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Photo of Kumar, Sidharth

Sidharth Kumar

Assistant Professor

Department of Computer Science

Pronouns: He/Him/His


Building & Room:

SEO 1330


851 S. Morgan, MC 152, Chicago, Il 60607

CV Link:

Sidharth Kumar


My research aims to provide a comprehensive framework for high-performance data management, critical in extracting meaningful insights from large and complex computational systems. Broadly, I have an interest in developing scalable algorithms and data structures that can inject interactivity and performance into data- and compute-intensive applications. My core expertise is HPC, but my overall research relies on the effective use of techniques from a range of areas including databases, topology, visualization, and machine learning.

As an undergraduate at DAIICT, I was fascinated by computer graphics, ultimately leading me to join The University of Utah. While at the U, I spent a summer at Argonne National Lab, where I was exposed to the world of supercomputing. I was thrilled by the idea of using supercomputers to solve computationally massive problems. Ever since, I have worked at the intersection of HPC, analytics, and visualization, helping domain scientists extract knowledge from massive amounts of complex data.


Selected Grants

NSF, SHF: Small: Scalable and Extensible I/O Runtime and Tools for Next Generation Adaptive Data Layouts, Principal Investigator

NSF, RII Track-4:NSF: Relational Algebra on Heterogeneous Extreme-scale Systems, Principal Investigator

NSF, PPoSS: A Full-stack Approach to Declarative Analytics at Scale, co-PI

NSF, PPoSS: Large: A Full-stack Approach to Declarative Analytics at Scale, co-PI

Selected Publications

The robustness of persistent homology of brain networks to data acquisition‐related non‐neural variability in resting state fMRI. Sidharth Kumar, Ahmedur Rahman Shovon, Gopikrishna Deshpande. Human Brain Mapping, 2023. Impact factor – 5.038

Towards iterated relational algebra on the GPU. Ahmedur Rahman Shovon, Thomas Gilray, Kristopher Micinski, Sidharth Kumar. USENIX Annual Technical Conference USENIX 2023, acceptance rate – 19%

Communication-Avoiding Recursive Aggregation. Yihao Sun, Sidharth Kumar, Thomas Gilray, Kristopher Micinski. IEEE International Conference on Cluster Computing. Cluster 2023, acceptance rate – 24%.

Speculative Progressive Raycasting for Memory Constrained Isosurface Visualization of Massive Volumes. Will Usher, Landon Dyken, Sidharth Kumar. The 13th IEEE Symposium on Large Data Analysis and Visualization.  LDAV 2023, acceptance rate – 38%.

GraphWaGu: GPU Powered Large Scale Graph Layout. Landon Dyken, Pravin Poudel, Steve Petruzza, Will Usher, Jake Chen Sidharth Kumar. Computation and Rendering for the Web. Eurographics Symposium on Parallel Graphics and Visualization.  EGPGV 2022, acceptance rate – 58%.

Optimizing the Bruck algorithm for non-uniform all-to-all communication.Ke Fan, Thomas Gilray, Kristopher Micinski, Valerio Pascucci, Sidharth Kumar. ACM International Symposium on High-Performance Parallel and Distributed Computing. HPDC 2022, acceptance rate – 19%.

Load-balancing Parallel I/O of Compressed Hierarchical Layouts. Ke Fan, Duong Hoang, Steve Petruzza, Thomas Gilray, Valerio Pascucci, Sidharth Kumar. IEEE Conference On High-Performance Computing, Data, and Analytics. HiPC 2021, acceptance rate – 23%.

Compiling Data-parallel Datalog. Thomas Gilray, Sidharth Kumar, Kristopher Micinski. International Conference on Compiler Construction, 2021. CC 2021.

Load-balancing Parallel Relational Algebra. Sidharth Kumar, Thomas Gilray. The International Supercomputing Conference, 2020. ISC 2020, acceptance rate – 31%, Hans Meuer Best paper award.

Distributed Relational Algebra at Scale. Sidharth Kumar, Thomas Gilray. IEEE Conference On High-Performance Computing, Data, and Analytics. HiPC 2019,  acceptance rate – 23%, Best Paper award.

Service to Community

  • ACM/IEEE Supercomputing Conference (SC 2023), technical program committee
  • International Supercomputing Conference (ISC 2023), technical program committee
  • IEEE International Parallel and Distributed Processing Symposium (IPDPS 2022), PC Chairs Team
  • IEEE International Conference on high-performance Computing, data, and Analytics (HiPC 2022), technical program committee
  • ACM/IEEE Supercomputing Conference (SC 2022), technical program committee
  • EuroMPI/USA 2022, technical program committee
  • IEEE International Conference on high-performance Computing, data, and Analytics (HiPC 2021), technical Program Committee
  • IEEE International Conference on high-performance Computing, data, and Analytics (HiPC 2020), technical program committee

Notable Honors

2019, Best research paper award, IEEE International conference on high-performance computing, data, and analytics (HiPC 2019)

2020, Hans Meuer best research paper award, The International Supercomputing Conference, 2020 (ISC 2020)

2021, Best Research Poster Award, 28th HiPC Student Research Symposium (SRS), 2021

2022, EPSCoR Research Fellow, NSF


Ph.D. in Computing (2016)
Scientific Computing and Imaging Institute, University of Utah
Advisor: Valerio Pascucci

Bachelor of Technology in Information and Communication Technology (2009)
DAIICT, Gandhinagar, India
Advisors: Gautam Dutta and Naresh Jotwani