Anwar Said
Institute for Software Integrated Systems,Department of Computer Science,
Vanderbilt University,
Nashville, TN, USA.
AI Research Scientist
About Me
I am currently an AI Research Scientist at the Institute for Software Integrated Systems, Vanderbilt University, TN, USA. Prior to that, I was a postdoc at Vanderbilt University. I received my PhD in Computer Science from Information Technology University (ITU) and was a member of Artificial Intelligence Lab. My research primarily intersects the fields of data science and machine learning, with a focus on graph representation learning, social network analysis, and graph theory with applications in Anhedonia detection, drug discovery and development, social networks and education. I actively participate in top conferences and journals in these domains, contributing through both publications and serving as a PC member and a reviewer. My works have been published in renowned venues such as NeurIPS, Neurocomputing, Applied Soft Computing, and TKDE, among others.
Latest
Paper accepted
Paper entitled A Graph Neural Network Framework for Imbalanced Bus Ridership Forecasting accepted at SMARTCOMp 2024
(Apr. 2024)Paper accepted
Paper entitled Improving Graph Machine Learning Performance Through Feature Augmentation Based on Network Control Theory accepted at Mediterranean Conference on Control and Automation (MED2024)
(Apr. 2024)Paper accepted
Paper entitled Control-based Graph Embeddings with Data Augmentation for Contrastive Learning accepted at American Control Conference (ACC2024)
(Jan. 2024)Gave a hands-on tutorial session on reproducible project management at the BrainHack event hosted at Vanderbilt University
I will be attending NeurIPS 2023 in New Orleans, LA and will be presenting our poster on benchmarks for graph machine learning in Brain Connectomics. If you are attending the conference, please feel free to stop by and chat, or drop me a message!
Paper accepted
(Nov. 2023)Paper entitled Graph Unlearing: A Review is now online!
Paper entitled NeuroGraph: Benchmarks for Graph Machine Learning in Brain Connectomics accepted at NeurIPS23 Datasets and Benchmark Track
Paper entitled Enhanced Graph Neural Networks with Ego-Centric Spectral Subgraph Embeddings Augmentation accepted at ICMLA23
Paper entitled ASBiNE: Dynamic Bipartite Network Embedding for incorporating structural and attribute information accepted at World Wide Web
I will be attending KDD 2023 in Long Beach, CA and will be presenting our paper on benchmarks for graph machine learning in Brain Connectomics. I am also co-organizaing Data Science for Social Good Workshop (DSSG) at KDD. If you are attending the conference, please feel free to stop by and chat, or drop me a message!
We have released the documentation for NeuroGraph package!
Paper entitled NeuroGraph: Benchmarks for Graph Machine Learning in Brain Connectomics is now online!
Gave a guest lecture at Agriculture Sciences, Tennessee State University on "Machine Learning at the Forefront of Molecular Analysis". Link to the slides
Paper entitled Sequential Graph Neural Networks for Code Vulnerability Identification accepted at HotSoS23
Paper entitled On Augmenting Topological Graph Representations for Attributed Graphs accepted at Applied Soft Computing (2023)
Paper entitled Circuit Design completion using Graph Neural Networks accepted at Neural Computing and Applications (2023)
Selected Publications
for complete list, please see my google scholar2023
NeuroGraph: Benchmarks for Graph Machine Learning in Brain Connectomics
Anwar Said, et al. "NeuroGraph: Benchmarks for Graph Machine Learning in Brain Connectomics." arXiv preprint arXiv:2306.06202 (2023).
Circuit design completion using graph neural networks
Said, A., Shabbir, M., Hassan, S. U., Hassan, Z. R., Ahmed, A., & Koutsoukos, X. (2023). On augmenting topological graph representations for attributed graphs. Applied Soft Computing, 110104.
On Augmenting Topological Graph Representations for Attributed Graphs
Anwar Said, Mudassir Shabbir, Brian Broll, Waseem Abbas, Peter Volgyesi, Xenofon Koutsoukos, Neural Computing and Applications 2023
DGSD: Distributed graph representation via graph statistical properties
Said, A., Hassan, S. U., Tuarob, S., Nawaz, R., & Shabbir, M. (2021) Future Generation Computer Systems, 119, 166-175
Detailed analysis of Ethereum network on transaction behavior, community structure and link prediction.
Said, Anwar, et al (2021) PeerJ Computer Science 7 : e815
NetKI: A kirchhoff index based statistical graph embedding in nearly linear time..
Said, A., Hassan, S. U., Abbas, W., & Shabbir, M. (2021), Neurocomputing, 433, 108-118.
2019
Leveraging deep learning and SNA approaches for smart city policing in the developing world.
Hassan, S. U., Shabbir, M., Iqbal, S., Said, A., Kamiran, F., Nawaz, R., & Saif, U. (2019),International Journal of Information Management, 102045.
2019
Mining network-level properties of Twitter altmetrics data.
Said, A., Bowman, T. D., Abbasi, R. A., Aljohani, N. R., Hassan, S. U., & Nawaz, R. (2019) Mining network-level properties of Twitter altmetrics data. Scientometrics, 120(1), 217-235.
2019
Proactive caching at the edge leveraging influential user detection in cellular D2D networks.
Said, A., Shah, S. W. H., Farooq, H., Mian, A. N., Imran, A., & Crowcroft, J. (2018). Future Internet, 10(10), 93.
2018
Exploiting social networks of Twitter in altmetrics big data
Imran, M., Akhtar, A., Said, A., Safder, I., Hassan, S. U., & Aljohani, N. R. (2018, September) In 23rd international conference on science and technology indicators (STI 2018) (pp. 12-14).