About

Henning S. Mortveit is an associate professor in the Department of Systems and Information Engineering and the UVA Biocomplexity Institute at the University of Virginia. He received his doctorate in mathematics from the Norwegian University of Science and Technology in 2000. His research interests include the theory, modeling, and simulation of massively interacting systems, computational architectures for systems of systems, and the development of theories and formalisms for capturing, analyzing, and simulating networked systems, including applications in machine learning. Prior to joining UVA, he was a technical staff member at Los Alamos National Laboratory and an associate professor at Virginia Tech.

Research topics

• Computer Security
• Computer Science
• Virology
• Medicine
• Marketing
• International trade
• Transport engineering
• Business
• Geography
• Engineering

Selected publications

• Towards High Resolution Probabilistic Coastal Inundation Forecasting from Sparse Observations

  Proceedings of the AAAI Conference on Artificial Intelligence · 2026-03-14

  articleOpen access

  Coastal flooding poses increasing threats to communities worldwide, necessitating accurate and hyper-local inundation forecasting for effective emergency response. However, real-world deployment of forecasting systems is often constrained by sparse sensor networks, where only a limited subset of locations may have sensors due to budget constraints. To approach this challenge, we present Diff-Sparse, a masked conditional diffusion model designed for probabilistic coastal inundation forecasting from sparse sensor observations. Diff-Sparse primarily utilizes the inundation history of a location and its neighboring locations from a context time window as spatiotemporal context. The fundamental challenge of spatiotemporal prediction based on sparse observations in the context window is addressed by introducing a novel masking strategy during training. Digital elevation data and temporal co-variates are utilized as additional spatial and temporal contexts, respectively. A convolutional neural network and a conditional UNet architecture with cross-attention mechanism are employed to capture the spatiotemporal dynamics in the data. We trained and tested Diff-Sparse on coastal inundation data from the Eastern Shore of Virginia and systematically assessed the performance of Diff-Sparse across different sparsity levels (0%, 50%, 95% missing observations). Our experiment results show that Diff-Sparse achieves upto 62% improvement in terms of two forecasting performance metrics compared to existing methods, at 95% sparsity level. Moreover, our ablation studies reveal that digital elevation data becomes more useful at high sparsity levels compared to temporal co-variates.

  PublisherOA PDFDOI

• Pandemics in Silico: Scaling Agent-Based Simulations on Realistic Social Contact Networks

  2025-06-03

  article

  Preventing the spread of infectious diseases requires implementing interventions at various levels of government and evaluating the potential impact and efficacy of those preemptive measures. Agent-based modeling can be used for detailed studies of the spread of such diseases in the presence of possible interventions. The computational cost of modeling epidemic diffusion through large social contact networks necessitates the use of parallel algorithms and resources in order to achieve quick turnaround times. In this work, we present Loimos, a scalable parallel framework for simulating epidemic diffusion. Loimos uses a hybrid of time-stepping and discrete event simulation to model disease spread, and is implemented on top of Charm++, an asynchronous, many-task runtime that enables over-decomposition and adaptive overlap of computation and communication. We demonstrate that Loimos is able to achieve significant speedups while scaling to large core counts. In particular, Loimos is able to simulate 200 days of a COVID19 outbreak on a digital twin of California in about 42 seconds, for an average of 4.6 billion traversed edges per second (TEPS), using 4096 cores on Perlmutter at NERSC.

  PublisherDOI

• DIMPLES: Distributed Influence Maximization for Pandemic pLanning on Exascale Systems

  2025-06-08

  article
  PublisherDOI

• Towards High Resolution Probabilistic Coastal Inundation Forecasting from Sparse Observations

  arXiv (Cornell University) · 2025-05-08 · 1 citations

  preprintOpen access

  Coastal flooding poses increasing threats to communities worldwide, necessitating accurate and hyper-local inundation forecasting for effective emergency response. However, real-world deployment of forecasting systems is often constrained by sparse sensor networks, where only a limited subset of locations may have sensors due to budget constraints. To approach this challenge, we present DIFF -SPARSE, a masked conditional diffusion model designed for probabilistic coastal inundation forecasting from sparse sensor observations. DIFF -SPARSE primarily utilizes the inundation history of a location and its neighboring locations from a context time window as spatiotemporal context. The fundamental challenge of spatiotemporal prediction based on sparse observations in the context window is addressed by introducing a novel masking strategy during training. Digital elevation data and temporal co-variates are utilized as additional spatial and temporal contexts, respectively. A convolutional neural network and a conditional UNet architecture with cross-attention mechanism are employed to capture the spatiotemporal dynamics in the data. We trained and tested DIFF -SPARSE on coastal inundation data from the Eastern Shore of Virginia and systematically assessed the performance of DIFF -SPARSE across different sparsity levels 0%, 50%, 95% missing observations. Our experiment results show that DIFF -SPARSE achieves upto 62% improvement in terms of two forecasting performance metrics compared to existing methods, at 95% sparsity level. Moreover, our ablation studies reveal that digital elevation data becomes more useful at high sparsity levels compared to temporal co-variates.

  PublisherOA PDFDOI

• Hazard Function Guided Agent-Based Models: A Case Study of Return Migration from Poland to Ukraine

  2025-09-01

  article

  The Russian invasion of Ukraine in February 2022 has led to the largest forced migration crisis in Europe since World War II, with millions displaced both internally and internationally. Among the displaced, approximately 4.2 million individuals have returned, highlighting the significance of return migration as a critical phase in the migration continuum. Existing studies on return migration are limited in scope, relying on survey-based approaches that suffer from demographic bias, lack of validation against ground truth, and inability to account for uncertainty. We propose a novel computational framework for modeling the return of conflict-induced migrants, using agent-based models (ABMs) and their surrogates. These models are grounded in hazard functions and account for sociopolitical contexts. Our proposed ABMs outperform baseline methods in estimating return migration from Poland to Ukraine by at least 42% and by as much as 57% in terms of normalized root mean squared error (NRMSE). Further, to illustrate the utility of such models for policymakers, we conduct two case studies that estimate the duration of displacement and characterize the demographic breakdown among the returnees.

  PublisherDOI

• Theoretical Note: On the Practical Uses of Mathematical Theory for Attitude Research

  ArXiv.org · 2025-09-17

  preprintOpen accessSenior author

  In attitude theory, formal theoretical predictions come largely from the simulation of computational models. We argue that to push attitude theory further, we should employ mathematical analysis/analytic methods alongside of computational simulation, something that other sciences and engineering consider standard practice. Our work first attempts to portray the complementary nature of mathematical analysis along side of computational simulation using as an example the Causal Attitude Network model of attitudes (Dalege et al., 2016). We then introduce a mathematical theory, Graph Dynamical Systems (GDS), as a broad theoretical framework for network models of attitudes. We illustrate the use of GDS, in the context of the Attitudes as Constraint Satistfaction (ACS) theory of attitude dynamics (Monroe & Read, 2008), as a generator of precise, quantitative theoretical predictions. We conclude by pointing out the value of improved attitude theory for the so-called replication crisis in psychology. KEYWORDS: attitudes, neural networks, dynamical systems, psychological networks

  PublisherOA PDFDOI

• An agent-based framework to study forced migration: A case study of Ukraine

  PNAS Nexus · 2024-02-29 · 8 citations

  articleOpen access

  The ongoing Russian aggression against Ukraine has forced over eight million people to migrate out of Ukraine. Understanding the dynamics of forced migration is essential for policy-making and for delivering humanitarian assistance. Existing work is hindered by a reliance on observational data which is only available well after the fact. In this work, we study the efficacy of a data-driven agent-based framework motivated by social and behavioral theory in predicting outflow of migrants as a result of conflict events during the initial phase of the Ukraine war. We discuss policy use cases for the proposed framework by demonstrating how it can leverage refugee demographic details to answer pressing policy questions. We also show how to incorporate conflict forecast scenarios to predict future conflict-induced migration flows. Detailed future migration estimates across various conflict scenarios can both help to reduce policymaker uncertainty and improve allocation and staging of limited humanitarian resources in crisis settings.

  PublisherOA PDFDOI

• A Scalable Game-theoretic Approach to Urban Evacuation Routing and Scheduling

  2024-12-15

  article

  Evacuation planning is an essential part of disaster management where the goal is to relocate people under imminent danger to safety. However, finding jointly optimal evacuation routes and a schedule that minimizes the average evacuation time or evacuation completion time, is a computationally hard problem. As a result, large-scale evacuation routing and scheduling continues to be a challenge. In this paper, we present a game-theoretic approach to tackle this problem. We start by formulating a strategic routing and scheduling game, named the Evacuation Game: Routing and Scheduling (EGRES), where players choose their route and time of departure. We show that: (i) every instance of EGRES has at least one pure strategy Nash equilibrium, and (ii) an optimal outcome in an instance will always be an equilibrium in that instance. We then provide bounds on how bad an equilibrium can be compared to an optimal outcome. Additionally, we present a polynomial-time algorithm, the Sequential Action Algorithm (SAA), for finding equilibria in a given instance under a special condition. We use Virginia Beach City in Virginia, and Harris County in Houston, Texas as study areas and construct two EGRES instances. Our results show that, by utilizing SAA, we can efficiently find equilibria in these instances that have social objective close to the optimal value.

  PublisherDOI

• Pandemics In Silico: Scaling an Agent-Based Simulation on Realistic Social Contact Networks

  arXiv (Cornell University) · 2024-01-16 · 1 citations

  preprintOpen access

  Preventing the spread of infectious diseases requires implementing interventions at various levels of government and evaluating the potential impact and efficacy of those preemptive measures. Agent-based modeling can be used for detailed studies of epidemic diffusion and possible interventions. Modeling of epidemic diffusion in large social contact networks requires the use of parallel algorithms and resources. In this work, we present Loimos, a scalable parallel framework for simulating epidemic diffusion. Loimos uses a hybrid of time-stepping and discrete-event simulation to model disease spread, and is implemented on top of an asynchronous, many-task runtime. We demonstrate that Loimos is to able to achieve significant speedups while scaling to large core counts. In particular, Loimos is able to simulate 200 days of a COVID-19 outbreak on a digital twin of California in about 42 seconds, for an average of 4.6 billion traversed edges per second (TEPS), using 4096 cores on Perlmutter at NERSC.

  PublisherOA PDFDOI

• Potential impact of annual vaccination with reformulated COVID-19 vaccines: Lessons from the US COVID-19 scenario modeling hub

  UNC Libraries · 2024-05-04

  articleOpen access

  Background AU Coronavirus Disease 2019 (COVID-19) continues to cause :significant hospitalizations and deaths in the United States. Its continued burden and the impact of annually reformulated vaccines remain unclear. Here, we present projections of COVID-19 hospitalizations and deaths in the United States for the next 2 years under 2 plausible assumptions about immune escape (20% per year and 50% per year) and 3 possible CDC recommendations for the use of annually reformulated vaccines (no recommendation, vaccination for those aged 65 years and over, vaccination for all eligible age groups based on FDA approval). Methods and findings The COVID-19 Scenario Modeling Hub solicited projections of COVID-19 hospitalization and deaths between April 15, 2023 and April 15, 2025 under 6 scenarios representing the intersection of considered levels of immune escape and vaccination. Annually reformulated vaccines are assumed to be 65% effective against symptomatic infection with strains circulating on June 15 of each year and to become available on September 1. Age- and state-specific coverage in recommended groups was assumed to match that seen for the first (fall 2021) COVID-19 booster. State and national projections from 8 modeling teams were ensembled to produce projections for each scenario and expected reductions in disease outcomes due to vaccination over the projection period. From April 15, 2023 to April 15, 2025, COVID-19 is projected to cause annual epidemics peaking November to January. In the most pessimistic scenario (high immune escape, no vaccination recommendation), we project 2.1 million (90% projection interval (PI) [1,438,000, 4,270,000]) hospitalizations and 209,000 (90% PI [139,000, 461,000]) deaths, exceeding pre-pandemic mortality of influenza and pneumonia. In high immune escape scenarios, vaccination of those aged 65+ results in 230,000 (95% confidence interval (CI) [104,000, 355,000]) fewer hospitalizations and 33,000 (95% CI [12,000, 54,000]) fewer deaths, while vaccination of all eligible individuals results in 431,000 (95% CI: 264,000–598,000) fewer hospitalizations and 49,000 (95% CI [29,000, 69,000]) fewer deaths. Conclusions COVID-19 is projected to be a significant public health threat over the coming 2 years. Broad vaccination has the potential to substantially reduce the burden of this disease, saving tens of thousands of lives each year.

  PublisherDOI

Frequent coauthors

• Madhav Marathe

  218 shared

• Anil Vullikanti

  University of Virginia

  121 shared

• Samarth Swarup

  University of Virginia

  109 shared

• Abhijin Adiga

  Warwick Hospital

  98 shared

• Jiangzhuo Chen

  University of Virginia

  95 shared

• Stephen Eubank

  University of Virginia

  90 shared

• Bryan Lewis

  Biocom

  88 shared

• Srinivasan Venkatramanan

  Biocom

  78 shared

Education

• Ph.D.

  Norwegian University of Science and Technology

  2000

Awards & honors

• Best paper award at the 39th ACM International Conference on…
• Honorable mention award at AAMAS 2020