About

Reyan Ahmed is an Assistant Professor of Practice in the Department of Computer Science at the University of Arizona. He holds a Ph.D. from The University of Arizona. His office is located in GS 831, and he can be contacted via abureyanahmed@arizona.edu. The page does not provide specific details about his research focus, background, or key contributions.

Research topics

• Computer Science
• Artificial Intelligence
• Theoretical computer science
• Algorithm

Selected publications

• Phytochemical and nutritional variation of country beans (Lablab purpureus) involving parents and hybrids

  Journal of the Sylhet Agricultural University · 2025-07-07 · 2 citations

  articleOpen access

  The fifteen country bean genotypes (Lablab purpureus) were grown to assess the nutritional status and phytochemical analysis. Morphological variation among the genotypes was also evaluated. The experiments were set up using one-way randomized complete block design with three replications. These 15 genotypes grown in winter season from August 2022 to January 2023 were IPSA Sheem -1(P1), Sikribi Sheem-1(P2), Sikribi Sheem-2(P3), BARI Sheem-1(P4), Golgadda Sheem (P5), and hybrids from P1× P2, P1× P3, P1× P4, P1× P5, P2× P3, P2× P4, P2× P5, P3× P4, P3× P5, and P4× P5. Nutritional parameters like crude protein (CP), antioxidant, dry matter (DM), and moisture content were studied. The highest CP (29.63%) was observed in IPSA Sheem-1 (P1). We found the highest antioxidant (Free radical scavenging activity of DPPH) in hybrid from P4× P5 (37.96%). P3× P5 hybrid had the highest DM (13.11%) and the highest moisture content (%) value was in P4× P5 (90.61%). In phytochemical analysis, the total phenolic content (TPC) and total flavonoid content (TFC) were studied. The highest TPC was (14.58 ug/mg) found in Golgadda Sheem (P5). P1× P4 hybrid had the highest TFC (4.25 ug/mg). Following morphological variation, the highest number (4.86) of seeds pod-1 was recorded in P1× P5. Based on nutrients IPSA Sheem -1(P1) was found to be more nutritious in the winter season compared to the other genotypes. J. Sylhet Agril. Univ. 11(2): 53-62, 2024

  PublisherOA PDFDOI

• Word2VecGD: Neural Graph Drawing with Cosine-Stress Optimization

  ArXiv.org · 2025-09-22

  preprintOpen accessSenior author

  We propose a novel graph visualization method leveraging random walk-based embeddings to replace costly graph-theoretical distance computations. Using word2vec-inspired embeddings, our approach captures both structural and semantic relationships efficiently. Instead of relying on exact shortest-path distances, we optimize layouts using cosine dissimilarities, significantly reducing computational overhead. Our framework integrates differentiable stress optimization with stochastic gradient descent (SGD), supporting multi-criteria layout objectives. Experimental results demonstrate that our method produces high-quality, semantically meaningful layouts while efficiently scaling to large graphs. Code available at: https://github.com/mlyann/graphv_nn

  PublisherOA PDFDOI

• Size Should not Matter: Scale-invariant Stress Metrics

  arXiv (Cornell University) · 2024-08-08

  preprintOpen access1st authorCorresponding

  The normalized stress metric measures how closely distances between vertices in a graph drawing match the graph-theoretic distances between those vertices. It is one of the most widely employed quality metrics for graph drawing, and is even the optimization goal of several popular graph layout algorithms. However, normalized stress can be misleading when used to compare the outputs of two or more algorithms, as it is sensitive to the size of the drawing compared to the graph-theoretic distances used. Uniformly scaling a layout will change the value of stress despite not meaningfully changing the drawing. In fact, the change in stress values can be so significant that a clearly better layout can appear to have a worse stress score than a random layout. In this paper, we study different variants for calculating stress used in the literature (raw stress, normalized stress, etc.) and show that many of them are affected by this problem, which threatens the validity of experiments that compare the quality of one algorithm to that of another. We then experimentally justify one of the stress calculation variants, scale-normalized stress, as one that fairly compares drawing outputs regardless of their size. We also describe an efficient computation for scale-normalized stress and provide an open source implementation.

  PublisherOA PDFDOI

• Graph Sparsifications using Neural Network Assisted Monte Carlo Tree Search

  arXiv (Cornell University) · 2023-11-17

  preprintOpen access

  Graph neural networks have been successful for machine learning, as well as for combinatorial and graph problems such as the Subgraph Isomorphism Problem and the Traveling Salesman Problem. We describe an approach for computing graph sparsifiers by combining a graph neural network and Monte Carlo Tree Search. We first train a graph neural network that takes as input a partial solution and proposes a new node to be added as output. This neural network is then used in a Monte Carlo search to compute a sparsifier. The proposed method consistently outperforms several standard approximation algorithms on different types of graphs and often finds the optimal solution.

  PublisherOA PDFDOI

• Nearly Optimal Steiner Trees using Graph Neural Network Assisted Monte Carlo Tree Search

  arXiv (Cornell University) · 2023-04-30 · 1 citations

  preprintOpen access1st authorCorresponding

  Graph neural networks are useful for learning problems, as well as for combinatorial and graph problems such as the Subgraph Isomorphism Problem and the Traveling Salesman Problem. We describe an approach for computing Steiner Trees by combining a graph neural network and Monte Carlo Tree Search. We first train a graph neural network that takes as input a partial solution and proposes a new node to be added as output. This neural network is then used in a Monte Carlo search to compute a Steiner tree. The proposed method consistently outperforms the standard 2-approximation algorithm on many different types of graphs and often finds the optimal solution.

  PublisherOA PDFDOI

• Visualizing Evolving Trees

  Lecture notes in computer science · 2023-01-01 · 4 citations

  book-chapterOpen accessCorresponding
  PublisherOA PDFDOI

• Multi-Priority Graph Sparsification

  arXiv (Cornell University) · 2023-01-29

  preprintOpen access1st authorCorresponding

  A \emph{sparsification} of a given graph $G$ is a sparser graph (typically a subgraph) which aims to approximate or preserve some property of $G$. Examples of sparsifications include but are not limited to spanning trees, Steiner trees, spanners, emulators, and distance preservers. Each vertex has the same priority in all of these problems. However, real-world graphs typically assign different ``priorities'' or ``levels'' to different vertices, in which higher-priority vertices require higher-quality connectivity between them. Multi-priority variants of the Steiner tree problem have been studied in prior literature but this generalization is much less studied for other sparsification problems. In this paper, we define a generalized multi-priority problem and present a rounding-up approach that can be used for a variety of graph sparsifications. Our analysis provides a systematic way to compute approximate solutions to multi-priority variants of a wide range of graph sparsification problems given access to a single-priority subroutine.

  PublisherOA PDFDOI

• A Scalable Method for Readable Tree Layouts

  arXiv (Cornell University) · 2023-05-17

  preprintOpen access

  Large tree structures are ubiquitous and real-world relational datasets often have information associated with nodes (e.g., labels or other attributes) and edges (e.g., weights or distances) that need to be communicated to the viewers. Yet, scalable, easy to read tree layouts are difficult to achieve. We consider tree layouts to be readable if they meet some basic requirements: node labels should not overlap, edges should not cross, edge lengths should be preserved, and the output should be compact. There are many algorithms for drawing trees, although very few take node labels or edge lengths into account, and none optimizes all requirements above. With this in mind, we propose a new scalable method for readable tree layouts. The algorithm guarantees that the layout has no edge crossings and no label overlaps, and optimizes one of the remaining aspects: desired edge lengths and compactness. We evaluate the performance of the new algorithm by comparison with related earlier approaches using several real-world datasets, ranging from a few thousand nodes to hundreds of thousands of nodes. Tree layout algorithms can be used to visualize large general graphs, by extracting a hierarchy of progressively larger trees. We illustrate this functionality by presenting several map-like visualizations generated by the new tree layout algorithm.

  PublisherOA PDFDOI

• Multi-priority Graph Sparsification

  Lecture notes in computer science · 2023-01-01 · 3 citations

  book-chapter1st authorCorresponding
  PublisherDOI

• Splitting Vertices in 2-Layer Graph Drawings

  IEEE Computer Graphics and Applications · 2023-04-06 · 12 citations

  article1st authorCorresponding

  Bipartite graphs model the relationships between two disjoint sets of entities in several applications and are naturally drawn as 2-layer graph drawings. In such drawings, the two sets of entities (vertices) are placed on two parallel lines (layers), and their relationships (edges) are represented by segments connecting vertices. Methods for constructing 2-layer drawings often try to minimize the number of edge crossings. We use vertex splitting to reduce the number of crossings, by replacing selected vertices on one layer by two (or more) copies and suitably distributing their incident edges among these copies. We study several optimization problems related to vertex splitting, either minimizing the number of crossings or removing all crossings with fewest splits. While we prove that some variants are ${\mathsf {NP}}$NP-complete, we obtain polynomial-time algorithms for others. We run our algorithms on a benchmark set of bipartite graphs representing the relationships between human anatomical structures and cell types.

  PublisherDOI

Frequent coauthors

• Stephen Kobourov

  University of Arizona

  49 shared

• Faryad Darabi Sahneh

  22 shared

• Keaton Hamm

  19 shared

• Richard Spence

  University of Arizona

  18 shared

• Greg Bodwin

  University of Michigan–Ann Arbor

  12 shared

• Felice De Luca

  University of Arizona

  10 shared

• Mohammad Javad Latifi Jebelli

  10 shared

• Sabin Devkota

  10 shared

Education

• MSc, Computer Science and Engineering

  Bangladesh University of Engineering and Technology

  2016

• BSc, Computer Science and Engineering

  Bangladesh University of Engineering and Technology

  2013