The Design Computation and Digital Engineering (DeCoDE) Lab at MIT Mechanical Engineering advances the science of AI-driven design. We envision a future where humans and AI design together to tackle the world’s most pressing challenges. To realize this vision, we develop fundamental machine learning and optimization methods that enhance the design of complex systems and support human teams in creating better products. Our goal is to build versatile approaches that apply across scales, levels of complexity, wickedness, and disciplines. We frame design problems as generalizable machine learning and optimization tasks, enabling new ways to explore, evaluate, and generate solutions. By harnessing advanced AI, we are reimagining the product design process—accelerating innovation and shaping the next generation of engineering design.

At DeCoDE Lab, our core values - Integrity, Inclusivity, Collaboration, and Excellence - guide us to conduct research with honesty and transparency, foster a diverse and welcoming environment, recognize the power of teamwork, and continually strive for the highest quality in all our endeavors. We are also advocates for reproducible and open-source science, and we contribute by sharing most of our research code and papers online.

Selected Publications [See all]


Nicholas Sung, Steven Spreizer, Mohamed Elrefaie, Kaira Samuel, Matthew C. Jones, Faez Ahmed
BlendedNet: A Blended Wing Body Aircraft Dataset and Surrogate Model for Aerodynamic Predictions
In IDETC 2025
Web Link     Code     Abstract

Anna C Doris, Md Ferdous Alam, Amin Heyrani Nobari, Faez Ahmed
CAD-Coder: An Open-Source Vision-Language Model for Computer-Aided Design Code Generation
In IDETC 2025
Web Link     Code     Abstract

Yu, N., Alam, M. F., Hart, A. J., Ahmed, F.
GenCAD-3D: CAD Program Generation using Multimodal Latent Space Alignment and Synthetic Dataset Balancing
In Journal of Mechanical Design
Project Page     Web Link     Abstract

Ahmed, F., Picard, C., Chen, W., McComb, C., Wang, P., Lee, I., Stankovic, T., Allaire, D., Menzel, S.
Design by Data: Cultivating Datasets for Engineering Design
In Journal of Mechanical Design
Web Link     Abstract

Brandon Man, Ghadi Nehme, Md Ferdous Alam, Faez Ahmed
VideoCAD: A Large-Scale Video Dataset for Learning UI Interactions and 3D Reasoning from CAD Software
Under Review
Web Link     Abstract

Kristen M. Edwards, Farnaz Tehranchi, Scarlett R. Miller, Faez Ahmed
AI Judges in Design: Statistical Perspectives on Achieving Human Expert Equivalence with Vision-Language Models
In IDETC 2025
Web Link     Abstract

Cyril Picard, Lyle Regenwetter, Amin Heyrani Nobari, Akash Srivastava, Faez Ahmed
Generative Optimization: A Perspective on AI-Enhanced Problem Solving in Engineering
Under Review
Web Link     Abstract

João Alves Ribeiro, Bruno Alves Ribeiro, Sérgio M. O. Tavares, Jie Zhang, Faez Ahmed
Offshore Wind Turbine Tower Design and Optimization: A Review and AI-Driven Future Directions
In Applied Energy 2025
PDF     Web Link     Abstract

Mohamed Elrefaie, Janet Qian, Raina Wu, Qian Chen, Angela Dai, Faez Ahmed
AI Agents in Engineering Design: A Multi-Agent Framework for Aesthetic and Aerodynamic Car Design
In IDETC 2025
Web Link     Abstract

Regenwetter, L., Giannone, G., Srivastava, A., Gutfreund, D., Ahmed, F.
Constraining Generative Models for Engineering Design with Negative Data
In Transactions on Machine Learning Research
Project Page     Web Link     Abstract

Alam, F., Ahmed, F.
GENCAD: Image-Conditioned Computer-Aided Design Generation with Transformer-Based Contrastive Representation and Diffusion Priors
In Transactions on Machine Learning Research
Project Page     Web Link     Abstract

Elrefaie, M., Morar, F., Dai, A., Ahmed, F.
DrivAerNet++: A Large-Scale Multimodal Car Dataset with Computational Fluid Dynamics Simulations and Deep Learning Benchmarks
In NeurIPS 2024
Project Page     Web Link     Abstract

Heyrani Nobari, A., Srivastava, A., Gutfreund, D., Xu, K., Ahmed, F.
LInK: Learning Joint Representations of Design and Performance Spaces through Contrastive Learning for Mechanism Synthesis
In Transactions on Machine Learning Research
PDF     Project Page     Web Link     Abstract

Yu, R., Picard, C., Ahmed, F.
Fast and Accurate Bayesian Optimization with Pre-trained Transformers for Constrained Engineering Problems
In Structural and Multidisciplinary Optimization
PDF     Web Link     Dataset     Abstract

Giannone, G., Srivastava, A., Winther, O., Ahmed, F.
Aligning Optimization Trajectories with Diffusion Models for Constrained Design Generation
In NeurIPS 2023
PDF     Project Page     Web Link     Abstract

Song, B., Zhou, R., Ahmed, F.
Multi-Modal Machine Learning in Engineering Design: A Review and Future Directions
In Journal of Computer and Information Science in Engineering 2023
PDF     Project Page     Web Link     Abstract

Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.
Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design
In Computer Aided Design 2023
PDF     Web Link     Abstract

Mazé, F., Ahmed, F.
Diffusion Models Beat GANs on Topology Optimization
In AAAI 2023
PDF     Project Page     Web Link     Abstract

Regenwetter, L., Heyrani Nobari, A., Ahmed, F.
Deep Generative Models in Engineering Design: A Review
In Journal of Mechanical Design 2022
PDF     Web Link     Dataset     Abstract

Announcements


Prospective Ph.D. students - If you are interested in joining the DeCoDE lab, you can apply to the Computational Science and Engineering Program or to the Mechanical Engineering department at MIT.

If you are interested in joining the DeCoDE lab, drop me an email with the following text in your subject line "Join DeCoDE:" followed by the position you are interested in (for example, a postdoc, intern, visiting student, etc.).

Datasets [See all datasets]


The following list contains engineering design datasets collected by DeCoDE lab. Kindly acknowledge the corresponding paper that introduced the dataset if you utilize a dataset for your work.

Blendednet - A large-scale aerodynamic dataset of 999 blended wing body geometries. Each geometry is simulated across about nine flight conditions, yielding 8830 converged RANS cases with the Spalart-Allmaras model and 9 to 14 million cells per case.

DrivAerNet++ - A large-scale multimodal dataset of 8000 detailed 3D car meshes and aerodynamic performance data comprising of full 3D pressure, velocity fields, and wall-shear stresses, point clouds and parts annotation.

DrivAerNet dataset - A dataset of 4000 detailed 3D car meshes and aerodynamic performance data comprising of full 3D pressure, velocity fields, and wall-shear stresses.

VLM dataset - A dataset of 1000+ tasks to evaluate vision language models.

Car Drag Coefficient - A dataset of 4,948 3D car meshes, their renderings, and their drag coefficients.

LINKS dataset - A dataset of 100 million planar linkage mechanisms and 1.1 billion coupler curves obtained from kinematic simulations. The dataset also contains curated curves, 100 million negative samples, and a publicly available simulation software.

Turbo-compressors dataset - A dataset of 22 million turbo-compressors and their performance under different operating conditions.

Airfoil dataset - A synthetic dataset of 48,503 airfoils and their aerodynamic performance computed using OpenFOAM.

Topodiff topology optimization dataset - A dataset of 33,000 images corresponding to optimal topologies for diverse boundary conditions. The dataset also contains their physical fields, compliance values, and an additional 42000 non-optimal topologies.

SHIP-D dataset - A dataset of 30,000 ship hulls each with design and functional performance information, including parameterization, mesh, point-cloud, and image representations, as well as 32 hydrodynamic drag measures under different operating conditions.

3D cars dataset - A diverse dataset of 9,070 high-quality 3D car meshes labeled by drag coefficients computed from computational fluid dynamics simulations.

BIKED dataset - A dataset of 4,500 community-designed bicycles in tabular and image format, along with images corresponding to different bike parts for each bicycle.

BIKED++ dataset - A dataset of 1.4 million bicycles represented in tabular and image format, along with CLIP embeddings of all designs.

FRAMED dataset - A dataset of 4,500 bicycle frames and ten performance metrics obtained from structural simulations.

Aircraft dataset - A dataset of lift and drag performance values of 4,045 3D aircraft models from Shapenet.

Milk Frother dataset - A multimodal dataset of 1,126 milk frother sketches and their text descriptions. The dataset is derived from a milk frother dataset collected at the Brite lab.

Autosurf aircraft dataset A dataset of 1,050 airplane models with segmentation labels, created using NASA's Open Vehicle Sketch Pad (OpenVSP).

Other engineering datasets A collection of datasets from the engineering design community, curated for our JMD review paper. Note that this list was made in 2022 and is not regularly updated.

Principal Investigator

Faez Ahmed

Associate Professor
Department of Mechanical Engineering
Massachusetts Institute of Technology
Email: faez at mit dot edu

Prof. Faez Ahmed is an Associate Professor in the Department of Mechanical Engineering at MIT, where he directs the Design Computation and Digital Engineering Lab. His research interests lie at the intersection of Artificial Intelligence and engineering design, focusing particularly on first‑principle generative AI and optimization algorithms, multi‑modal representation learning, and engineering design methodology with human–AI design co‑pilots. Before joining MIT, Prof. Ahmed was a postdoctoral fellow at Northwestern University and earned his Ph.D. in Mechanical Engineering from the University of Maryland. He also spent several years in Australia’s railway and mining sector, leading data‑driven predictive‑maintenance initiatives. Prof. Ahmed has received the NSF CAREER Award, the ASME DAC Young Investigator Award, the ASME DTM Young Investigator Award, the Google Research Scholar Award, and the Keenan Award for Innovation in Undergraduate Education. At MIT, he has held the Doherty, d’Arbeloff, and ABS Career Development Chairs. He currently serves as an Associate Editor for Computer‑Aided Design and as the Featured Articles Editor for the ASME Journal of Mechanical Design.

Current Members [See all members]


Cyril Picard
Cyril Picard

Cyril Picard

Research Affiliate

cyrilp@mit.edu

Ferdous Alam
Ferdous Alam

Ferdous Alam

Postdoctoral Associate

mfalam@mit.edu

Hongrui Chen
Hongrui Chen

Hongrui Chen

Postdoctoral Associate

hongrui@mit.edu

Kang Hyun Lee
Kang Hyun Lee

Kang Hyun Lee

Postdoctoral Associate

kanghl@mit.edu

Amin Heyrani Nobari
Amin Heyrani Nobari

Amin Heyrani Nobari

Ph.D. Candidate

ahnobari@mit.edu

Kristen M. Edwards
Kristen M. Edwards

Kristen M. Edwards

Ph.D. Candidate

kme@mit.edu

Lyle Regenwetter
Lyle Regenwetter

Lyle Regenwetter

Ph.D. Candidate

regenwet@mit.edu

Noah Joseph Bagazinski
Noah Joseph Bagazinski

Noah Joseph Bagazinski

Ph.D. Candidate

noahbagz@mit.edu

Manideep Rebbagondla
Manideep Rebbagondla

Manideep Rebbagondla

Ph.D. Candidate

manideep@mit.edu

Ghadi Nehme
Ghadi Nehme

Ghadi Nehme

Ph.D. Candidate

ghadi@mit.edu

Myles Wortham
Myles Wortham

Myles Wortham

Ph.D. Candidate

mwortham@mit.edu@mit.edu

Nicholas Wei Yong Sung
Nicholas Wei Yong Sung

Nicholas Wei Yong Sung

Graduate Student

nicksung@mit.edu

Nomi Yu
Nomi Yu

Nomi Yu

Graduate Student

yunomi@mit.edu

Rosen Yu
Rosen Yu

Rosen Yu

Graduate Student

rosenyu@mit.edu

Annie Clare Doris
Annie Clare Doris

Annie Clare Doris

Graduate Student

adoris@mit.edu

Mohamed Elrefaie
Mohamed Elrefaie

Mohamed Elrefaie

Graduate Student

moatef@mit.edu

Jacob Thomas Sony
Jacob Thomas Sony

Jacob Thomas Sony

Graduate Student

jacob_ts@mit.edu

Era Syla
Era Syla

Era Syla

Graduate Student

erasyla@mit.edu

Aditya Palaparthi
Aditya Palaparthi

Aditya Palaparthi

Graduate Student

apalapar@mit.edu

Bella Stewart
Bella Stewart

Bella Stewart

Graduate Student

istewart@mit.edu

Joao Paulo Alves Ribeiro
Joao Paulo Alves Ribeiro

Joao Paulo Alves Ribeiro

Visiting Graduate Student

jpar@mit.edu

Tyler Butler
Tyler Butler

Tyler Butler

Administrative Assistant

tjbutler@mit.edu

Fun fact: According to the Mathematics Genealogy Project, our academic ancestors include: Poisson, Laplace, Lagrange, Euler, Bernoulli, Leibniz, Copernicus, Nasir al-Din al-Tusi, and many more. Check out our academic family tree.

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