear solvers in PDE-based simulations. International Conference on Computational Science and Its Applications. 828--839.
Bhowmick, S., Raghavan, P., Teranishi, K. (2002). A combinatorial scheme for developing efficient composite solvers. International Conference on Computational Science. 325--334.
Journal Article
Qian, W., Bhowmick, S., O'Neill, M., Ramisetty-Mikler, S., Mikler, A. R. (2021). Applying a Probabilistic Infection Model for studying contagion processes in contact networks. Journal of Computational Science. 54, . Elsevier. https://www.sciencedirect.com/science/article/abs/pii/S1877750321001046?via%3Dihub
Srinivasan, S., Pollard, S., Das, S. K., Norris, B., Bhowmick, S. (2018). A Shared-Memory Algorithm for Updating Tree-Based Properties of Large Dynamic Networks. Other. IEEE.
Sarkar, S., Sikdar, S., Bhowmick, S., Mukherjee, A. (2018). Using core-periphery structure to predict high centrality nodes in time-varying networks. Other. 32(5), 1368--1396. Springer US.
Santra, A., Bhowmick, S., Chakravarthy, S. (2017). Efficient community re-creation in multilayer networks using boolean operations. Procedia Computer Science. 108, 58--67. Elsevier.
Chakraborty, T., Kumar, S., Ganguly, N., Mukherjee, A., Bhowmick, S. (2016). GenPerm: a unified method for detecting non-overlapping and overlapping communities. Other. 28(8), 2101--2114. IEEE.
Chakraborty, T., Srinivasan, S., Ganguly, N., Mukherjee, A., Bhowmick, S. (2016). Permanence and community structure in complex networks. Other. 11(2), 14. ACM.
Bhowmick, S., Chen, T., Halappanavar, M. (2015). A new augmentation based algorithm for extracting maximal chordal subgraphs. Other. 76, 132--144. Academic Press.
Dempsey, K., Ufimtsev, V., Bhowmick, S., Ali, H. (2014). A parallel template for implementing filters for biological correlation networks. Other. 12(4), 285--297.
Meyer, P., Siy, H., Bhowmick, S. (2014). Identifying important classes of large software systems through k-core decomposition. Other. 17(07n08), 1550004. World Scientific Publishing Company.
Hemmatazad, N., Gandhi, R., Zhu, Q., Bhowmick, S. (2014). The Intelligent Data Brokerage: A Utility-Enhancing Architecture for Algorithmic Anonymity Measures. Other. 2(1), 22--33. IGI Global.
Cooper, K. D., Ufimtsev, V., Bhowmick, S., Ali, H. (2013). A Parallel Template for Implementing Filters for Biological Correlation Networks.
Chakraborty, T., Srinivasan, S., Ganguly, N., Bhowmick, S., Mukherjee, A. (2013). Constant communities in complex networks. Scientific Reports. 3, 1825. Nature Publishing Group.
Ufimtsev, V. V., Bhowmick, S., Rykov, V. V. (2012). A Scalable Group Testing Based Algorithm for Finding d-highest Betweenness Centrality Vertices in Large Scale Networks.
Dempsey, K., Bhowmick, S., Ali, H. (2012). Function-preserving filters for sampling in biological networks. Procedia Computer Science. 9, 587--595. Elsevier.
Srinivasan, S., Chakraborty, T., Bhowmick, S. (2012). Identifying base clusters and their application to maximizing modularity.. Other. 588, 82.
Cooper, K. D., Chen, T., Bhowmick, S., Ali, H. (2012). On the design of advanced filters for biological networks using graph theoretic properties.
Cooper, K. D., Duraisamy, K., Bhowmick, S., Ali, H. (2012). The Development of Parallel Adaptive Sampling Algorithms for Analyzing Biological Networks.
Srinivasan, S., Bhowmick, S. (2012). Using stable communities for maximizing modularity. Other. DIMACS Atlanta, Georgia.
Cooper, K. D., Duraisamy, K., Ali, H., Bhowmick, S. (2011). A parallel graph sampling algorithm for analyzing gene correlation networks. Procedia Computer Science. 4(2011), 136.
Paymal, P., Patil, R., Bhomwick, S., Siy, H. (2011). Empirical study of software evolution using community detection. Other.
Bhowmick, S. (2010). Novel Applications of Graph Embedding Techniques. Georgia Institute of Technology.
Bhowmick, S., Shontz, S. M. (2010). Towards high-quality, untangled meshes via a force-directed graph embedding approach. Procedia Computer Science. 1(1), 357--366. Elsevier.
Bhowmick, S., Hovland, P. (2007). Exploiting Symmetry for Hessian Computation.
Bhowmick, S., Eijkhout, V., Freund, Y., Fuentes, E., Keyes, D. (2006). Application of machine learning to the selection of sparse linear solvers. Other.
Bhowmick, S., Raghavan, P., McInnes, L., Norris, B. (2004). Faster PDE-based simulations using robust composite linear solvers. Other. 20(3), 373--387. North-Holland.
Newspaper
Bhowmick, S., Buluc, A., Kepner, J., Miller, B., Caceres, R., Gadepally, V., Crouser, J., Webster, J. (2016). Data Mining Brings IT to SIAM Annual Meeting.
Technical Report
Rajamanickam, S., Ufimtsev, V., Bhowmick, S. (2014). Building Blocks for Graph Based Network Analysis.. Sandia National Lab.(SNL-NM), Albuquerque, NM (United States).
Berry, J. W., Leung, V. J., Phillips, C. A., Pinar, A., Robinson, D. G., Berger-Wolf, T., Bhowmick, S., Casleton, E., Kaiser, M., Nordman, D. J., others, (2014). Statistically significant relational data mining. Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States).
Bhowmick, S., Hovland, P. (2006). A Polynomial Time Algorithm for the Detection of Axial Symmetry in Directed Acyclic Graphs. ANL/MCS-P1314-0106, Argonne National Laboratory, Illinois.

Awarded Grants

Contracts, Grants and Sponsored Research

Grant - Research
Bhowmick, S. (Principal), Madduri, K. (Principal), Chakravarthy, S. (Principal), "NetSplicer," Sponsored by NSF, Federal, $320043 Funded. (20192023).
Bhowmick, S. (Co-Principal), Dantu, R. (Principal), Morozov, K., "Locating Super-Spreaders Through Partnership of Anonymization and Encryption," Sponsored by NSA, Federal, $299944 Funded. (20202022).
Bhowmick, S. (Principal), Taufer, M. (Principal), Jagode, H. (Co-Principal), "ANACIN-X: Analysis and modeling of Nondeterminism and Associated Costs in eXtreme scale applications," Sponsored by NSF, Federal, $300000 Funded. (September 01, 2019August 2022).
Bhowmick, S. (Principal), Das, S. (Principal), Norris, B. (Principal), "SPX: Collaborative Research: SANDY: Sparsification-Based Approach for Analyzing Network Dynamics," Sponsored by NSF, Federal, $225000 Funded. (September 2017August 2020).
Bhowmick, S., Das, S. (Principal), "XPS: EXPL: FP: Collaborative Research: SPANDAN: Scalable Parallel Algorithms for Network Dynamics Analysis," Sponsored by NSF, Federal, $146000 Funded. (September 2015August 2019).
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Overall
Summative Rating
Challenge and
Engagement Index
Response Rate

out of 5

out of 7
%
of
students responded
  • Overall Summative Rating (median):
    This rating represents the combined responses of students to the four global summative items and is presented to provide an overall index of the class’s quality. Overall summative statements include the following (response options include a Likert scale ranging from 5 = Excellent, 3 = Good, and 1= Very poor):
    • The course as a whole was
    • The course content was
    • The instructor’s contribution to the course was
    • The instructor’s effectiveness in teaching the subject matter was
  • Challenge and Engagement Index:
    This rating combines student responses to several SPOT items relating to how academically challenging students found the course to be and how engaged they were. Challenge and Engagement Index items include the following (response options include a Likert scale ranging from 7 = Much higher, 4 = Average, and 1 = Much lower):
    • Do you expect your grade in this course to be
    • The intellectual challenge presented was
    • The amount of effort you put into this course was
    • The amount of effort to succeed in this course was
    • Your involvement in course (doing assignments, attending classes, etc.) was
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