sidharthkumar.io

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Sidharth's Home Page

 Genereret Januar 24 2026 23:58 PM

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Sidharth's Home Page

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Overskrifter

H1 H2 H3 H4 H5 H6
0 1 8 1 7 61
  • [H2] Sidharth Kumar
  • [H3] About me
  • [H3] Research Projects
  • [H3] Awards
  • [H3] Research grants
  • [H3] Current research team
  • [H3] Selected Publications
  • [H3] Courses Taught
  • [H3] Service
  • [H4] Latest News
  • [H5] Associate professor, Computer Science
  • [H5] *Fully-funded Ph.D. positions open*
  • [H5]
  • [H5] Database Systems
  • [H5] Advanced algorithms
  • [H5] Data visualiaztion
  • [H5] Algorithms and data structures
  • [H6] Data management at exascale
  • [H6] MPI, Collectives
  • [H6] GPU accelerated visualiaztion for the WEB
  • [H6] Declarative analysis at Scale
  • [H6] Topology driven analytics
  • [H6] Honorable Mention, Pacific Vis 2025
  • [H6] Best Research Paper Award, Winner, LDAV 2023
  • [H6] Best Research Poster, Finalist, SC 2023
  • [H6] Best Research Poster Award (SRS), HiPC 2021
  • [H6] Hans Meuer Best Research Paper Award, ISC 2020
  • [H6] Best Research Paper Award, HiPC 2019
  • [H6] NSF PPoSS large 2023-2028
  • [H6] NSF EPSCoR RII Track4, 2022-2023
  • [H6] NSF Software and Hardware Foundation
  • [H6] NSF PPoSS Planning 2023-2028
  • [H6] Directors Discretionary (DD) award, ANL, 2019-current
  • [H6] Ke Fan, PhD Candidate
  • [H6] Ahmedur Rahman Shovon, PhD Student
  • [H6] Kunting Qi, PhD Student
  • [H6] Charis Hulu, PhD Student
  • [H6] Jiaxin Lu, PhD Student
  • [H6] Landon Dyken, PhD Student
  • [H6] Machine Learning Driven Auto-tuning for Non-uniform All-to-All Collectives
  • [H6] Topology Assisted Clustering of Temporal fMRI Brain Networks With Use-Case in Mitigating Non-Neural Multi-Site Variability
  • [H6] Faster Annotation for Elevation-Guided Flood Extent Mapping by Consistency-Enhanced Active Learning
  • [H6] Parameterized Algorithms for Non-uniform All-to-all
  • [H6] Multi-node Multi-GPU Datalog
  • [H6] Datalog with First-Class Facts
  • [H6] Accelerating Web-Based Graph Layout Computation with Bottom-Up GPU Quadtree Construction
  • [H6] Enabling Fast and Accurate Crowdsourced Annotation for Elevation-Aware Flood Extent Mapping
  • [H6] Column-Oriented Datalog on the GPU
  • [H6] Optimizing Datalog on the GPU
  • [H6] Interactive Isosurface Visualization in Memory Constrained Environments Using Deep Learning and Speculative Raycasting
  • [H6] TinyProf: Towards Continuous Performance Introspection through Scalable Parallel I/O
  • [H6] Configurable Algorithms for All-to-all Collectives
  • [H6] The robustness of persistent homology of brain networks to data acquisition-related non-neural variability in resting state fMRI
  • [H6] Towards iterated relational algebra on the GPU.
  • [H6] Using Digital phenotyping to understand health-related outcomes: A scoping review.
  • [H6] UALCAN: An update to the integrated cancer data analysis platform.
  • [H6] Scalable, interactive and hierarchical visualization of virus taxonomic data.
  • [H6] Communication-Avoiding Recursive Aggregation.
  • [H6] Speculative Progressive Raycasting for Memory Constrained Isosurface Visualization of Massive Volumes.
  • [H6] A Visual Guide to MPI All-to-all
  • [H6] Optimizing the Bruck Algorithm for Non-uniform All-to-all Communication.
  • [H6] GraphWaGu: GPU Powered Large Scale Graph Layout Computation and Rendering for the Web.
  • [H6] Load-balancing Parallel I/O of Compressed Hierarchical Layouts.
  • [H6] An empirical investigation of OpenMP based implementation of Simplex Algorithm.
  • [H6] Compiling Data-Parallel Datalog.
  • [H6] Adaptive Spatially Aware I/O for Multiresolution Particle Data Layouts.
  • [H6] Load-balancing Parallel Relational Algebra.
  • [H6] Distributed Relational Algebra at Scale.
  • [H6] Spatially-aware Parallel I/O for Particle Data.
  • [H6] VisIt-OSPRay: Toward an Exascale Volume Visualization System.
  • [H6] Scalable Data Management of the Uintah Simulation Framework for Next-Generation Engineering Problems with Radiation.
  • [H6] Reducing network congestion and synchronization overhead during aggregation of hierarchical data.
  • [H6] Evaluation of In-Situ Analysis Strategies at Scale for Power Efficiency and Scalability.
  • [H6] Efficient I/O and storage of adaptive resolution data.
  • [H6] Fast multi-resolution reads of massive simulation datasets.
  • [H6] Characterization and modeling of pidx parallel I/O for performance optimization.
  • [H6] Efficient data restructuring and aggregation for I/O acceleration in PIDX.
  • [H6] PIDX: efficient parallel I/O for multiresolution multi-dimensional scientific datasets.

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