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 build engineering-native AI — models, datasets, and solvers that work the way engineering actually works, through CAD, geometry, simulation, and optimization, under real physical and manufacturing constraints — to enhance the design of complex systems and support human teams in creating better products.
Our goal is to build versatile approaches that automate and augment engineering design across scales, levels of complexity, and disciplines, framing design problems as generalizable machine learning and optimization tasks. Much of our current research is organized around three pillars of engineering-native AI: workflow-native models and agents that operate directly on CAD programs, geometry, and topology; transferable foundation-model priors that keep prediction and optimization reliable when data are scarce and evaluations expensive; and open infrastructure — large datasets, objective-aware benchmarks, and fast solvers — that helps the whole field explore, evaluate, and generate solutions cumulatively. Together, these threads build toward our long-term goal: a General Artificial Designer — a dependable AI collaborator for engineering.
CIE Best Conference Paper & AI/ML TC Best Paper, IDETC 2026
In IDETC 2026 (Accepted)
Directed energy deposition (DED) produces complex thermo-mechanical responses that can lead to distortion and reduced dimensional accuracy of a manufactured part. Thermo-mechanical finite element simulations are widely used to estimate these effects, but their computational cost and the complexity of accurately capturing DED physics limit their use in design iteration and process optimization. This paper introduces FLARE (Field Prediction via Linear Affine Reconstruction in wEight-space), a data-efficient surrogate modeling framework for predicting post-cooling displacement fields in DED from geometric and process parameters. We develop a predefined-geometry DED simulation workflow using an open-source finite element framework and generate a dataset of simulations with varying geometry, laser power, and deposition velocity. Each simulation provides full-field displacement, stress, strain, and temperature data throughout the manufacturing process. FLARE encodes each simulation as an implicit neural field and regularizes the corresponding neural-network weights so that they follow the affine structure of the input parameter space. This enables prediction of unseen parameter combinations by reconstructing network weights through affine mixing of training examples. On this DED benchmark, the method shows improved accuracy compared to baseline methods in both in-distribution and extrapolation settings. Although the present study focuses on DED displacement prediction, the proposed affine weight-space reconstruction framework offers a promising approach for data-efficient surrogate modeling of physical fields.
In Structural and Multidisciplinary Optimization (in press)
Projected Gradient Descent (PGD) methods offer a simple and scalable approach to topology optimization (TO), yet they often struggle with nonlinear and multi-constraint problems due to the complexity of active-set detection. This paper introduces PGD-TO, a framework that reformulates the projection step into a regularized convex quadratic problem, eliminating the need for active-set search and ensuring well-posedness even when constraints are infeasible. The framework employs a semismooth Newton solver for general multi-constraint cases and a binary search projection for single or independent constraints, achieving fast and reliable convergence. It further integrates spectral step-size adaptation and nonlinear conjugate-gradient directions for improved stability and efficiency. We evaluate PGD-TO on four benchmark families representing the breadth of TO problems: (i) minimum compliance with a linear volume constraint, (ii) minimum volume under a nonlinear compliance constraint, (iii) multi-material minimum compliance with four independent volume constraints, and (iv) minimum compliance with coupled volume and center-of-mass constraints. Across these single- and multi-constraint, linear and nonlinear cases, PGD-TO achieves convergence and final compliance comparable to the Method of Moving Asymptotes (MMA) and Optimality Criteria (OC), while reducing per-iteration computation time by 10–43x on general problems and 115–312x when constraints are independent. Overall, PGD-TO establishes a fast, robust, and scalable alternative to MMA, advancing topology optimization toward practical large-scale, multi-constraint, and nonlinear design problems. Public code available at: https://github.com/ahnobari/pyFANTOM.
In Journal of Mechanical Design 2026
The subjective evaluation of early-stage engineering designs, such as concept sketches, traditionally relies on human experts. However, expert evaluations are time-consuming, expensive, and sometimes inconsistent. Recent advances in vision-language models (VLMs) offer the potential to automate design assessments, but it is crucial to ensure that these artificial intelligence (AI) “judges” perform on par with human experts. This work introduces in-context learning (ICL)-enhanced VLM judges and a comprehensive statistical framework (including agreement, error, correlation, statistical difference checks, equivalence testing, and top-set overlap) to rigorously assess AI–expert equivalence. Across two case studies, we show that reasoning-enabled VLMs are the strongest-performing AI judges. They consistently outperform two-third trained novices across all metrics, and for measures such as uniqueness, creativity, and drawing quality, they approach expert-equivalent performance. In specific cases, they even exceed expert–expert agreement, attaining lower mean absolute error and higher rank correlations than the expert baseline. These findings suggest that, on certain statistical tests, AI judges are not only approaching expert–expert equivalence but in some cases surpassing it.
In ICML 2026
Despite recent progress, recovering parametric CAD construction sequences from geometric input, such as meshes or point clouds, is a key challenge for design and manufacturing, as existing CAD reconstruction and generation methods are largely restricted to difficult-to-edit formats like meshes or Breps or editable simple sketch-and-extrude pipelines and low-complexity datasets. We introduce CADFit, a hybrid optimization-based CAD reconstruction framework that recovers complex, editable CAD construction sequences from meshes by incrementally fitting and validating parametric operations using geometric feedback. Our approach is distinguished by formulating reconstruction as an IoU-driven optimization over structured CAD programs and supporting a rich set of operations, including extrusions, revolutions, fillets, and chamfers. Experiments on multiple CAD benchmarks show that CADFit outperforms state-of-the-art mesh-to-CAD methods in volumetric Intersection-over-Union and Chamfer Distance, while substantially reducing the Invalid Ratio of reconstructed CAD programs, particularly for complex designs. We further present a multimodal pipeline that enables end-to-end reconstruction of CAD construction sequences from images by combining image-based geometry reconstruction with CADFit. By enabling accurate reconstruction of higher-complexity CAD models, CADFit provides a practical foundation for generating richer datasets and advancing future learning-based approaches to CAD reverse engineering. The code is available at: https://github.com/ghadinehme/CADFit
Spotlight Paper: Top 2.2% of 23,918 Submissions
In ICML 2026
Multi-fidelity (MF) regression often operates in regimes of extreme data imbalance, where the commonly-used Gaussian-process (GP) surrogates struggle with cubic scaling costs and overfit to sparse high-fidelity observations, limiting efficiency and generalization in real-world applications. We introduce FIRE, a training-free MF framework that couples tabular foundation models (TFMs) to perform zero-shot in-context Bayesian inference via a high-fidelity correction model conditioned on the low-fidelity model's posterior predictive distributions. This cross-fidelity information transfer via distributional summaries captures heteroscedastic errors, enabling robust residual learning without model retraining. Across 31 benchmark problems spanning synthetic and real-world tasks (e.g., DrivAerNet, LCBench), FIRE delivers a stronger performance-time trade-off than seven state-of-the-art GP-based or deep learning MF regression methods, ranking highest in accuracy and uncertainty quantification with runtime advantages. Limitations include context window constraints and dependence on the quality of the pre-trained TFM's.
In ICML 2026
Generating high-fidelity 3D geometries that satisfy specific parameter constraints has broad applications in design and engineering. However, current methods typically rely on large training datasets and struggle with controllability and generalization beyond the training distributions. To overcome these limitations, we introduce LAMP (Linear Affine Mixing of Parametric shapes), a data-efficient framework for controllable and interpretable 3D generation. LAMP first aligns signed distance function (SDF) decoders by overfitting each exemplar from a shared initialization, then synthesizes new geometries by solving a parameter-constrained mixing problem in the aligned weight space. To ensure robustness, we further propose a safety metric that detects geometry validity via linearity mismatch. We evaluate LAMP on two 3D parametric benchmarks: DrivAerNet++ and BlendedNet. We found that LAMP enables (i) controlled interpolation within bounds with as few as 100 samples, (ii) safe extrapolation by up to 100% parameter difference beyond training ranges, and (iii) physics performance-guided optimization under fixed parameters. LAMP significantly outperforms conditional autoencoder and Deep Network Interpolation (DNI) baselines in both extrapolation and data efficiency. Our results demonstrate that LAMP advances controllable, data-efficient, and safe 3D generation for design exploration, dataset generation, and performance-driven optimization.
In ICLR 2026
Bayesian optimization (BO) struggles in high dimensions, where Gaussian-process surrogates demand heavy retraining and brittle assumptions, slowing progress on real engineering and design problems. We introduce GIT-BO, a Gradient-Informed BO framework that couples TabPFN v2, a tabular foundation model (TFM) that performs zero-shot Bayesian inference in context, with an active-subspace mechanism computed from the model's own predictive-mean gradients. This aligns exploration to an intrinsic low-dimensional subspace via a Fisher-information estimate and selects queries with a UCB acquisition, requiring no online retraining. Across 60 problem variants spanning 20 benchmarks — nine scalable synthetic families and eleven real-world tasks (e.g., power systems, Rover, MOPTA08, Mazda) — up to 500 dimensions, GIT-BO delivers a better performance-time trade-off than state-of-the-art GP-based methods (SAASBO, TuRBO, Vanilla BO, BAxUS), ranking highest in performance and with runtime advantages that grow with dimensionality. Limitations include memory footprint and dependence on the capacity of the underlying TFM.
MIT Initiative for New Manufacturing
This work examines how AI, especially agentic systems, is being adopted in engineering and manufacturing workflows, what value it provides today, and what is needed for broader deployment. This is an exploratory and qualitative state-of-practice study grounded in over 30 interviews across four stakeholder groups (large enterprises, small/medium firms, AI developers, and CAD/CAM/CAE vendors). We find that near-term AI gains cluster around structured, repetitive work and data-intensive synthesis, while higher-value agentic gains come from orchestrating multi-step workflows across tools. Adoption is constrained less by model capability than by fragmented and machine-unfriendly data, stringent security and regulatory requirements, and limited API-accessible legacy toolchains. Reliability, verification, and auditability are central requirements for adoption, driving human-in-the-loop frameworks and governance aligned with existing engineering reviews. Beyond technical barriers there are also organizational ones: a persistent AI literacy gap, cultural heterogeneity, and governance structures that have not yet caught up with agentic capabilities. Together, the findings point to a staged progression of AI utility from low-consequence assistance toward higher-order automation, as trust, infrastructure, and verification mature. This highlights key breakthroughs needed, including integration with traditional engineering tools and data types, robust verification frameworks, and improved spatial and physical reasoning.
In NeurIPS 2025
Structural topology optimization (TO) is central to engineering design but remains computationally intensive due to complex physics and hard constraints. Existing deep-learning methods are limited to fixed square grids, a few hand-coded boundary conditions, and post-hoc optimization, preventing general deployment. We introduce Optimize Any Topology (OAT), a foundation-model framework that directly predicts minimum-compliance layouts for arbitrary aspect ratios, resolutions, volume fractions, loads, and fixtures. OAT combines a resolution- and shape-agnostic autoencoder with an implicit neural-field decoder and a conditional latent-diffusion model trained on OpenTO, a new corpus of 2.2 million optimized structures covering 2 million unique boundary-condition configurations. On four public benchmarks and two challenging unseen tests, OAT lowers mean compliance up to 90% relative to the best prior models and delivers sub-1 second inference on a single GPU across resolutions from 64 x 64 to 256 x 256 and aspect ratios as high as 10:1. These results establish OAT as a general, fast, and resolution-free framework for physics-aware topology optimization and provide a large-scale dataset to spur further research in generative modeling for inverse design.
In NeurIPS 2025
Computer-Aided Design (CAD) is a time-consuming and complex process, requiring precise, long-horizon user interactions with intricate 3D interfaces. While recent advances in AI-driven user interface (UI) agents show promise, most existing datasets and methods focus on short, low-complexity tasks in mobile or web applications, failing to capture the demands of professional engineering tools. In this work, we introduce VideoCAD, the first attempt at engineering UI interaction learning for precision tasks. Specifically, VideoCAD is a large-scale synthetic dataset consisting of over 41K annotated video recordings of CAD operations, generated using an automated framework for collecting high-fidelity UI action data from human-made CAD designs. Compared to existing datasets, VideoCAD offers an order of magnitude higher complexity in UI interaction learning for real-world engineering tasks, having up to a 20x longer time horizon than other datasets. We show two important downstream applications of VideoCAD: learning UI interactions from professional precision 3D CAD tools and a visual question-answering (VQA) benchmark designed to evaluate multimodal large language models' (LLM) spatial reasoning and video understanding abilities. To learn the UI interactions, we propose VideoCADFormer - a state-of-the-art model in learning CAD interactions directly from video, which outperforms multiple behavior cloning baselines. Both VideoCADFormer and the VQA benchmark derived from VideoCAD reveal key challenges in the current state of video-based UI understanding, including the need for precise action grounding, multi-modal and spatial reasoning, and long-horizon dependencies.
In Applied Energy 2025
Offshore wind energy leverages the high intensity and consistency of oceanic winds, playing a key role in the transition to renewable energy. As energy demands grow, larger turbines are required to optimize power generation and reduce the Levelized Cost of Energy (LCoE), which represents the average cost of electricity over a project’s lifetime. However, upscaling turbines introduces engineering challenges, particularly in the design of supporting structures, especially towers. These towers must support increased loads while maintaining structural integrity, cost-efficiency, and transportability, making them essential to offshore wind projects’ success. This paper presents a comprehensive review of the latest advancements, challenges, and future directions driven by Artificial Intelligence (AI) in the design optimization of Offshore Wind Turbine (OWT) structures, with a focus on towers. It provides an in-depth background on key areas such as design types, load types, analysis methods, design processes, monitoring systems, Digital Twin (DT), software, standards, reference turbines, economic factors, and optimization techniques. Additionally, it includes a state-of-the-art review of optimization studies related to tower design optimization, presenting a detailed examination of turbine, software, loads, optimization method, design variables and constraints, analysis, and findings, motivating future research to refine design approaches for effective turbine upscaling and improved efficiency. Lastly, the paper explores future directions where AI can revolutionize tower design optimization, enabling the development of efficient, scalable, and sustainable structures. By addressing the upscaling challenges and supporting the growth of renewable energy, this work contributes to shaping the future of offshore wind turbine towers and others supporting structures.
In IDETC 2025
BlendedNet is a publicly available aerodynamic dataset of 999 blended wing body (BWB) 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. The dataset is generated by sampling geometric design parameters and flight conditions, and includes detailed pointwise surface quantities needed to study lift and drag. We also introduce an end-to-end surrogate framework for pointwise aerodynamic prediction. The pipeline first uses a permutation-invariant PointNet regressor to predict geometric parameters from sampled surface point clouds, then conditions a Feature-wise Linear Modulation (FiLM) network on the predicted parameters and flight conditions to predict pointwise coefficients Cp, Cfx, and Cfz. Experiments show low errors in surface predictions across diverse BWBs. BlendedNet addresses data scarcity for unconventional configurations and enables research on data-driven surrogate modeling for aerodynamic design.
In NeurIPS 2024
We present DrivAerNet++, the largest and most comprehensive multimodal dataset for aerodynamic car design. DrivAerNet++ comprises 8,000 diverse car designs modeled with high-fidelity computational fluid dynamics (CFD) simulations. The dataset includes diverse car configurations such as fastback, notchback, and es- tateback, with different underbody and wheel designs to represent both internal combustion engines and electric vehicles. Each entry in the dataset features detailed 3D meshes, parametric models, aerodynamic coefficients, and extensive flow and surface field data, along with segmented parts for car classification and point cloud data. This dataset supports a wide array of machine learning applications including data-driven design optimization, generative modeling, surrogate model training, CFD simulation acceleration, and geometric classification. With more than 39 TB of publicly available engineering data, DrivAerNet++ fills a significant gap in available resources, providing high-quality, diverse data to enhance model training, promote generalization, and accelerate automotive design processes. Along with rigorous dataset validation, we also provide ML benchmarking results on the task of aerodynamic drag prediction, showcasing the breadth of applications supported by our dataset. This dataset is set to significantly impact automotive design and broader engineering disciplines by fostering innovation and improving the fidelity of aerodynamic evaluations.
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.).
Engineering design datasets from the DeCoDE lab. Please cite the corresponding paper when using a dataset.
Interactive project pages for our publications.
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 he has received the NSF CAREER Award, the AFOSR Young Investigator Award, the ASME DAC and DTM Young Investigator Awards, the Google Research Scholar and Amazon Research Awards, and MIT's Keenan Award for Innovation in Undergraduate Education. He holds the Esther and Harold E. Edgerton Career Development Professorship, having previously held the Doherty, ABS, and d'Arbeloff chairs, and serves as Associate Editor of Design Science and on the editorial boards of Computer-Aided Design and Machine Learning: Engineering.
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.
August 2026
A big IDETC-CIE 2026 in Houston is coming up for the lab as we have three papers and seven additional presentations. Our FLARE paper with GE Vernova won both the CIE Best Conference Paper Award and the AI/ML Technical Committee Best Paper Award. We are also organizing the fourth "From Data to Design" workshop and a student hackathon with nTop and MIT Lincoln Laboratory.
July 2026
Dr. Ahmed received the Air Force Office of Scientific Research (AFOSR) Young Investigator Program award (selected in 2025, with the grant effective 2026).
July 2026
Dr. Ahmed was appointed to the Esther and Harold E. Edgerton Career Development Professorship at MIT.
July 2026
The lab presented four papers at ICML 2026, with FIRE selected as a Spotlight (top 2.2% of submissions). Earlier in April, GIT-BO was presented at ICLR 2026.
July 2026
Congratulations to Ghadi Nehme on receiving the GE Vernova Fellowship and to Rosen Yu on receiving the MathWorks Fellowship!
July 2026
On July 16, the DeCoDE Lab moved to its new home in Building 35, Room 35-332. Come visit us in our new space!
June 2026
Our AI-driven tower redesign with UPorto was adopted as the official floating tower in the IEA Wind 22-MW reference turbine repository maintained by the National Laboratory of the Rockies and DTU. See the repository.
May 2026
At Commencement, we celebrated the DeCoDE Lab's first two Ph.D. graduates, Lyle Regenwetter and Amin Heyrani Nobari. We were also proud to celebrate the MS thesis milestones of Era Syla, Brandon Man, Annie Doris, Nomi Yu, Nicholas Sung, and Aditya Palaparthi. Excited for what comes next for all of them!
May 2026
Lyle Regenwetter successfully defended his Ph.D. and will be joining the University of Illinois Urbana-Champaign as an Assistant Professor. Congratulations, Dr. Regenwetter.
May 2026
Dr. Ahmed delivered a keynote at the DESIGN 2026 conference in Cavtat, Croatia.
April 2026
Congratulations to Ghadi Nehme on receiving MIT MechE's Thomas Sheridan Award for creativity in human-machine integration!
March 2026
Our DesignQA paper received the Editor's Award from the ASME Journal of Computing and Information Science in Engineering.
February 2026
DeCoDE alumnus Dr. Kang-Hyun Lee accepted a tenure-track faculty position at Soongsil University.
January 2026
Amin Heyrani Nobari successfully defended his thesis, becoming the first Ph.D. graduate of the DeCoDE Lab. He continues with the lab as a Postdoctoral Associate. Congratulations, Dr. Nobari!
January 2026
DeCoDE alumnus Dr. Md Ferdous Alam accepted a tenure-track faculty position at the Georgia Institute of Technology.
January 2026
Dr. Ahmed visited Toyota at the Higashi-Fuji Technical Center in Japan, discussing AI for engineering design with Toyota researchers.
December 2025
The lab presented four papers at NeurIPS 2025, including Optimize Any Topology, VideoCAD, BikeBench, and Activation-Informed Merging. Read more in this post.
December 2025
Dr. Ahmed visited the Tata Steel plant in Jamshedpur, along with visits to Tata Motors in Pune and Tata Power in Mumbai, exploring opportunities for AI in manufacturing and energy.
October 2025
Dr. Ahmed received an Amazon Research Award, supporting the lab's work with AWS resources.
October 2025
Dr. Ahmed co-organized the 2025 NSF Workshop on Artificial Intelligence in Engineering Design and Systems Engineering (AI-EDSE), held October 16-17, 2025 at Purdue University, bringing together researchers to discuss the future of AI in engineering design.
August 2025
DeCoDE alumnus Dr. Francis Ogoke started a tenure-track faculty position at Carnegie Mellon University.
August 2025
DeCoDE alumnus Dr. Cyril Picard joined P-1 AI, a technology startup.
August 2025
CAD-Coder paper by Annie Doris and team received the "DAC Best Paper Award"
August 2025
Dr. Ahmed received the 2025 DAC Young Investigator Award at the ASME IDETC conference.
July 2025
Dr. Ahmed received MIT MechE's Keenan Award for Innovation in Undergraduate Education.
May 2025
Congratulations to Annie Doris, Noah Bagazinski, and Amin Heyrani Nobari on receiving MIT MechE student awards!
April 2025
Dr. Ahmed received the National Science Foundation CAREER Award. Read more at MIT News.
January 2025
Dr. Ahmed co-created "Designing Solutions with AI," an MIT Challenge delivered worldwide through Nord Anglia Education, drawing more than 1,800 submitted projects from about 50 schools in over 20 countries. Watch the launch video.
December 2024
DrivAerNet++, our 39 TB dataset of 8,000 cars with high-fidelity CFD simulations, was published at NeurIPS 2024 and received an MIT Open Data Prize.
November 2024
The DeCoDE Lab hosted a workshop with visitors from Toyota Research and Toyota Japan.
August 2024
Dr. Ahmed received the 2024 DTM Young Investigator Award at the ASME IDETC conference.
August 2024
DeCoDE lab members attended and presented work at the ASME IDETC 2024 conference.
August 2024
We conducted the second Data2Design workshop during IDETC 2024. We were grateful for the thoughtful discussions, insightful questions, and genuine engagement from all the attendees. Check out the webpage to see the program here.
July 2024
Dr. Ahmed was appointed to the Doherty Chair in Ocean Utilization at MIT.
May 2024
Dr. Srivastava and Dr. Ahmed co-organized the first-ever InstructLab workshop on LLMs at the MIT IBM office.
May 2024
Dr. Ahmed and Dr. Alam received the Google Research Scholar Award 2024 in Applied Science.
March 2024
Lyle Regenwetter was named a Design Fellow by the MIT Morningside Academy for Design (MAD).
February 2024
Kristen spoke about her research which she and her team published in the ASME Journal of Mechanical Design: ADVISE: Accelerating the Creation of Evidence Syntheses for Global Development Using Natural Language Processing-Supported Human-Artificial Intelligence Collaboration.
December 2023
Rui Zhou won the MIT Pillar AI Collective Fellowship.
December 2023
Lyle, Amin, and DeCoDE alumni Giorgio and Binyang attended and presented their work at NeurIPS 2023.
October 2023
Rui Zhou won the MIT Flagship award and Noah Bagazinski and Kristen Edwards won the runner-up prize at the MIT IGNITE Generative AI Entrepreneurship Competition.
September 2023
We announced that Dr. Binyang Song had accepted a tenure-track faculty position at Virginia Tech in the Department of Industrial and Systems Engineering. Learn more.
July 2023
Dr. Ahmed was appointed to the American Bureau of Shipping (ABS) Career Development Professorship at MIT.
September 2023
We welcomed Annie, Nomi, Nicholas, Rosen, Brandon, and Kaira to the DeCoDE lab.
August 2023
We saw a large number of participants join our workshop during IDETC 2023. We were grateful for the thoughtful discussions, insightful questions, and genuine engagement from all the attendees. Check out the webpage to see the program here.
August 2023
Noah Bagazinski won an award for Exemplary Poster Presentation in Naval Architecture and Ocean Engineering at the NDSEG Fellowship Conference in San Antonio, Texas.
July 2023
Amin received the 2023 Mathworks Fellowship at MIT.
May 2023
Kristen passed the MechE qualifying exams.
April 2023
DeCoDE lab launched its student-run Instagram page follow us.
March 2023
DeCoDE lab celebrated IDETC conference submissions in North End.
February 2023
Kristen and Noah won awards for their posters at MERE 2023.
February 2023
Noah and Amin passed MechE qualifying exams.
January 2023
We thanked Dr. Conrad Tucker and the AiPEX lab team for visiting us and sharing their fantastic work.
August 2022
Amin received the 2022 Mathworks Fellowship at MIT.
August 2022
Noah and Lyle won the third prize at two ASME IDETC Hackathons.
August 2022
Dr. Ahmed won the Honorable Mention award in the JMD editors’ choice award for their PaDGAN paper.
August 2022
Kristen completed her master's degree in MechE.
July 2022
Dr. Ahmed received the 3M Non-Tenured Faculty Award.
June 2022
Noah won the ASME Student Poster Travel Award.
May 2022
Lyle passed MechE qualifying exams and became DeCoDE lab's first Ph.D. candidate.
May 2022
Lyle and Amin completed their master's degrees in MechE.
April 2022
Lyle received honorable mention in the NSF GRFP award.
April 2022
Kristen received the NSF GRFP award.
September 2021
Amin received the 2021 Mathworks Fellowship at MIT.
July 2021
Dr. Ahmed received the 2022 Alumni Excellence Research Award from the UMD Alumni Association.
July 2020
Dr. Ahmed was appointed to the d'Arbeloff Career Development Professorship at MIT upon joining the Department of Mechanical Engineering.
No Math AI
MIT Open Learning
MIT Mechanical Engineering
