Shaikh Arifuzzaman, Ph.D.
- Assistant Professor in Ubiquitous Data (AI and Big Data),
- Dept. of Computer Science,
- University of Nevada, Las Vegas (UNLV).
-
- Director, Data-intensive Scalable Computing Laboratory (DiSC Lab).
- Co-Liaison, UNLV AI and Big Data iRDA.
-
- 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 focuses on algorithmic foundations of large-scale, data-intensive computing, with particular emphasis on artificial intelligence (AI)/machine learning (ML) modeling and high-performance computing (HPC). Application areas include data-rich domains, e.g., cybersecurity, health/bio/neuro sciences, social networks and web, infrastructure systems, and sports.
Dr. Arifuzzaman is also a visiting faculty member at Lawrence Berkeley National Laboratory, collaborating on large scale computing projects and evaluating algorithms/methods on leadership‑class HPC systems. Previously, he was an assistant professor at the University of New Orleans and held positions at Sandia National Laboratories and the Biocomplexity Institute at Virginia Tech, where he also earned a Ph.D. in Computer Science. His research has been supported by National Science Foundation (NSF), US Dept. Of Energy (DoE), and UNLV OSP/FOA, among others. His work has been recognized with honors including 2X US DoE SRP‑HPC Fellowships (2023, 2019), Nevada Dept. of Edu. AI partnership Award (2025), first place in 2019 IEEE Big Data Conference-Big Data Cup Competition, 2026 UNLV TTDGRA award, and 2X UNLV President's innovation/research challenges winner.
See details on Dr. Arifuzzaman's research here: DiSC Lab.
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)
Research at a Glance
- 1. AI and HPC on Graphs: [IPDPS 2026 (Preprint)], [HPEC 2025], [ASONAM 2025], [JPP 2024], [ACM TKDD 2020], [IEEE HPEC 2024], [ICPP 2023], [IEEE IPDPS 2024], [App. Net. Sci. 2023].
- 2. AI in Performance Engineering: [HPEC 2025], [IEEE IPDPS 2024].
- 3. Social Network/Digital Media Mining using NLP and ML: [J.MLWA 26], [SNAM 2024], [Applied Sciences 2021].
- 4. Scalable Network/Graph Methods and Applications: [SNAM 2025], [SEDE 2025], [JPP 2024], [App. Net. Sci. 2023], [IEEE DependSys 2019], [IJBDI 2019], [IEEE eScience 2014].
- 5. AI/ML Methods and Interdisciplinary Applications: [J.MLWA 26], [HPEC 2025], [CyberSec-GNN Preprint25], [IEEE IECON 2024], [IEEE BigData 2020], [IEEE IPDPS 2020].
- [UNLV DiSC TechReport-25-001]: A GCN approach to intelligent CUDA parameter tuning
- [UNLV DiSC TechReport-25-002]: Sampling to scale: Performance trade-offs in approximate triangle and square counting
- [UNLV DiSC TechReport-25-003]: TopoGNN: A topology-aware GNN framework for uncovering IoT attack signatures
- [UNLV DiSC TechReport-25-004]: Modeling Epidemic Spread using Dynamic Graphs
Most Recent Papers
- IPDPS 2026 Efficient Communication-Aware Distributed ∆-Stepping for Single-Source Shortest Paths. [Paper Preprint]
- J. Machine Learning with Applications 2026 Towards an intelligent review helpfulness estimation: A novel dataset and machine learning framework. [Paper PDF]
- ASONAM 25 + J. SNAM 2025 Leveraging social network analysis and mobility data for modeling epidemic spread in urban tourist destinations. [J. SNAM/Springer Nature]
- HPEC 2025 GCN-Driven CUDA Parameter Optimization for Parallel Triangle Counting in Graphs. [HPEC 2025/IEEE Xplore]
- HPEC 2025 Sampling to scale: Performance trade-offs in approximate triangle and square counting. [PDF/Preprint]
- Preprint 25 TopoGNN: A topology-aware GNN framework for uncovering IoT attack signatures. [Preprint]
- Journal of Parallel Programming 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: Oct 20, 2025
Recent News
-
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.
-
2026“Efficient Communication-Aware Distributed ∆-Stepping for Single-Source Shortest Paths.” [Paper Preprint]. Thanks and congrats to my Ph.D. student Rakibul.
-
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.
-
2025Congratulations to Hasan Arikan on publishing his first paper as the lead author, involving GCN into predicting GPU CUDA Block size. See HPEC’25.
-
2025Congratulations to Nitika Pathania on her first paper, covering epidemic spread modeling. See [DOI/Springer] .
-
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.
-
2025Congratulations to Daniel on publishing the paper, covering network analysis with software engineering. See [SEDE 2025] .
-
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.
-
2024Congratulations to Ph.D. students Rakibul and Shubhashish on their first papers, covering parallel SSSP and subgraph matching. See HPEC’24 subgraph paper.
-
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).
-
2024Autonomous systems & graph/Laplacian‑based modeling. [paper] Collaboration with Mashuq Shafkat Islam, Anderson Sunda‑Meya, Peter X. Liu, Abdulmotaleb El Saddik, and Shafiqul Islam.
-
2024“Sentiment analysis of tweets on Social Security and Medicare.” [paper] First‑authored by high‑school student Unmesh (now headed to Carnegie Mellon University).
-
2023Faysal completed his Ph.D. (Fall 2023). He is currently a postdoc at Texas State with Prof. T.Z. Islam.
-
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).
-
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.
-
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.
-
2023One of five recipients. Interdisciplinary project with Dr. Brian Labus (Biostatistics & Epidemiology) on data‑driven epidemiology in urban settings.
-
2023“Fast Community Detection in Graphs with Infomap using Accelerated Sparse Accumulation,” with colleagues from LBNL.
-
2022Continued service to the Supercomputing community at SC22.
-
2022“Fast Algorithms for Mining and Analysis of Evolving Patterns in Large Dynamic Graphs,” in collaboration with Berkeley Lab.
-
2022Safrin completed her Ph.D. (Summer 2022) and joined Oak Ridge National Laboratory as a postdoc.
-
2022Joined UNLV in July 2022; grateful to UNO and excited for new collaborations and impact.
-
2021Safrin’s dissertation selected among top 10 for the SC21 Doctoral Showcase; additional poster accepted; continued TPC service.
-
2021“Innovative Architecture for High Performance Data Analytics,” with Berkeley Lab / University of California.
-
2020“Community Detection using Semi‑supervised Learning with Graph Convolutional Networks on GPUs.” [paper] Co‑chaired the 7th BigGraphs Workshop.
-
2020“Data‑Parallel Large Sparse Deep Neural Network on GPU,” accepted to ScaDL’20 Workshop with IPDPS 2020. [paper]
-
2020“Fast Parallel Algorithms for Counting Triangles in Big Graphs,” ACM Transactions on Knowledge Discovery from Data (TKDD), 14(1), 2020. [paper]
-
2019Team “BDTigers” won 1st place in the Brain Data Bank Challenge; multiple papers presented; co‑organized BigGraphs’19.
-
2019Served on the Best Poster Judging Committee; presented work at PDSW workshop.
-
2019Began collaboration with Berkeley Lab (Computational Research Division) on algorithms, performance modeling/benchmarking, and ML.
-
2019Support for work in ML and high‑performance computing.
-
2019Recognized as a top research idea; theme: algorithmic foundations of dynamic data. See ARCS 2019 awards.
-
2018Collaboration with Stennis Space Center on scalable systems for oceanographic data and analytics. (Conference held Nov 2019.)
-
2018Presented at PDSW‑DISCS workshop; served as SC Mentor; engaged with Early Career Workshop activities.
-
2018Co‑PI on high‑resolution human connectome analysis (with Psychology and CS).
-
2017Top‑rated proposal on parallel/randomized algorithms for large‑scale graph mining.
-
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.
- Scalable graph analytics on heterogeneous architectures (dynamic community detection, motif sampling, streaming).
- Performance portability & autotuning on GPUs/CPUs (Kokkos, CUDA, runtime modeling).
- Trustworthy & efficient AI (e.g., GNN-based intrusion detection for IoT; topology-aware learning).
- Exascale/HPC systems: communication-aware algorithms and memory-efficient graph kernels.
- Applied analytics in cybersecurity, neuroscience, public health/epidemiology, and software ecosystems.
Preferred backgrounds:
- C++ / CUDA
- Parallel programming (Kokkos, OpenMP)
- Distributed systems (MPI)
- Graph algorithms & analytics
- Machine Learning (GNNs, PyTorch)
- HPC performance analysis