Mudassir Shabbir
Associate Professor, Department of Computer Science, LUMS
mudassir.shabbir [AT] lums.edu.pk
Bio
Dr. Mudassir Shabbir is an Associate Professor in the Department of Computer Science at the Lahore University of Management Sciences (LUMS), Pakistan. He earned his Ph.D. from the Division of Computer Science at Rutgers University, NJ, USA, in 2014, where he was a Fulbright Scholar and Rutgers Honors Fellow (2011–12). Prior to joining LUMS in 2026, Dr. Shabbir was at the Information Technology University (ITU), Lahore, where he served as Associate Professor and Chairperson of the Department of Computer Science. He has also held positions as Research Assistant Professor at Vanderbilt University, Nashville, TN, and has contributed at Los Alamos National Laboratory (NM) and Bloomberg L.P. (New York, NY). His primary research focus is Graph Machine Learning and Robustness in Networks, with applications in brain network analysis (fMRI data), social network analysis, and Resilient Network Systems. His work on succinct representations of large datasets has direct applications in non-parametric statistical analysis, drawing on techniques from algorithmic and discrete geometry.
Recent
- Joined Lums as Associate Professor[2026]
Publications
Most recent publications on Google Scholar.
‡ indicates equal contribution.
- Sequential Graph Neural Networks for Source Code Vulnerability Identification
ArXiv (2023)
We introduce CVEFGE, a curated C/C++ source code vulnerability dataset, and SEGNN, a sequential GNN that achieves state-of-the-art vulnerability identification by learning code semantic representations via graph convolution.
- Learning-Based Heuristic for Combinatorial Optimization of the Minimum Dominating Set Problem using Graph Convolutional Networks
ArXiv (2023)
We propose a graph convolutional network heuristic for the NP-hard minimum dominating set problem that outperforms classical greedy approximation and generalizes to graphs larger than those seen during training.
- NeuroGraph: Benchmarks for Graph Machine Learning in Brain Connectomics
ArXiv (2023)
We introduce NeuroGraph, a benchmark suite of 35 graph-based neuroimaging datasets spanning behavioral and cognitive traits, with 15+ baseline methods and an open-source Python package for graph ML in brain connectomics.
- Resilient Multi-agent Reinforcement Learning Using Medoid and Soft-medoid Based Aggregation
2022 IEEE International Conference on Assured Autonomy (ICAA) (2022)
We propose medoid and soft-medoid aggregation rules for multi-agent reinforcement learning that provably converge to the optimum under adversarial attacks, outperforming average and median-based alternatives.
- What a drag! Streamlining the UAV design process with design grammars and drag surrogates
(2022)
We propose a UAV design pipeline combining a design grammar for geometry generation with a GNN-based drag surrogate trained on simulation data, accelerating design space exploration without costly CAD and simulation routines.
- Attack-Resilient Multi-Agent Flocking Control Using Graph Neural Networks
2022 30th Mediterranean Conference on Control and Automation (MED) (2022)
We design a GCN-based distributed flocking controller with median-based aggregation that maintains flock structure under adversarial communication attacks, where standard average-based aggregation provably fails.
- Leader Selection for Strong Structural Controllability in Networks using Zero Forcing Sets
2022 American Control Conference (ACC) (2022)
We present a linear-time optimal algorithm for minimum zero forcing sets in trees and a game-theoretic formulation for general graphs, with direct application to leader selection for strong structural controllability.
- Byzantine Resilient Distributed Learning in Multirobot Systems
IEEE Transactions on Robotics (2022)
We propose centerpoint-based aggregation for distributed SGD in multi-robot networks, guaranteeing convergence to the optimum even when Byzantine agents send arbitrary estimates — outperforming coordinate-wise and geometric median rules.
- Edge Augmentation With Controllability Constraints in Directed Laplacian Networks
IEEE Control Systems Letters (2021)
We study maximally adding edges to directed networks while preserving strong structural controllability bounds, providing an exact algorithm for zero-forcing constraints and an alpha-approximate randomized algorithm for distance-based constraints.
- Resilient Distributed Vector Consensus Using Centerpoint
(2021)
We show that centerpoints provide a complete characterization of safe points for resilient vector consensus, yielding tight necessary and sufficient conditions on adversary count that improve over Tverberg-based methods.
- Computing Graph Descriptors on Edge Streams
ACM Transactions on Knowledge Discovery from Data (2021)
We present streaming algorithms to compute three scalable graph descriptors from edge streams, achieving classification accuracy comparable to state-of-the-art methods while using only 25% of the memory, scaling to graphs with millions of edges.
- SEMOUR: A Scripted Emotional Speech Repository for Urdu
Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (2021)
We introduce SEMOUR, the first scripted emotion-tagged speech database in Urdu with 15,040 instances recorded by professional actors, enabling state-of-the-art 92% accuracy on speech emotion recognition.
- Resilient Distributed Diffusion for Multi-Robot Systems Using Centerpoint
Robotics: Science and Systems XVI (2020)
We propose centerpoint-based aggregation for distributed diffusion in multi-robot systems, proving resilient convergence when fewer than n/(d+1) neighbors are adversarial — a condition under which coordinate-wise and geometric median rules provably fail.
- Graph-Theoretic Approach for Increasing Participation in Networks With Assorted Resources
IEEE Transactions on Network Science and Engineering (2020)
We introduce the (r,s)-core model for resource-sharing networks and the anchor selection problem to prevent cascading withdrawal, classifying solvability as polynomial, NP-complete, or inapproximable depending on network parameters.
- Improving Network Robustness through Edge Augmentation While Preserving Strong Structural Controllability
2020 American Control Conference (ACC) (2020)
We formulate edge augmentation with distance-preserving constraints to jointly increase network robustness and maintain strong structural controllability, showing that optimal solutions form clique chains.
- A Simpler NP-Hardness Proof for Familial Graph Compression
ArXiv (2020)
We present a simpler proof of the NP-hardness of the Familial Graph Compression problem.
- Resilient Vector Consensus in Multi-Agent Networks Using Centerpoints
2020 American Control Conference (ACC) (2020)
We replace Tverberg partitions with centerpoints in resilient vector consensus algorithms, improving resilience guarantees and providing a complete characterization of necessary and sufficient conditions on adversary count.
- Interplay Between Resilience and Accuracy in Resilient Vector Consensus in Multi-Agent Networks
2020 59th IEEE Conference on Decision and Control (CDC) (2020)
We show that resilience in multidimensional consensus can be traded for accuracy via dimensionality projection, and propose a bounded consensus algorithm with formal guarantees on both resilience and convergence accuracy.
- Resilient multi-robot target pursuit
Proceedings of the 7th Symposium on Hot Topics in the Science of Security (2020)
We propose centerpoint-based aggregation for distributed LMS diffusion in multi-robot pursuit tasks, proving resilient convergence to the true target state under Byzantine attacks where median-based rules fail.
- Strong Structural Controllability of Diffusively Coupled Networks: Comparison of Bounds Based on Distances and Zero Forcing
2020 59th IEEE Conference on Decision and Control (CDC) (2020)
We compare distance-based and zero-forcing-based lower bounds on the strong structurally controllable subspace and present a combined bound that is always at least as tight as either approach individually.
- Resilient Distributed Vector Consensus Using Centerpoints
(2020)
We characterize safe points for resilient vector consensus as centerpoints, deriving tight necessary and sufficient conditions on adversary count that improve over Tverberg-based approaches in both resilience and computational efficiency.
- Tradeoff Between Controllability and Robustness in Diffusively Coupled Networks
IEEE Transactions on Control of Network Systems (2020)
We identify a fundamental conflict between robustness and controllability in linear dynamical networks, showing that maximizing robustness (via Kirchhoff index) increases the number of leaders required for strong structural controllability.
- Computation of the Distance-Based Bound on Strong Structural Controllability in Networks
IEEE Transactions on Automatic Control (2019)
We develop polynomial-time exact and linearithmic approximation algorithms for computing the distance-based lower bound on the strongly structurally controllable subspace using distance-to-leader vector sequences.
- Structural Robustness to Noise in Consensus Networks: Impact of Average Degrees and Average Distances
2019 IEEE 58th Conference on Decision and Control (CDC) (2019)
We derive tight bounds on structural robustness to noise in consensus networks using average node degree and distance, and show that random regular graphs typically achieve near-optimal robustness among graphs of the same size and degree.
- On the Trade-off Between Controllability and Robustness in Networks of Diffusively Coupled Agents
2019 American Control Conference (ACC) (2019)
We study the tension between Kirchhoff-index robustness and strong structural controllability in diffusively coupled networks, identifying maximally robust networks and determining the minimum leader sets needed for complete controllability.
- On the Computation of the Distance-based Lower Bound on Strong Structural Controllability in Networks
2019 IEEE 58th Conference on Decision and Control (CDC) (2019)
We provide polynomial-time exact and linearithmic approximation algorithms for computing distance-based lower bounds on strong structural controllability, outperforming zero-forcing bounds especially in partially controllable networks.
- Structural Robustness to Noise in Consensus Networks: Impact of Degrees and Distances, Fundamental Limits, and Extremal Graphs
IEEE Transactions on Automatic Control (2019)
We establish tight bounds on structural robustness to noise using degree and distance, prove a fundamental sparsity-robustness tradeoff, and show random k-regular graphs are near-optimal among graphs of the same size and average degree.
- Affine-Invariant Outlier Detection and Data Visualization
None (2017)
We develop algorithms for Ray Shooting Depth, an affine-invariant statistical ranking of 2D data, along with an open-source visualization tool with applications to outlier detection and distribution estimation.
- Scalable Approximation Algorithm for Network Immunization
None (2017)
We formulate network immunization as budgeted combinatorial optimization and design a spectral greedy algorithm that outperforms state-of-the-art methods in epidemic containment on large real-world networks.
- Spectral Methods for Immunization of Large Networks
ArXiv (2017)
We use spectral graph theory to define node relevance and design a scalable immunization algorithm that outperforms existing methods in epidemic containment, with theoretical guarantees on runtime and approximation quality.
- k-Centerpoints Conjectures for Pointsets in ℝd
Int. J. Comput. Geom. Appl. (2015)
We introduce k-centerpoints, unifying the classical centerpoint theorem and the ray-shooting theorem, prove equivalence of affine and topological variants in R^2, and derive the first non-trivial bounds in higher dimensions.
- SOME RESULTS IN COMPUTATIONAL AND COMBINATORIAL GEOMETRY
(2014)
PhD thesis presenting algorithms for hitting sets in convex ranges, ray-shooting depth, and related problems in discrete and computational geometry.
- Ray-Shooting Depth in R 2 - Algorithms and Applications
(2011)
We study computational aspects of ray-shooting depth in 2D, presenting algorithms and complexity results, and advocate its use as an affine-invariant statistical depth measure with applications in data analysis.
- Acceleration of Smith-Waterman using Recursive Variable Expansion
2008 11th EUROMICRO Conference on Digital System Design Architectures, Methods and Tools (2008)
We apply recursive variable expansion to the Smith-Waterman sequence alignment algorithm, exposing additional parallelism and achieving a minimum 400x speedup over serial execution, outperforming all prior published methods.
- Acceleration of Biological Sequence Alignment using Recursive Variable Expansion
(2007)
We apply partial recursive variable expansion to the Needleman-Wunsch global alignment algorithm for FPGA implementation, exposing more parallelism than existing methods and achieving a 1.55x speedup.
- Efficient Approximation Algorithms for String Kernel Based Sequence Classification
(2017)
We develop efficient algorithms to approximate the mismatch string kernel, enabling larger k and m values for sequence classification with theoretical guarantees, achieving higher accuracy on biological and music datasets.
- Circuit design completion using graph neural networks
Neural Computing and Applications (2023)
- Sequential Graph Neural Networks for Source Code Vulnerability Identification
ArXiv (2023)
- Learning-Based Heuristic for Combinatorial Optimization of the Minimum Dominating Set Problem using Graph Convolutional Networks
ArXiv (2023)
- NeuroGraph: Benchmarks for Graph Machine Learning in Brain Connectomics
ArXiv (2023)
- On augmenting topological graph representations for attributed graphs
Appl. Soft Comput. (2023)
- Resilient distributed vector consensus using centerpoint
Automatica (2022)
- Resilient Multi-agent Reinforcement Learning Using Medoid and Soft-medoid Based Aggregation
2022 IEEE International Conference on Assured Autonomy (ICAA) (2022)
- Data driven smart policing: A novel road distance-based k-median model for optimal substation placement
Comput. Hum. Behav. (2022)
- What a drag! Streamlining the UAV design process with design grammars and drag surrogates
(2022)
- Speech emotion recognition for the Urdu language
Language Resources and Evaluation (2022)
- Attack-Resilient Multi-Agent Flocking Control Using Graph Neural Networks
2022 30th Mediterranean Conference on Control and Automation (MED) (2022)
- Strong structural controllability of networks: Comparison of bounds using distances and zero forcing
Autom. (2022)
- Leader Selection for Strong Structural Controllability in Networks using Zero Forcing Sets
2022 American Control Conference (ACC) (2022)
- Byzantine Resilient Distributed Learning in Multirobot Systems
IEEE Transactions on Robotics (2022)
- Edge Augmentation With Controllability Constraints in Directed Laplacian Networks
IEEE Control Systems Letters (2021)
- Resilient Distributed Vector Consensus Using Centerpoint
(2021)
- DGSD: Distributed graph representation via graph statistical properties
Future Gener. Comput. Syst. (2021)
- Seymour's Second Neighborhood Conjecture for 6-antitransitive digraphs
Discret. Appl. Math. (2021)
- NetKI: A kirchhoff index based statistical graph embedding in nearly linear time
Neurocomputing (2021)
- Optimal school site selection in Urban areas using deep neural networks
Journal of Ambient Intelligence and Humanized Computing (2021)
- Computing Graph Descriptors on Edge Streams
ACM Transactions on Knowledge Discovery from Data (2021)
- Leveraging Deep Learning and SNA approaches for Smart City Policing in the Developing World
Int. J. Inf. Manag. (2021)
- SEMOUR: A Scripted Emotional Speech Repository for Urdu
Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (2021)
- Optimal school site selection in Urban areas using deep neural networks
Journal of Ambient Intelligence and Humanized Computing (2021)
- Tweet Coupling: a social media methodology for clustering scientific publications
Scientometrics (2020)
- Interpretable multi-scale graph descriptors via structural compression
Inf. Sci. (2020)
- Resilient Distributed Diffusion for Multi-Robot Systems Using Centerpoint
Robotics: Science and Systems XVI (2020)
- Graph-Theoretic Approach for Increasing Participation in Networks With Assorted Resources
IEEE Transactions on Network Science and Engineering (2020)
- Improving Network Robustness through Edge Augmentation While Preserving Strong Structural Controllability
2020 American Control Conference (ACC) (2020)
- A Simpler NP-Hardness Proof for Familial Graph Compression
ArXiv (2020)
- Resilient Vector Consensus in Multi-Agent Networks Using Centerpoints
2020 American Control Conference (ACC) (2020)
- Interplay Between Resilience and Accuracy in Resilient Vector Consensus in Multi-Agent Networks
2020 59th IEEE Conference on Decision and Control (CDC) (2020)
- Resilient multi-robot target pursuit
Proceedings of the 7th Symposium on Hot Topics in the Science of Security (2020)
- Strong Structural Controllability of Diffusively Coupled Networks: Comparison of Bounds Based on Distances and Zero Forcing
2020 59th IEEE Conference on Decision and Control (CDC) (2020)
- Resilient Distributed Vector Consensus Using Centerpoints
(2020)
- Estimating Descriptors for Large Graphs
Advances in Knowledge Discovery and Data Mining (2020)
- Tradeoff Between Controllability and Robustness in Diffusively Coupled Networks
IEEE Transactions on Control of Network Systems (2020)
- Tweet Coupling: a social media methodology for clustering scientific publications
Scientometrics (2020)
- Computation of the Distance-Based Bound on Strong Structural Controllability in Networks
IEEE Transactions on Automatic Control (2019)
- Combinatorial Trace Method for Network Immunization
Inf. Sci. (2019)
- Structural Robustness to Noise in Consensus Networks: Impact of Average Degrees and Average Distances
2019 IEEE 58th Conference on Decision and Control (CDC) (2019)
- On the Trade-off Between Controllability and Robustness in Networks of Diffusively Coupled Agents
2019 American Control Conference (ACC) (2019)
- Influential tweeters in relation to highly cited articles in altmetric big data
Scientometrics (2019)
- Influential tweeters in relation to highly cited articles in altmetric big data
Scientometrics (2019)
- On the Computation of the Distance-based Lower Bound on Strong Structural Controllability in Networks
2019 IEEE 58th Conference on Decision and Control (CDC) (2019)
- Structural Robustness to Noise in Consensus Networks: Impact of Degrees and Distances, Fundamental Limits, and Extremal Graphs
IEEE Transactions on Automatic Control (2019)
- Affine-Invariant Outlier Detection and Data Visualization
None (2017)
- Deep Stylometry and Lexical & Syntactic Features Based Author Attribution on PLoS Digital Repository
None (2017)
- Scalable Approximation Algorithm for Network Immunization
None (2017)
- Spectral Methods for Immunization of Large Networks
ArXiv (2017)
- k-Centerpoints Conjectures for Pointsets in ℝd
Int. J. Comput. Geom. Appl. (2015)
- Network Decontamination with a Single Agent
Graphs and Combinatorics (2015)
- SOME RESULTS IN COMPUTATIONAL AND COMBINATORIAL GEOMETRY
(2014)
- Network Decontamination with a Single Agent
Graphs and Combinatorics (2013)
- Ray-Shooting Depth in R 2 - Algorithms and Applications
(2011)
- Ray-Shooting Depth: Computing Statistical Data Depth of Point Sets in the Plane
None (2011)
- Acceleration of Smith-Waterman using Recursive Variable Expansion
2008 11th EUROMICRO Conference on Digital System Design Architectures, Methods and Tools (2008)
- Acceleration of Biological Sequence Alignment using Recursive Variable Expansion
(2007)
- Efficient Approximation Algorithms for String Kernel Based Sequence Classification
(2017)
Projects
Graph Machine Learning for Brain Connectomics
Byzantine-Resilient Consensus and Distributed Learning
Learning-Based Heuristics for NP-Hard Graph Problems
Zero Forcing, Edge Augmentation, and Structural Controllability
Streaming Algorithms for Graph Descriptors on Large Networks
Accelerating UAV Design Space Exploration with Graph Neural Networks
Graph Machine Learning for Brain Connectomics
Byzantine-Resilient Consensus and Distributed Learning
Learning-Based Heuristics for NP-Hard Graph Problems
Zero Forcing, Edge Augmentation, and Structural Controllability
Streaming Algorithms for Graph Descriptors on Large Networks
Accelerating UAV Design Space Exploration with Graph Neural Networks
Vitæ
Full Resume in PDF.
-
LUMS 2026 - nowAssociate Professor
Department of Computer Science -
Vanderbilt University 2021 - 2023Research Assistant Professor
-
Information Technology University 2021Associate Professor - got promoted
-
Information Technology University 2014 - 2026Assistant / Associate Professor
Theory Group
Chairperson, Department of Computer Science (until 2025) -
Bloomberg L.P. 2013Research Intern
New York, NY -
Los Alamos National Laboratory 2011Research Intern
Los Alamos, NM -
Rutgers University 2007 - 2014Ph.D. Student
Division of Computer Science
Fulbright Scholarship
Rutgers Honors Fellow 2011-12