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Introduction

Welcome to my tenure review webpage. I am Tanzima Z. Islam, currently an Assistant Professor in the Department of Computer Science at Texas State University. This page provides an overview of my academic career, including my CV, professional statements, and selected publications.  I have published over 30 peer-reviewed conference and journal articles in top venues such as SC and IPDPS. My work addresses key challenges in making HPC applications more efficient and scalable, contributing to advancements in computational science and engineering. I am actively involved in the academic and professional community. I review for numerous conferences and journals and have chaired or co-chaired the performance track at prominent conferences, including SC, IPDPS, and IEEE Cluster. My service extends to organizing workshops and special sessions, fostering collaboration and knowledge exchange among researchers in the HPC community. I am also a member of several professional organizations, including ACM and IEEE.

 

I have received multiple awards and recognitions for my contributions to the field. Some of my notable accolades include the Presidential Seminar Award at Texas State University in 2023-2024, the R&D 100 Award in 2019 for the Scalable Checkpoint/Restart Library, and the LLNL Director’s Science & Technology Award for Excellence in Publication in 2014, and most recently the College of Science and Engineering's 2024 Research Millionaire award. I am regularly invited by national laboratories and industry organizations to give technical talks, reflecting the impact and relevance of my research. According to Google Scholar, my citation count is 730+, and my h-index is 11, demonstrating the influence and reach of my work in the scientific community.

 

Thank you for visiting my tenure review webpage. Here you will find detailed information about my academic journey, my research contributions, and my professional activities.

 

Tenure & Promotion Guidelines at TXST

Computer Science Department Guideline

College of Science and Engineering Guideline

Tenure and Promotion University Policy

Research, Teaching & Service

Curriculum Vitae

Research Statement

Teaching Statement

Service Statement

Up to 10 Selected Publications as Required

  1. Patki, T., Thiagarajan, J. J., Ayala, A., & Islam, T. Z. (2019). Performance optimality or reproducibility: that is the question. In International Conference for High Performance Computing, Networking, Storage and Analysis, 1–30.

  2. Ramadan, T., Lahiry, A., & Islam, T. Z. (2023). Novel representation learning technique using graphs for performance analytics. In 2023 International Conference on Machine Learning and Applications (ICMLA), 1311–1318. IEEE.

  3. Phelps, C., Lahiry, A., Islam, T. Z., & Pouchard, L. (2024). Reimagine application performance as a graph: Novel graph-based method for performance anomaly classification in high-performance computing. In 48th Annual Computers, Software, and Applications Conference (COMPSAC). IEEE. (Accepted).

  4. Ramadan, T., Islam, T. Z., Phelps, C., Pinnow, N., & Thiagarajan, J. J. (2021). Comparative code structure analysis using deep learning for performance prediction. In 2021 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS), 151–161. IEEE.

  5. Tramm, J., Siegel, A., Islam, T., & Schulz, M. (2014). XSBench—the development and verification of a performance abstraction for Monte Carlo reactor analysis. In The Role of Reactor Physics toward a Sustainable Future (PHYSOR).

  6. Islam, T. Z., Thiagarajan, J. J., Bhatele, A., Schulz, M., & Gamblin, T. (2016). A machine learning framework for performance coverage analysis of proxy applications. In SC’16: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, 538–549. IEEE.

  7. Banerjee, T., Hackl, J., Shringarpure, M., Islam, T., Balachandar, S., Jackson, T., & Ranka, S. (2016). CMT-Bone—a proxy application for compressible multiphase turbulent flows. In IEEE 23rd International Conference on High Performance Computing (HiPC), 173–182. IEEE.

  8. Thiagarajan, J. J., Anirudh, R., Kailkhura, B., Jain, N., Islam, T., Bhatele, A., Yeom, J., & Gamblin, T. (2018). PADDLE: Performance analysis using a data-driven learning environment. In 2018 IEEE International Parallel and Distributed Processing Symposium (IPDPS), 784–793. IEEE.

  9. Islam, T., Ayala, A., Jensen, Q., & Ibrahim, K. (2019). Toward a programmable analysis and visualization framework for interactive performance analytics. In 2019 IEEE/ACM International Workshop on Programming and Performance Visualization Tools (ProTools), 70–77. IEEE.

  10. Banday, B. H., Islam, T. Z., & Marathe, A. (2024). PerfGen: A synthesis and evaluation framework for performance data using generative AI. In 48th Annual Computers, Software, and Applications Conference (COMPSAC). IEEE. (Accepted).

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