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Postdoctoral Research Associate at the University of Virginia working at the intersection of scientific machine learning, AI for cyber-physical systems, digital twins, and resilient infrastructure. My research combines graph neural networks, neural operators, physics-informed learning, multi-fidelity modeling, generative AI, and uncertainty quantification for forecasting, optimization, and decision support in flood, power, and transportation systems.
Research Interests
- AI for cyber-physical systems and digital twins
- Physics-informed machine learning and neural operators
- Generative AI for resilient infrastructure design
- Multi-fidelity modeling for scientific machine learning
- Uncertainty quantification and trustworthy AI
Core Strengths
- Graph neural networks, neural operators, and physics-informed ML
- Multi-fidelity learning for data-efficient surrogate modeling
- Large-scale geospatial and connected-vehicle data analytics
- Scientific computing workflows across simulation, uncertainty, and optimization
- Python, PyTorch, Spark, HPC, and research-grade software development
Academic Appointment
- Postdoctoral Research Associate, University of Virginia Environmental Institute, 2025-Present
- Joint departmental research appointment spanning Computer Science and Civil & Environmental Engineering
- Current focus: optimal joint expansion, transition, and resilience planning of power systems responsive to climate change and uncertainty
- Ongoing research directions include uncertainty-aware flood-state reconstruction, projected diffusion policies for constrained stormwater control, inference-driven flood sensing and sensor design, and diffusion-based optimization for resilient power systems
- Also contributing to broader graph-learning research across transportation, power, water, and structural infrastructure systems
- Advisors: Negin Alemazkoor, Ferdinando Fioretto, and Somayeh Asadi
Research Experience
- Research Assistant, University of Virginia, 2022-2025
- Developed multi-fidelity graph neural networks, geometry-informed neural operators, and physics-informed models for flood forecasting, PDE surrogate modeling, and power flow analysis
- Contributed HydroGraphNet to NVIDIA PhysicsNeMo, including model implementation, configuration, documentation, and testing
- Analyzed more than 15.6 billion connected-vehicle events and roughly 2 TB of data to identify multimodal safety patterns and hotspots
- Served as lead student researcher on multiple funded projects spanning multimodal safety, cyberinfrastructure, and uncertainty quantification
- Transportation System Risk Analyzer, Sharif University of Technology, 2021-2022
- Led work on bridge retrofit prioritization in Tehran’s highway system using probabilistic models, simulation, and machine-learning-based vulnerability prediction
- Collaborated with local stakeholders on transportation resilience planning
- Graduate Researcher, Infrastructure Resilience and Uncertainty, Sharif University of Technology, 2018-2020
- Developed probabilistic models for transportation-system resilience and community recovery analysis
Education
- Ph.D. in Civil Engineering, University of Virginia, 2022-2025
- GPA: 4.0/4.0
- Dissertation focused on graph-based learning for flood forecasting, data-efficient modeling, physical consistency, and domain-adaptive generalization
- Advisor: Negin Alemazkoor
- M.Sc. in Civil Engineering (Earthquake Engineering), Sharif University of Technology, 2017-2020
- GPA: 4.0/4.0
- Thesis topic: probabilistic modeling of transportation infrastructure and its interdependencies in community resilience analysis
- Advisor: Mojtaba Mahsuli
- B.Sc. in Civil Engineering, Sharif University of Technology, 2013-2017
- GPA: 3.73/4.0
- Advisor: Homayoon Estekanchi
Awards and Honors
- Olsen Graduate Fellowship, University of Virginia
- CEE Outstanding Graduate Research Award, University of Virginia
- Link Lab Early-Stage Research Award, University of Virginia
- Link Lab Student Flash Talk Award, University of Virginia
- Scholarship and direct admission to M.Sc. studies at Sharif University of Technology
- Ranked in the top 0.2% of more than 252,000 participants in the Iranian National University Entrance Exam
- Multimodal Safety and Equity Evaluation using Big Connected Vehicle Data, Virginia Department of Transportation, $50,000, 2024
- Graph Neural Networks for Smart Infrastructure Systems with Heterogeneous IoT Sensors, Commonwealth Cyber Initiative, $100,000, 2024
- Risk and Uncertainty Quantification using Multi-fidelity CHEST Devices, CHEST, $50,000, 2023
- Probabilistic Modeling of Transportation Infrastructure in Community Resilience Analysis, INSF, $30,000, 2021
Certifications and Training
- 5-Day Gen AI Intensive, Google, 2024
- Generative AI with Large Language Models, Coursera / DeepLearning.AI, 2023
- Building Transformer-Based Natural Language Processing Applications, NVIDIA, 2023
- Deep Learning Specialization, Coursera / DeepLearning.AI, 2022-2023
- Build Better Generative Adversarial Networks (GANs), Coursera, 2022
- Fundamentals of Reinforcement Learning, Coursera, 2022
- Machine Learning, Coursera, 2021
Technical Skills
- Machine Learning and AI
- Graph neural networks, neural operators, physics-informed ML, domain adaptation, uncertainty quantification
- Generative modeling, diffusion models, GANs, and surrogate modeling for PDE-driven systems
- Frameworks and Tools
- Python, C++, R, MATLAB, PyTorch, TensorFlow, scikit-learn, NumPy, Pandas, SciPy
- Apache Spark, CUDA, Git/GitHub, Jupyter, LaTeX, ArcGIS, Linux, HPC environments
- Applications
- PDE surrogate modeling, flood forecasting, probabilistic power flow, and transportation-system resilience
- Large-scale geospatial analytics, connected-vehicle data analysis, uncertainty-aware modeling, constrained optimization, and sensor-informed decision support
Selected Professional Signals
- 9 peer-reviewed publications and 2 under-review manuscripts in the updated academic CV
- 10 presentations across venues including AGU, EMI, ICOSSAR, UVA Link Lab, and related research forums
- Reviewer for journals and conferences spanning scientific ML, cyber-physical systems, infrastructure, and transportation
- Mentored undergraduate and graduate students across UVA and Sharif University of Technology
- Current postdoctoral work spans both methodological research and systems-level infrastructure AI, including sensing, control, optimization, and cross-domain graph learning
Publications
Peer-Reviewed Publications
Taghizadeh, M., & Mahsuli, M. (2025). "Probabilistic Modeling of the Risk and Recovery of the Transportation Systems for Community Resilience Analysis." Reliability Engineering & System Safety.
Taghizadeh, M., Zandsalimi, Z., Nabian, M. A., Goodall, J. L., & Alemazkoor, N. (2025). "FloodForecaster: A Domain-Adaptive Geometry-Informed Neural Operator Framework for Rapid Flood Forecasting." Journal of Hydrology.
Taghizadeh, M., Zandsalimi, Z., Shafiee-Jood, M., & Alemazkoor, N. (2025). "Multi-Fidelity Graph Neural Networks for Efficient and Accurate Flood Hazard Mapping." Environmental Modelling & Software.
Taghizadeh, M., Zandsalimi, Z., Nabian, M. A., Shafiee-Jood, M., & Alemazkoor, N. (2025). "Interpretable Physics-Informed Graph Neural Networks for Flood Forecasting." Computer-Aided Civil and Infrastructure Engineering.
Taghizadeh, M., Nabian, M. A., & Alemazkoor, N. (2024). "Multi-fidelity Physics-informed Generative Adversarial Network for Solving Partial Differential Equations." ASME Journal of Computing and Information Science in Engineering.
Taghizadeh, M., Khayambashi, K., Hasnat, M. A., & Alemazkoor, N. (2024). "Multi-fidelity Graph Neural Networks for Efficient Power Flow Analysis under High-Dimensional Demand and Renewable Generation Uncertainty." Electric Power Systems Research.
Taghizadeh, M., Nabian, M. A., & Alemazkoor, N. (2024). "Multifidelity Graph Neural Networks for Efficient and Accurate Mesh-based Partial Differential Equations Surrogate Modeling." Computer-Aided Civil and Infrastructure Engineering.
Taghizadeh, M., Mahsuli, M., & Poorzahedy, H. (2023). "Probabilistic Framework for Evaluating the Seismic Resilience of Transportation Systems during Emergency Medical Response." Reliability Engineering & System Safety.
Taghizadeh, M., Xiu, D., & Alemazkoor, N. (2023). "Improving Accuracy and Computational Efficiency of Optimal Design of Experiment via Greedy Backward Approach." International Journal for Uncertainty Quantification.
Under Review
Zandsalimi, Z., Taghizadeh, M., Lynn, S. L., Goodall, J. L., Shafiee-Jood, M., & Alemazkoor, N. (under review). "End-to-End Graph Neural Networks for Real-Time Hydraulic Prediction in Stormwater Systems." Hydrology and Earth System Sciences.
Anand, H., Khayambashi, K., Zandsalimi, Z., Taghizadeh, M., Hasnat, M. A., & Alemazkoor, N. (under review). "Applications of Graph Neural Networks in Civil Infrastructures: A Review on Transportation, Power, Water, and Structural Systems." Engineering Applications of Artificial Intelligence.
Teaching and Mentoring
- Teaching support for
CE 3000 Civil Engineering Systems Analysis - Teaching support for
SYS 6582 Introduction to Uncertainty Quantification - Mentored students across UVA and Sharif University of Technology, including computer science and civil engineering trainees
Service
- Reviewer for
Engineering Applications of Artificial Intelligence, Scientific Reports, Engineering Structures, Environmental Modelling & Software, Journal of Supercomputing, Separation and Purification Technology, Optimization and Engineering, and Journal of Machine Learning for Modeling and Computing - Reviewer for
ACM/IEEE International Conference on Cyber-Physical Systems and IEEE International Intelligent Transportation Systems Conference - Member of
ASCE, EMI, ITE, and IRSA
