I joined the Department of Statistics at the University of Manitoba in 2024 as an Assistant Professor. I have recently completed an interdisciplinary Ph.D. in Mathematical and Statistical Modelling from Wilfrid Laurier University (WLU). My Ph.D. dissertation was titled ``Multiscale Modelling of Brain Networks and the Analysis of Dynamic Processes in Neurodegenerative Disorders". Before that, I possess an MSc degree in Applicable Mathematics from the University of Greenwich, London, England.

I have always been fascinated by my mathematical studies and have a flair for the subject. I relish the challenge of problem-solving that mathematics provides. For me, it is an endlessly intriguing subject, as the discipline appears limitless, allowing so much scope for further study and research. My current research expertise is highly multi- and inter-disciplinary and includes vast areas in the field of applied mathematics, statistics, data science, bioinformatics, data analysis, stochastic processes and biostatistical sciences, viz., multiscale modelling of brain networks, hybrid and statistical modelling, data-driven brain networks, neuroscience, machine learning, computational statistics, high dimensional data analysis, and the analysis of dynamic processes in neurodegenerative disorders.

About

Hina Shaheen, P.h.D.

Assistant Professor
Department of Statistics
366 Machray Hall
University of Manitoba
Winnipeg MB R3T 2N2 Canada

Our Lab is dedicated to advancing the understanding and treatment of brain diseases through cutting-edge statistical analysis and computational modelling techniques. At NeuroStats Lab, we harness the power of high-dimensional machine learning algorithms and cloud computing to tackle complex challenges in neuroscience and healthcare. Our research involves developing and optimizing cutting-edge machine-learning techniques capable of handling vast amounts of data. By leveraging cloud computing resources, we accelerate computation and analysis, enabling rapid insights and advancements in understanding brain diseases. Through our interdisciplinary approach, we aim to push the boundaries of knowledge and innovation in the field, ultimately leading to improved diagnosis, treatment, and management of neurological disorders.

Research

Conference Publications

Hina Shaheen. (2026). Multiscale Modelling with Data-Driven Brain Networks: Misfolded Proteins and Astrocytic Clearance in Alzheimer’s Disease. 2026 SSC Annual Meeting in Hamilton , Abstract
Mohammad Alamgir Chowdhury*, Hina Shaheen. (2026). Familial Mutation-Aware Modeling of A-β Propagation in Alzheimer’s Disease. 2026 SSC Annual Meeting in Hamilton, , Abstract
Bowala, S*; Thavaneswaran, A; Shaheen, H; et al. (2025). On the Superiority of Data-Driven CombinedForecasts Based on Deep Learning Models. IEEE. The 49th IEEE International Conference on Computers, Software, and Applications (COMPSAC 2025), Toronto, Canada (1-10), Conference Date: 2025/7
Shaheen, H; Melnik, R. (2025). STOCHASTIC MODELLING OF PROTEIN AGGREGATION AND CLEARANCE IN BRAIN REGIONS USING A MULTISCALE FRAMEWORK FOR SIMULATING ALZHEIMER’S DISEASE. XI International Conference on Coupled Problems in Science and Engineering, Villasimius, Italy, Conference Date: 2025/6
Shaheen, H; Melnik, R. (2024). Neural Dynamics in Parkinson's Disease: Integrating Machine Learning and Stochastic Modelling with Connectomic Data. Springer International Publishing. 24th International Conference on Computational Science 2024, Malaga, Spain (46-60), Conference Date: 2024/7
Shaheen, H; Melnik, R; Singh, S. (2023). Interplay between Amyloid-beta and Calcium Dynamics in Alzheimer’s Disease: A Physics-Informed Bayesian Approach. Springer International Publishing. The VIth International AMMCS Interdisciplinary Conference Waterloo, Ontario, Canada, Waterloo, Canada, Conference Date: 2023/8 Abstract
Pal, S; Shaheen, H; Melnik, R. (2022). The Influence of Amyloid-Beta on Calcium Dynamics in Alzheimer’s Disease: A Spatio-Temporal Study. Springer International Publishing. The 22nd International Conference on Computational Science and Its Applications, Malaga, Spain (308--322), Conference Date: 2022/7
Shaheen, H; Melnik, R. (2022). Multiscale modelling of brain network with the influence of deep brain stimulation. Springer International Publishing. The Virtual 13th Conference on Dynamical Systems Applied to Biology and Natural Sciences, DSABNS 2022, Barcelo, Spain, Conference Date: 2022/2 Abstract
Shaheen, H; Melnik, R. (2021). Deep brain stimulation with a computational model for the cortex-thalamusbasal-ganglia system and network dynamics of neurological disorder.Springer International Publishing. 21st International Conference on Computational and Mathematical Methods in Science and Engineering, Waterloo, Canada, Conference Date: 2021/7 Abstract
Shaheen, H; Singh, S; Melnik, R. (2021). Mathematical modeling of calcium-mediated exosomal dynamics in neural cells. Springer International Publishing. Second International Nonlinear Dynamics Conference (NODYCON 2021), Rome, Italy (83--92), Conference Date: 2021/2
Shaheen, H; Melnik, R; Singh, S. (2021). Analysis of cortical spreading depression in brain with multiscale mathematical models. Springer International Publishing. International Conference on Applied Mathematics, Modeling and Computational Science, AMMCS 2019, Waterloo, Canada (197--207), Conference Date: 2019/8

Journal Publications

A. Herrera* ; M. Chowdhury*; H. Shaheen. (2026). Computational modelling of Parkinson’s disease: A multiscale approach with deep brain stimulation and stochastic noise. Journal of Neuroscience Methods.
Shaheen, H; Melnik, R. (2026). Bayesian modelling of amyloid-beta dynamics and astrocyte influence in Alzheimer’s disease. Journal of Neuroscience Methods. 433: 12520.
Bergmann, T*; Movshovich, M*; Shao, Y*; Ryznar, J*; Nemoga-Stout, X*; Marquez, I*; Herath, I*; Sainbhi, AS*; Vakitbilir, N*; Silvaggio, N*; Hasan, R*; Stein, KY; Shaheen, H; Moon, J; Zeiler, FA et al. (2026). Missing Data Gap Imputation Methods in Electroencephalogram (EEG) Signals: A Systematic Scoping Review. Sensors. 26(8): 2431.
Chowdhury, M*; Shaheen, H. (2026). Mutation-Aware Modeling of Amyloid-β Propagation in Alzheimer’s Disease. Trends in Neurosciences. Co-Author Submitted,
Shaheen, H; Melnik R. (2025). Brain Network Dynamics and Multiscale Modelling of Neurodegenerative Disorders: A Review. IEEE Access. 13: 33074–33100.
MacIver, A* ; Shaheen, H. (2025). Machine Learning Approaches For Alzheimer’s Disease Cognitive Decline Prediction. Neurocomputing, submitted, Elsevier.
MacIver, A* ; Shaheen, H. (2025). Modelling Alzheimer’s Protein Dynamics: A Data-Driven Integration of Stochastic Methods, Machine Learning, and Connectome Insights. Scientific Reports, Nature. arXiv:2411: 02644.
Shaheen, Hina; Melnik, Roderick. (2025). Bayesian Approaches for Revealing Complex Neural Network Dynamics in Parkinson’s Disease. Journal of Computational Science, Elsevier. 85: p.102525.
Shaheen, H; Melnik, R; Singh, S. (2024). Data-driven Stochastic Model for Quantifying the Interplay Between Amyloid-beta and Calcium Levels in Alzheimer's Disease. Statistical Analysis and Data Mining: The ASA Data Science Journal. 17(2): e11679.
Shaheen, H; Pal, S; Melnik, R. (2023). Astrocytic clearance and fragmentation of toxic proteins in Alzheimer’s disease on large-scale brain networks. Physica D: Nonlinear Phenomena. 454: 133839.
Shaheen, H; Melnik, R. (2022). Deep brain stimulation with a computational model for the cortex-thalamusbasal-ganglia system and network dynamics of neurological disorders. Computational and Mathematical Methods. 2022: Article ID 8998150.
Shaheen, H; Pal, S; Melnik, R. (2022). Multiscale co-simulation of deep brain stimulation with brain networks in neurodegenerative disorders. Brain Multiphysics. 3: 100058.
Shaheen, H; Singh, S; Melnik, R. (2021). A neuron-glial model of exosomal release in the onset and progression of Alzheimer's disease. Frontiers in Computational Neuroscience. 15: 653097.

Mohammad Alamgir Chowdhury. (2026). Familial Mutation-Aware Modeling of A-β Propagation in Alzheimer’s Disease. 2026 SSC Annual Meeting in Hamilton, hamilton, Canada
Hina Shaheen (2026). Multiscale Modelling with Data-Driven Brain Networks: Misfolded Proteins and Astrocytic Clearance in Alzheimer’s Disease. 2026 SSC Annual Meeting in Hamilton, hamilton, Canada
Hina Shaheen (2025). Data-Driven Modelling of Cerebral Physiology and Brain Network Dynamics. Seminar- Mathematics Department, University of Greenwich, London, United Kingdom,
(2025). Multiscale Modelling with Data-Driven Brain Networks: Misfolded Proteins and Astrocytic Clearance in Alzheimer’s Disease. XI International Conference on Coupled Problems in Science and Engineering, Villasimius, Sardinia, Italy
Hina Shaheen (2025). Bayesian approaches for revealing complex neural network dynamics in Parkinson’s disease. The 8th International Conference on Econometrics and Statistics (EcoSta 2025), Japan
Hina Shaheen (2025). Multiscale modelling of brain networks and the analysis of AI neurodegenerative techniques. Seminar in the Department of Mathematics at Comsats University Islamabad, Pakistan
Hina Shaheen (2025). Bayesian inference and role of astrocytes in amyloid-beta dynamics with modelling of Alzheimer's disease using clinical data. Data Science Seminar at Thompson Rivers University, Kamloops, Canada
Hina Shaheen (2024). Exploring Brain Network Dynamics: Multiscale Modelling and Its Implications in Neurodegenerative Disorders. Maud Menten Institute HQP (Highly Qualified Personnel) Summit, Vancouver, Canada
Hina Shaheen (2024). Neural Dynamics in Parkinson's Disease: Integrating Machine Learning and Stochastic Modelling with Connectomic Data. International Conference on Computational Science, 2024, Málaga, Spain
Hina Shaheen (2024). Data-driven stochastic model for quantifying the interplay between amyloid-beta and calcium levels in Alzheimer's disease. Seminar--School of Mathematical and Statistical Sciences and College of Health Professions at the University of Texas Rio Grande Valley, Region of Texas, United States of America
Hina Shaheen (2024). Data-driven stochastic model for quantifying the interplay between amyloid-beta and calcium levels in Alzheimer's disease. Talk in Data Science Seminar at Thompson River University, Kelowna, Canada
Hina Shaheen (2023). Interplay between Amyloid-beta and Calcium Dynamics in Alzheimer’s Disease: A Physics-Informed Bayesian Approach. The VI Interdisciplinary International Conference on Applied Mathematics, Modelling and Computational Science (AMMCS) 2023, Waterloo, Canada
Hina Shaheen (2022). Multiscale modelling of brain network with the influence of deep brain stimulation. 13th Conference on Dynamical Systems Applied to Biology and Natural Sciences Virtual DSABNS, February 8-11, 2022, Barcelo, Spain., Barcelo, Spain
Hina Shaheen (2021). Multiscale Modelling of Brain Networks and the Analysis of Dynamic Processes in Neurogenerative Disorders. July 2021, Wilfrid Laurier University, Ontario, Canada, Ph.D. Math Research Symposium, waterloo, Canada
Hina Shaheen (2021). Deep brain stimulation with a computational model for the cortex-thalamus-basal-ganglia system and network dynamics of neurological disorder. 21st International Conference on Computational and Mathematical Methods in Science and Engineering, Barcelo, Spain
Hina Shaheen (2021). Mathematical modelling of calcium-mediated exosomal dynamics in neural cells. The Second. International Nonlinear Dynamics Conference (NODYCON 2021), Rome, Italy
Hina Shaheen (2019). Analysis of Cortical Spreading Depression in Brain with Multiscale Mathematical Models.The V Interdisciplinary International Conference on Applied Mathematics, Modelling and Computational Science (AMMCS) 2019, Waterloo, Canada
(2018). Probabilistic numerical methods for Stochastic Partial Differential Equations. Mathematics Symposium, London, United Kingdom