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I will defend my PhD thesis, “Physics-Constrained Generative Models for Computational Design,” on Tuesday, September 8, 2026, at PM ET. Location: MIT MIT CSAIL 32-G449 Zoom: Committee: Kaiming He, Bill Freeman, Wojciech Matusik Generative models can now propose candidate designs at high throughput, yet the physical world remains the... show more

Minghao Guo

1,332 subscribers

78,288 views • 11 days ago •via X (Twitter)

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36 Comments

Robert Scoble11 days ago

@MIT_CSAIL Your work caught my eye. Thank you and knock it out of the park!

Minghao Guo11 days ago

Relying on downstream validation creates a fundamental trade-off between throughput and physical realizability: generating a candidate may take seconds, while validating it can take hours to months. Physical realizability is a scaling problem, not just a quality problem. (2/7)

Minghao Guo11 days ago

Inspired by Marr’s separation of levels of analysis, we organize generative computational design into three levels: physical, computational, and generative. Because each level abstracts the one below, physical requirements can be weakened or lost at the interfaces between them. (3/7)

Minghao Guo11 days ago

This thesis treats physical requirements as first-class citizens of the generative system. The central idea is to carry those requirements bottom-up through explicit interfaces, from the physical world through computation and into generation. We formulate computational design as constrained optimization. (4/7)

Minghao Guo11 days ago

We realize these interfaces through three core elements: representation, constraints, and objective evaluation. At the representation interface, procedural molecular grammars, medial skeletons, and tetrahedral sphere primitives define candidate spaces that are valid by construction. At the constraint interface, differentiable projection modules enforce physical feasibility during generation. (5/7)

Minghao Guo11 days ago

At the objective interface, unified simulators evaluate design behavior with scientific fidelity at generative throughput, spanning physical regimes from molecular dynamics to aerodynamics. Together, these contributions lay the foundation for universal precision design: general-purpose systems that tailor physically realizable designs to the specific requirements of a problem, individual, or environment, at scale. (6/7)

Minghao Guo11 days ago

A research map of selected publications across my PhD study: #GenerativeAI #AIforScience #ComputationalDesign #ScientificMachineLearning #PhDDefense #worldmodel

Minghao Guo11 days ago

Missing the time haha! It’s 2-3pm ET!

중심11 days ago

@MIT_CSAIL at what time?

BrenJ11 days ago

@MIT_CSAIL @evelovesolive @beffjezos felt like this was up y'alls alley.

DrKnowItAll11 days ago

@MIT_CSAIL Great work! This looks really impressive.

rob11 days ago

@MIT_CSAIL Great stuff here.

samir sawarkar11 days ago

@MIT_CSAIL The next leap in generative AI isn’t generating more designs. It’s generating designs that cannot violate the physics. That’s the shift from “AI can imagine it” to “AI can engineer it.”

Aman Chourasia11 days ago

@MIT_CSAIL This is great!

Daniel Samanez11 days ago

@MIT_CSAIL 👍 at what time is it?

Ralf Sigmund🇺🇦🇪🇺 @sistar-hh.bsky.social10 days ago

@MIT_CSAIL Congrats. It was a honor to listen to You leading us through Your work. All the best!

Xenonostra'11 days ago

@MIT_CSAIL I wish you the best of luck I'm sure you will do well

GSC11 days ago

@MIT_CSAIL

Zhiyuan11 days ago

@MIT_CSAIL solid work! may you good luck!

Yuxi Xiao11 days ago

@MIT_CSAIL Wow nice video for the phd thesis!!! congrats

Big data annotation11 days ago

@MIT_CSAIL 大佬，牛逼

Raj Kachhadiya (KARAM)11 days ago

@MIT_CSAIL Interesting, congratulations!

Anand Kumar11 days ago

@MIT_CSAIL Interesting. Looking forward to it.

Neil8 days ago

@MIT_CSAIL Wow this is such an interesting research direction, do you think the future of 3D is headed this way?

Yossi Eliaz11 days ago

@MIT_CSAIL loved the video!

likhesh11 days ago

@MIT_CSAIL Amazing, what time exactly?

Mandar Wagh11 days ago

congrats, and that is a committee. the split i keep getting stuck on: physics constraints are mostly differentiable, so they go straight into a loss. manufacturing constraints mostly are not — tool reach, support-free overhang, demoldability, minimum wall are combinatorial, and you cannot gradient your way to a draft angle. does the thesis treat those as one kind of constraint, or as two different problems? genuinely curious which way you landed.

the guy11 days ago

@MIT_CSAIL 🧡

Jesse Jr Lim (林振燊)11 days ago

@MIT_CSAIL wow very cool.. paused the vid alot to see whats going on

X.M.11 days ago

@MIT_CSAIL all the best king

Volatile Markets11 days ago

@MIT_CSAIL Love this!

Verdi11 days ago

@MIT_CSAIL congrats!! you didnt share a time tho? > "on Tuesday, September 8, 2026, at PM ET."

Jacques Mₜ=∏ₛ((1−λₛ)+λₛEₛ)11 days ago

@MIT_CSAIL PhaGGOT

Curtis10 days ago

@MIT_CSAIL Grest work, great slides, great marketing video!

Black Canvas11 days ago

@MIT_CSAIL Wish you the best and interesting work!

Supratik Bhattacharya11 days ago

@MIT_CSAIL Wow! this is super interesting. all the best!

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1,443,875 views • 10 months ago

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Peter Thiel on the question he asks every startup founder he invests in “Why will the 20th talented person join your company when they can get paid way more at Google, they will have to work way less hard at Google, and it will look better on their resume to go to Google?” The 20th employee won’t get the equity or prestige that someone on the founding team will get, so there needs to be another incentive if you’re going to build a truly great company and attract the best people in the world. Thiel believes the best answer is something along the lines of: “This is the only place in the world where you can work on this incredibly important problem.” He continues: “It has to—on some dimension—be a really important problem that at least some people think is the most important problem in the world. Those are the kind of businesses that are unique, and when they work, they end up being leaders in their respective markets.” If it’s a problem that a bunch of other team are working on, it will be hard to attract a truly world-class employee #20.

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316,188 views • 1 year ago

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MTS

11,676 views • 4 months ago

Demis Hassabis on the limit in today’s AI: language can describe the world, but it cannot contain it - and why "World Models" are his "longest standing passion". Language models absorbed far more structure about reality from text than many researchers expected, because human language quietly carries physics, psychology, culture, tools, plans, and cause-and-effect. But text is still a compressed residue of experience, not experience itself. A sentence can say a cup falls from a table, yet it does not fully encode weight, grip, balance, friction, timing, sound, surprise, or the tiny motor corrections a body makes before it even notices them. The world is not only made of facts that can be named; it is made of constraints that have to be lived through, touched, predicted, violated, and repaired. That is why world models matter. They aim to learn the hidden grammar of physical reality: how objects persist, how forces unfold, how space changes when an agent moves, and how action creates feedback. Language models can often reason about the world because people have written so much about it. World models try to learn what the world is like before it becomes words. The difference is exactly what matters because intelligence is not just answering well; it is knowing what would happen next if you moved, reached, pushed, smelled, slipped, or failed. A mind trained only on descriptions may become brilliant at explanation. A mind trained on experience may become better at consequence. --- Full video from "Google DeepMind" and "Hannah Fry" YT channel (link in comment)

Demis Hassabis on the limit in today’s AI: language can describe the world, but it cannot contain it - and why "World Models" are his "longest standing passion". Language models absorbed far more structure about reality from text than many researchers expected, because human language quietly carries physics, psychology, culture, tools, plans, and cause-and-effect. But text is still a compressed residue of experience, not experience itself. A sentence can say a cup falls from a table, yet it does not fully encode weight, grip, balance, friction, timing, sound, surprise, or the tiny motor corrections a body makes before it even notices them. The world is not only made of facts that can be named; it is made of constraints that have to be lived through, touched, predicted, violated, and repaired. That is why world models matter. They aim to learn the hidden grammar of physical reality: how objects persist, how forces unfold, how space changes when an agent moves, and how action creates feedback. Language models can often reason about the world because people have written so much about it. World models try to learn what the world is like before it becomes words. The difference is exactly what matters because intelligence is not just answering well; it is knowing what would happen next if you moved, reached, pushed, smelled, slipped, or failed. A mind trained only on descriptions may become brilliant at explanation. A mind trained on experience may become better at consequence. --- Full video from "Google DeepMind" and "Hannah Fry" YT channel (link in comment)

Rohan Paul

49,938 views • 3 months ago

Working on a 3D/Pixel Art tileset to use with my custom level design tools Tiles can be textures or entire models, like on the larger walls (grass, moss and other details are procedural) At the end of the video I show how it looks in-game #gamedev #indiegame #lowpoly #pixelart

Working on a 3D/Pixel Art tileset to use with my custom level design tools Tiles can be textures or entire models, like on the larger walls (grass, moss and other details are procedural) At the end of the video I show how it looks in-game #gamedev #indiegame #lowpoly #pixelart

Tiani Pixel | Follow me on BSky!

75,960 views • 2 years ago

Can Minnesota Go Red? Minnesota Senate candidate Michele Tafoya on Somali fraud, ICE ops, the SAVE Act, and U.S. hockey Gang, 2026 will be a pivotal year for the nation’s future. Can the Republicans hold the Congress? Will ICE deportations continue? And: How many of our tax dollars have been leaving our country due to various frauds? You know Michele Tafoya from Gutfeld! and other Fox shows — as well as her previous years as a sideline reporter in numerous other sports. At this writing, she is the leading candidate for the Republican candidate for senate in Minnesota. Can she pull it off? And if so… can she turn a Minnesota Senate seat red? In a state that seems to be at the point of the spear for so many of the nation’s 2026 controversies? Join us for a fun conversation with Michele Tafoya, Republican candidate for Senate from Minnesota (who also happens to be very funny…!). So click here for a sizzle... and subscribe for the full pod.

Can Minnesota Go Red? Minnesota Senate candidate Michele Tafoya on Somali fraud, ICE ops, the SAVE Act, and U.S. hockey Gang, 2026 will be a pivotal year for the nation’s future. Can the Republicans hold the Congress? Will ICE deportations continue? And: How many of our tax dollars have been leaving our country due to various frauds? You know Michele Tafoya from Gutfeld! and other Fox shows — as well as her previous years as a sideline reporter in numerous other sports. At this writing, she is the leading candidate for the Republican candidate for senate in Minnesota. Can she pull it off? And if so… can she turn a Minnesota Senate seat red? In a state that seems to be at the point of the spear for so many of the nation’s 2026 controversies? Join us for a fun conversation with Michele Tafoya, Republican candidate for Senate from Minnesota (who also happens to be very funny…!). So click here for a sizzle... and subscribe for the full pod.

Paul Mauro

23,268 views • 6 months ago

My conversation with Kanishka Rao, who went from working on particle physics at CERN to driving robotics at Google DeepMind. We get into the Gemini Robotics 2 release, trending training recipes in the industry, physical AGI, world models, and more. 1:00 Intro 1:47 Does AI's pace feel crazy? 4:04 From particle physics PHD to robotics 6:03 Speech recognition's deep learning moment 8:43 Whole-body humanoid control 10:47 The trash-bag tying demo 13:07 The robotics data pyramid 14:13 Transferring skills across robots 16:30 How does Motion Transfer work 16:56 Why a separate reasoning model 19:02 Can distillation work in robotics? 20:02 The ASIMOV safety benchmark 22:47 VLAs vs world models 25:10 The missing tactile sensing piece 27:48 How far to physical AGI? 30:07 Fundamental breakthrough needed? 31:53 World models for evaluation 33:45 Will the model layer commoditize? 35:11 Humanoid as a form factor

My conversation with Kanishka Rao, who went from working on particle physics at CERN to driving robotics at Google DeepMind. We get into the Gemini Robotics 2 release, trending training recipes in the industry, physical AGI, world models, and more. 1:00 Intro 1:47 Does AI's pace feel crazy? 4:04 From particle physics PHD to robotics 6:03 Speech recognition's deep learning moment 8:43 Whole-body humanoid control 10:47 The trash-bag tying demo 13:07 The robotics data pyramid 14:13 Transferring skills across robots 16:30 How does Motion Transfer work 16:56 Why a separate reasoning model 19:02 Can distillation work in robotics? 20:02 The ASIMOV safety benchmark 22:47 VLAs vs world models 25:10 The missing tactile sensing piece 27:48 How far to physical AGI? 30:07 Fundamental breakthrough needed? 31:53 World models for evaluation 33:45 Will the model layer commoditize? 35:11 Humanoid as a form factor

The Humanoid Hub

22,037 views • 1 month ago

Introducing WorldForge: testable world-model workflows for physical AI systems. You can think of it, loosely, as “LangChain for world models”. The problem is that “world model” has become an overloaded label. Depending on context, it can mean a video generator, a cost model, a robot policy, a JEPA-style latent predictor, etc. They share almost nothing, different inputs, runtimes, failure modes. I built WorldForge to stop pretending they're interchangeable. Front-door demo: a real Hugging Face LeRobot (LeRobot) diffusion_pusht policy combined with LeWorldModel by Lucas Maes checkpoint for scoring. Both run locally on my MacBook in the demo video. LeWM is extremely efficient (~15M params), can plan up to 48× faster, and runs on commodity hardware. WorldForge wires the loop: policy → candidates → score → select Replay happens in a local TUI today, but the same loop could drive a real robot in the world. Would love feedback from people working on world models, physical AI, robotics, ML infra, and adjacent tooling. Fully open source. Contributions very welcome. Plan in the dream, replay in real world.

Introducing WorldForge: testable world-model workflows for physical AI systems. You can think of it, loosely, as “LangChain for world models”. The problem is that “world model” has become an overloaded label. Depending on context, it can mean a video generator, a cost model, a robot policy, a JEPA-style latent predictor, etc. They share almost nothing, different inputs, runtimes, failure modes. I built WorldForge to stop pretending they're interchangeable. Front-door demo: a real Hugging Face LeRobot (LeRobot) diffusion_pusht policy combined with LeWorldModel by Lucas Maes checkpoint for scoring. Both run locally on my MacBook in the demo video. LeWM is extremely efficient (~15M params), can plan up to 48× faster, and runs on commodity hardware. WorldForge wires the loop: policy → candidates → score → select Replay happens in a local TUI today, but the same loop could drive a real robot in the world. Would love feedback from people working on world models, physical AI, robotics, ML infra, and adjacent tooling. Fully open source. Contributions very welcome. Plan in the dream, replay in real world.

abdel

17,001 views • 4 months ago