Journal Papers

1. Keil, J. M., Mondal, D., Moradi, E., & Nekrich, Y. (2022). Finding a Maximum Clique

in a Grounded 1-Bend String Graph. Journal of Graph Algorithms and Applications,

26(4), 553–575.

2. Fazlali, M., Moradi, E., & Tabatabaee, H. (2017). Adaptive Parallel Louvain Community

Detection on a Multicore Platform. Microprocessors and Microsystems.

3. Nazari, P., Moradi, E., Nazari, S., Khaligh, J., & Shayestehfar, M. (2014). Applying

Genetic Algorithm in Selecting Providers of Supply Chain in Big Stores. Journal of

Advances in Mathematics (JAM).

Conference Papers

1. Moradi, E., Shvets, M., & Mondal, D. (2025). Visualization of Node-Centric Hierarchical

Structures in Directed Graphs. IV2025: InfVis - Information Visualisation Theory &

Practice.

2. Hossein, I., Moradi, E.(presenter), Mondal, D., Kobourov, S. (2025). Map Visualiza-

tions for Graphs with Group Restrictions. Proceedings of Graphics Interface 2025 (GI

2025), Kelowna, BC, Canada. CORE 2021 Rank: B.

3. Moradi, E., Mondal, D. (2023). BigGraphVis: Visualizing Communities in Big Graphs

Leveraging GPU-Accelerated Streaming Algorithms. 18th International Joint Conference

on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISI-

GRAPP - IVAPP). CORE2021 Rank B.

4. Keil, J. M., Mondal, D., Moradi, E. (2020). Finding a Maximum Clique in a Grounded

1-Bend String Graph. Canadian Conference on Computational Geometry.

5. Moradi, E., Fazlali, M., & Tabatabaee, H. (2015). Fast Parallel Community Detec-

tion Algorithm Based on Modularity. 18th CSI International Symposium on Computer

Architecture & Digital Systems (CADS 2015).

Poster Presentations

1. Moradi, E., Mondal, D. (2020). Visualizing Massive Networks by GPU-Accelerated

Streaming Algorithms. 28th International Symposium on Graph Drawing and Network

Visualization (GD 2020). CORE2021 Rank A.

2. Shvets, M., Moradi, E. (2022). Visualizing Node-Specific Hierarchies in Directed Net-

works. 30th International Symposium on Graph Drawing and Network Visualization (GD

2022). CORE2021 Rank A.

In Preparation

1-Moradi, E., Mondal, D. Quality-Space Trade-offs in Revealing Clusters in Large Network

Visualizations, 2025.

2-Moradi, E., Shvets, M., Mondal, D. Visualizing Node-Specific Hierarchies in Directed

Networks, 2025.

Collaborations

1-University College Cork (UCC), Ireland: Smart Manufacturing Project, Constraint

Programming, Euler Path Applications.

2-Global Institute for Water Security, University of Saskatchewan: Prairie Water

Project, Dynamic Visualization.

3-City of Saskatoon: Transit data visualization and optimization.

– CMPT 215.02: : Introduction to Computer Organization and Architecture (2025)

– CMPT 270.3: Developing Object-Oriented Systems (2022,2025)

– CMPT 260: Mathematical Logic and Computing (2023)

– CMPT 145.3: Principles of Computer Science (2023)

Technical and Vocational University, Iran

– Data Structures(2018)

– Algorithms(2017)

– Web Programming(2016)

– English for Computer Science students(2015).

• Teaching Assistant 2020 – Present
  University of Saskatchewan, Saskatoon, SK, Canada
  • Supported courses in Algorithms, Data Structures, Concurrent Programming, Parallel
  Programming for Scientific Computing, Principles of Computer Science and Information
  Visualization.

Explainable Models GPGPU Graph Parallel Processing Visualization

Dedicated PhD candidate in Computer Science at the University of Saskatchewan, specializing

in GPU-accelerated visualization methods for massive networks. Proven expertise in research,

teaching, and project leadership, with a strong technical background in programming, dis-

tributed systems, and network visualization. Demonstrated ability to publish in high-impact

journals and conferences, lead interdisciplinary projects, and deliver effective instruction in

computer science. PhD thesis defense scheduled.

Graph Drawing and Network Visualization

<img src="netvis.gif" alt="" class="inline">

Graph drawing focuses on finding geometric representations of graphs. Aside from theoretical interest, graph drawing is used in vast varieties of practical applications such as VLSI circuit layout, social network analysis, software system visualization, geometric routing, and bioinformatics. We examine theoretical bounds on the drawing aesthetics, and develop algorithms to draw graphs satisfying the constraints that arise from practical applications.

Computer Science, PhD Candidate.

Technical Skills
• Programming Languages: C++, C#, Java, Python, Delphi, PHP, TCL, HTML, CSS
• Data Visualization: D3.js, Tableau, Plotly, Matplotlib
• Deep Learning: TensorFlow, Keras, PyTorch
• Frameworks: NetworkX, .NET, OpenMP, MPI, CUDA
• Networking: Cisco (CCNA), Microsoft (MCSE)