April 28, 2026 | Saint John’s Resort, Plymouth, MI
The event is free, but registration is required.
Explore. Experience. Engage.
Attend MathWorks Automotive Conference to connect with MathWorks and industry experts and learn more about the latest automotive trends. Discover new features and capabilities for MATLAB and Simulink and hear firsthand how customers are successfully using them.
Featured Topics
- Software Transformation
- Generative AI
- AI and Machine Learning in Engineering
- Virtual Vehicles
- Electrification and Hybridization
Why Attend?
Get inspired by the new automotive trends and customer success stories from leading MathWorks customers.
Advance your skills with Technology Showcase demos.
Gain insight from trusted experts who are transforming the automotive industry.
Connect with other engineers, researchers, and scientists in the automotive community who use MATLAB and Simulink.
Featured Sessions
Phil Koopman, Carnegie Mellon
Embodied AI Safety: Reimagining Safety Engineering for AI in Physical Systems
Embodied AI (eAI) uses artificial intelligence based on machine learning to interact with the physical world. eAI is now being deployed in the real world in robotaxis, smart medical devices, household robots, and other applications. However, many are struggling with challenges concerning the safety of these devices: how to design for safety, how to evaluate safety, and how to think about whether any particular eAI system is acceptably safe.
This talk provides an overview of Professor Phil Koopman’s new book on this topic, with robotaxi safety as a concrete example. Anyone working in this area needs a basic understanding of four core areas: safety engineering, cybersecurity engineering, machine learning technology, and human/computer interaction. The talk also discusses eAI safety issues in the wild, the complexities of establishing what risks might be acceptable, and open challenges in eAI safety.
Talk highlights include:
- Identifying key principles in the areas of system safety, cybersecurity, machine learning, human/computer interaction, and liability
- Illustrating how things change when a human operator is replaced by a computer, using examples from the robotaxi industry
- Explaining the need to reframe system safety from risk optimization to a multi-constraint satisfaction approach
Phil Koopman
Carnegie Mellon
Professor Philip Koopman is an internationally recognized expert on autonomous vehicle (AV) safety whose work in that area spans almost 30 years. He has also worked extensively in general embedded system design, software quality, and safety across numerous transportation, industrial, and defense application domains, including conventional automotive software and hardware systems. He originated the UL 4600 autonomous vehicle safety standard and received the Industry Legend award at the 2024 Self-Driving Industry Awards. He is faculty emeritus at Carnegie Mellon University.
Anamitra Bhattacharyya, General Motors
Adeel Ahmed, General Motors
GM’s VERDE Toolchain: Seamless Multiplatform Simulation Capability
This presentation highlights the platform-independent execution capabilities and expanded application scope of the GM® VERDE Toolchain, developed using MATLAB®, Simulink®, and App Designer. By leveraging the FMI framework, the toolchain enables seamless cosimulation across a variety of GM tools, supporting controls coupling, thermal analysis, track performance evaluation, and standalone propulsion model development. A newly introduced FMU generation tool based in App Designer streamlines model creation through configurable templates for inputs, outputs, and tunable parameters. The VERDE Toolchain supports deployment across GM’s EV, ICE, and hybrid platforms, with applications spanning predictive thermal modeling, propulsion system simulation, and SIL/HIL/DIL environments. Ongoing development efforts focus on memory optimization, expanded parameter support, and integration of AI and machine learning technologies.
Anamitra Bhattacharyya
General Motors
Anamitra Bhattacharyya is a global technical specialist for vehicle energy model and toolchain development at General Motors. He leads the architecture and deployment of VERDE, including modular Simulink and Simscape propulsion models, FMI/FMU export, and cosimulation with thermal, dynamics, and controls environments. Anamitra has over 20 years of experience in propulsion modeling, vehicle simulation, and controls integration, with prior roles at GM, EcoMotors, Cummins, and FEV. He holds a master’s degree in mechanical engineering from Wayne State University.
Adeel Ahmed
General Motors
Adeel Ahmed is an engineering group manager for the energy model and toolchain development team at General Motors. His team owns VERDE, GM’s enterprise vehicle energy and range simulation environment built on MATLAB, Simulink, and Simscape. Since joining GM as a high-school co-op, Adeel has worked in calibration and diagnostics, energy execution, mass integration, and thermal, HVAC, and PTC—all with a consistent focus on advancing modelbased engineering and virtual toolchains. He holds a B.S. in mechanical engineering from Kettering University and an M.S. in energy systems engineering from the University of Michigan.
Guy Rouleau, MathWorks
Building Efficient and Fast Models: Sharing User Experience
Adopting best practices for large scale models can lead to a significant improvement in model performance and maintainability. Over the last few years, MathWorks has worked with many organizations to implement these best practices, evaluate the improvements, and even uncover challenges that are beyond best practices. In this talk, we will cover:
- A quick recap of best practices
- Example benefits from applying best practices
- Product enhancements to improve model performance and efficiency
- Techniques to identify and diagnose the problematic areas of the model or process
- Countermeasures
Guy Rouleau
MathWorks
Guy Rouleau is a consulting advanced support engineer at MathWorks and has been supporting Simulink users since 2007. In his role, Guy works closely with customers on technical challenges, bringing his expertise in engineering systems and applied modeling to support advanced MATLAB and Simulink workflows. Guy is the author of the Guy on Simulink blog, where he shares tips and tricks he’s learned while supporting Simulink users. He holds a master’s degree in mechanical engineering from McGill University, in collaboration with the Canadian Space Agency.
Yudong Lin, PACCAR
Creating a Digital Thread for Vehicle Function Requirements Using System Composer
Defining vehicle functions and translating them into clear, actionable requirements is essential for effective collaboration across multiple teams. At PACCAR, the goal is to enhance traceability in system-level vehicle software design and reduce defect rates during downstream implementation.
Traditionally, the Kenworth software team has relied on textual requirements alone to trace software design decisions. The absence of a cohesive system-level design workflow can lead to internal logic defects and unexpected software behaviors, especially as the volume of textual requirements grows and tracing them becomes increasingly complex. To address this, the team introduced a workflow that uses System Composer™ to define and review functional architecture and connect it to desktop prototypes (DPs) built in Simulink®. Both artifacts are connected to textual requirements, creating a digital thread that links requirements, architecture, and simulation models. This approach improves communication across functional teams and enables early validation, improving the clarity of intended functionality.
This session shows how forward and backward traceability in System Composer provides a comprehensive view of how requirements and design constraints are related and fulfilled within the software. Furthermore, the Kenworth team has explored a method that combines MATLAB® scripts with the System Composer API to automate the generation of system placeholder models from textual specifications, facilitating efficient knowledge transfer into the System Composer. This integration has the potential to improve traceability, consistency, and collaboration throughout the software development lifecycle.
Yudong Lin
PACCAR
Yudong Lin is a vehicle control engineer at Kenworth Truck Company, a division of PACCAR, where he leads the vehicle function definition team’s transition to Model-Based Design. He integrates desktop prototyping with systems engineering tools such as System Composer to enhance the traceability and clarity of intended automotive functionalities. Yudong specializes in developing and modeling supervisory vehicle control systems, including speed control, ADAS, and power takeoff systems.
Yudong is passionate about innovation and continuously strives to push the boundaries of automotive technology. Outside of work, he mentors university students with electric truck capstone projects. Yudong holds a Ph.D. in mechanical engineering with a focus on cohesive multi-agent robotic systems from the University of Washington.
Avinash Nehemiah, MathWorks
Model-Based Design Meets GenAI: Delivering Speed and Quality
See how generative AI and Model-Based Design converge to deliver speed, quality, and safety in automotive engineering. Learn how Model-Based Design is evolving to combine AI-driven innovation with proven development practices.
Avinash Nehemiah
MathWorks
Avinash Nehemiah leads a global team of over 60 product marketers and product managers at MathWorks, specializing in AI, Model-Based Design, wireless communication, computer vision, code generation, and more. His department is responsible for connecting customer and market needs with product roadmaps and supporting strategic customers with product adoption. Avinash has over 15 years of experience as a product manager in computer vision, deep learning, and automated driving. Prior to joining MathWorks, he successfully led a team in developing an embedded computer vision-based solution to enhance patient safety in hospital rooms.
Avinash earned a master’s degree in electrical and computer engineering from Carnegie Mellon University, where he focused his research on object recognition in radar imagery.
Technology Showcase
Visit the demo booths at MathWorks Automotive Conference 2026 North America to see product demonstrations, speak with experts, and experience the latest features in MATLAB and Simulink.
Exhibitors
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