Shaikh Arifuzzaman, Ph.D.
- Assistant Professor in Ubiquitous Data (AI and Big Data),
- Dept. of Computer Science,
- Howard R. Hughes College of Engineering,
- University of Nevada, Las Vegas (UNLV).
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- Director, Data-intensive Scalable Computing Laboratory (DiSC Lab).
- Co-Liaison, UNLV AI and Big Data iRDA.
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- Faculty Affiliate, Lawrence Berkeley National Laboratory, Berkeley, CA.
Dr. Shaikh Arifuzzaman is an Assistant Professor of Computer Science at the University of Nevada, Las Vegas (UNLV) and leads the Data‑intensive Scalable Computing Laboratory (DiSC Lab). His research builds algorithms, systems, and learning methods for data that is too large, too irregular, or too incomplete for standard tools. He develops graph machine learning and graph neural network methods, along with the parallel and distributed algorithms for massive, dynamic graphs. Much of this rests on high-performance computing, which he also studies in its own right for AI and scientific discovery. A related line of work examines how reliably machine learning and language-model pipelines behave on imperfect data. Application areas include Earth-system science and geohazards, urban and infrastructure resilience, cybersecurity, epidemiology and public health, and social and web data.
Dr. Arifuzzaman is lead PI of a $1.04M NSF award integrating artificial intelligence and neotectonics for data-driven global active fault mapping, and recently completed an NSF EPSCoR award on parallel algorithms for large dynamic graphs as sole principal investigator. As a Faculty Affiliate at Lawrence Berkeley National Laboratory, he collaborates with Berkeley Lab staff scientists and develops and evaluates methods on DOE leadership-class systems, including NERSC platforms. Previously, he was an assistant professor at the University of New Orleans and held positions at Sandia National Laboratories and Virginia Tech, where he also earned a Ph.D. in Computer Science.
His research has been supported by the National Science Foundation, the U.S. Department of Energy, and UNLV. His work has been recognized with two U.S. DOE SRP‑HPC Fellowships (2019, 2023), the Nevada Department of Education AI Partnership Award (2025), the UNLV Top Tier Doctoral Graduate Research Assistantship Award (2026), two UNLV President's innovation and research challenge wins, and first place in the 2019 IEEE Big Data Cup Competition. He serves on the Nevada Department of Education AI Executive Steering Committee and has served as a review panelist for NSF, DOE, and DoD programs.
The DiSC Lab is currently recruiting Ph.D. students—see current projects and lab members. Ready-to-use short, medium, and long biographies for speaker introductions and program materials are available on the bio page.
Honors & Awards
- 2026 UNLV TTDGRA Award (Top research award at UNLV)
- 2025 AI Partnership Award, Nevada Department of Education
- 2024 UNLV President’s Interdisciplinary Research Accelerator Competition Winner
- 2024 UNLV President’s AI Innovation Challenge—First Prize, Faculty advisor
- 2023, 2019 2X U.S. Department of Energy SRP‑High Performance Computing Fellowship
- 2021 Top 10 Ph.D. dissertations globally to showcase in ACM/IEEE SC21 Conference, Ph.D. Advisor
- 2019 First Place, Big Data Cup Challenge, IEEE Big Data Conference
- 2021, 2019 2X UNO Annual Research Award (SCoRe Award)
Latest Funded Grants
- Lead PI — NSF CAIG: Integrating AI and Neotectonics for Global Active Fault Mapping, 2026–2029, Total: $1.04M, UNLV: $590K
- PI — UNLV TTDGRA: AI Framework for Simulating Urban Resilience in Extreme Environments, 2026–2029, ~$100,000
- PI — NSF EPSCoR RII-Track4: Parallel Dynamic Graph Algorithms, 2023–2026, $247K
- PI — DOE SRP-HPC: Performance Portability of Graph Algorithms, 2023–2023, ~$60,000
- PI — UNLV FOA: Dynamic Graph Models of Epidemic Spread, 2023–2025, $35,000
Research at a Glance
- Graph machine learning: robustness, sparsification, and security. How GNN accuracy degrades on incomplete or perturbed graphs, and how to preserve it. [IEEE Access 2026], [ICMLA 2026], [IEEE HPEC 2026].
- Parallel and distributed algorithms for massive, dynamic graphs. Community detection, motif counting, and shortest paths on large HPC systems. [ACM ICPP 2026], [IEEE IPDPSW 2026], [J. Par. Prog. 2025], [ACM TKDD 2020]. AI-guided performance engineering and portability: [IEEE HPEC 2025], [IEEE IPDPSW/GrAPL 2024].
- AI for science and societal resilience. Active fault mapping, urban epidemic spread, and digital twins for infrastructure systems. [J. SNAM 2025], [Springer CCIS 2025], [IEEE IECON 2024].
- Language, text, and social data. Silent failures in LLM data pipelines, and NLP for social and digital media mining. [ICMLA 2026], [ICMLA 2026], [J. MLWA 2026].
55+ peer-reviewed publications · ~880 citations · h-index 16 · full publication list
Most Recent Papers
- ACM ICPP 2026 SQUASH: Distributed Square Estimation with Provable Error Bounds for Dynamic Graphs. [Paper Preprint]
- IEEE Access 2026 Structural Sensitivity of Graph Neural Networks for Intrusion Detection: An Intervention Study. [Paper Link]
- IEEE IPDPSW 2026 Efficient Communication-Aware Distributed ∆-Stepping for Single-Source Shortest Paths. [Paper Preprint]
- IEEE ICMLA 2026 Learning on Incomplete Graphs: Benchmarking GNN Robustness to Sparsification. [Paper Preprint]
- IEEE HPEC 2026 An Empirical Study of Kernel-Aware Graph Sparsification for Parallel BFS and SSSP. [Paper PDF]
- J. MLWA 2026 Towards an intelligent review helpfulness estimation: A novel dataset and machine learning framework. [Paper PDF]
- IEEE ICMLA 2026 Dual-Axis Benchmark and Preventive Scaffold: Surfacing and Mitigating Silent Risks in LLM Data Preparation. [Paper Preprint]
- IEEE ICMLA 2026 Iterative Silver-Label Refinement for Biomedical Temporal Information Extraction. [Paper Preprint]
- ASONAM 25 + J. SNAM 2025 Leveraging social network analysis and mobility data for modeling epidemic spread in urban tourist destinations. [J. SNAM/Springer Nature]
- IEEE HPEC 2025 GCN-Driven CUDA Parameter Optimization for Parallel Triangle Counting in Graphs. [HPEC 2025/IEEE Xplore]
- IEEE HPEC 2025 Sampling to scale: Performance trade-offs in approximate triangle and square counting. [PDF/Preprint]
- Int. J. Parallel Prog. 2025 DyG‑DPCD: A Distributed Parallel Community Detection for Dynamic Graphs [Int J Parallel Prog/Springer Nature]
See the full list of publications here: Publications • Updated: August 10, 2026
Recent News
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2026“An Empirical Study of Kernel-Aware Graph Sparsification for Parallel BFS and SSSP.” [Paper PDF]. Congratulations to my Ph.D. student Rakibul. The study shows that sparsification should be chosen based on target graph kernels, reaching 3.30× speedup for parallel BFS and 3.85× for Delta-Stepping SSSP.
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2026Awarded through the NSF Collaborations in Artificial Intelligence and Geosciences (CAIG) program, 2026–2029. UNLV is the lead institution ($590K to UNLV) in collaboration with the University of Kansas (PI: Michael Taylor). The project develops graph neural network methods for mapping active faults from global geoscience data, and funds two AI-focused Ph.D. students starting Spring 2027.
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2026Congratulations to Rakibul (GNN robustness to graph sparsification), Ming (silent risks in LLM data preparation), and Chan (biomedical temporal information extraction) on their papers at the 25th IEEE International Conference on Machine Learning and Applications.
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2026Top research award at UNLV. The award will help me recruite a new PhD student, to start in Fall 2026. Looking for a student with expertise and interests in temporal GNN, dynamic graph algorithms, and anomaly detection for infrastructure systems.
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2026Graph Neural Network for IoT intrusion detection. [Paper Link]. Congratulations and thanks to PhD student author Ki Chan and Profs. Kim and Jo.
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2026“SQUASH: Distributed Square Estimation with Provable Error Bounds for Dynamic Graphs.” [Paper Preprint]. Thanks and congrats to my Ph.D. student Shubhashish.
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2026“Efficient Communication-Aware Distributed ∆-Stepping for Single-Source Shortest Paths.” [Paper Preprint]. Thanks and congrats to my Ph.D. student Rakibul.
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2026“Towards an intelligent review helpfulness estimation: A novel dataset and machine learning framework” [Paper PDF]. Thanks and congratulations to co-authors Rakibul, Shubhashish (both my PhD students), and Dr. Fonseca.
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2025Congratulations to Hasan Arikan on publishing his first paper as the lead author, involving GCN into predicting GPU CUDA Block size. See HPEC’25.
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2025Congratulations to Nitika Pathania on her first paper, covering epidemic spread modeling. See [DOI/Springer] .
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2025Recognized on stage with the AI Partnership Award for contributions to Nevada’s K–12/Higher‑Ed AI policy. Delivered an invited talk, “Building AI‑Ready Students in a Post‑ChatGPT World,” to educators and administrators.
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2025Congratulations to Daniel on publishing the paper, covering network analysis with software engineering. See [SEDE 2025] .
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2024As faculty advisor, mentored the winning student team to the Grand Prize ($25K) at UNLV’s President’s Innovation Challenge. Students are preparing a startup focused on AI for education.
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2024Congratulations to Ph.D. students Rakibul and Shubhashish on their first papers, covering parallel SSSP and subgraph matching. See HPEC’24 subgraph paper.
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2024“Unlocking the Potential: Performance Portability of Graph Algorithms on Kokkos Framework.” [paper] Thanks to Ph.D. student Faysal, undergrad Hasan, and LBNL collaborators (Max Bremer, John Shalf, and Doru Thom Popovici).
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2024Autonomous systems & graph/Laplacian‑based modeling. [paper] Collaboration with Mashuq Shafkat Islam, Anderson Sunda‑Meya, Peter X. Liu, Abdulmotaleb El Saddik, and Shafiqul Islam.
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2024“Sentiment analysis of tweets on Social Security and Medicare.” [paper] First‑authored by high‑school student Unmesh (now headed to Carnegie Mellon University).
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2023Faysal completed his Ph.D. (Fall 2023). He is currently a postdoc at Texas State with Prof. T.Z. Islam.
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2023“Fast Parallel Index Construction for Efficient K‑truss‑based Local Community Detection in Large Graphs.” [paper] With Ph.D. student Faysal and LBNL collaborators (Max Bremer, Cy Chan, John Shalf).
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2023“Exploring temporal community evolution: algorithmic approaches and parallel optimization for dynamic community detection.” [open access] With former Ph.D. student Safrin and LBNL collaborators Aydın Buluç and Khaled Ibrahim.
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2023Summer at Berkeley Lab (Performance & Algorithms Research). Joined by students Faysal (Ph.D.) and Hasan (UG). Collaborations with Thom, John, Max, Cy, Khaled, and Aydın.
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2023One of five recipients. Interdisciplinary project with Dr. Brian Labus (Biostatistics & Epidemiology) on data‑driven epidemiology in urban settings.
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2023“Fast Community Detection in Graphs with Infomap using Accelerated Sparse Accumulation,” with colleagues from LBNL.
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2022Continued service to the Supercomputing community at SC22.
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2022“Fast Algorithms for Mining and Analysis of Evolving Patterns in Large Dynamic Graphs,” in collaboration with Berkeley Lab.
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2022Safrin completed her Ph.D. (Summer 2022) and joined Oak Ridge National Laboratory as a postdoc.
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2022Joined UNLV in July 2022; grateful to UNO and excited for new collaborations and impact.
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2021Safrin’s dissertation selected among top 10 for the SC21 Doctoral Showcase; additional poster accepted; continued TPC service.
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2021“Innovative Architecture for High Performance Data Analytics,” with Berkeley Lab / University of California.
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2020“Community Detection using Semi‑supervised Learning with Graph Convolutional Networks on GPUs.” [paper] Co‑chaired the 7th BigGraphs Workshop.
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2020“Data‑Parallel Large Sparse Deep Neural Network on GPU,” accepted to ScaDL’20 Workshop with IPDPS 2020. [paper]
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2020“Fast Parallel Algorithms for Counting Triangles in Big Graphs,” ACM Transactions on Knowledge Discovery from Data (TKDD), 14(1), 2020. [paper]
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2019Team “BDTigers” won 1st place in the Brain Data Bank Challenge; multiple papers presented; co‑organized BigGraphs’19.
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2019Served on the Best Poster Judging Committee; presented work at PDSW workshop.
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2019Began collaboration with Berkeley Lab (Computational Research Division) on algorithms, performance modeling/benchmarking, and ML.
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2019Support for work in ML and high‑performance computing.
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2019Recognized as a top research idea; theme: algorithmic foundations of dynamic data. See ARCS 2019 awards.
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2018Collaboration with Stennis Space Center on scalable systems for oceanographic data and analytics. (Conference held Nov 2019.)
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2018Presented at PDSW‑DISCS workshop; served as SC Mentor; engaged with Early Career Workshop activities.
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2018Co‑PI on high‑resolution human connectome analysis (with Psychology and CS).
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2017Top‑rated proposal on parallel/randomized algorithms for large‑scale graph mining.
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2017Project: Scalable Mining and Analysis of Protein‑Protein Interaction (PPI) Networks.
Lab Highlights
Hiring Announcement
Positions Alert!
I am hiring a postdoc and multiple Ph.D. students to work on exciting projects in AI, HPC/Systems, and Algorithms. Motivated and talented BS and MS students are also welcome to apply for the GA positions.
- Graph ML, robustness, and security: GNN behavior on incomplete and perturbed graphs; topology-aware learning; GNN-based intrusion detection for IoT.
- Parallel and distributed graph algorithms: dynamic community detection, motif sampling, streaming updates, communication-aware and memory-efficient kernels at extreme scale.
- AI-guided performance engineering: autotuning and performance portability across GPUs/CPUs (Kokkos, CUDA, learned cost models).
- AI for science and societal resilience: geoscience fault mapping (NSF CAIG), urban resilience modeling, epidemiology, software ecosystems.
- Language models and applied ML: reliability of LLM data pipelines; information extraction for biomedical and social data.
Preferred backgrounds:
- C++ / CUDA
- Parallel programming (Kokkos, OpenMP)
- Distributed systems (MPI)
- Graph algorithms & analytics
- Machine Learning (GNNs, PyTorch)
- HPC performance analysis