Have you ever had an idea for something that looked cool, but wouldn’t work well in practice? When it comes to designing things like decor and personal accessories, generative artificial intelligence (genAI) models can relate. They can produce creative and elaborate 3D designs, but when you try to fabricate such blueprints into real-world objects, they usually don’t sustain everyday use.

The underlying problem is that genAI models often lack an understanding of physics. While tools like Microsoft’s TRELLIS system can create a 3D model from a text prompt or image, its design for a chair, for example, may be unstable, or have disconnected parts. The model doesn’t fully understand what your intended object is designed to do, so even if your seat can be 3D printed, it would likely fall apart under the force of someone sitting down.

In an attempt to make these designs work in the real world, researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) are giving generative AI models a reality check. Their “PhysiOpt” system augments these tools with physics simulations, making blueprints for personal items such as cups, keyholders, and bookends work as intended when they’re 3D printed. It rapidly tests if the structure of your 3D model is viable, gently modifying smaller shapes while ensuring the overall appearance and function of the design is preserved.

You can simply type what you want to create and what it’ll be used for into PhysiOpt, or upload an image to the system’s user interface, and in roughly half a minute, you’ll get a realistic 3D object to fabricate. For example, CSAIL researchers prompted it to generate a “flamingo-shaped glass for drinking,” which they 3D printed into a drinking glass with a handle and base resembling the tropical bird’s leg. As the design was generated, PhysiOpt made tiny refinements to ensure the design was structurally sound.

“PhysiOpt combines GenAI and physically-based shape optimization, helping virtually anyone generate the designs they want for unique accessories and decorations,” says MIT electrical engineering and computer science (EECS) PhD student and CSAIL researcher Xiao Sean Zhan SM ’25, who is a co-lead author on a paper presenting the work. “It’s an automatic system that allows you to make the shape physically manufacturable, given some constraints. PhysiOpt can iterate on its creations as often as you’d like, without any extra training.”

This approach enables you to create a “smart design,” where the AI generator crafts your item based on users’ specifications, while considering functionality. You can plug in your favorite 3D generative AI model, and after typing out what you want to generate, you specify how much force or weight the object should handle. It’s a neat way to simulate real-world use, such as predicting whether a hook will be strong enough to hold up your coat. Users also specify what materials they’ll fabricate the item with (such as plastics or wood), and how it’s supported — for instance, a cup stands on the ground, whereas a bookend leans against a collection of books.

Given the specifics, PhysiOpt begins to iteratively optimize the object. Under the hood, it runs a physics simulation called a “finite element analysis” to stress test the design. This comprehensive scan provides a heat map over your 3D model, which indicates where your blueprint isn’t well-supported. If you were generating, say, a birdhouse, you may find that the support beams under the house were colored bright red, meaning the house will crumble if it’s not reinforced.

PhysiOpt can create even bolder pieces. Researchers saw this versatility firsthand when they fabricated a steampunk (a style that blends Victorian and futuristic aesthetics) keyholder featuring intricate, robotic-looking hooks, and a “giraffe table” with a flat back that you can place items on. But how did it know what “steampunk” is, or even how such a unique piece of furniture should look?

Remarkably, the answer isn’t extensive training — at least, not from the researchers. Instead, PhysiOpt uses a pre-trained model that’s already seen thousands of shapes and objects. “Existing systems often need lots of additional training to have a semantic understanding of what you want to see,” adds co-lead author Clément Jambon, who is also an MIT EECS PhD student and CSAIL researcher. “But we use a model with that feel for what you want to create already baked in, so PhysiOpt is training-free.”

By working with a pre-trained model, PhysiOpt can use “shape priors,” or knowledge of how shapes should look based on earlier training, to generate what users want to see. It’s sort of like an artist recreating the style of a famous painter. Their expertise is rooted in closely studying a variety of artistic approaches, so they’ll likely be able to mirror that particular aesthetic. Likewise, a pre-trained model’s familiarity with shapes helps it generate 3D models.

CSAIL researchers observed that PhysiOpt’s visual know-how helped it create 3D models more efficiently than “DiffIPC,” a comparable method that simulates and optimizes shapes. When both approaches were tasked with generating 3D designs for items like chairs, CSAIL’s system was nearly 10 times faster per iteration, while creating more realistic objects.

PhysiOpt presents a potential bridge between ideas and real-world personal items. What you may think is a great idea for a coffee mug, for instance, could soon make the jump from your computer screen to your desk. And while PhysiOpt already does the stress-testing for designers, it may soon be able to predict constraints such as loads and boundaries, instead of users needing to provide those details. This more autonomous, common-sense approach could be made possible by incorporating vision language models, which combine an understanding of human language with computer vision.

What’s more, Zhan and Jambon intend to remove the artifacts, or random fragments that occasionally appear in PhysiOpt’s 3D models, by making the system even more physics-aware. The MIT scientists are also considering how they can model more complex constraints for various fabrication techniques, such as minimizing overhanging components for 3D printing.

Zhan and Jambon wrote their paper with MIT-IBM Watson AI Lab Principal Research Scientist Kenney Ng ’89, SM ’90, PhD ’00 and two CSAIL colleagues: undergraduate researcher Evan Thompson and Assistant Professor Mina Konaković Luković, who is a principal investigator at the lab. 

The researchers’ work was supported, in part, by the MIT-IBM Watson AI Laboratory and the Wistron Corp. They presented it in December at the Association for Computing Machinery’s SIGGRAPH Conference and Exhibition on Computer Graphics and Interactive Techniques in Asia.

Studying gene expression in a cancer patient’s cells can help clinical biologists understand the cancer’s origin and predict the success of different treatments. But cells are complex and contain many layers, so how the biologist conducts measurements affects which data they can obtain. For instance, measuring proteins in a cell could yield different information about the effects of cancer than measuring gene expression or cell morphology.

Where in the cell the information comes from matters. But to capture complete information about the state of the cell, scientists often must conduct many measurements using different techniques and analyze them one at a time. Machine-learning methods can speed up the process, but existing methods lump all the information from each measurement modality together, making it difficult to figure out which data came from which part of the cell.

To overcome this problem, researchers at the Broad Institute of MIT and Harvard and ETH Zurich/Paul Scherrer Institute (PSI) developed an artificial intelligence-driven framework that learns which information about a cell’s state is shared across different measurement modalities and which information is unique to a particular measurement type.

By pinpointing which information came from which cell parts, the approach provides a more holistic view of the cell’s state, making it easier for a biologist to see the complete picture of cellular interactions. This could help scientists understand disease mechanisms and track the progression of cancer, neurodegenerative disorders such as Alzheimer’s, and metabolic diseases like diabetes.

“When we study cells, one measurement is often not sufficient, so scientists develop new technologies to measure different aspects of cells. While we have many ways of looking at a cell, at the end of the day we only have one underlying cell state. By putting the information from all these measurement modalities together in a smarter way, we could have a fuller picture of the state of the cell,” says lead author Xinyi Zhang SM ’22, PhD ’25, a former graduate student in the MIT Department of Electrical Engineering and Computer Science (EECS) and an affiliate of the Eric and Wendy Schmidt Center at the Broad Institute of MIT and Harvard, who is now a group leader at AITHYRA in Vienna, Austria.

Zhang is joined on a paper about the work by G.V. Shivashankar, a professor in the Department of Health Sciences and Technology at ETH Zurich and head of the Laboratory of Multiscale Bioimaging at PSI; and senior author Caroline Uhler, a professor in EECS and the Institute for Data, Systems, and Society (IDSS) at MIT, member of MIT’s Laboratory for Information and Decision Systems (LIDS), and director of the Eric and Wendy Schmidt Center at the Broad Institute. The research appears today in Nature Computational Science.

Manipulating multiple measurements

There are many tools scientists can use to capture information about a cell’s state. For instance, they can measure RNA to see if the cell is growing, or they can measure chromatin morphology to see if the cell is dealing with external physical or chemical signals.

“When scientists perform multimodal analysis, they gather information using multiple measurement modalities and integrate it to better understand the underlying state of the cell. Some information is captured by one modality only, while other information is shared across modalities. To fully understand what is happening inside the cell, it is important to know where the information came from,” says Shivashankar.

Often, for scientists, the only way to sort this out is to conduct multiple individual experiments and compare the results. This slow and cumbersome process limits the amount of information they can gather.

In the new work, the researchers built a machine-learning framework that specifically understands which information overlaps between different modalities, and which information is unique to a particular modality but not captured by others.

“As a user, you can simply input your cell data and it automatically tells you which data are shared and which data are modality-specific,” Zhang says.

To build this framework, the researchers rethought the typical way machine-learning models are designed to capture and interpret multimodal cellular measurements.

Usually these methods, known as autoencoders, have one model for each measurement modality, and each model encodes a separate representation for the data captured by that modality. The representation is a compressed version of the input data that discards any irrelevant details.

The MIT method has a shared representation space where data that overlap between multiple modalities are encoded, as well as separate spaces where unique data from each modality are encoded.

In essence, one can think of it like a Venn diagram of cellular data.

The researchers also used a special, two-step training procedure that helps their model handle the complexity involved in deciding which data are shared across multiple data modalities. After training, the model can identify which data are shared and which are unique when fed cell data it has never seen before.

Distinguishing data

In tests on synthetic datasets, the framework correctly captured known shared and modality-specific information. When they applied their method to real-world single-cell datasets, it comprehensively and automatically distinguished between gene activity captured jointly by two measurement modalities, such as transcriptomics and chromatin accessibility, while also correctly identifying which information came from only one of those modalities.

In addition, the researchers used their method to identify which measurement modality captured a certain protein marker that indicates DNA damage in cancer patients. Knowing where this information came from would help a clinical scientist determine which technique they should use to measure that marker.

“There are too many modalities in a cell and we can’t possibly measure them all, so we need a prediction tool. But then the question is: Which modalities should we measure and which modalities should we predict? Our method can answer that question,” Uhler says.

In the future, the researchers want to enable the model to provide more interpretable information about the state of the cell. They also want to conduct additional experiments to ensure it correctly disentangles cellular information and apply the model to a wider range of clinical questions.

“It is not sufficient to just integrate the information from all these modalities,” Uhler says. “We can learn a lot about the state of a cell if we carefully compare the different modalities to understand how different components of cells regulate each other.”

This research is funded, in part, by the Eric and Wendy Schmidt Center at the Broad Institute, the Swiss National Science Foundation, the U.S. National Institutes of Health, the U.S. Office of Naval Research, AstraZeneca, the MIT-IBM Watson AI Lab, the MIT J-Clinic for Machine Learning and Health, and a Simons Investigator Award.

With the impact artificial intelligence is having on how companies operate, the environment for how MIT students are learning entrepreneurship and choosing to create new ventures is seeing rapid changes as well. To address how these student startups are being built, the Martin Trust Center for MIT Entrepreneurship undertook a months-long series of discussions with key stakeholders to help shape a new direction for delta v, MIT’s capstone entrepreneurship accelerator for student founders.

Two of Boston’s most successful tech entrepreneurs have stepped forward to fund this growth of new MIT ventures through a combined $6 million gift that supports the delta v accelerator run out of the Trust Center. Ed Hallen MBA ’12 and Andrew Bialecki, co-founders of Boston-based customer relationship management firm Klaviyo, are providing the donation to support the next wave of innovation-driven entrepreneurship taking place at MIT.

“In the early days of Klaviyo, we learned almost everything by building, testing assumptions, making mistakes, and figuring things out as we went,” Hallen says. “MIT delta v creates that same learning-by-doing environment for students, while surrounding them with mentorship and resources that help founders build with clarity and momentum. We’ve seen the difference delta v can make for founders, and we’re excited to help the Trust Center extend that opportunity to the next generation of students.”

“We’ve always believed the world needs more entrepreneurs, and that Boston should be one of the places leading the way,” adds Bialecki. “Boston is a hub of innovation with ambitious students and a strong community of builders. MIT delta v plays a critical role in developing founders early, not just helping them start companies but helping them build companies that last. Supporting that mission is something Ed and I care deeply about.”

The Martin Trust Center plans to “accelerate the accelerator” with the funding. Recognizing the opportunity that exists as AI impacts how students are able to build companies, along with the increased interest being shown by students to learn about entrepreneurship during their time on campus, is a major driver for these changes. One of the main impacts will be the ability of delta v participants to earn up to $75,000 in equity-free funding during the program, an increase from $20,000 in years past. 

Also, delta v will be introducing a partner model composed of leading founders from companies such as HubSpot, Okta, and Kayak, C-suite operators, subject matter experts, and early-stage investors who will all be providing significant guidance and mentorship to the student ventures.

“Core to MIT’s mission is developing the innovative technologies and solutions that can help solve tough problems at global scale,” says MIT Provost Anantha Chandrakasan. “The AI revolution is creating exciting new opportunities for MIT students to build the next wave of impactful companies, and the delta v accelerator is a perfect vehicle to help them make that happen.”

In recent years MIT-founded startups such as Cursor and Delve who use AI as a core part of their business have seen explosive growth in both customers and revenue as well as valuation. In addition, delta v alumni entrepreneurs and their companies such as Klarity and Reducto are providing software-as-a-service (SaaS) platforms using AI tools while Vertical Semiconductor is growing thanks to providing the energy solutions that data centers need to power today’s computing demands. These are just some of the businesses MIT students are looking to as models they can follow to build and launch successfully, whether they are working on solutions in health care, climate, finance, the future of work, or another global challenge.

“MIT Sloan is the place for entrepreneurship education, part of a unique ecosystem of collaboration across MIT to solve problems," says Richard M. Locke, the John C Head III Dean at the MIT Sloan School of Management. “The delta v program is a great example of how MIT students dedicate their energy to starting a venture, connect with mentors, and incorporate proven frameworks for disciplined entrepreneurship. This gift from Ed Hallen and Andrew Bialecki will provide additional funding for this important program, and I’m so grateful for their support of entrepreneurship education at MIT.” 

“I remember when Ed and Andrew were giving birth to Klaviyo at the Trust Center,” says Bill Aulet, the Ethernet Inventors Professor of the Practice and managing director of the Trust Center. “Through their ingenuity and drive, they have created an iconic tech company here in Boston with the support of our ecosystem. Through their willingness to give back, many more students will now be able to follow their path and become entrepreneurs who can create extraordinary positive impact in the world.”

Applications for the next delta v cohort will open on March 1 and close on April 1. Teams will be announced in May for the summer 2026 accelerator.

“MIT delta v is about creating belief in our most exceptional entrepreneurial talent — and turning that belief into consequential impact for the world. By supporting early-stage founders who take bold ideas from improbable to possible, we help them build companies that matter,” says Ana Bakshi, the Trust Center’s executive director. “Our students are the next generation of job creators, economic drivers, and thought leaders. To realize this potential, it is critical that we continue to invest in and scale startup programs and spaces so they can build at unprecedented levels. Ed and Andrew’s generosity gives us a powerful opportunity to change velocity—and make that future possible.”

Founded in 1991, the award-winning Martin Trust Center for MIT Entrepreneurship is today focused on teaching entrepreneurship as a craft. It combines evidence-based entrepreneurship frameworks, used in over a thousand other organizations, with experiential learning, experiences, and community building inside and outside the classroom to create the next generation of innovation-driven entrepreneurs. Alumni who have gone through Trust Center programs have started companies including Cursor, Delve, Okta, HubSpot, PillPack, Honey, WHOOP, Reducto, Klarity, and Biobot Analytics, and thousands more in industries as diverse as biotech, climate and energy, AI, health care, fintech, business and consumer software, and more. 

In the first 10 years of delta v, the program's alumni have helped create entrepreneurs who have gone on to experience extraordinary success. The five-year survival rate of their companies has been 69%, and they have raised well over $3 billion in funding while addressing the world’s greatest challenges — evidenced by the fact that 89% are directly aligned with the UN Sustainable Development goals.