Abstract: For decades, 3D mechanical design has relied on boundary representation (B-rep), a static description of geometry that underpins modern CAD, simulation, and manufacturing. While powerful, this paradigm has also produced engineering workflows dominated by graphical interfaces, manual operations, and disconnected software tools. This creates a fundamental bottleneck for modern agentic AI, as mechanical design and engineering still lack the programmable infrastructure needed for AI systems to interact with, reason about, and modify complex 3D designs. This raises a simple question: what if engineering design were treated as code? In this talk, I explore the idea of representing 3D CAD models as executable programs rather than direct static geometry. Programmatic representations expose the structure and logic of how designs are constructed, enabling automation and providing a natural foundation for training foundational generative AI models that can generate functional mechanical designs. I will briefly revisit earlier attempts at intelligent design systems and discuss how recent advances in artificial intelligence open the door to a new generation of AI-native engineering software. The discussion focuses on 3D mechanical design but points toward a broader future in which engineering workflows become programmable, generative, and AI-native.

Bio: Ferdous Alam is an Assistant Professor in the George W. Woodruff School of Mechanical Engineering at the Georgia Institute of Technology and an affiliated faculty member of ML@GT. He is also a core faculty member at the Institute for Robotics and Intelligent Machines (IRIM). He leads the Inference Lab, where his research focuses on the computational foundations of AI-driven engineering design and robotic manufacturing, including generative modeling of mechanical systems, representation learning for 3D geometry, and robot learning for manufacturing tasks. Before joining Georgia Tech, Dr. Alam was a postdoctoral researcher at MIT, and he previously received his PhD from The Ohio State University. His research has been recognized with several Best Paper Awards at leading ASME conferences, as well as the Google Research Scholar Award in Applied Science.

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Bio: I am an applied mathematician interested in the interplay of scientific computing and artificial intelligence. I am a Winship Distinguished Research Associate Professor in the Department of Mathematics and the Department of Computer Science at Emory University and a member of Emory’s Scientific Computing Group. I lead the Emory REU/RET site for Computational Mathematics for Data Science. Prior to joining Emory, I was a postdoc at the University of British Columbia and I held PhD positions at the University of Lübeck and the University of Münster.

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Bio: Dr. Navid Azizan is the Alfred H. (1929) and Jean M. Hayes Associate Professor at MIT, where he holds dual appointments in the Department of Mechanical Engineering (in Control, Instrumentation & Robotics) and the Schwarzman College of Computing's Institute for Data, Systems & Society (IDSS) and is a Principal Investigator in the Laboratory for Information & Decision Systems (LIDS). He is also a core faculty member of the Statistics and Data Science Center and the Center for Computational Science and Engineering. His research interests broadly lie in machine learning, systems and control, and mathematical optimization. His research lab focuses on various aspects of reliable AI systems, with applications to high-stakes and safety-critical settings. He obtained his PhD in Computing and Mathematical Sciences (CMS) from the California Institute of Technology (Caltech) in 2020, his MSc in electrical engineering from the University of Southern California in 2015, and his BSc in electrical engineering and physics from Sharif University of Technology in 2013. Prior to joining MIT, he completed a postdoc at Stanford University in 2021. Additionally, he was a research scientist intern at Google DeepMind in 2019. He is a recipient of several awards, including the National Science Foundation CAREER Award, research awards from Amazon, Google, and MathWorks, among others, and the inaugural Information Theory and Applications (ITA) “Sun” (Gold) Graduation Award. His work has been recognized with Best Paper awards at several venues, including the Learning for Dynamics and Control (L4DC), INFORMS JFIG, and ACM Greenmetrics. He was named in the list of Outstanding Academic Leaders in Data by the CDO Magazine for two consecutive years in 2024 and 2023. His teaching and mentorship have been recognized with the Joseph A. Martore (1975) Excellence in Teaching Award in 2026, the Frank E. Perkins Award for Excellence in Graduate Advising (MIT Institute Award) in 2025, and the UROP Outstanding Mentor Award in 2023.

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Bio: The Ghosh group engages in cross-disciplinary collaborations and welcomes students from diverse academic backgrounds. Most projects focus on addressing challenges in fundamental physics and astrophysics using computational and AI/ML tools, making the group a natural fit for students with strong skills or interests in these areas. We develop methods to automate theoretical physics calculations using reinforcement learning and LLM agents, enabling rapid testing of new theories. We also work on simulation, experimental design and high-dimensional statistical inference techniques powered by AI to accelerate scientific discovery. Data analysis problems at the scale of the Large Hadron Collider or multi-messenger astronomy often demand rapid decision-making, and we design efficient AI algorithms that can be deployed on fast hardware to meet these challenges.

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Bio: Hal Daumé III is a professor of computer science with appointments in the Maryland Language Science Center and the University of Maryland Institute for Advanced Computer Studies, where he is also the director of TRAILS. In addition to fairness and natural language processing, his research focuses on understanding computational properties of learning and language as well as trustworthy AI.

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Bio: Enric Boix-Adsera is an assistant professor of statistics and data science. His parents are originally from Barcelona, and he grew up mostly in Princeton, New Jersey. Before coming to Wharton, he earned a doctorate in electrical engineering and computer science from MIT.

Boix-Adsera studies the learning processes of artificial intelligence models, such as ChatGPT, with the goal of improving their efficiency and reliability.

 For CODA guest access, please contact shatcher8@gatech.edu at least 2 business days prior to the event.