Inference Lab

Research

We create novel artificial intelligence methods for computational design, robotic manufacturing, and AI-native engineering. We deploy these methods in real-world systems for intelligent, real-time decision-making in unstructured environments. Guided by our motto intelligent integration, we aim to unify design, simulation, optimization, and manufacturing into a single scalable platform, what we call universal engineering intelligence, and some call artificial general engineering intelligence.

Foundation Models for AI-native Engineering

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Physics and knowledge-integrated AI

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Embodied Manufacturing Intelligence

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Team

Ferdous AlamMember of Technical Staff

John Doe 1Member of Technical Staff

Jane Doe 2Member of Technical Staff

John Doe 3Member of Technical Staff

Jane Doe 4Member of Technical Staff

Publications

Explore our latest publications in top conferences and journals.

VideoCAD: A Large-Scale Video Dataset for Learning UI Interactions and 3D Reasoning from CAD Software Brandon Man, Ghadi Nehme, Md Ferdous Alam, Faez Ahmed NeurIPS CodePaper 2025

GenCAD-3D: CAD Program Generation using Multimodal Latent Space Alignment and Synthetic Dataset Balancing Nomi Wu, Md Ferdous Alam, A. John Hart, Faez Ahmed Journal of Mechanical Design (JMD) WebsitePaper 2025

Cad-Coder: An Open-Source Vision-Language Model for Computer-Aided Design Code Generation Anna Doris, Md Ferdous Alam, Amin Heyrani Nobari, Faez Ahmed IDETC PaperFord Best Paper Award 2025

GenCAD-Self-Repairing: Feasibility Enhancement for 3D CAD Generation Chikaha Tsuji, Enrique Flores Medina, Harshit Gupta, Md Ferdous Alam IDETC PaperSEIKM Best Paper Award 2025

From concept to manufacturing: Evaluating vision-language models for engineering design Cyril Picard, Kristen M Edwards, Anna C Doris, Brandon Man, Giorgio Giannone, Md Ferdous Alam, Faez Ahmed Artificial Intelligence Review Paper 2025

GenCAD: Image-Conditioned Computer-Aided Design Generation with Transformer-Based Contrastive Representation and Diffusion Priors Md Ferdous Alam, Faez Ahmed Transactions on Machine Learning Research (TMLR) WebsitePaper 2025

Representation learning for sequential volumetric design tasks Md Ferdous Alam, Yi Wang, Chih-Yi Cheng, Rodger Luo Journal of Mechanical Design (JMD) WebsitePaper 2025

DesignQA: A Multimodal Benchmark for Evaluating Large Language Models’ Understanding of Engineering Documentation Anna C Doris, Daniele Grandi, Ryan Tomich, Md Ferdous Alam, Hyunmin Cheong & Faez Ahmed Journal of Computing and Information Science in Engineering (JCISE) Paper 2025

Physics-guided transfer reinforcement learning for data efficient embodied intelligence in autonomous manufacturing systems Md Ferdous Alam, Sarp Sezer, Zhi Zhang, Max Shtein, Kira Barton & David J. Hoelzle Code 2024

Advantage-based policy transfer with metrics of transferability for Reinforcement Learning Md Ferdous Alam, Parinaz Naghizadeh & David J. Hoelzle arXiv CodePaper 2024

From automation to augmentation: policy and practice to redefine engineering design and manufacturing in the age of nextgen-ai Md Ferdous Alam, Austin Lentsch, Nomi Yu, Sylvia Barmack, Suhin Kim, Daron Acemoglu, John Hart, Simon Johnson, Faez Ahmed MIT Press Paper 2023

An Additive Manufacturing Testbed to Evaluate Machine Learning-Based Autonomous Manufacturing Zhi Zhang, Antony George, Md Ferdous Alam, Chris Eubel, Chaitanya Krishna Prasad Vallabh, Max Shtein, Kira Barton, David J Hoelzle Journal of Manufacturing Science and Engineering (JMSE) Paper 2023

Reinforcement learning enabled autonomous manufacturing using transfer learning and probabilistic reward modeling Md Ferdous Alam, Max Shtein, Kira Barton & David J. Hoelzle IEEE Control Systems Letters (L-CSS) Paper 2022

Sample efficient transfer in reinforcement learning for high variable cost environments with an inaccurate source reward model Md Ferdous Alam, Max Shtein, Kira Barton & David J. Hoelzle American Control Conference (ACC) CodePaper 2022

A physics guided reinforcement learning framework for an autonomous manufacturing system Md Ferdous Alam, Max Shtein, Kira Barton & David J. Hoelzle American Control Conference (ACC) CodePaper 2021

Autonomous Manufacturing Using Machine Learning: A Computational Case Study With a Limited Manufacturing Budget Md Ferdous Alam, Max Shtein, Kira Barton & David J. Hoelzle Manufacturing Science and Engineering Conference (MSEC) PaperBest Paper Award 2020

Code

We share open-source code and tools for the community. Here are some of our most accomplished repositories:

Contact

Get in touch with us for collaborations, questions, or opportunities. For any official queries contactProfessor Ferdous Alam directly.