Barcelona
April 8-9, 2026
The program for the CDFAM Barcelona to be held on April 8-9, 2026 is currently under development. If you are interested in joining us to present, submissions are now open.
Full details can be found on the submissions page and previous presentations can be found in the CDFAM archives.
The Barcelona program will feature two days of technical presentations and case studies spanning computational design, simulation-driven engineering, advanced manufacturing, and architectural systems.
The agenda will highlight the emerging role of AI and machine-learning–based methods within these practices, with contributions from industry, academia, and software developers focused on practical workflows, underlying computational frameworks, and real-world application. The full program and speaker lineup will be updated continually leading up to the event.
The symposium will take place at the Barcelona Biomedical Research Park (PRBB), a major international research center located on the Mediterranean coastline.
Carrer del Doctor Aiguader, 88, Ciutat Vella, 08003 Barcelona, Spain
The venue sits directly on the waterfront, with networking sessions held on the outdoor deck overlooking the ocean, offering space for informal discussion alongside the technical program.
Program Under Development
Organization:
CDFAM
Presenter:
Duann Scott
Welcome to CDFAM Barcelona
Presentation Abstract
Welcome to CDFAM Barcelona and opening remarks giving context to the event and it’s program.
Speaker Bio
Duann Scott is the founder of the CDFAM – Computational Design Symposium, bringing together experts across design, engineering, and software development to explore the future of computational design at all scales.
He is also the Executive Director of the 3MF Consortium, driving the development of an open data standard for additive manufacturing.
With a background spanning industry, academia, and software, Duann focuses on advancing innovation in digital design and advanced manufacturing.
Functional AI for 3D Design Automation — From Path Finding to Generative Modeling for Building Construction
Presentation Abstract
Great strides made recently in 3D generative artificial intelligence (GenAI) have been propelled by the rapid scaling of large foundation models and advances in generative models such as diffusion and flow matching. However, current neural generators have predominantly been constructed by optimization against image-space losses. Should appearance be the main criterion for 3D design and content generation? Not really. The 3D world we live in is not only to be observed. Accordingly, the main goal for 3D GenAI should be for the generated 3D entities to be used and interacted with, so as to serve their intended functions, just as in the real world.
We introduce Functional AI to 3D design automation and demonstrate its importance and potential for the built environment. Fundamentally, any constructed building, and all the objects and structures therein, must fulfill the desired functional requirements, from architecture and complex structural layouts down to the placements and intricate interplay between mechanical equipment, heat or water pipes, and electrical conduits. Our technical coverages will encompass functionalization of 3D objects and scenes, agentic AI for path finding, and generative modeling of complex building structures, with the ultimate goal of establishing a foundation model for building data with construction intelligence.
Acquiring the requisite semantic and spatial understanding, and developing GenAI tools to attain functional designs, is especially challenging for non-residential constructions, specifically commercial, medical, institutional, and mission critical buildings such as data centres. This is the hard problem Augmenta has been focusing on. Our presentation will showcase recent successes of bringing large-scale design automation and generative design to practice, highlighting two elementary schools in Michigan – the first buildings in the world with an electrical system modeled and delivered by AI-powered generative design.
Speaker Bio
Hao (Richard) Zhang is Vice President of AI and R&D at Augmenta, and a Professor in the School of Computing Science at Simon Fraser University, Canada. Augmenta is a Canadian start-up that aspires to automate building designs, from mechanical and electrical to structural and architectural. Richard is leading the company’s efforts in developing advanced AI tools to achieve these goals with scaling, efficiency, and sustainability
Richard is a Fellow of the IEEE, holds a Distinguished University Professorship, and is a member of the ACM SIGGRAPH Academy. From 2021 to 2025, he was an Amazon Scholar. Richard obtained his Ph.D. from the University of Toronto, and MMath and BMath degrees from the University of Waterloo. His research is in computer graphics and visual computing with special interests in spatial and functional AI, geometric and generative modeling, 3D vision, geometric deep learning, as well as computational design and fabrication. Awards won by Richard include a Canadian Human-Computer Communications Society Achievement Award in Computer Graphics (2022), a Google Faculty Award (2019), an NSERC Discovery Accelerator Supplement Award (2014), a Best Dataset Award from ChinaGraph (2020), as well as faculty grants/gifts from Adobe, Autodesk, and Google. He and his students and collaborators have won the SIGGRAPH 2025 Test of Time Award, CVPR 2020 Best Student Paper Award, and Best Paper Awards at Symposium on Geometry Processing (SGP) 2008, and CAD/Graphics 2017. Richard was the Technical Papers Chair for SIGGRAPH 2025.
Organization:
University of Stuttgart, IntCDC
Presenter:
Claudia Valverde
Architected Porosity Informed by Real-World Data for More-Than-Human Thermal Comfort
Presentation Abstract
This presentation introduces a design and research framework that integrates geometry generation with real-world climatic and ecological data to support more-than-human thermal comfort in the exterior of building envelopes. Over the past two years, I have developed architected porous cellular structures—periodic and non-periodic—based on adaptive density minimal surfaces (ADMS) and triply periodic minimal surfaces (TPMS). These structures serve as protective envelopes for nesting tubes used by cavity-nesting wild bees.
The novelty of this work lies not in the digital modelling itself, but in demonstrating how pore size and spatial gradients can be tuned to buffer heat threats inside nesting cavities, and how these porous morphologies behave under real outdoor conditions. Full-scale and small-scale prototypes were installed on real building settings, where they were exposed to solar radiation, diurnal temperature swings, summer heat events and varying humidity. Continuous monitoring revealed how these structures process and respond to environmental information—delaying heat peaks, modulating temperature transfer, and interacting with passive evaporative cooling strategies.
In parallel, wild bee occupation of the prototypes provided biological feedback, confirming which geometries are perceived as suitable nesting habitats. Bringing together digital modelling, outdoor performance testing and ecological observation, this research proposes a design approach that situates building envelopes as active interfaces capable of supporting more-than-human thermal comfort in urban environments.
Speaker Bio
Claudia Valverde is an architect and researcher exploring more-than-human thermal comfort and bioreceptivity through architected geometry and material performance. She holds a Master in Industrial Design for Architecture from the Politecnico di Milano and is currently a PhD candidate and Research Associate at the University of Stuttgart’s Cluster of Excellence IntCDC (ITKE). Her work investigates how porous morphologies, additive manufacturing and façade-integrated systems can support non-human species—particularly cavity-nesting wild bees—within real outdoor building conditions. Through generative design methods, fabrication experiments and environmental monitoring, she develops prototypes that expand the ecological role of architectural surfaces and position geometry as a mediator between climate, materials and living organisms.
Beyond Surrogates: Foundational AI for Physics-Native Design
Presentation Abstract
A new generation of AI models is emerging — not just faster approximators, but intelligent systems that understand and generate physics.
In this talk, Alan Patterson, CEO of BeyondMath, will explore how foundational physics AI breaks from the surrogate modeling paradigm. Unlike models that rely on customer-provided simulation data or narrow datasets, BeyondMath’s models are trained on self-generated data rooted in first principles — not interpolating outcomes, but learning the physical laws and structure of the design space itself.
This approach enables something radically new: generalizable, physics-consistent predictions at near-CFD fidelity, delivered in seconds — and without the need to retrain when a geometry changes. It opens the door to simulation-native design workflows, where simulation is not a bottleneck but a continuous, integrated part of ideation and optimization.
Alan will share:
– Why surrogate AI models struggle in real-world engineering
– What it means to build a foundational model that learns physics, not data correlations
– Case studies from sectors like motorsport and energy
– How these models enable new kinds of design tools and thinking
This is not an evolution of simulation — it’s a rethinking of how AI and physics interact. Foundational AI for physics is here, and it’s reshaping the very act of designing the physical world.
Speaker Bio
Alan Patterson is the Co-founder and CEO of BeyondMath, a company pioneering foundational AI that learns and generates physics — enabling real-time, physics-native design without relying on surrogate models or customer data. BeyondMath’s models are trained on first-principles-derived data, offering engineers high-fidelity simulation at unprecedented speed and scale.
With over 30 years of experience in applied machine learning and software engineering, Alan has led innovation across sectors from healthcare to aerospace. He’s built and exited three machine learning startups (acquired by Google, Amazon, and Rolls Royce), and previously held senior roles at Google, eBay, and HomeX. A hands-on builder and thought leader, he’s known for turning cutting-edge ML into real-world impact.
A New Ecological Simulation Framework for Rhino/Grasshopper
Presentation Abstract
Rhino.Ecologic® is an advanced ecological simulation framework built for Rhino and Grasshopper, designed specifically for landscape architects and urban planners working with computational design. It allows users to seamlessly integrate ecological simulations into their existing 3D design workflows, supporting nature-inclusive strategies at both large and small scales.
With Rhino.Ecologic®, designers can generate location- and time-specific 3D species distribution maps, along with biomass and biodiversity simulations, enabling data-driven ecological decision-making throughout the design process.
With this presentation, you will get a concise overview of the new Rhino/Grasshopper plugin, an introduction to ecological simulation in AEC, and a demo showcasing how the tool works in practice.
Speaker Bio
Verena Vogler leads the R&D team and the programming curriculum at McNeel Europe. She holds a Diploma (Leipzig University of Applied Sciences) and a Master’s in Architecture and Computational Design (IaaC), and earned her doctorate in Engineering from the Chair of Computer Science in Architecture at Bauhaus University Weimar.
An Engineer’s Approach to Integrating Machine Learning in Generative Design Tools
Presentation Abstract
Machine learning and Artificial Intelligence have enormous potential as tools for generative design in engineering. However, most industry efforts remain stuck in research prototypes, brittle bespoke models, or disconnected add-ons that rarely survive real engineering workflows. In this talk, we will present an engineer’s approach to integrating machine learning directly into production-ready generative design tools, drawing on our experience building the fastest physics-driven thermo-fluid optimization platform on the market. Rather than replacing physics with opaque black boxes, our methodology uses ML only where it strengthens engineering outcomes.
I will show how ToffeeX’s ML developments accelerate design exploration and automation while preserving full control of engineering intent, seamlessly extending our existing topology optimization engine which is already used daily in real production environments. This talk highlights why our approach, built on smart algorithmic design rather than brute-force model training, achieves the reliability, manufacturability, and speed required for real-world engineering.
Speaker Bio
Thomas is an aerospace engineer specialized in fluid dynamics, heat transfer, and optimization. He holds MEng, MRes, and PhD degrees from the Department of Aeronautics at Imperial College London and currently leads R&D efforts at ToffeeX. Known for combining rigor with curiousity and a sense of humour (he once tried to cook a steak in a hypersonic wind tunnel — it had a good sear, but was cooked blue), he has nearly a decade of experience building CAE tools and engineering processes.
Computational Design of Personalized CPAP masks
Presentation Abstract
CPAP therapy is used to treat patients with sleep apnea by providing constant air pressure through a mask during sleep. For many patients (75,4%), standard masks fit poorly or require tight adjustment, causing issues such as pressure points or leakage resulting in dry eyes, skin irritation, and finally even therapy discontinuation (30%). Personalized CPAP masks can improve fit and comfort and reduce leakage.
However, early designs face challenges due to the interaction between soft facial tissue and mask materials. During sleep, facial deformation and mask compliance vary with posture, often leading to leakage. In this study, we analyzed 3D scans of individuals lying in multiple positions to model how posture affects mask shape. To improve further, a sensor instrument was developed to measure pressure points while wearing existing masks, this data was used to optimize a computational model that integrates head posture, 3D scan data, and facial softness to guide mask personalization.
Two mask concepts were developed: (1) a fully personalized mask with a 3D-printed soft interface and a rigid hard part available in three sizes, and (2) an intermediate personalized layer that fits between a standard mask and the face to improve fit without replacing the entire mask. Both designs were optimized for 3D printing with Lynxter silicone -improving slicing, print time, neatness, and thickness distribution – as well as for manufacturing with SLA cocoon molding and silicone casting.
Clinical testing in a dutch hospital is planned for March to evaluate the fit of both designs. Results are expected to provide insights into personalized mask design, the feasibility of a 3D scan–model–print workflow in a regional hospital, and the potential of 3D-printed masks for tailored CPAP therapy. When this strategy for generating the model and manufacturing proves potential, the strategy can be extended to other respiratory masks.
Speaker Bio
Anne Pasman studied Industrial Design with a focus on Emerging Technology Design. She currently works at the Industrial Design research group on a line exploring new technology in relation to design. With research areas combining AM and computational design for both personalized medical products and experimenting with innovative materials such as wood, concrete, and silicones.
Redefining mechanical engineering in the age of AI
Presentation Abstract
Mechanical engineering has been limited by the capabilities of traditional CAD software, most of which are built on architectures that are more than 30 years old. How can we design the products of the next 30 years using technology created three decades ago?
AI can introduce a real paradigm shift in how we conceive and develop products, but engineers must remain at the centre of the process. As an engineer, I know that the workflow is often more important than the final result. A great result coming from a black box is never truly great.
In this talk, I will discuss the changing role of mechanical engineering in the age of AI, the key bottlenecks slowing down AI adoption, and practical ways to integrate AI into engineering workflows in a sustainable way for the aerospace and automotive industries, where standards and certification requirements are highly restrictive.
Speaker Bio
Rhushik Matroja is the CEO and Co-Founder of Cognitive Design Systems, a deep-tech company building next-generation software for generative design and manufacturability intelligence. With more than a decade of experience in mechanical engineering, additive manufacturing, and simulation, he focuses on reducing design cycle time for aerospace, automotive, and industrial sectors. His work brings advanced automation, performance optimisation, and design exploration into everyday engineering workflows, helping companies accelerate product development and make better decisions.
Computational Color and Texture for Additive Manufacturing
Presentation Abstract
While we continue to push the boundaries of computational geometry, the integration of color and texture represents a vital and under-explored frontier in additive design. As multi-material hardware and sophisticated file formats become increasingly accessible, the ability to program color and texture directly into the design workflow emerges as a powerful new tool set. This session explores our experiences building up such a tool set and will dive into effects that traditional modeling tools cannot easily capture. Our talk will include:
• Optical Phenomena: Implementing shimmering and view-dependent effects.
• Embedded Data: Integrating “hidden” messages and encoded information within a part’s skin.
• Procedural Topography: Using color-driven surface displacement.
• Gradient Control: Managing complex geometry-driven color and texture transitions.
• Realistic In-workflow Rendering: Displaying accurate as-printed colors and textures within the design workflow.
• Procedural Customization: Automating the generation of unique colorways and textures across product families, including the use of AI-generated content.
We will examine the workflows required to translate digital intent into reliable physical results, while highlighting common pitfalls and how to navigate them. To bridge the gap between pixels and atoms, we will pass around physical samples for a hands-on look at these techniques.
Speaker Bio
Mary Baker is the proprietor of Palace3D, a consulting and contracting firm for computational design for additive. From 2018-2025, she was an Architect in Computational Design for Additive Manufacturing at HP Inc. where she helped introduce procedural design tools such as Houdini to the additive design community. She also co-led the automated design of 3D-printed molded fiber tooling parts for HP’s sustainable packaging business, wrote the back-end automation engine for turning AI-generated content into 3D-printable customized products, designed HP’s 316 Stainless 3D printed metal jewelry, and built a variety of software tools to help make additive design for color more intuitive and reliable. Before HP she was on the faculty of the CS and EE departments at Stanford University where she graduated 7 PhD students.
Randall West is a Senior Mechanical Engineer for HP in their 3D design services group. He works with companies to develop their additive products and build their computational design workflows in industries such as orthotics and prosthetics, consumer goods, medical devices, and drones. Before designing for additive, he designed additive machines and spent a significant portion of his career developing and delivering HP MJF technology. Merging his product engineering background with non-traditional procedural design tools like Houdini, has been a powerful and rewarding skill expansion over the last 5 years of his career. Randall is also the creator of Chroma Scales, a custom pocket knife handle company known for producing far-out designs with full-color MJF that look like donuts, corn on the cob, or the surface of the moon.
How cloud computing enables informed decision making in Design and Engineering
Presentation Abstract
During our presentation, Simon Lut (Lead Developer of Arup InForm) and Rick Titulaer (Computational Design Skills Manager Europe) will explore how cloud computing enables informed decision making for projects in Arup. A few project examples will be shown to demonstrate this, and through these demonstrations, Arup InForm will be explained.
Speaker Bio
Rick Titulaer is a Senior Computational Designer and Structural Engineer at Arup in Amsterdam. He is driven by a passion for holistic and sustainable design solutions and applies computational methods to create optimal outcomes for clients. His experience covers complex projects including residential towers, stadiums, data centers, and sculptural structures. Since 2016 he has contributed to landmark projects around the world, such as the Santiago Bernabéu Stadium in Madrid, the Stedelijk Base in Amsterdam, the Westblaak Tower in Rotterdam, and Elements in Amsterdam.
Rick is an active member of the parametric design community in the Netherlands and across Europe. He speaks at international conferences, delivers guest lectures at technical universities, and serves as the Computational Design Skills Network Manager for Arup in Europe.
Organization:
Universidad Europea de Madrid
Airbus Operations SL
Presenter:
Raul C. Llamas-Sandin
Unlocking Large-Scale Structural Synthesis: High-Performance GPU Topology Optimization for Architectural and Civil Engineering Applications
Presentation Abstract
This presentation introduces a novel, high-performance topology optimization solver developed to bridge the gap between computational efficiency and the massive scale required for architectural and civil engineering projects. While traditional topology optimization is often limited by mesh resolution and computational cost we leverage the massive parallelism of GPUs to solve structural synthesis problems with millions of elements in minutes on a workstation.
Key features relevant to the built environment include robust stress constraints with asymmetric tension-compression limits, active thermal loading for thermoelastic analysis, and the ability to enforce strict geometric constraints using external geometry to define non-designable void or solid regions—essential for integrating fixed features or architectural fenestration.
For civil engineering, we showcase applications in bridge design and high-rise structural cores, where the solver optimizes material distribution within large domains (e.g., 80m spans) under complex loading scenarios. In architectural design, the software enables the synthesis of free-form, structurally efficient forms that are immediately exportable as watertight, smoothed STL files for digital manufacturing or rapid prototyping. By decoupling the physical problem definition from the numerical solver via modular configurations, the software offers a flexible, production-ready workflow that empowers designers to explore the limits of structural efficiency without compromising on resolution or speed. This tool represents a significant step forward in democratizing high-fidelity structural synthesis for the Architectural and Engineering design.
Speaker Bio
Raul C. Llamas-Sandin is an aerospace engineer and academic with over two decades of experience in advanced aircraft design and technological innovation. Since 2001, he has served as a Future Projects Engineer at Airbus, where he specializes in conceptual design and leads research initiatives in European R&T framework programmes.
Parallel to his industrial career, Raul has been an Assistant Professor of Aerospace Engineering at Universidad Europea since 2011, teaching courses on Flight Mechanics and Advanced Aircraft Design. His technical expertise spans aerodynamics, structural engineering, and preliminary sizing—roots established during his early tenure as a Structures Engineer at BAE Systems in the UK.
Raul holds an MSc in Aerospace Vehicle Design from Cranfield University, an MSc in Physics from UNED, an Aerospace Engineering degree from the Polytechnic University of Madrid and is currently pursuing a PhD in Mechanical Engineering at the University of Seville. He holds 14 patents in areas such as aerodynamic improvement devices and plasma actuation, and authoring numerous publications on topics ranging from AI in structural sizing to experimental ice shape analysis.
Design for real world engineering: integrating uncertainty into product assessment
Presentation Abstract
Traditional engineering design processes rely on fixed input values for loading conditions, geometry and material properties. Although this deterministic approach is straightforward, it does not embrace real world uncertainty and design approaches focus on very conservative worst-case scenarios or non-appropriate conditions. As a result, components may be overdesigned or may still fail when conditions differ from the assumed limits. To address this challenge, we have developed a design toolkit that incorporates uncertainty directly into computational analysis.
The toolkit integrates Monte Carlo simulation methods into the modelling process. Instead of using single values, statistical distributions are assigned to key inputs. The software then performs large numbers of simulations to produce a clear picture of how likely different performance outcomes are, allowing failure probabilities and sensitive parameters to be identified.
We demonstrated the toolkit using a sensor mounting bracket from a smart wearable. A traditional optimisation reduced the bracket’s weight by 30% while our probabilistic approach revealed that the design was tuned to an extremely unlikely drop event and still carried thermal failure risks. The output of our process provided additional detailed information to the design and engineering teams, enabling them to re-optimise and achieve a 50% weight reduction while maintaining an extremely low failure probability. This work highlights how probabilistic tools can support more reliable and efficient product development.
Speaker Bio
Greg is an engineering consultant with extensive experience in the development of consumer electronic products and specialist expertise in digital twin technologies and advanced simulation workflows. His work centres on combining physics based modelling with data driven methods to enhance product reliability, optimise performance and support informed decision making throughout the development cycle. He has significant experience in finite element analysis, predictive maintenance and the development of automated, reproducible pipelines for complex engineering computations.
From Surrogates to Large Physics Models: Making AI-Native Engineering Work in Production
Presentation Abstract
Engineering is undergoing a fundamental shift. AI is no longer a point solution for accelerating isolated simulations — it is becoming a core layer of the engineering stack, reshaping how physical systems are designed, tested, and brought to production.
In this talk, Nico will share how PhysicsX is moving beyond narrow, task-specific surrogates toward Large Physics Models (LPMs). Using external aerodynamics as a concrete example, he will introduce PhysicsX’s latest model, trained on a large and diverse corpus of vehicle geometries, and show how it delivers real-time aerodynamic intelligence that generalizes to new concepts with minimal fine-tuning. Through production case studies, the session will demonstrate how AI-native workflows are compressing development cycles from months to weeks — enabling broader design-space exploration, continuous optimization, and faster, more confident engineering decisions.
Beyond aerodynamics, Nico will place this work in a broader AI-native engineering context. He will explore how Large Physics and Geometry Models, combined with adaptive workflows, connect design, simulation, and manufacturing into a seamless continuum. The result is not simply faster iteration, but a structural change in how engineering teams operate — where physics AI augments human judgment at scale, unlocking levels of performance and creativity that were previously out of reach.
Speaker Bio
Nico is the Co-Founder and Director of Engineering at PhysicsX, a pioneering deeptech company at the forefront of AI and engineering, dedicated to driving breakthrough innovations. Collaborating across Delivery, R&D, and Platform teams, he leads the development of advanced computer-aided engineering (CAE) and AI-accelerated design optimization methodologies. With extensive expertise in both engineering and deep learning, Nico works at the intersection of these fields to meet the important challenges of our time and build beyond human imagination. Prior to co-founding PhysicsX in late 2019, Nico built his career in the automotive and motorsport industries working for companies like Bentley Motors, Audi Motorsport and Mercedes-Benz.
nTop
Presentation Abstract
Speaker Bio
Bradley Rothenberg is the CEO and founder of nTop, an engineering design software company based in New York City. Since its founding in 2015, nTop has served the aerospace, automotive, medical, and consumer products industries with engineering software that enables users to design, test, and iterate faster on highly complex parts for production. Bradley has been developing computational design tools for more than 15 years. He actively works to advance the industry, often speaking at industry events around the world, including Develop3DLive, Talk3D, and formnext. He is often quoted in trade publications, interviewed on industry podcasts, and included in Forbes Magazine. He studied architecture at Pratt Institute in Brooklyn, New York.
Personalized Proprioception: Automated Infill Generation for Gradient Compressibility
Presentation Abstract
This presentation will discuss the creation of the Sequence Dynamic Insole Generator, an automated and bespoke tool to generate custom additively manufactured insoles based upon human biomechanics data. This process was built using SideFX Houdini to leverage proceduralism and automated data handling in order to create a robust, operator-controlled process for the creation of individual biomechanic insoles. Rather than creating 3D CAD Geometry and relying on an off-the-shelf slicer, this workflow generates a variable density infill toolpath pattern based on input parameters and geometry.
Custom pathfinding algorithms were developed for print optimization and manufacturability, embedding constraints and print parameters directly into G-Code Generation. Gradient compressibility and energy absorption enable the insoles to modulate proprioceptive input across the body’s kinematic chain, supporting adaptive changes in the user’s dynamic movement patterns. We will discuss algorithmic strategies for toolpath generation, automated adherence to printing parameters, and integration with digital fabrication methods, demonstrating how procedural design environments can bridge biomechanics, computation, and manufacturing in next-generation product creation systems.
Speaker Bio
David Burpee is a multidisciplinary Computational Design Leader based in the Pacific Northwest, with expertise spanning Footwear, Apparel, Consumer Goods, Automotive, Medical, and Architecture industries. He lectures on Computational Design and Algorithmic Thinking at the University of Washington and is a Computational Researcher on a National Science Foundation grant exploring Engineered Living Materials (ELMs).
With over a decade of Computational Design experience, David has delivered advanced design strategy, tools, and training for companies including Nike, PUMA, FILA, General Motors, Harry’s Razors, and EQLZ. His work demonstrates a proven methodology that merges creativity, deep technical capabilities, and broad market impact.
Originally trained as an Architectural Designer with a Master of Architecture from USC, David has contributed to highrise and supertall projects in Los Angeles, Seattle, and across Asia. His work integrates computational approaches at every scale, from skyscrapers to small installations.
Driven by a passion for biomimicry, generative systems, and sustainable innovation, David applies computational design to address complex ecological and social challenges through creative, high-performance solutions.
Synera
Presentation Abstract
Speaker Bio
At Synera, Andrew Sartorelli is the VP of Software Partnerships and Head of Product Management where helps brings to market solutions for customer pains using internally developed solutions, as well as leveraging partner solutions. He’s spent the past 10 years working for a variety of engineering software companies including Autodesk, nTopology, Hexagon, and now Synera.
Registration
Barcelona, 2026
Discounted Registration
€800
Until March 8th
Early Bird Registration
€650
Until February 1st
Academic Registration
€450
Until April 10th
For other payment options including direct transfer, invoices or discount pricing for group discounts, contact info@cdfam.com
