Ali Yar Khan

NSF Graduate Research Fellow
Ph.D. Candidate, Computer Science & Engineering
University of North Texas

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I am on the job market for postdoctoral and tenure-track faculty positions starting in 2027.

About

I'm a Ph.D. candidate at the University of North Texas, advised by Dr. Sanjukta Bhowmick, and a 2023 National Science Foundation Graduate Research Fellow. I build scalable algorithms and high-performance systems for analyzing large, dynamic networks, drawing on graph algorithms, sparse linear algebra, parallel computing, and compiler technology.

My dissertation, Subgraph Counting: Methods, Frameworks, and Applications, is expected in Spring 2027. I worked with Sandia National Laboratories as an intern from 2023 to 2025, and I currently serve as a teaching assistant in UNT's Department of Computer Science and Engineering.

News

• Nov 2026Presenting my dissertation work at the SC26 Doctoral Symposium.
• Sep 2026Started as a Teaching Assistant in UNT CSE.
• Mar 2026Invited talk at the UC Santa Cruz Theory Seminar: Graphs in Flux: Dynamic Motif Counting and Portable Algebraic Compilation at Scale.
• May 2025ParaDyMS published at CCGrid 2025; PhD Forum poster at IPDPS 2025.

Research

• Parallel dynamic motif counting
  Scalable algorithms for counting small subgraph patterns in large graphs that change over time, handling both single-edge and batched updates. This work led to ParaDyMS (CCGrid 2025). [code]
• Portable compilation for graph analytics
  A compiler pipeline that lowers Python-GraphBLAS sparse linear algebra to performance-portable Kokkos C++ through MLIR and LAPIS, in collaboration with Sandia National Laboratories.
• Graphlet-based subgraph matching
  Using small graphlets as building blocks for exact subgraph matching, with a focus on many queries at once over dynamic data graphs.

Publications

• A. Khan, N. Tan, J. Marquez, M. Taufer, S. Bhowmick. ParaDyMS: Parallel Dynamic Motif Counting at Scale. IEEE/ACM CCGrid, 2025. [code]
• A. Shinde, V. Sabhaya, K. Farokhrou, F. Irany, A. Khan, S. Bhowmick, A. Santra, S. Chakravarthy. MLN-geeWhiz: Dashboard for Modeling, Analysis, Drill-Down, and Visualization of Complex Data Sets using Multilayer Networks. BDA, 2024.
• A. Khan, B. Kelley, K. Liegeois, S. Rajamanickam. Generating Kokkos Code From Sparse Tensor Algebra in Python Using MLIR. Sandia CSRI Summer Proceedings, SAND2023-13916R, 2023. Tech report
• Y. Guo, D. Brock, A. Lin, T. Doan, A. Khan, P. Tarau. Dependency Graphs for Summarization and Keyphrase Extraction. NLPIR, 2022.
• S.H. Ali, A. Goel, A. Singirikonda, A.Y. Khan, T. Xiao. Towards a Comprehensive Dataset for Next-Day Wildfire Prediction. IEEE QRS-C, 2022.
• Y. Guo, W. Sun, A. Khan, T. Doan, P. Tarau. DocTalk: Combining Dependency-Based Text Graphs and Deep Learning into a Practical Dialog Engine. FQAS, 2021.

Selected Posters & Talks

• Subgraph Counting: Methods, Frameworks, and Applications. SC26 Doctoral Symposium, 2026.
• Graphs in Flux: Dynamic Motif Counting and Portable Algebraic Compilation at Scale. UC Santa Cruz Theory Seminar, 2026. Invited
• Algorithms and Applications of Dynamic Network Analysis using CANDY. SC25, 2025.
• Parallel Motif Counting for Large-Scale Datasets. IPDPS PhD Forum, 2025.
• Scalable Motif Counting on Large-Scale Dynamic Graphs. SIAM CSE, 2025.
• Scalable Motif Counting on Large-Scale Dynamic Graphs. SC24 ACM Student Research Competition, 2024.
• Towards Scalable Identification of Motifs Representing Non-Determinism in HPC Simulations. SC22 ACM Student Research Competition, 2022.

Teaching & Mentoring

I teach through active, collaborative work: pair programming, so students learn to debug and reason together; a semester-long group project that ties the course together; and a flipped-classroom format that spends class time on problem solving. I see AI tools as something to teach students to use well, as a way to extend what they know rather than replace the thinking.

• Teaching Assistant, Department of Computer Science and Engineering, UNT · 2026 – present
• Research mentoring
  Mentored undergraduate and graduate students in parallel programming and algorithm design, including an undergraduate who went on to a Ph.D. in our lab, graduate students in the UNT AI Summer Research Program (2022), and high school students whose project won a category award at an international science fair.
• HPC education
  Presented High-Performance Computing for Graph AI: A Top-Down Perspective at the NSF/TCPP Workshop on Parallel and Distributed Computing Education (EduPar), IPDPS 2025.

Experience

• NSF Graduate Research Fellow — University of North Texas 2023 – 2026 Parallel motif counting and large-scale dynamic graph analytics.
• Graduate Research Intern & Co-op — Sandia National Laboratories 2023 – 2025 MLIR tooling for Kokkos code generation and HPC graph analysis workflows (Computer Science Research Institute).
• Graduate Research Assistant — University of North Texas 2022 – 2023 Scalable event-graph algorithms for finding sources of nondeterminism in HPC applications (ANACIN-X).
• Undergraduate Researcher — University of North Texas 2021 – 2022 NSF REU on community detection in multilayer networks with GNNs; emergency-response planning dashboard for county health officials (RE-PLAN).

Education

• Ph.D., Computer Science — University of North Texasexpected 2027
• M.S., Computer Science — University of North Texas2025
• B.S., Computer Science and Engineering — University of North Texas2021

Awards & Service

• NSF Graduate Research Fellowship (2023)
• IEEE-CS TCHPC/TCPP HPC Student Cohort (SC24, IPDPS25)
• SIGHPC Student Research Competition travel grants (SC22, SC24)
• Outstanding Senior, UNT Department of Computer Science and Engineering
• Program Committee, CCLS Symposium: AI for Biology and Medicine (2025)
• Student Volunteer, SC21 and SC22