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July 18, 2019


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ILP Insider
July 1, 2019

Digital help for behavioral health

CompanionMx combines a mobile app with an AI-based platform to help clinicians help people with mood disorders.
“Mood disorder” is a mild term for a major problem in public health, which includes depression, bipolar disorder and other chronic conditions that worsen someone’s emotional state. These conditions are surprisingly common—for example, each year about 11 million U.S. adults suffer at least one major depressive episode—and the clinical monitoring that helps to avoid such episodes has been surprisingly unchanged for decades.

CompanionMx, an MIT STEX25 company, takes on this problem with Companion™, a mobile app-based service designed to help clinicians make smarter decisions for their mood disorder patients. Using AI-based algorithms built from MIT research, Companion measures and analyzes voice and cell phone metadata to quantify symptoms of mental health. These symptoms are then reported to clinicians.

Subhrangshu Datta
CEO, CompanionMx

Early clinical trials have shown that incorporating Companion into regular care for people with diagnosed mood disorders can make a statistically significant improvement in their care outcomes, says Subhrangshu (Sub) Datta, chief executive officer of CompanionMx. “We’re able to provide the right care at the right time from the right level of care provider for people with these disorders,” he says.

A second-generation spinoff
Companion’s core technology is derived from research on automated conversational analysis carried out at the MIT Media Lab’s Human Dynamics Laboratory, led by Professor Alex (Sandy) Pentland. In 2007 the technology was spun out into Cogito Corp., which has pursued two main opportunities. The first is in assisting agents in customer service and call centers, and the second is in behavioral health with Companion.

Much of the early funding for what became Companion came from the Department of Defense. In a first clinical trial at a Veterans Administration clinic in 2013, a prototype system performed well in monitoring veterans with depression and post-traumatic stress disorder (PTSD). The National Institute of Mental Health supported further work with the next-stage Companion product, including a clinical trial at two Boston hospitals in which 73 participants with at least one symptom of PTSD or depression completed a 12-week field trial. Reported in 2017, the trial showed that Companion successfully predicted symptoms of depression and PTSD, and that participants were reasonably comfortable using the app.

When CompanionMx spun out of Cogito to fully commercialize the product in 2018, Datta joined as CEO with a personal mission. After Datta himself experienced an episode of major depressive disorder, he had been surprised and disappointed to see that caregivers were making their ongoing decisions based only on intermittent, qualitative self-reported scoring by patients.

In his career as an executive with medical device business units within large manufacturers, “the healthcare system that I knew is completely driven by data that is continuous, repeatable and reproducible,” Datta says. “I just couldn't believe that system didn’t exist for mood disorders. There's no reason for anybody to have episodes like the one I had. We should be able to manage it early so things don't escalate to that point.”

Details on disorders
Companion passively collects two broad types of data from the user’s cell phone, Datta explains. One is voice; the user is regularly prompted to record a quick “audio diary,” the program does not pull voice data from calls or other communications. The other type is passively collected cell phone metadata: call log, text logs and geolocation.

Next, Companion’s cloud service brings AI-based algorithms to quantify behavior health symptoms. Examining the collected audio diaries, “Companion is not looking at words but at speech patterns, which makes it extremely powerful, difficult to game and language-agnostic,” says Datta. “Similarly in phone metadata, it’s looking for patterns on the frequency, diversity and timing of interactions.”

The system’s proprietary AI driven algorithms convert this data into quantitative measures for four symptoms of behavioral health grounded in standard clinical practice: interest, social isolation, mood and energy level.

This information then is summarized in a dashboard delivered to clinicians (as well as the user). “The dashboard gives greater visibility to the user’s behavioral health symptoms on a continuous, repeatable and reproducible basis, so clinicians can detect worsening of symptoms early and do something about it,” Datta says. In a recent clinical study at a Harvard teaching hospital, clinicians used insights from the Companion dashboard to predict mania up to two weeks in advance—advance notice that could help to avoid hospitalization or worse outcomes.

Companion’s clinical component is designed to fit within a healthcare provider’s current workflow, with a social worker or behavioral health counselor periodically checking the dashboard. The social worker pulls in a psychiatrist at the appropriate time when needed, both ensuring appropriate care and improving resource utilization, Datta says.

Building partnerships for behavioral health
Because Companion acts solely as an advisory service for clinicians, it hasn’t required Food & Drug Administration approval. CompanionMx is now rolling out the service commercially.

In March 2019, CompanionMx was selected for the MIT Startup Exchange’s STEX25, an accelerator for “industry-ready” startups that are ready for significant growth. The company seeks to partner with technology firms, pharmaceutical companies and large organizations with employee wellness programs.

Companion’s ability to provide proactive remote monitoring of behavioral health symptoms has shown significant appeal for technology companies, Datta says. At Veteran Affairs facilities, for instance, “we could help open up a whole set of opportunities to really make a difference to address the scourge of suicide among veterans that's happening today,” Datta says.

His company already is working with one pharmaceutical firm, in a clinical study in which Companion acts as a measurement of efficacy for their firm’s treatment.

Among large employers, “mood disorders are a huge challenge that many are trying to address,” Datta says. “They’re looking for partners who can give them the ability to proactively help individuals who have already been diagnosed.” Organizations must, however, consider the organizational culture and commitment to help individuals with mood disorders, “because there often is stigma around it,” he says.

Over time, CompanionMx plans to expand and sharpen its software tools for addressing anxiety and stress related disorders. The company also wants to tailor Companion for both geriatric patients and adolescents—both populations in which depression is a major problem. “Suicide has become the number-two cause of deaths amongst college students today,” he points out.

Additionally, CompanionMx may provide services for people for whom behavioral health concerns are heightened by comorbid health conditions such as heart disease or diabetes. “There are many opportunities on that front where we could make a big difference immediately,” Datta says.

Overall, such behavioral health services fit well in today’s quest for “value-based” healthcare among insurers, providers and government agencies. “There are clear outcome improvement and cost savings opportunities when you bring in true evidence-based care driven by objective measures grounded in a strong clinical foundation,” he emphasizes. “This we firmly believe will help us in our mission to ensure no one has a significant mental health episode ever again.”

About MIT Startup Exchange, STEX25, and MIT’s Industrial Liaison Program (ILP)
MIT Startup Exchange actively promotes collaboration and partnerships between MIT-connected startups and industry. Qualified startups are those founded and/or led by MIT faculty, staff, or alumni, or are based on MIT-licensed technology. Industry participants are principally members of MIT’s Industrial Liaison Program (ILP).

MIT Startup Exchange maintains a propriety database of over 1,500 MIT-connected startups with roots across MIT departments, labs and centers; it hosts a robust schedule of startup workshops and showcases, and facilitates networking and introductions between startups and corporate executives.

STEX25 is a startup accelerator within MIT Startup Exchange, featuring 25 “industry ready” startups that have proven to be exceptional with early use cases, clients, demos, or partnerships, and are poised for significant growth. STEX25 startups receive promotion, travel, and advisory support, and are prioritized for meetings with ILP’s 230 member companies.

MIT Startup Exchange and ILP are integrated programs of MIT Corporate Relations.

MIT Sloan Management Review
June 27, 2019

Can we really test people for potential?

We need a more nuanced approach to predicting job performance.
Have you ever taken an aptitude or work personality test? Maybe it was part of a job application, one of the many ways your prospective employer tried to figure out whether you were the right fit. Or perhaps you took it for a leadership development program, at an offsite team-building retreat, or as a quiz in a best-selling business book. Regardless of the circumstances, the hope was probably more or less the same: that a brief test would unlock deep insight into who you are and how you work, which in turn would lead you to a perfect-match job and heretofore unseen leaps in your productivity, people skills, and all-around potential.

How’s that working out for you and your organization?

My guess is that results have been mixed at best. On the one hand, a good psychometric test can easily outperform a résumé scan and interview at predicting job performance and retention. The most recent review of a century’s worth of research on selection methods, for example, found that tests of general mental ability (intelligence) are the best available predictors of job performance, especially when paired with an integrity test.1 Yet, assessing candidates’ and employees’ potential presents significant challenges. We’ll look at some of them here. Read Full Article
MIT Research News
June 26, 2019

New AI programming language goes beyond deep learning

General-purpose language works for computer vision, robotics, statistics, and more.
A team of MIT researchers is making it easier for novices to get their feet wet with artificial intelligence, while also helping experts advance the field.

In a paper presented at the Programming Language Design and Implementation conference this week, the researchers describe a novel probabilistic-programming system named “Gen.” Users write models and algorithms from multiple fields where AI techniques are applied — such as computer vision, robotics, and statistics — without having to deal with equations or manually write high-performance code. Gen also lets expert researchers write sophisticated models and inference algorithms — used for prediction tasks — that were previously infeasible.

In their paper, for instance, the researchers demonstrate that a short Gen program can infer 3-D body poses, a difficult computer-vision inference task that has applications in autonomous systems, human-machine interactions, and augmented reality. Behind the scenes, this program includes components that perform graphics rendering, deep-learning, and types of probability simulations. The combination of these diverse techniques leads to better accuracy and speed on this task than earlier systems developed by some of the researchers. Read Full Article at MIT News Office
ILP Insider
June 25, 2019

Building a silk road for agriculture

Benedetto Marelli designs materials based on silk proteins that can preserve crops and make seeds more productive.
How, exactly, did Benedetto Marelli begin wrapping up fruit in silk?

“It came about just by chance,” says Marelli, Paul M. Cook Career Development Assistant Professor of Civil and Environmental Engineering. Working as a postdoctoral researcher in the lab of Tufts University’s Fiorenzo Omenetto, he was competing in a contest to cook with silk, and had the idea of coating strawberries.

“It turned out that these strawberries didn't really change in color or in texture,” he says. “I didn't think that was very interesting, so I simply left the strawberries on the bench and forgot about it. Then after seven days I came back and saw that the strawberries that were coated remained in very good shape, while the few strawberries that I didn't coat were completely spoiled.”

Silk is a structural biopolymer, one of the building blocks of nature. Beginning as an undergraduate in biomedical engineering, “my work has always been about structural biopolymers, and how we can reinvent them as building blocks to engineer advanced materials,” he says.

Benedetto Marelli
Paul M. Cook Career Development Professor,
Associate Professor of Civil
and Environmental Engineering, MIT CEE

Marelli focused in his Tufts postdoc primarily on one of the silk proteins, known as silk fibroin, whose strength, stability and other advantages have long found use in medicine and now increasingly in optoelectronics.

Since joining MIT in 2015, however, his main focus has been on using silk fibroin in agriculture. “My work is about feeding the nine billion people we’ll have by 2050,” he says. This work has led to a silk-coating startup firm, Cambridge Crops, which is investigating partnerships with many firms along the food supply chain.

Marelli’s lab also continues more fundamental research on the nanostructure and self-assembly of silk—an intriguing material for a surprisingly broad set of applications.

Spinning up silk advances
Silk fibroin is a multi-talented material, starting with its stability at temperatures up to 200 degrees Celsius and in environments at various levels of acidity. Even more interesting to materials scientists, silk fibroin can be made into strikingly different types of films and solids. “We can make a transparent silk film with a thickness of a few nanometers, or a screw, or a bioplastic,” he says. “We also can engineer the shelf life of silk, so that it ranges literally from seconds to months or years.”

Moreover, silk can be stable in different conformations. For instance, it can be either completely water soluble or completely water insoluble. “We can process silk in water, so we can regenerate silken engineered materials using completely water-based processes, but at the same time we can form a material that eventually becomes water-insoluble,” Marelli says. “That’s unique to silk.”

Additionally, silk can be processed at atmospheric pressure and room temperature, so engineers can enhance its functionality by adding dopants. “We can have multiple functions in a single material format—for example, I can add an enzyme or an inorganic chemical,” Marelli says.

That’s a wrap
So how could this rich collection of material strengths help out in agriculture?

As Marelli’s original strawberry experiment suggested, silk coatings might help to minimize the waste of crops between harvest and consumption.

Currently about a quarter of food globally is discarded, and most of that loss happens soon after harvest. “The moment you pick a crop, the crop starts to lose value,” Marelli says. “We thought, can we address that by providing growers or other stakeholders in the food industry with a coating material that doesn't alter the taste or the appearance of the crop, but can delay or prevent spoilage?”

He and his colleagues have shown they can form such coatings by drop-casting fruit in a carefully tailored silk solution, or spraying the fruit with the solution.

“You simply let the water evaporate and the material assembles, using no energy,” he says. “That technique allows you to make a membrane that can regulate water evaporation and oxygen permeation. We found that, for example, we can add 10 days to the shelf life of a strawberry outside the fridge, and similar times for bananas and many other crops.”

Marelli joined with Tufts’s Omenetto and two other cofounders to spin off their discoveries into Cambridge Crops, a startup firm finding methods to scale the technology up to industrial use and figuring the best ways to bring it to market. The startup is discussing partnerships with stakeholders across the food industry ranging from growers to retailers, and including manufacturers of chemical-based agricultural products who seek to develop more ecologically friendly materials.

Smarter seeds
Reengineered silk may make another major contribution earlier in the food chain—coating seeds to make them grow better.

Feeding nine billion people by 2050 will require using farmland that may suffer from soil depletion, saline soil, water shortages, blights and pest infestations. These problems can’t be solved merely by doubling down on agrochemicals or adopting more efficient use of current resources. “Smart seeds” also may help, Marelli suggest.

Among the many talents of silk fibroin coatings, they can help to guard helpful bacteria from degradation by water, oxygen and light. Marelli and co-workers are developing seed coatings that incorporate rhizobacteria to aid nitrogen fixing, boost germination rates and perform better in drought and saline soils.

The MIT researchers are collaborating with investigators at Mohammed VI Polytechnic University (UM6P) in Morocco on a proof of concept with barley seeds. Materials will be tested in the lab and in an experimental farm run by UM6P, modeling the persistent drought and soil salinity found in the country’s Rehamna Province. The researchers will then work to improve the coatings and find ways to scale up production to sufficient volume for commercial agriculture.

Self-assembling new applications
Additionally, Marelli’s lab performs basic materials science on the self-assembly and directed assembly of silk fibroin (and other biopolymers) to create novel synthetic materials for a very broad range of tasks, including many uses in biomedicine and optoelectronics.

“The beauty of this bioinspired research is that we’re starting with this nanomaterial that has been engineered by nature to be a very high performing fiber with a hierarchical structure,” he adds. “We can discover new processes to obtain these advanced materials, and then think of universal rules that allow us to apply these processes to synthetic materials.”
MIT Sloan Management Review
June 24, 2019

The surprising value of obvious insights

Confirming what people already believe can help organizations overcome barriers to change.
A few years ago, the people analytics experts at Google stunned me with one of their recommendations to managers. They had been studying how to onboard new hires effectively. After running surveys and experiments, they came back with a list of tips. Here’s the one that jumped out at me:

Meet your new hires on their first day.

People analytics has transformed HR and talent management into a data-driven field. Since Google was a pioneer in the field, I was expecting an aha moment. Instead, I got a duh-ha moment — a sudden flash of the blindingly obvious.

As an organizational psychologist, my trade has been to highlight the counterintuitive, the unexpected, the overlooked. For the past decade and a half I’ve regularly referred people to classic advice from sociologist Murray Davis:1 If you want to be interesting, challenge the (weakly held) assumptions of your audience. I’ve argued that it is not storytelling but questioning conventional wisdom that makes Malcolm Gladwell fascinating (though he found that point obvious from the get-go). Read Full Article
MIT Research News
June 21, 2019

“Nanoemulsion” gels offer new way to deliver drugs through the skin

Novel materials made with FDA-approved components could deliver large payloads of active ingredients.
MIT chemical engineers have devised a new way to create very tiny droplets of one liquid suspended within another liquid, known as nanoemulsions. Such emulsions are similar to the mixture that forms when you shake an oil-and-vinegar salad dressing, but with much smaller droplets. Their tiny size allows them to remain stable for relatively long periods of time.

The researchers also found a way to easily convert the liquid nanoemulsions to a gel when they reach body temperature (37 degrees Celsius), which could be useful for developing materials that can deliver medication when rubbed on the skin or injected into the body.

“The pharmaceutical industry is hugely interested in nanoemulsions as a way of delivering small molecule therapeutics. That could be topically, through ingestion, or by spraying into the nose, because once you start getting into the size range of hundreds of nanometers you can permeate much more effectively into the skin,” says Patrick Doyle, the Robert T. Haslam Professor of Chemical Engineering and the senior author of the study. Read Full Article at MIT News Office
MIT Sloan Management Review
June 20, 2019

The only way manufacturers can survive

Digital transformation is no longer optional for industrial companies. The problem is it’s really, really hard.
Leading a corporate transformation of any kind is difficult, and it hasn’t become any easier over time. But starting and sustaining a digital transformation in a manufacturing company? That’s tougher than managing any other change initiative — from total quality management to Six Sigma to lean manufacturing — and, believe us, we’ve lived through, or seen, them all over the last three decades.

Becoming digital is a requisite for survival today. However, while waves of technology — automation, additive manufacturing, AI — are washing over the corporate world, redefining the nature of work and productivity, there are no playbooks and few best practices for manufacturers’ digital transformation. Few industrial companies even paid attention to digital technologies until recently. Just nine years ago, for instance, General Electric didn’t track them closely, never thought about how they could fit in with the machines it manufactured, and, above all, didn’t realize it could make money from them. Digitalization was far removed from GE’s industrial reality. Read Full Article
MIT Research News
June 19, 2019

Spotting objects amid clutter

New approach quickly finds hidden objects in dense point clouds, for use in driverless cars or work spaces with robotic assistants.
A new MIT-developed technique enables robots to quickly identify objects hidden in a three-dimensional cloud of data, reminiscent of how some people can make sense of a densely patterned “Magic Eye” image if they observe it in just the right way.

Robots typically “see” their environment through sensors that collect and translate a visual scene into a matrix of dots. Think of the world of, well, “The Matrix,” except that the 1s and 0s seen by the fictional character Neo are replaced by dots — lots of dots — whose patterns and densities outline the objects in a particular scene.

Conventional techniques that try to pick out objects from such clouds of dots, or point clouds, can do so with either speed or accuracy, but not both. Read Full Article at MIT News Office
MIT Sloan Management Review
June 17, 2019

Understanding China's next wave of innovation

Three types of emerging innovators in China are making it increasingly difficult for Western multinationals to compete.
In recent years, a handful of Chinese companies have emerged as global innovators and have garnered a lot of attention. This group includes online retail giant Alibaba, appliance maker Haier, search and data technology provider Baidu, and Tencent, the social communication and gaming ecosystem. These companies are challenging the R&D strategies of foreign companies to keep up with the pace in China, and they are providing valuable lessons on how to make ideas commercially viable. But there’s another, less obvious force to be reckoned with in China as well: thousands of innovative companies that are quietly disrupting numerous industries, overtaking incumbents, and developing new products and new business models. For a variety of reasons we’ll discuss here, these emerging innovators are not easy to identify — yet they pose real threats, often in unexpected places. Read Full Article
MIT Research News
June 14, 2019

Toward artificial intelligence that learns to write code

Researchers combine deep learning and symbolic reasoning for a more flexible way of teaching computers to program.
Learning to code involves recognizing how to structure a program, and how to fill in every last detail correctly. No wonder it can be so frustrating.

A new program-writing AI, SketchAdapt, offers a way out. Trained on tens of thousands of program examples, SketchAdapt learns how to compose short, high-level programs, while letting a second set of algorithms find the right sub-programs to fill in the details. Unlike similar approaches for automated program-writing, SketchAdapt knows when to switch from statistical pattern-matching to a less efficient, but more versatile, symbolic reasoning mode to fill in the gaps. Read Full Article at MIT News Office
MIT Sloan Management Review
June 13, 2019

How digital leadership is(n't) different

Leaders must blend traditional and new skills to effectively guide their organizations into the future.
Disruption scholars have focused on how established companies, complacent in their industry position, fail to anticipate their collapse. The companies wither not because they are surpassed in their core capabilities but because they don’t recognize that the competencies that once made them distinctive no longer define success. These stories have a whiff of tragedy — companies that used to be frontrunners are overtaken by a changing world and stick with the status quo rather than investing in capabilities that will bring the next win.

When volatility puts a leading company at risk, it also threatens the leaders, managers, and others who work for it — and that exacerbates the problem, because their insight is precisely what’s needed to curb the company’s tendency not to adopt new capabilities in the face of volatility. Companies of course can shift and enhance their institutional know-how by hiring new people, but individuals cannot swap out well-honed skills quickly enough to suit changing markets. As human capital theory tells us, even as people recognize their need to gain new skills, they seldom adapt rapidly. That’s largely because skills are accumulated slowly through years of formal education, training, and work experience. Learning simply takes time. Read Full Article
ILP Insider
June 11, 2019

Archiving petabytes in pellets of DNA

CATALOG taps into DNA’s extreme density and robustness to meet the soaring demand for massive data storage and parallel computation.
Forget mere “Big Data.” Around the globe, we are generating data at an incomprehensible rate. By one estimate, we’ll create 160 zettabytes (trillions of gigabytes) annually by 2025. And this tsunami of data is now raising a previously unthinkable challenge.

“That’s a lot more useful data than we will have the ability to store,” says Hyunjun Park, cofounder and chief executive officer of CATALOG, an MIT STEX25 startup company.

CATALOG aims to solve this problem with a novel technology that employs the first known form of information storage on this planet: DNA.

Hyunjun Park
Cofounder & CEO,

In recent years, a number of labs have shown the ability to encode and store digital information in synthetic DNA. As odd as it may seem to use the molecule that captures biology’s genetic code for digital tasks, DNA offers compelling potential advantages. “DNA has incredible information density; you can store about a million times as many bits in the same volume as compared to flash drives or magnetic media such as hard drives and data tape,” Park says. “It’s also got an extremely long shelf life; DNA can last for thousands of years.”

The DNA data storage techniques demonstrated in labs, however, have been extremely slow and expensive compared to current storage technologies. One key bottleneck is the time required to synthesize the data-encoding DNA. CATALOG is bringing a distinctive technical approach to speed this process, readying a demonstration system for commercial service this year.

Based in Boston, CATALOG is looking for partnerships with large organizations who struggle with extreme data archival needs—and perhaps take an interest in even more radical technologies down the road to perform parallel computing in DNA itself.

Combining prefab DNA to encode data
CATALOG began with a connection in the lab of Timothy Lu, MIT associate professor of biological engineering and of electrical engineering and computer science. Park, who trained as a microbiologist and was working as a postdoctoral researcher, began talking with Nathaniel Roquet, who was finishing up a doctorate in biophysics.

Roquet was studying a class of enzymes called recombinases that can recognize and manipulate specific sequences within a longer piece of DNA. “These enzymes offer a way to change the state of a DNA molecule, so if you think about it, it is a way to store arbitrary digital information using those different states of DNA molecules, working in test tubes instead of inside of a cell,” Park says.

In CATALOG’s technology demonstrations, “a computer reads the binary data and generates instructions for our liquid handler to move around our premade short pieces of DNA, and combine them in combinations that represents the ones and zeros that we want to store,” Park says. Another machine then collects the encoded DNA molecules and concentrates them into pellet form. To retrieve the information, the pellets are rehydrated and the DNA molecules are read by a genome sequencer, in a method that is essentially error-free.

By midyear, CATALOG expects to complete a prototype machine that can encode about 125 gigabytes of information into DNA every 24 hours, “at a cost that's about a million times cheaper than what's been possible before with DNA,” Park says. The company will offer storage as a service to organizations interested in examining the technology. It plans another major milestone for a next-generation platform offering 125-terabyte-per-day encoding by 2022, as a fully commercial product.

Formed in 2016, the company has raised $10.5 million in funding to date. It faces competition from very large firms such as Microsoft as well as several other DNA synthesis startups. However, “CATALOG is in a unique position where we're positioned to make this a reality within the next year or two, rather than in five or six years,” Park says.

Partnering for pilots
In January 2019, CATALOG was selected for membership in STEX25, a startup accelerator within the MIT Startup Exchange that includes 25 “industry ready” startups that are prepared for significant growth. “The Startup Exchange has been really valuable for us in getting warm introductions to ILP member corporations that could become partners in the long run,” Park says.

“We’re looking for organizations with lots of data that are interested in long-term partnerships who can pilot our machine with us, to see how this totally new storage medium could fit within their data pipelines,” says Park.

Many companies in industries such as entertainment and petroleum production, and numerous government agencies, are faced with the need to archive gigantic amounts of data. “If you’re a large entity like these, you're already looking for a new solution for data archival,” Park says. The two current options are to maintain an inhouse tape library or to outsource the archive to a cloud provider. Both options are far from perfect, with limitations in storage capacity, high expense and serious concerns about the reliability of data retrieval over the years.

“Our partners will influence how we develop our software layer as well as the technical features in the final product,” Park says. “We want to have as many technical and business conversations with these partners as possible throughout the processes, so that we can build the right product around their needs.”

CATALOG is particularly interested in partners who are also intrigued by the possibility of taking an even more radical step into digital DNA computation. “Eventually, we want to be able to compute directly on data that's stored in DNA,” Park says. “We want to build an active information storage system, rather than something that just keeps data on the shelf forever.”

“Using other DNA molecules or enzymes, we could do highly parallel computation on a massive dataset in a way that isn't really possible with classical computing,” he says. “That could solve a lot of problems in computing that are difficult to solve right now.” This approach also could potentially save on computing costs, because it avoids the need to move data from storage into memory for computing and then back again, which demands a lot of energy.

“Our long-term goal is to bring computation to the data,” Park says. “Organizations that want to explore both DNA-based storage and computation could be an ideal match for us.”

About MIT Startup Exchange, STEX25, and MIT’s Industrial Liaison Program (ILP)
MIT Startup Exchange actively promotes collaboration and partnerships between MIT-connected startups and industry. Qualified startups are those founded and/or led by MIT faculty, staff, or alumni, or are based on MIT-licensed technology. Industry participants are principally members of MIT’s Industrial Liaison Program (ILP).

MIT Startup Exchange maintains a propriety database of over 1,500 MIT-connected startups with roots across MIT departments, labs and centers; it hosts a robust schedule of startup workshops and showcases, and facilitates networking and introductions between startups and corporate executives.

STEX25 is a startup accelerator within MIT Startup Exchange, featuring 25 “industry ready” startups that have proven to be exceptional with early use cases, clients, demos, or partnerships, and are poised for significant growth. STEX25 startups receive promotion, travel, and advisory support, and are prioritized for meetings with ILP’s 230 member companies.

MIT Startup Exchange and ILP are integrated programs of MIT Corporate Relations.