MDS26 focuses on high-dimensional data analysis, scalability, and parallel algorithms, highlighting advances in mathematical and computational methods. Researchers and practitioners will engage with emerging ideas, share insights, and define the next generation of mathematics for data science.
The conference focuses on mathematical issues in imaging, expanding its reach in science and medicine. Challenges span multiple disciplines, including physics, engineering, mathematics, and data science.
The conference brings together specialists and practitioners to share insights on optimization theory, algorithms, software, and applications. Key research challenges will be addressed, fostering an ideal environment for exchange and collaboration.
The 2026 SIAM Annual Meeting will bring together experts in the fields of life sciences, mathematics of planet earth, and applied math education. Registration is now open for these joint conferences, providing a unique opportunity for attendees to engage with diverse topics.
The conference combines three events: SIAM Annual Meeting, Applied Math Education, and Life Sciences, focusing on the intersection of math and planetary systems. It provides a platform for interdisciplinary research and collaboration.
The SIAM Conference on Applied Mathematics Education (ED26) aims to advance educational programs in applied mathematics. Key topics include innovation, practice, improvement, and faculty development.
The SIAM Conference on Parallel Processing for Scientific Computing emphasizes high-performance scientific computing and scalable algorithms. The conference attracts applied mathematics, computer science, and computational science and engineering communities.
The conference will focus on mathematical and statistical foundations, data-driven approaches, and computational advances in uncertainty quantification. Major themes include applications in biology, medicine, environmental sciences, decision making for societal benefit, and physical science and engineering.
Mathematical modeling can assess toxicants' impact on river populations without endangering the environment. A new model developed by Peng Zhou and Qihua Huang describes the interactions between a population and a toxicant in an advective environment.
Researchers develop novel models to investigate the impact of drug-eluting stents on arterial tissue permeability and blood flow. The study identifies key factors contributing to restenosis near stent edges, highlighting potential avenues for design improvements.
Researchers explored optimizing disease control in prisons using rapid tests, identifying the optimal strategy to minimize costs and reduce infection. The study found that switching between full and no testing depends on various parameters, including contagion rates and test sensitivity.
A new study explores how changes in transportation networks impact disease spread, providing insights for future disease intervention strategies. The researchers developed techniques to quantify the effectiveness of different approaches in controlling disease outbreaks by analyzing network structure and hot spot placement.
A mathematical model suggests repaying loans quickly can minimize cost, but delaying payments may be more beneficial for large balances. Borrowers should consider maximizing early payments and switching to income-based repayment when forgiveness is near.
Researchers introduce a novel algorithm to estimate speed and angle of rotating objects in space, then apply these estimates to develop high-resolution images. The method achieves improved resolution without being heavily affected by atmospheric fluctuations.
Researchers use mathematical modeling to study synchronization changes during seizure onset, identifying key factors that influence the transition from desynchronization to high levels of synchronization. The study reveals a new phenomenon where amplitude coupling can accelerate or decelerate the domino effect.
Researchers develop an innovative algorithm inspired by weakly electric fish to detect and locate objects via electrosensing. The multi-scale approach combines information gathered at different distances from the object, providing a more accurate understanding of its features.
Researchers propose a data-driven model to forecast U.S. elections by tracking democratic and republican voters over time, predicting vote margins in each state. The authors' simple yet effective approach enables early forecasts with high accuracy, making it a valuable tool for election prediction.
A study investigated human dispersal's effect on disease control and total extent of an infection's spread. Researchers used the susceptible-infected-susceptible model to analyze the impact of human migration on infection sizes and disease prevalence in different regions.
Researchers developed an algorithm that uses first-order echoes from microphones to reconstruct a room's shape. The method is a theoretical problem but has potential applications in various fields, such as vehicle navigation and sound localization.
Researchers have developed a theoretical mechanical model to study how spider orb-webs detect prey through vibrations. The model reveals that web dynamics are crucial in localizing prey, and it has potential applications for bioinspired materials.
Researchers developed a mathematical model to explore interactions between bacteria and drug-eluting medical devices. The model indicates that successful therapy depends on a balance between the drug's action and bacterial proliferation, influenced by coating properties and porosity.
A mathematical model estimates chlamydia's R0 value, indicating the disease's potential spread. The multi-group SIR model with age structure yields an R0 estimate between 1.0148 and 1.0535 for Japan's outbreak.
Researchers develop new mathematical model to optimize geothermal heat exchanger design, ensuring energy efficiency and economic viability. The model avoids unrealistic assumptions, providing accurate predictions for long-term thermal response behavior.
Researchers develop a time-varying network model that factors in individual dynamics and burstiness to better explain the relationship between social activity and disease spread. The model shows that self-excitement mechanisms lower the epidemic threshold, increasing disease communicability.
A new mathematical model, inspired by age-dependent population models, analyzes urban burglary patterns and identifies key factors influencing repeat victimization. The model takes into account a burglar's age and a house's susceptibility to robbery based on its age, providing a flexible framework for prevention strategies.
Researchers develop a multidimensional model to study tree sap transport, capturing radial variations and geometry effects. The model validates findings via numerical method, providing new insights into flow regimes and their dependence on physical parameters.
The Society for Industrial and Applied Mathematics (SIAM) has awarded 16 distinguished mathematicians for their outstanding research and contributions to the field of applied mathematics and computational science. The recipients were honored at the SIAM Annual Meeting Prizes and Awards Luncheon in Portland, Oregon.
Researchers employed a concrete interplay model in quenched multiplex networks to study the connection between adaptive human behavior and epidemic spread. The model accurately describes actual epidemic spread in complex networks while characterizing interactions between transmission and human behaviors.
A mathematical model proposes a threshold strategy for governments to counterbalance the costs of reducing public debt. The optimal threshold is endogenously determined based on inflation levels.
Researchers use ordinary differential equation-based model to calculate effective method of introducing Wolbachia to wild mosquitoes, reducing spread of life-threatening diseases. The two-sex model accounts for aquatic life stage, heterosexual transmission, and multiple pregnant states, capturing entire transmission cycle.
Researchers present a mathematical model for autoignition in free round turbulent jets, enabling more efficient supercritical water oxidation technology. The model simplifies complex dynamics into one differential equation, allowing for sharp characterization of autoignition events.
Researchers Ryosuke Omori and Jianhong Wu develop an inductive algorithm to study site-specific nucleotide frequencies using a multi-strain SIR model. The algorithm calculates Tajima's D, a statistical test that measures natural selection at a specific site.
A mathematical model reveals that adding a pendulum between the container and carrying hand reduces sloshing by diminishing resonant frequency. The study shows that this design significantly reduces liquid movement, minimizing spills and burns.
Researchers propose a mathematical model to investigate the effects of drug parameters and dosing schedules on HIV latent reservoirs and viral load dynamics. The study suggests that drugs with proper pharmacodynamic properties can potentially prevent or postpone establishment of viral infection.
Researchers develop a computational level set method to optimize sensor placement for maximum surveillance in complex environments. The model acknowledges finite range, limited viewing angle, and nonzero failure rate of realistic sensors, yielding accurate sensory constraints and optimal viewing directions.
Lim's work impacts public through real-world applications, including neural fiber mapping and Human Connectome Project. He received multiple awards for his research on tensors, hypermatrices, and computational geometry/topology.
A new reconstruction method for Electrical Impedance Tomography (EIT) is proposed to estimate abdominal fat, improving reproducibility and spatial resolution. The technique has shown promising results in detecting subcutaneous fat thickness, with further research needed to assess visceral fat volume.
A new continuous-discrete hybrid population model describes the invasive dynamics of zebra mussels in North American rivers. The model shows that population persistence is contingent upon moderate water temperatures and low flow velocities, and that upstream invasion success depends on these factors.
A periodic time-delayed model of Lyme disease predicts its spread in North America and Canada, incorporating seasonality and climate factors. The model suggests that reducing tick recruitment rates can help eliminate the disease.
Researchers developed a method to compute optimal traffic light settings for urban intersections by applying traffic flow conservation laws on networks. The approach uses partial outer convexification, splitting the problem into two stages: nonlinear dynamic optimization and linear mixed-integer programming.
Climate change impacts mosquito life cycle and malaria parasite development, increasing transmission risk in sub-Saharan Africa. A new model predicts disease spread using periodic vector-bias effects, improving accuracy over previous models.
The study models coffee extraction in drip filter machines, considering bed dimensions, flow rates, and grind size distribution. Approximate solutions are used to relate brewing performance with coffee, water, and equipment properties.
Researchers designed an equilibrium model to understand the factors that contribute to lens comfort, revealing the importance of suction pressure, radial tension, and hoop tension. The study aims to improve contact lens design and comfort, potentially leading to novel applications like drug administration and sensory enhancement.
A novel wavelet variational model is proposed to segment ultrasound videos efficiently, tackling low contrast, shadow effects, and complex noise statistics. The model achieves accurate ROI tracking with robustness and flexibility, making it suitable for real-time clinical applications.
SIAM has announced its 2016 Class of Fellows, a group of 30 distinguished researchers recognized for their outstanding contributions to applied mathematics and computational science. The recipients were chosen for their exceptional research and service to the community.
Researchers apply global sensitivity analysis to pinpoint key HIV model parameters affecting treatment plans. By fixing non-influential inputs and minimizing parameter dimensions, they better understand HIV dynamics and develop optimal treatment strategies.
Researchers develop a closed formula to reduce motion blur in camera images by optimizing flutter shutter codes for any probability density of expected scene velocities. The formula links optimal codes with velocity distributions and surpasses the previously-established 1.17 bound gain for known velocities.
Using video footage and geodesic Lagrangian coherent structures theory, researchers found unsteady material transport barriers surrounding Jupiter's Great Red Spot and jet streams. This analysis enhances knowledge of the planet's atmosphere, with potential applications in oceanography, meteorology, and environmental monitoring.
The study proposes adaptive control techniques to manage pests, which can help reduce crop losses due to pest control. The approach tolerates uncertainty in pest dynamics, making it suitable for developing better models or adopting design approaches that tolerate the likely level of uncertainty.
Researchers propose a stochastic differential equation model to simulate musical performance synchronization. The model accounts for internal tempo preservation, noise distraction, and phase correction.