MIT tops among single-campus universities in US patents granted
In an era defined by unprecedented challenges and opportunities, MIT remains at the forefront of pioneering research and innovation.
The Institute’s relentless pursuit of knowledge has once again been recognized, with MIT securing 343 utility patents issued by the United States Patent and Trademark Office in 2023. This marks the 10th consecutive year that the National Academy of Inventors has both ranked worldwide colleges for number of U.S. patents issued and recognized MIT as the top single-campus university for patents granted. (The University of California system, which comprises 10 campuses and six academic health centers across the state, is No. 1 overall.)
Technology transfer is at the core of MIT’s mission to advance knowledge for the benefit of the world, and the Technology Licensing Office (TLO) plays a transformative role in bridging the gap between groundbreaking research and societal impact. Impact is recognized in many ways through startups, small- to medium-sized companies, and large corporations. The TLO’s efforts in patenting and licensing are vital for transforming academic discoveries into practical solutions that address societal needs, drive economic growth, and create new opportunities.
Each year, the TLO receives over 600 invention disclosures, resulting in a high volume of issued patents. The TLO’s ongoing strategic licensing efforts bolster MIT’s endeavors across six clear impact areas: healthy living, sustainable futures, connected worlds, advanced materials, climate stabilization, and the exploration of uncharted frontiers. These areas, intentionally curated to reflect the interests and priorities of MIT’s faculty and research staff, drive meaningful change through translation and tech transfer.
Lesley Millar-Nicholson, the executive director of the TLO, further underscores the importance of aligning efforts with President Sally Kornbluth’s vision for MIT. “Our collaborative efforts ensure that the innovations born here at MIT make a difference across the globe, addressing some of the most pressing challenges of our time,” Millar-Nicholson states. “This reflects a shared commitment to what Kornbluth described in her inaugural address about climate change, ‘… [this is] the kind of grand creative enterprise in which the energy you release together is greater than what you each put in. A nuclear fusion of problem-solving and possibility!’”
Verdox and Cognito Therapeutics are two of the many startups that epitomize a grand creative enterprise. Verdox, a startup from the lab of T. Alan Hatton, the Ralph Landau Professor of Chemical Engineering Practice and director of the David H. Koch School of Chemical Engineering Practice, is on a mission to combat climate change by capturing carbon dioxide with unrivaled efficiency using electricity. Cognito, which sprang from the labs of Li-Huei Tsai, professor of neuroscience and director of the Picower Institute for Learning and Memory, and Edward Boyden, the Y. Eva Tan Professor in Neurotechnology and member of the McGovern Institute for Brain Research, pioneers treatments for neurodegenerative diseases, including dementias, offering Alzheimer’s patients a beacon of noninvasive hope with their neuro-stimulatory therapy. These enterprises, just two of many that have licensed and are developing MIT’s intellectual property, embody the spirit of MIT — they are not merely companies; they are catalysts for a more sustainable, healthier world.
Technology Licensing Officer Nestor Franco highlights the daily journey of MIT’s research from concept to commercialization: “Our commitment to out-license these innovations not only amplify MIT’s contribution to global progress but also reinforces our dedication to societal betterment,” he says.
As MIT continues to push the boundaries of what is possible, from deep space to quantum computing, the TLO remains a cornerstone of the Institute’s strategy for impact.
To explore the cutting-edge technologies emerging from MIT, visit patents.mit.edu. Here, you can discover the innovations available for licensing that are set to shape the future. To delve deeper into the work and initiatives of the TLO, and to understand how MIT’s inventions are transformed into societal solutions, visit tlo.mit.edu.
Growing our donated organ supply
For those in need of one, an organ transplant is a matter of life and death.
Every year, the medical procedure gives thousands of people with advanced or end-stage diseases extended life. This “second chance” is heavily dependent on the availability, compatibility, and proximity of a precious resource that can’t be simply bought, grown, or manufactured — at least not yet.
Instead, organs must be given — cut from one body and implanted into another. And because living organ donation is only viable in certain cases, many organs are only available for donation after the donor’s death.
Unsurprisingly, the logistical and ethical complexity of distributing a limited number of transplant organs to a growing wait list of patients has received much attention. There’s an important part of the process that has received less focus, however, and which may hold significant untapped potential: organ procurement itself.
“If you have a donated organ, who should you give it to? This question has been extensively studied in operations research, economics, and even applied computer science,” says Hammaad Adam, a graduate student in the Social and Engineering Systems (SES) doctoral program at the MIT Institute for Data, Systems, and Society (IDSS). “But there’s been a lot less research on where that organ comes from in the first place.”
In the United States, nonprofits called organ procurement organizations, or OPOs, are responsible for finding and evaluating potential donors, interacting with grieving families and hospital administrations, and recovering and delivering organs — all while following the federal laws that serve as both their mandate and guardrails. Recent studies estimate that obstacles and inefficiencies lead to thousands of organs going uncollected every year, even as the demand for transplants continues to grow.
“There’s been little transparent data on organ procurement,” argues Adam. Working with MIT computer science professors Marzyeh Ghassemi and Ashia Wilson, and in collaboration with stakeholders in organ procurement, Adam led a project to create a dataset called ORCHID: Organ Retrieval and Collection of Health Information for Donation. ORCHID contains a decade of clinical, financial, and administrative data from six OPOs.
“Our goal is for the ORCHID database to have an impact in how organ procurement is understood, internally and externally,” says Ghassemi.
Efficiency and equity
It was looking to make an impact that drew Adam to SES and MIT. With a background in applied math and experience in strategy consulting, solving problems with technical components sits right in his wheelhouse.
“I really missed challenging technical problems from a statistics and machine learning standpoint,” he says of his time in consulting. “So I went back and got a master’s in data science, and over the course of my master’s got involved in a bunch of academic research projects in a few different fields, including biology, management science, and public policy. What I enjoyed most were some of the more social science-focused projects that had immediate impact.”
As a grad student in SES, Adam’s research focuses on using statistical tools to uncover health-care inequities, and developing machine learning approaches to address them. “Part of my dissertation research focuses on building tools that can improve equity in clinical trials and other randomized experiments,” he explains.
One recent example of Adam’s work: developing a novel method to stop clinical trials early if the treatment has an unintended harmful effect for a minority group of participants. “I’ve also been thinking about ways to increase minority representation in clinical trials through improved patient recruitment,” he adds.
Racial inequities in health care extend into organ transplantation, where a majority of wait-listed patients are not white — far in excess of their demographic groups’ proportion to the overall population. There are fewer organ donations from many of these communities, due to various obstacles in need of better understanding if they are to be overcome.
“My work in organ transplantation began on the allocation side,” explains Adam. “In work under review, we examined the role of race in the acceptance of heart, liver, and lung transplant offers by physicians on behalf of their patients. We found that Black race of the patient was associated with significantly lower odds of organ offer acceptance — in other words, transplant doctors seemed more likely to turn down organs offered to Black patients. This trend may have multiple explanations, but it is nevertheless concerning.”
Adam’s research has also found that donor-candidate race match was associated with significantly higher odds of offer acceptance, an association that Adam says “highlights the importance of organ donation from racial minority communities, and has motivated our work on equitable organ procurement.”
Working with Ghassemi through the IDSS Initiative on Combatting Systemic Racism, Adam was introduced to OPO stakeholders looking to collaborate. “It’s this opportunity to impact not only health-care efficiency, but also health-care equity, that really got me interested in this research,” says Adam.
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MIT Initiative on Combatting Systemic Racism – HealthcareVideo: IDSS
Making an impact
Creating a database like ORCHID means solving problems in multiple domains, from the technical to the political. Some efforts never overcome the first step: getting data in the first place. Thankfully, several OPOs were already seeking collaborations and looking to improve their performance.
“We have been lucky to have a strong partnership with the OPOs, and we hope to work together to find important insights to improve efficiency and equity,” says Ghassemi.
The value of a database like ORCHID is in its potential for generating new insights, especially through quantitative analysis with statistics and computing tools like machine learning. The potential value in ORCHID was recognized with an MIT Prize for Open Data, an MIT Libraries award highlighting the importance and impact of research data that is openly shared.
“It’s nice that the work got some recognition,” says Adam of the prize. “And it was cool to see some of the other great open data work that’s happening at MIT. I think there’s real impact in releasing publicly available data in an important and understudied domain.”
All the same, Adam knows that building the database is only the first step.
“I’m very interested in understanding the bottlenecks in the organ procurement process,” he explains. “As part of my thesis research, I’m exploring this by modeling OPO decision-making using causal inference and structural econometrics.”
Using insights from this research, Adam also aims to evaluate policy changes that can improve both equity and efficiency in organ procurement. “And we’re hoping to recruit more OPOs, and increase the amount of data we’re releasing,” he says. “The dream state is every OPO joins our collaboration and provides updated data every year.”
Adam is excited to see how other researchers might use the data to address inefficiencies in organ procurement. “Every organ donor saves between three and four lives,” he says. “So every research project that comes out of this dataset could make a real impact.”
New AI method captures uncertainty in medical images
In biomedicine, segmentation involves annotating pixels from an important structure in a medical image, like an organ or cell. Artificial intelligence models can help clinicians by highlighting pixels that may show signs of a certain disease or anomaly.
However, these models typically only provide one answer, while the problem of medical image segmentation is often far from black and white. Five expert human annotators might provide five different segmentations, perhaps disagreeing on the existence or extent of the borders of a nodule in a lung CT image.
“Having options can help in decision-making. Even just seeing that there is uncertainty in a medical image can influence someone’s decisions, so it is important to take this uncertainty into account,” says Marianne Rakic, an MIT computer science PhD candidate.
Rakic is lead author of a paper with others at MIT, the Broad Institute of MIT and Harvard, and Massachusetts General Hospital that introduces a new AI tool that can capture the uncertainty in a medical image.
Known as Tyche (named for the Greek divinity of chance), the system provides multiple plausible segmentations that each highlight slightly different areas of a medical image. A user can specify how many options Tyche outputs and select the most appropriate one for their purpose.
Importantly, Tyche can tackle new segmentation tasks without needing to be retrained. Training is a data-intensive process that involves showing a model many examples and requires extensive machine-learning experience.
Because it doesn’t need retraining, Tyche could be easier for clinicians and biomedical researchers to use than some other methods. It could be applied “out of the box” for a variety of tasks, from identifying lesions in a lung X-ray to pinpointing anomalies in a brain MRI.
Ultimately, this system could improve diagnoses or aid in biomedical research by calling attention to potentially crucial information that other AI tools might miss.
“Ambiguity has been understudied. If your model completely misses a nodule that three experts say is there and two experts say is not, that is probably something you should pay attention to,” adds senior author Adrian Dalca, an assistant professor at Harvard Medical School and MGH, and a research scientist in the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL).
Their co-authors include Hallee Wong, a graduate student in electrical engineering and computer science; Jose Javier Gonzalez Ortiz PhD ’23; Beth Cimini, associate director for bioimage analysis at the Broad Institute; and John Guttag, the Dugald C. Jackson Professor of Computer Science and Electrical Engineering. Rakic will present Tyche at the IEEE Conference on Computer Vision and Pattern Recognition, where Tyche has been selected as a highlight.
Addressing ambiguity
AI systems for medical image segmentation typically use neural networks. Loosely based on the human brain, neural networks are machine-learning models comprising many interconnected layers of nodes, or neurons, that process data.
After speaking with collaborators at the Broad Institute and MGH who use these systems, the researchers realized two major issues limit their effectiveness. The models cannot capture uncertainty and they must be retrained for even a slightly different segmentation task.
Some methods try to overcome one pitfall, but tackling both problems with a single solution has proven especially tricky, Rakic says.
“If you want to take ambiguity into account, you often have to use an extremely complicated model. With the method we propose, our goal is to make it easy to use with a relatively small model so that it can make predictions quickly,” she says.
The researchers built Tyche by modifying a straightforward neural network architecture.
A user first feeds Tyche a few examples that show the segmentation task. For instance, examples could include several images of lesions in a heart MRI that have been segmented by different human experts so the model can learn the task and see that there is ambiguity.
The researchers found that just 16 example images, called a “context set,” is enough for the model to make good predictions, but there is no limit to the number of examples one can use. The context set enables Tyche to solve new tasks without retraining.
For Tyche to capture uncertainty, the researchers modified the neural network so it outputs multiple predictions based on one medical image input and the context set. They adjusted the network’s layers so that, as data move from layer to layer, the candidate segmentations produced at each step can “talk” to each other and the examples in the context set.
In this way, the model can ensure that candidate segmentations are all a bit different, but still solve the task.
“It is like rolling dice. If your model can roll a two, three, or four, but doesn’t know you have a two and a four already, then either one might appear again,” she says.
They also modified the training process so it is rewarded by maximizing the quality of its best prediction.
If the user asked for five predictions, at the end they can see all five medical image segmentations Tyche produced, even though one might be better than the others.
The researchers also developed a version of Tyche that can be used with an existing, pretrained model for medical image segmentation. In this case, Tyche enables the model to output multiple candidates by making slight transformations to images.
Better, faster predictions
When the researchers tested Tyche with datasets of annotated medical images, they found that its predictions captured the diversity of human annotators, and that its best predictions were better than any from the baseline models. Tyche also performed faster than most models.
“Outputting multiple candidates and ensuring they are different from one another really gives you an edge,” Rakic says.
The researchers also saw that Tyche could outperform more complex models that have been trained using a large, specialized dataset.
For future work, they plan to try using a more flexible context set, perhaps including text or multiple types of images. In addition, they want to explore methods that could improve Tyche’s worst predictions and enhance the system so it can recommend the best segmentation candidates.
This research is funded, in part, by the National Institutes of Health, the Eric and Wendy Schmidt Center at the Broad Institute of MIT and Harvard, and Quanta Computer.
Improving drug development with a vast map of the immune system
The human immune system is a network made up of trillions of cells that are constantly circulating throughout the body. The cellular network orchestrates interactions with every organ and tissue to carry out an impossibly long list of functions that scientists are still working to understand. All that complexity limits our ability to predict which patients will respond to treatments and which ones might suffer debilitating side effects.
The issue often leads pharmaceutical companies to stop developing drugs that could help certain patients, halting clinical trials even when drugs show promising results for some people.
Now, Immunai is helping to predict how patients will respond to treatments by building a comprehensive map of the immune system. The company has assembled a vast database it calls AMICA, that combines multiple layers of gene and protein expression data in cells with clinical trial data to match the right drugs to the right patients.
“Our starting point was creating what I call the Google Maps for the immune system,” Immunai co-founder and CEO Noam Solomon says. “We started with single-cell RNA sequencing, and over time we’ve added more and more ‘omics’: genomics, proteomics, epigenomics, all to measure the immune system’s cellular expression and function, to measure the immune environment holistically. Then we started working with pharmaceutical companies and hospitals to profile the immune systems of patients undergoing treatments to really get to the root mechanisms of action and resistance for therapeutics.”
Immunai’s big data foundation is a result of its founders’ unique background. Solomon and co-founder Luis Voloch ’13, SM ’15 hold degrees in mathematics and computer science. In fact, Solomon was a postdoc in MIT’s Department of Mathematics at the time of Immunai’s founding.
Solomon frames Immunai’s mission as stopping the decades-long divergence of computer science and the life sciences. He believes the single biggest factor driving the explosion of computing has been Moore’s Law — our ability to exponentially increase the number of transistors on a chip over the past 60 years. In the pharmaceutical industry, the reverse is happening: By one estimate, the cost of developing a new drug roughly doubles every nine years. The phenomenon has been dubbed Eroom’s Law (“Eroom” for “Moore” spelled backward).
Solomon sees the trend eroding the case for developing new drugs, with huge consequences for patients.
“Why should pharmaceutical companies invest in discovery if they won’t get a return on investment?” Solomon asks. “Today, there’s only a 5 to 10 percent chance that any given clinical trial will be successful. What we’ve built through a very robust and granular mapping of the immune system is a chance to improve the preclinical and clinical stages of drug development.”
A change in plans
Solomon entered Tel Aviv University when he was 14 and earned his bachelor’s degree in computer science by 19. He earned two PhDs in Israel, one in computer science and the other in mathematics, before coming to MIT in 2017 as a postdoc to continue his mathematical research career.
That year Solomon met Voloch, who had already earned bachelor’s and master’s degrees in math and computer science from MIT. But the researchers were soon exposed to a problem that would take them out of their comfort zones and change the course of their careers.
Voloch’s grandfather was receiving a cocktail of treatments for cancer at the time. The cancer went into remission, but he suffered terrible side effects that caused him to stop taking his medication.
Voloch and Solomon began wondering if their expertise could help patients like Voloch’s grandfather.
“When we realized we could make an impact, we made the difficult decision to stop our academic pursuits and start a new journey,” Solomon recalls. “That was the starting point for Immunai.”
Voloch and Solomon soon partnered with Immunai scientific co-founders Ansu Satpathy, a researcher at Stanford University at the time, and Danny Wells, a researcher at the Parker Institute for Cancer Immunotherapy. Satpathy and Wells had shown that single-cell RNA sequencing could be used to gain insights into why patients respond differently to a common cancer treatment.
The team began analyzing single-cell RNA sequencing data published in scientific papers, trying to link common biomarkers with patient outcomes. Then they integrated data from the United Kingdom’s Biobank public health database, finding they were able to improve their models’ predictions. Soon they were incorporating data from hospitals, academic research institutions, and pharmaceutical companies, analyzing information about the structure, function, and environment of cells — multiomics — to get a clearer picture of immune activity.
“Single cell sequencing gives you metrics you can measure in thousands of cells, where you can look at 20,000 different genes, and those metrics give you an immune profile,” Solomon explains. “When you measure all of that over time, especially before and after getting therapy, and compare patients who do respond with patients who don’t, you can apply machine learning models to understand why.”
Those data and models make up AMICA, what Immunai calls the world’s largest cell-level immune knowledge base. AMICA stands for Annotated Multiomic Immune Cell Atlas. It analyzes single cell multiomic data from almost 10,000 patients and bulk-RNA data from 100,000 patients across more than 800 cell types and 500 diseases.
At the core of Immunai’s approach is a focus on the immune system, which other companies shy away from because of its complexity.
“We don’t want to be like other groups that are studying mainly tumor microenvironments,” Solomon says. “We look at the immune system because the immune system is the common denominator. It’s the one system that is implicated in every disease, in your body’s response to everything that you encounter, whether it’s a viral infection or bacterial infection or a drug that you are receiving — even how you are aging.”
Turning data into better treatments
Immunai has already partnered with some of the largest pharmaceutical companies in the world to help them identify promising treatments and set up their clinical trials for success. Immunai’s insights can help partners make critical decisions about treatment schedules, dosing, drug combinations, patient selection, and more.
“Everyone is talking about AI, but I think the most exciting aspect of the platform we have built is the fact that it’s vertically integrated, from wet lab to computational modeling with multiple iterations,” Solomon says. “For example, we may do single-cell immune profiling of patient samples, then we upload that data to the cloud and our computational models come up with insights, and with those insights we do in vitro or in vivo validation to see if our models are right and iteratively improve them.”
Ultimately Immunai wants to enable a future where lab experiments can more reliably turn into impactful new recommendations and treatments for patients.
“Scientists can cure nearly every type of cancer, but only in mice,” Solomon says. “In preclinical models we know how to cure cancer. In human beings, in most cases, we still don’t. To overcome that, most scientists are looking for better ex vivo or in vivo models. Our approach is to be more agnostic as to the model system, but feed the machine with more and more data from multiple model systems. We’re demonstrating that our algorithms can repeatedly beat the top benchmarks in identifying the top preclinical immune features that match to patient outcomes.”