Technical Leader of Artificial Intelligence

2026 Military Health System Research Symposium (MHSRS). July 15, 2026 August 3-6, 2026 | orlando, florida | Booth #624. Kitware is proud to return to the Military Health System Research Symposium (MHSRS), the premier scientific meeting dedicated to advancing research and technologies that support Warfighter health and medical readiness. Kitware Inc. is exhibiting at Booth 624, where Kitware Inc. will demonstrate its open source software for military medical applications, including: * Physiological modeling of trauma, disease, and treatment response * Digital twins * Trauma imaging and triage * AI/ML and LLMs * Medical image analysis and visualization * Simulation environments for training * Hardware-in-the-Loop testing and evaluation Polytrauma and treatment simulation. Kitware's Pulse Physiology Engine provides validated physiological and injury modeling that enables realistic trauma simulation without relying on animal or human testing. Researchers and program teams can simulate a variety of medical conditions and scenarios, including: * Hemorrhage * Burn * Respiratory compromise * Shock states * Blast and impact injuries * Treatment responses These capabilities can be used to create realistic training environments, improve battlefield medical readiness and care in the field, and accelerate the development and evaluation of medical devices for real-world use. DEMO: Kitware Inc. is excited to demonstrate its new digital moulage wound simulation tool, which uses flexible wearable displays to present realistic injuries on a manikin or live actor. The system creates an interactive environment for wound assessment, progression, and care training. Within a single training scenario, students can: * Adjust wound transparency to reveal the underlying anatomy and vasculature * View wound progression at various time points, such as the point of injury, after cleaning, four hours later, or four days later * Display real wound photographs or custom wound imagery on the wearable screen * Modify wound characteristics in real-time, including severity, dirtiness, scabbing, infection, and skin tone * Observe multiple synchronized injuries at different locations on the patient Visit Booth #624 to see its flexible screen wound simulation. AI-Driven perception and decision-making. Its team has been leveraging AI to support medical decision-making in resource-constrained environments, including the battlefield and natural disasters. Its solutions help clinicians and care teams analyze medical data, interpret imaging, and identify critical findings more quickly and consistently. These capabilities can help improve triage, accelerate treatment decisions, and deliver actionable insights at the point of care to support warfighter health and enable more informed medical decisions when resources are limited. DEMO: Its team will be demonstrating a rugged, headset-mounted system designed to support Tactical Combat Casualty Care (TCCC). Using edge AI, the system will automatically recognize patients, track the care they receive, and generate records under challenging field conditions. By reducing the need for manual documentation, this technology helps medics remain focused on patient care while improving continuity of care beyond the point of injury. Visit Booth #624 to see its TCCC technology in action. Examples of its work. DARPA In the Moment (ITM): Kitware is supporting DARPA's In the Moment (ITM) program, which is exploring how AI can assist decision-makers in complex, high-stakes environments. During Phase 1, the program focused on military medical triage in resource-constrained settings. Kitware developed algorithmic decision-makers designed to align with human judgment and decision-making priorities. The team also used the Pulse Physiology Engine to generate synthetic patient data and digital twins representing a wide range of injuries, physiological conditions, and treatment scenarios. This work supports the development and evaluation of ethical, explainable AI systems for medical decision support. Army Predictive Simulation of Injury Progression (PreSIP): Kitware developed technologies to support prolonged casualty care training, where injuries and wounds must be managed over hours to days rather than only during the initial stages of Tactical Combat Casualty Care (TCCC). The project combined flexible-screen technology, the Pulse Physiology Engine, and tablet-based scenario control to depict wound progression and underlying anatomy on manikins and live training participants. DHA Real-Time Automated Patient Identification and Documentation - Tactical Combat Casualty Care (RAPID-TC3): Kitware is developing RAPID-TC3 to assist medics by automating Tactical Combat Casualty Care (TCCC) reporting. The project uses a headset-mounted camera and edge AI to detect casualties, recognize medical procedures, and generate TCCC reports under challenging field conditions. RAPID-TC3 is designed to track patients throughout the scene and associate medical interventions with each casualty to support documentation. AFRL Ventilation Management Trainer: Kitware supported an AFRL project to develop an advanced ventilator simulator for training clinicians in mechanical ventilation. The project combined the Pulse Physiology Engine with validated training scenarios to provide realistic physiological responses and automated feedback during training. This work supported mechanical ventilation training for military and civilian clinicians in prolonged care, transport, and other complex care environments. Additional information on the project can be found here. DARPA GOLDen hour extended EVACuation (GOLDEVAC): Kitware supported DARPA's GOLDen hour extended EVACuation (GOLDEVAC) program, which investigated new approaches to sustaining critically injured patients during prolonged medical evacuation. Its team used the Pulse Physiology Engine to model patient physiology and evaluated technologies designed to provide resuscitation and oxygenation through a single intravascular device. This work supports research into technologies for prolonged casualty care during medical evacuation. Additional information about the project can be found here. ARPA-H Precision Surgical Interventions (PSI): Kitware is a key industry partner on the ARPA-H Precision Surgical Interventions (PSI) program. The project is developing next-generation surgical technologies for tumor removal. Kitware is leading the software infrastructure for the breast surgical system. The work focuses on integrating multimodal imaging, AI-powered analysis, and real-time surgical guidance to support pre-operative planning and decision-making. Additional information can be found here. NIH Surgical Training Simulation: Kitware led an NIH-funded project to develop a physics-based surgical simulator for training clinicians to respond to rare and adverse events during surgery. The project combines the Pulse Physiology Engine with the Interactive Medical Simulation Toolkit (iMSTK) to create realistic surgical simulations that incorporate patient physiologic feedback. This work advances virtual surgical training through hemorrhage simulation, thermal injury modeling, and anatomical variation. While exposing clinicians to unexpected complications they may encounter in practice. AI-Powered Ultrasound for Newborn Heart Screening: Kitware is leading an ARPA-H-funded project to develop an AI-assisted ultrasound system for screening newborns for critical congenital heart defects (C-CHD). The project combines autonomous ultrasound imaging with AI algorithms that guide image acquisition and evaluate whether key cardiac views have been successfully captured. This work aims to make cardiac screening more accessible in routine clinical settings and support earlier identification of life-threatening heart defects. Learn more here. Medical image analysis, visualization, and computer vision at Kitware. Kitware's team of software engineers, machine learning experts, and medical imaging researchers has decades of experience supporting government health agencies and their contractors. Its technical capabilities include: * Anatomic segmentation * Autonomous medicine * Clinical exploration * Computational modeling * Digital pathology * Digital twins * Interactive AI * Medical image quality assurance * Multimodal LLMs * Point-of-care ultrasound * Surgical planning * Synthetic data generation * Virtual medical training Kitware Inc. understand the critical need for robust, reliable, and high-performance software support in developing and deploying AI solutions in medical applications. Kitware Inc. innovate new methodologies, generate prototypes for proof of concept and feasibility study, or participate in full product development for FDA approval. Its solutions come with unlimited rights to the U.S. government, including no licensing fees or restrictions. Visit Booth #624 at MHSRS 2026 or contact Kitware Inc. to speak with its experts.

GEOINT Symposium 2026. April 15, 2026 May 3-6, 2026 | aurora, colorado | Booth #2230. At the USGIF GEOINT Symposium 2026, the leading event for the geospatial intelligence community, Kitware will present its latest advancements in AI test and evaluation (T&E), computer vision, and interactive visualization. Its work supports national security missions by enabling organizations to better analyze complex data and make informed decisions with confidence. Kitware Inc. invite you to connect with its team at Booth #2230 and join its training sessions and lightning talks to see how its open source technologies and applied research are addressing today's most pressing GEOINT challenges. Advancing AI test & evaluation for geospatial applications. Kitware is advancing the development and deployment of AI systems through a strong focus on test and evaluation. Its approach spans the full lifecycle of AI, from data preparation and model development to rigorous evaluation and operational transition, ensuring systems are reliable, effective, and aligned with mission requirements. As part of this effort, Kitware Inc. is contributing to DARPA's In the Moment (ITM) program, where Kitware Inc. design AI systems that align with human decision-making processes in complex environments. By prioritizing transparency and alignment with human-defined criteria, these systems provide more interpretable and actionable outputs. For large-scale geospatial workflows, Kitware offers open source platforms such as GeoWATCH and RDWATCH, which enable users to train, evaluate, and deploy AI on satellite imagery through intuitive, web-based tools. These platforms are built to integrate into existing pipelines and support efficient analysis at scale. Kitware Inc. also emphasize responsible and explainable AI, recognizing its importance in operational settings. Its XAITK Toolkit helps users understand how models arrive at their decisions, providing tools for evaluation, visualization, and explanation that strengthen trust and improve human-machine collaboration. In addition, Kitware's 3D vision technologies, including its open source TeleSculptor platform, convert aerial imagery and video into detailed 3D models using structure-from-motion techniques. These capabilities support mapping, object detection, and situational awareness - even in environments where metadata is incomplete or unavailable. Kitware develops its technologies in close collaboration with government and industry partners, delivering open source solutions that emphasize transparency, interoperability, and long-term impact. Visit Booth #2230 to experience these capabilities firsthand and connect with its team. Kitware training sessions and lightning talks. Training Session | Monday, May 4 from 7:30-8:30 AM Presenter: Matt Leotta, Ph.D. Vision-Language Models (VLMs) let you find and segment objects in large imagery datasets just by describing them in natural language, avoiding the need for costly data labeling and retraining. This session explains how these models work and how they can be applied to geospatial tasks like object detection, segmentation, and even 3D analysis. Training Session | Tuesday, May 5 from 2:00 - 3:00 PM Presenter: Scott McCloskey, Ph.D. Event-Based Sensing (EBS) is a new imaging approach where sensors capture only changes in brightness instead of full frames, enabling extremely fast, efficient, and high-dynamic-range data collection. This session introduces how EBS works and how it can be used in geospatial applications like tracking fast-moving objects and identifying vehicles using AI-driven analysis. Training Session | Wednesday, May 6 from 7:30-8:30 AM Presenter: Arslan Basharat, Ph.D. This session explores how Large Language Models can be adapted and aligned to match the specialized reasoning of GEOINT analysts, improving their usefulness in real-world decision-making. It also demonstrates techniques like fine-tuning and prompt training, along with multimodal AI applications, to enhance geospatial analysis workflows. Few-shot Building Damage Assessment Lightning Talk | Monday, May 4th from 3:50-3:55 pm Authors/Presenters: Dennis Melamed, Trevor Stout, and Cameron Johnson AI-based damage assessment models perform well with large labeled datasets but struggle to adapt quickly to new disasters where labeled data is scarce. This work presents a label-efficient approach that uses pretraining to extract general features, enabling accurate damage classification with as few as 100 labeled samples. The result is faster, more scalable damage assessment with significantly reduced labeling effort, accelerating the delivery of actionable intelligence. Understanding Sensor-based Robustness of Object Detection Models for Overhead Imagery Lightning Talk | Monday, May 4th from 3:45-3:50 pm Author/Presenters: Anthony Hoogs, Ph.D. AI object detection models for overhead imagery often struggle when deployed under sensor conditions different from their training data. This work uses the Natural Robustness Toolkit (NRTK) to simulate varied sensor parameters and systematically evaluate how these changes impact model performance. The results provide insight into model sensitivity and robustness, helping guide better training, evaluation, and deployment strategies. Formal Guarantees of AI Model Robustness for GEOINT Applications Lightning Talk | Tuesday, May 5th from 3:05-3:10 pm Author/Presenter: Anthony Hoogs, Ph.D. MAGNET is an open source toolkit developed under DARPA's AIQ program to evaluate and improve the reliability and generalization of AI models in real-world deployments. It provides a flexible framework for testing models across text, image, and multimodal tasks using structured evaluations and performance prediction methods. The goal is to help identify model limitations before deployment, supporting more robust and trustworthy AI systems for applications like GEOINT. Label What Matters: Open-Vocabulary 3D Semantic Segmentation for GEOINT Lightning Talk | Tuesday, May 5th from 2:10-2:15 pm Author/Presenter: Matt Leotta, Ph.D 3D models from UAS and satellite imagery are valuable for GEOINT but lack semantic labels, limiting their usefulness. GU3SS addresses this by using vision-language models to enable open-vocabulary 3D segmentation, allowing analysts to define targets with simple text prompts. This flexible approach reduces retraining needs and improves adaptability across changing missions and environments. Computer vision and AI at Kitware. Kitware is a recognized leader in developing advanced artificial intelligence and computer vision solutions for mission-critical applications. Kitware Inc. build systems that enable organizations to analyze imagery, video, and multimodal data at scale, with a focus on performance, transparency, and real-world deployment. Its work spans a wide range of technical areas, including: * AI test and evaluation for geospatial and mission systems * Human-aligned AI and decision support * Responsible and trustworthy AI * Geospatial analytics, remote sensing, and 3D reconstruction * Object detection, classification, and tracking * Multimedia integrity and activity detection * Open source platforms for operational AI deployment Kitware Inc. bring deep expertise across the full AI lifecycle, from data curation and model development to evaluation and transition, ensuring systems are robust, reliable, and aligned with mission needs in complex environments. Working in close collaboration with government agencies, industry partners, and academic institutions, Kitware Inc. deliver solutions that support a wide range of operational domains. Its technologies are designed to adapt to evolving challenges and provide lasting value across diverse mission areas. Contact its team to learn more about how Kitware Inc. can partner with you.

Exploring urban infrastructure risk with GeoDatalytics. April 10, 2026 Urban infrastructure systems, such as transportation networks, are increasingly vulnerable to extreme weather events, aging infrastructure, and growing urban demand. Understanding how these pressures affect interconnected systems is critical for cities and infrastructure operators working to improve resilience. GeoDatalytics is an open source platform developed through a collaboration between Kitware and Northeastern University to address these challenges. It enables teams to organize complex urban datasets, explore scenario-based analyses, and evaluate how disruptions impact infrastructure systems across a city. Through an integrated workflow that combines simulation and geospatial analytics, GeoDatalytics helps bring fragmented data together into a unified environment for analysis and decision-making. Challenges in urban infrastructure analysis. Urban infrastructure systems are highly interconnected, making it difficult to assess how disruptions in one area affect others. Events such as flooding can simultaneously impact transportation networks, accessibility, and emergency response. Infrastructure teams often work with fragmented datasets and limited tools for exploring "what-if" scenarios, making it challenging to fully understand system-wide impacts. Addressing these limitations requires a more integrated approach to data management, modeling, and visualization. GeoDatalytics for integrated infrastructure analysis. GeoDatalytics provides a structured environment for managing projects and datasets, allowing users to work with transportation, environmental, and other infrastructure data in a consistent and reproducible way. The platform supports interactive geospatial visualization, enabling users to explore infrastructure systems and better understand spatial relationships across a city. These capabilities are paired with scenario-based workflows that allow users to simulate disruptions and evaluate their impacts. In the webinar, Kitware Inc. demonstrate an AI-driven flood simulation that models how environmental conditions affect infrastructure systems. This simulation is combined with transportation network analysis to assess how flooding disrupts mobility and accessibility. By integrating these capabilities, GeoDatalytics provides insight into how disruptions propagate across systems, helping users better understand infrastructure risk. Perspectives from the GeoDatalytics development team provide insight into the platform's design goals, current capabilities, and ongoing development. As an open source project, GeoDatalytics continues to evolve with an emphasis on expanding analytics workflows and supporting additional infrastructure domains. Partnering with Kitware for urban infrastructure analytics. With deep expertise in open source software, geospatial analytics, and large-scale data systems, Kitware developed GeoDatalytics to make infrastructure analysis more accessible, flexible, and scalable. Whether you're integrating diverse urban datasets, modeling disruption scenarios, or analyzing system-wide impacts, GeoDatalytics provides the tools needed to support informed, data-driven decision-making. If you're interested in exploring how GeoDatalytics can support your infrastructure resilience and planning efforts, its team can provide technical guidance, collaboration opportunities, and real-world applications.