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December 16, 2018

BROWSE NEWS RESULTS

48 Results | Page 1 | 2 | 3 | 4 | Last | Next
 
StartupExchange
November 27, 2018

Feature Labs: Eliminating bottlenecks in data science with automated feature engineering

Feature Labs’ CEO Max Kanter is building software that helps enterprises integrate machine learning into their businesses.
We live in a world of powerful computer hardware, greater availability to an abundance of data than ever before, and ever improving algorithms—all of which contribute to the increased capabilities of machine learning methods and the growing interest in machine learning tools. However, organizations often struggle to chart a successful path from collecting raw data to deploying a workable machine learning model. According to Max Kanter, co-founder and CEO of MIT spinout Feature Labs, part of the problem is that enterprises often approach machine learning projects as research endeavors that end in papers or PowerPoint presentations, which means they fail to positively impact how business operates.

“One of the best ways a company can build their first machine learning model or accelerate their machine learning process, is by focusing on the quickest path to value. Our automation technology helps companies do that by shortening the path from raw data to deployed model while involving key stakeholders in the process,” says Kanter.


Max Kanter
Cofounder & CEO, Feature Labs


Which is exactly what Spanish Bank BBVA achieved using Feature Labs Software. One industry report estimates that more than $100 billion worth of legitimate transactions are incorrectly rejected per year. By applying Feature Labs’ automated feature engineering to over 900 million recorded transactions, BBVA was able to decrease the number of transactions they incorrectly classified as fraudulent by 54 percent. This was accomplished not by creating a new or better machine learning model, but instead by using Feature Labs’ technology to determine better explanatory variables to feed into existing algorithms. “When we looked into the financial impact of eliminating these false positives for BBVA, we found the potential for millions of dollars of savings across all the transactions processed every single day,” says Kanter.

Feature engineering is the process of taking a raw data set and extracting the explanatory variables that are fed into a machine learning model to make the algorithms work. The features, or variables, are used to train the model to make predictions. If we take the BBVA example, the variables extracted might have included client location when using their card, if clients used a chip as opposed to swiping their card, or length of time since the previous transaction. Feature engineering is an essential step in the process to make machine learning algorithms work, but until now it has been time consuming and tedious. Kanter and Feature Labs have developed the most advanced software for automating the process of feature engineering. They call their process Deep Feature Synthesis.

Kanter came up with the idea for Feature Labs while he was a machine learning researcher at MIT CSAIL in the Data to AI (DAI) Group with his future co-founder Kalyan Veeramachaneni, who is a Principal Investigator at MIT’s Laboratory for Information and Decision Systems. They focused on building tools for applied machine learning and data science, particularly as they applied to the real-world problems of their sponsors. What Kanter and Veeramachaneni found was that their biggest challenge wasn’t building accurate machine learning models, but rather the time it took to get to those solutions. “There was a clear shortage of tools in the market to help with the tedious step of transforming and extracting the features to create accurate machine learning models,” he says.




In 2015, Kanter and Veeramachaneni published the results of their research in a paper called “Deep Feature Synthesis: Towards Automating Data Science Endeavors.” They garnered overwhelming industry attention that encouraged Kanter and Veeramachaneni to found Feature Labs. They were later joined by fellow MIT researcher Ben Schreck. One of their first customers was Accenture, the global management consulting and professional services company. Feature Labs software helped them take all of their historical project management information to successfully build a project manager powered by artificial intelligence.

Their successful collaboration with Accenture was a milestone for the young startup, and it gave them the confidence they needed to proceed. Now well-funded, they have continued to prove themselves and the value of their technology, demonstrated in part through mutually beneficial partnerships with Kohls, Monsanto, DARPA, and most recently with Carahsoft Technology Corp.

Based on experiences with clients from a wide variety of fields, Feature Labs recently released Machine Learning 2.0. “It’s a new paradigm for developing and creating new machine learning products and services,” says Kanter. It’s a set of seven structured steps that enterprises can follow to translate raw data into a model for rapid deployment and impact.

At the heart of it all is a desire to innovate, to improve the space in which data scientists work, as evidenced by Feature Labs’ open source software, Featuretools. “Creating Featuretools was a labor of love for all of us at Feature Labs. Based on our many years as data scientists in the trenches, we knew that the technology we had built was going to change the way people built predictive models, and we were really excited to make that available to anyone in the world for free.”

Featuretools is proving popular with data scientists everywhere, from people new to machine learning using the tool to build their first predictive models to consultants building models and proof of concepts for their customers.

Feature Labs also provided their software to MIT’s Office of Digital Learning for their big data and advanced analytics course, giving professional learners the opportunity to train their models with the most advanced technology on the market.

Feature Labs has collaborated with a diverse group of organizations across industries, the tie that binds being the desire to adopt machine learning and increase the rate at which they deploy predictive models. “The common thread,” says Kanter, “is interesting raw data sets that have untapped potential. If a company has questions but doesn’t have the data scientists or resources to answer them, Feature Labs is a great piece of software to help them accelerate that process.”



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.

StartupExchange
October 30, 2018

Formlabs: The democratization of professional desktop 3D printers

Max Lobovsky and the team at Formlabs make powerful, affordable 3D printers for professionals.
Stereolithography (SLA) was the first 3D printing process developed. “It’s the gold standard for high-resolution accuracy and quality, detailed components,” says Max Lobovsky, CEO and co-founder of Formlabs. But it is also the most expensive and difficult process to use. In 2011, when Formlabs was just starting out, a typical SLA machine cost between $70,000-100,000. Around that time, Lobovsky and his team introduced their first 3D printer, the Form 1, at a drastically reduced starting price of $3,000.

Lobovsky cites three key reasons for his ability to drive down prices and innovate in a crowded space. First, they redesigned 3D printing technologies from the ground up, leveraging modern consumer electronics technology—applying a laser source developed for Blu-ray disc players. They also aimed for higher volume, banking on the fact that there would be a larger market. “It took a leap of faith, but it paid off,” says Lobovsky. Today no other competitor even comes close to producing 3D printers at the same volume as Formlabs. “Finally, the most important thing we did is get a lot of smart engineers from MIT and other great places together, applied ourselves to the problem, and figured out a way to do things differently,” he says. It’s not a bad recipe for success.


Max Lobovsky
CEO & Cofounder, Formlabs


Long before co-founding Formlabs with fellow MIT Media Lab alums David Cranor and Natan Linder, Max Lobovsky was fascinated with 3D printing technologies for their ability to expedite the process of turning digital designs into real-world products. “I had always been someone with more ideas than the ability to make them real, but the 3D printer is something that conceptually shortens that path from idea to reality.” As an undergraduate at Cornell University he was inspired by the Fab@Home project, which was one of the first low-cost, open-source desktop 3D printers.

But it was during his time as a graduate student, working with Professor Neil Gershenfeld of the MIT Center for Bits and Atoms, that Lobovsky was truly struck by the impact of democratizing high-tech fabrication techniques. By exploring Gershenfeld’s Fab Labs, which provide modern means for widespread access to invention, Lobovsky recognized the ingenuity and sophistication of the tools and products produced.

And while most 3D printers at the time were geared toward hobbyists, Lobovsky and his colleagues saw an opening in a different direction: “We realized that the real potential in the realm of 3D printing was for a professional desktop 3D printer: a product that didn’t exist at the time. And that is what we set out to build.”

In 2012, Formlabs gained significant media attention for their successful Kickstarter campaign, which saw them raise nearly $3 million. It was the most well-funded tech project in the history of the crowdfunding platform and allowed Formlabs to grow from a three-person venture to a 35-person team. Their auspicious start drew the attention of the largest 3D printing company in the world, who promptly slapped them with a patent infringement lawsuit. Conventional wisdom suggested that with a lawsuit looming and no units shipped, finding funding and filling out the team would prove difficult if not impossible. “The lawsuit doubled the difficulty of everything we had to do, but we pushed through. Ultimately, we weren’t infringing on the technology. We built the company, the patents expired, and all that is behind us,” says Lobovsky. It speaks not only to the resilience of the young CEO and his team but also to the market demand for their product.




Today, Formlabs is the leader in their field of desktop SLA, shipping tens of thousands of professional 3D printers per year, or 80-90% of the units in that space. “Formlabs ships all around the world. We’ve got offices in Europe, China, and Japan, and we’re selling in all of those markets,” says Lobovsky. Their flagship product is the Form 2. It’s an update on the original Form 1, but still sells for just $3350. They’ve also introduced a suite of materials used in the printing process, and the Form Cell, a start-to-finish automated system that is critical in driving 3D printing into high-volume usage. Finally, they’ve developed the Fuse 1, which is a selective laser-centering system (SLS) with a different range of material properties. SLS typically cost hundreds of thousands of dollars. Formlabs offers the Fuse 1 for $10,000.

These days, one of the greatest challenges they face is meeting the demands of such a broad range of customers across various markets. The majority of their clients are in the fields of product design and engineering, from small product design firms to huge manufacturers. “Today, you can name almost any big, American company that makes something, and they’ve got multiple of our printers,” says Lobovsky. They also have their products in most major consumer electronics companies, including Apple, Google, and Samsung. And the range of industries they’re working with is truly staggering. Formlabs prints high-quality, affordable 3D solutions for industries as diverse as automotive and aerospace to dental, healthcare, even education.

With more than 50 percent of their sales being done outside of the U.S., Lobovsky sees a tremendous amount of growth opportunity in Asia. “A lot of our competitive advantage comes from our ability to manufacture really new, difficult-to-make hardware, and China has a lot of expertise in that area. Our relationship with that part of the world will only continue to strengthen over time,” he says. With Formlabs having demonstrated a close to 100 percent year over year growth for five consecutive years running and an annual revenue run rate exceeding $100 million, it’s no surprise they’re attracting the interest of big names like former CEO of GE, Jeff Immelt, who recently joined their board of directors.

In the last year, Lobovsky and the team at Formlabs focused significant attention on enterprise solutions for big companies, with the intention of driving 3D printing technologies forward. Perhaps the best example of this type of partnership is with the multinational shoemaker New Balance. “It’s been amazing to work with New Balance to develop new materials and new capabilities to solve their specific problems. Through MIT ILP, we want to build more partnerships with other large companies looking to use not just the 3D printing technology that’s available today. At Formlabs, we’re interested in discovering what we can bring to the world in the next few years.”



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.

StartupExchange
October 19, 2018

Advanced Potash Technologies: Solving the food crisis with a better fertilizer

Since 2011, Advanced Potash Technologies (APT) has focused on one mission – to address the food shortage problem.
Since 2011, Advanced Potash Technologies (APT) has focused on one mission – to address the food shortage problem. For this global goal, the Brazilian company took a top-down approach, says Philip Wender, APT’s managing director. With a growing worldwide population, the need for crops would only intensify, and, for many regions, the soil is sub-par and no commercial potassium fertilizer is affordable or accessible. The answer became clear: make a new version that took advantage of feldspar, an abundant source that is potassium-rich and can be found on any continent. Although this mineral had been considered in the past, it was the introduction of MIT technology that allowed for a low-temperature production pathway that resulted in a cost effective and chlorine-free fertilizer, which could be deployed all over the world. “Many farmers had limited access to fertilizers that were not ideal for their soils,” Wender says. “Now they can access a far more efficient and economic fertilizer to drive yields of their indigenous crops.”


Philip Wender,
Managing Director,
Advanced Potash Technologies


Taking advantage of local resources
APT’s roots are in Wender’s home country of Brazil, a place where agriculture is central. The soil, though, is often sandy and responds poorly to traditional fertilizer methods, a common problem in tropical regions that see heavy rainfall. Wender says that the problem will only intensify in the coming years as resources will be further stressed.

Specifically, worldwide population is expected to grow from the current 7.6 billion people to 9.8 billion in 2050. More food will have to be grown – and grown on a local level. Additionally, crops and grains will have to be produced to feed all the associated livestock in each country. The challenge is that although demand is expected to increase quickly, the availability of arable land is unlikely to keep pace. While there are a variety of solutions to consider, Wender says that by far the most impactful on crop yields is effective fertilization, ideally by a source that is domestic and scalable within the local economy.

As it stands, potash (KCl) is the industry standard potassium fertilizer, but it comes with a few problems, Wender says. While it contains potassium, it also has roughly 50 percent chloride by weight. Along with being sensitive to chloride, crops forced to ingest this highly soluble salt must do so all at once, otherwise risk losing all the potassium as it leaches away from the roots.

Potash is also geographically concentrated. Five countries, all developed economies in the northern hemisphere, control approximately 85 percent of its traded production. The complex importation of this nutrient, particularly to the south, makes it cost-prohibitive for many farmers. “There’s a big disconnect between where it’s needed and where it’s produced,” he says. In these locations, “the benefit of the fertilizer will not compensate the price that the farmer is paying.”

With this dilemma in mind, APT started with a simple question: Is there a way to produce potassium fertilizer in Brazil?

The answer was yes, given the country is rich in high potassium feldspar deposits. Unfortunately, the solution wasn’t as simple as gently milling the rocks, Wender says. The crystalline structure is rigid and plant roots wouldn’t be able to absorb the potassium. The trapped nutrient needed to be made available. The missing piece was the technology to do this.

At a 2012 conference, Wender met with members of the Department of Materials Science and Engineering at MIT, who he says quickly understood the magnitude and impact of the project and wanted to be involved. After less than a year of sponsored research, the team at the Allanore Group proposed a low-temperature, hydrothermal pathway. Rather than extracting potassium out of a rock, this process disrupted the crystal structure enough to allow the plant’s roots to do the rest of the work. “Professor Antoine Allanore realized that plants have evolved for millions of years thanks to their abilities to extract nutrition from the ground,” Wender says. “Why not simply take advantage of this natural mechanism?”

The MIT research program is ongoing and has resulted in HydroPotash, a fertilizer with controlled potassium release and no chloride, making it applicable to any soil type. By operating at a lower temperature and for shorter times, Allanore’s approach also requires less energy, making APT’s production process scalable in ways that other methods could not be, Wender says. Simultaneously, APT is researching additional areas of potential value, including the remediation of heavy metals in soil, combining the product with growth-supporting bacteria, and mitigating run-off and volatilization losses in nitrogen fertilizers.

Adding to the benefits, feldspar is abundant throughout the world and is found on the earth’s surface, thereby avoiding deep underground mining as is required with potash. Since it’s simple and cost-effective, Wender says that the APT process can be pursued anywhere. Fertilizer can then be produced close to agricultural areas, making it accessible to farmers of all sizes and in all regions.

Given that feldspar has never had a previous economic value, its deposits have never been thoroughly mapped. One edge APT offers, Wender says, is that the company has deep geological expertise, and, with its in-house team of scientists, has created a proprietary method to identify worldwide deposits. At this point, the company owns 6 sites in Brazil, all near agricultural hubs, and is exploring sites in California, Missouri, Arkansas, and Australia.

The company has been able to produce its potash on a small scale, and has been working with a variety of companies and institutions, including the International Fertilizer Development Center (IFDC) in the United States, Embrapa in Brazil, as well as some of the largest farming cooperatives and research centers in South America. The company has performed several greenhouse tests with different soil and crop types to demonstrate the superiority of HydroPotash, says Wender, adding that the company is working alongside a top tier Engineering, Procurement, and Construction (EPC) company to successfully scale up the technology. The plan is to have a pilot plant operating in 2019 that can produce a few thousand tons of fertilizer a year, with a full, industrial-scale plant planned for 2021.

Expanding its footprint up north
The initial partnership with MIT wasn’t a hard decision, Wender says. The university has a well-earned reputation for taking on big problems. “You can be sure they have a high-quality technology that stays focused on precisely what you’re looking to achieve,” he says. As the relationship has evolved, the company decided to establish its world headquarters and R&D facility right outside of Boston. This, combined with its well-established presence in Brazil, has given APT the perfect balance of product development and market engagement.

The company remains close to farmers and to where the product will be used. It also gets to stay near campus. It has hired MIT scientists, some of whom worked on the original project, and they come with not only a knowledge of the technology but also an expertise in an array of relevant industries. “The location is also known as an innovation hub. It’s a worldwide reference,” Wender says. “It encourages high-level conversations, and it attracts people who value impact and want to make a difference globally.”



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.

StartupExchange
October 17, 2018

MIT Corporate Relations and MIT Startup Exchange announce STEX25 additions

MIT Startup Exchange is pleased to announce the addition of five companies to its roster of STEX25 startups. New additions to the program include: BMF Material Technology Inc., ClimaCell, Distilled Analytics, Interpretable AI, and IQ3Connect.
MIT Startup Exchange is pleased to announce the addition of five companies to its roster of STEX25 startups. New additions to the program include: BMF Material Technology Inc. (micro/nano-scale 3D printing), ClimaCell (micro weather), Distilled Analytics (predictive identity), Interpretable AI (automated predictive systems), and IQ3Connect (collaborative VR).

“MIT fosters the creation of strong tech startups, and the caliber of technology and talent in this STEX25 induction is no exception. Our ILP members utilize MIT Startup Exchange to effectively filter and match emerging startups with their companies, which result in many advanced discussions and partnerships,” said Executive Director of MIT Corporate Relations Karl Koster.
STEX25 is a startup accelerator
within MIT Startup Exchange
featuring 25 "industry-ready" startups.


MIT Startup Exchange is adding startups to STEX25 on a roughly quarterly basis, from among more than 1,600 startups in the database. STEX25 startups receive promotion, travel, and advisory support and are prioritized for meetings by the MIT Industrial Liaison Program’s (ILP) industry liaisons. STEX25 is adding tailored services to further collaboration with ILP corporate members, including targeted Startup Exchange workshops and showcases, exhibits at ILP conferences, and other events tailored towards industry.

“Within these new additions, we see creative use of data for incredibly practical and industry-changing applications, with a large emphasis on AI. Throughout the Institute and industry, the prevalence of AI-based companies and technologies have greatly shaped our startup landscape,” said MIT Startup Exchange Program Director Marcus Dahllöf.

The broad group of STEX25 startups currently represent a variety of important fields, including artificial intelligence, automation, data analytics, energy, healthcare, internet of things (IoT), life science, advanced manufacturing, machine learning, materials, nanotech, sensors, and more.

MIT Startup Exchange draws from the Institute’s plethora of startups founded and/or led by MIT faculty, staff, or alumni, or based on MIT-licensed technology. With nearly 20% of MIT faculty acting as serial entrepreneurs, the breadth of faculty involvement in STEX25’s new additions comes as no surprise to Program Director Marcus Dahllöf. “As always, our MIT faculty is well-represented within these startups, including faculty founders at BMF Material Technology (Nicholas Fang, MIT MechE), Distilled Analytics (Alex ‘Sandy’ Pentland, MIT Media Lab), and Interpretable AI (Dimitris Bertsimas, MIT Sloan).”

To learn more about STEX25 and MIT Startup Exchange, visit http://ilp.mit.edu/stex25.jsp.




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.

StartupExchange
September 25, 2018

Legit: Reducing R&D waste with artificial intelligence

Matt Osman is CEO and cofounder of Legit, the Cambridge-based startup using proprietary natural language processing algorithms to dramatically streamline the R&D process.
In 2015, Matt Osman was the youngest VP at a $1 billion structured credit hedge fund in London. He also just happened to be a trained attorney with a degree in philosophy, politics, and economics from Oxford University and a fascination with artificial intelligence. In particular, he was interested in the effect he thought AI would have on professional services. “I became very interested in structured data sets like legal documents and what AI could do using them as a data source,” he says. Meanwhile, Jacob Rosen was finishing his MS at MIT CSAIL and had just published his paper, “Tax Non-Compliance Detection Using Co-Evolution of Tax Evasion Risk and Audit Likelihood," which won the Best Innovative Application Paper Award at ICAIL 2015.

When Rosen’s research presenting an algorithm that could anticipate tax evasion found its way onto Osman’s desk, Osman was understandably impressed. “I thought it was so cool he had basically built a tax attorney out of these algorithms that I stepped out of my office and called him immediately,” he says.


Matt Osman
CEO & Cofounder, Legit


Legit was formed with the belief that they could apply Rosen’s work on coevolutionary algorithms to the world of research and development. “We founded Legit because we saw huge inefficiencies with the way that engineers, researchers, and scientists determined whether or not their ideas were novel or applicable in other technology areas,” says Osman. Their third co-founder and Chief Scientist is Anthony Bucci, a highly esteemed computer scientist and Tufts University lecturer with a specialization in coevolutionary algorithms.

It works like this: Within a corporate R&D context, the user plugs their description of a new product idea into the Legit platform. In real time, Legit uses natural language processing, a subset of AI, to extract all the concepts from that idea and cross reference them with 30 million pieces of technical literature. Within seconds, an R&D team knows if they have a novel idea on their hands or how similar two (or three or four) technical ideas are to each other, thereby aiding R&D teams to verify what’s new and valuable at lighting speed.

According to Deloitte’s innovation practice, Doblin Group, 96 percent of innovations fail to return the cost of capital. Osman notes that a key reason these projects don’t succeed is because a product has been tried before or there is a failure to differentiate from the competition. In other words, if you consider the fact that there is a tremendous amount of capital invested pursuing dead-end ideas, Legit is clearing the path to innovation and saving quite a bit of money in the process.

Legit launches new features approximately every two weeks, meaning they are intent on rapidly increasing their capabilities and reach. And the value of their platform has not gone unnoticed. They’re currently working with Stanley Black & Decker’s Breakthrough Innovation team in Boston. For engineers, Legit functions as a single repository for all their ideas in various stages: “A bit like a CRM that provides feedback on how valuable their ideas are likely to be and how similar they are to other ideas,” says Osman. But it isn’t just the fortune 500 American manufacturer that is using the platform to reduce waste. Legit also works with early-stage medical device companies and large life sciences companies. Regardless of size, scale, or industry, Osman is confident that if you have an R&D team, you can benefit from partnering with Legit.

An essential feature of the platform is its ability to identify collaboration opportunities across a siloed R&D team or organization. For example, one of their larger clients has disparate R&D teams spread throughout the world. “An engineer in Paris can be working on something and we can identify how similar that is to what the engineers in San Diego, for example, are working on—in real time,” says Osman. “In terms of combining real-time feedback or novelty and value, deep competitive analysis, and then also this very engaging environment for engineers and R&D teams to live in, I think we’re among the first to combine all of them.”




With Rosen as CTO, it’s no surprise that MIT has featured heavily in the Legit story. Rosen’s work in the ALFA Group at CSAIL led to ALFA Group’s Principal Research Scientist Dr. Una-May O’Reilly playing a key role in bringing the founding team together by introducing Rosen and Osman to Bucci. Dr. O’Reilly also functioned as technical advisor to the fledgling startup and, according to Osman, is still very close to the team. Which is why it should come as no surprise that they’ve chosen to stay close to home, with offices in Cambridge, MA. “This is probably the most innovative square mile or two on earth—it’s certainly up there. Given that we’re a company devoted to increasing R&D efficiency, I think we’re in the right place,” says Osman.

With regard to his experiences with MIT ILP, Osman says the experience has been extremely useful. “I think that having people who are focused on listening to the needs of large corporations, particularly R&D departments, has been invaluable. The introductions have always been incredibly well curated, and the events are a fantastic way for us to meet the people we’re interested in collaborating with.”

Osman has a very clear and concise message for ILP member companies: “The technology we provide is the first step in building the future of R&D. We’re offering ILP member companies the opportunity to be a part of building that future.”

He notes that what they are looking for in partners is the spirit of innovation and active collaboration. Interestingly, and perhaps unsurprisingly, Legit has their own R&D department, which is rare for a company of their size. It’s part of why he thinks Legit makes such an interesting partner for large corporates. They understand the R&D pain points, which means they understand how difficult it is to innovate. “We’re trying to sell the ingredients of innovation,” says Osman. At the moment Legit sells software applications, but the goal is much more expansive. “Eventually we’ll start brokering introductions to the right talent, then to the right suppliers and materials. In time, we’ll become a one-stop shop for the ingredients of innovation.”



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.

StartupExchange
August 2, 2018

Arundo: Pushing data analytics to the edge

After many years leading the global technology practice for asset-heavy industries, MIT alumnus Tor Jakob Ramsøy launched Arundo two and a half years ago to provide large-scale analytics and increased asset utilization.
The declining costs of data acquisition, data storage, and cloud computing have transformed the economics of numerous industries, from consumer products to retail to financial services. However, fields such as energy, maritime, chemical, and manufacturing haven’t experienced the benefits at the same rate. These sectors face unique challenges with their capital-intensive physical assets and related information technology investments. MIT alumnus Tor Jakob Ramsøy (S.M. ‘95) understood these dynamics, and launched Arundo two and a half years ago to provide large-scale analytics. The company’s technology enables machine learning for performance insights and automates processes for equipment monitoring, safety, logistics, and scheduling, helping its clients to minimize unplanned downtime and increase profitability, because as Ramsøy says, “It’s all about increased asset utilization.” Tor Jakob Ramsøy, CEO of Arundo Analytics

Reviving data science
Arundo was founded in 2015; the inspiration came a couple of years earlier. Ramsøy had been a senior partner at McKinsey & Company, leading the global technology practice for asset-heavy industries. In that role, he saw a recurring theme. Big companies with old assets had lots of data, but they weren’t storing or using it in any systematic or accessible way. Even if they tried to use the latest machine learning tools, he says, “A lot of data science was dying in PowerPoint.”

More than merely rescuing information, Ramsøy says that he wanted to apply consumer business algorithms, such as how Netflix makes recommendations based on past viewing, to industrial companies. The intent, he says, was to be dynamic and to anticipate and predict issues in real time, such as equipment or operation failures and points of improvement. Three offices were then opened: Silicon Valley for technology innovation; Oslo, by the North Sea, for shipping and renewables; and Houston for oil and gas. A Boston office was added in January 2018 to take advantage of MIT’s talents and the ILP’s partnerships with large industrial companies that could benefit from Arundo’s capabilities.

While oil and gas was the initial focus – the field basically invented big data 30-40 years ago, Ramsøy says – Arundo’s industry targets share common ground. They have advanced equipment with sensors, making data harvesting and sharing that much easier. There’s also the need to minimize downtime without a compromise in safety. For example, with an oil rig in the North Sea, the average utilization is 84 percent, but the planned utilization is 95. As Ramsøy says, if a company produced 50,000 barrels a day at $45 per barrel, that 11 percent gap could mean hundreds of millions of dollars in lost revenue. With 50 oil rigs in play, it’s a multi-billion dollar opportunity. “The magnitude of the business case is enormous,” he says.

Going to the edge
Arundo’s initial customer was Statoil. For the Norwegian multinational energy company, Arundo built algorithms and developed the ability to not only capture data, but also compare it to performance history and be able to offer predictive maintenance, either with a specific recommendation or even by implementing something like a controlled stop, Ramsøy says.

That’s one part, the ability to pinpoint the cause of downtime, Ramsøy says, and fix it. What sets the company apart is being able to easily introduce machine learning at scale into daily operations, and it’s due to leveraging the cloud, which he says is accessible, safe and inexpensive. Because of the abundance of sensors, Arundo can tap into heavy industrial equipment, stream data, build and train machine learning models, and then publish those models into a business process, with “one click,” he says. Additionally, Arundo provides “edge” analytics, which involves a few things. The company can sample and intelligently stream data from rugged or remote industrial sites, which may not always have internet connectivity, into the cloud. It can provide edge compute capabilities to compress data with local calculations and stream just the results back to the cloud. And, most uniquely, it can push trained machine learning models down to the edge, enabling them to interact with local operators and decision processes and sync with a cloud-based model management framework when connectivity is available. “So then you have the wisdom of the crowd. All machine learning models are learning from each other,” he says.

All this wouldn’t mean much without speed. For one client, a manufacturing company with 35 plants around the world, Arundo was able to pull streaming machine data from a plant in China in just one day, and deploy real-time cloud analytics in just a couple of weeks, once the right hardware was in place. Ramsøy says that at minimum every customer is guaranteed delivered business value in less than 90 days.

Being ready for changes
Ramsøy says that he’s happy with the state of his company, but he’s not satisfied, as untapped opportunities remain. One is MIT. Arundo recently joined the STEX25, and while the relationship is still taking shape, Ramsøy knows that high expectations and optimism aren’t unrealistic in being part of the ecosystem. What’s often said is true, he adds. MIT offers expertise and talent with its faculty, researchers and students, who can help improve Arundo’s technology, along with being the current and future generations of employees. “We see MIT as a great meeting place between industrial companies, research, and start-ups like ourselves,” he says. “We hope to take an active and leading role in this with our IoT products.”

All that will certainly assist in the other great potential that Ramsøy sees. No industry wants to merely buy a pump anymore. It wants to buy pumping. This industrial shift from product to service economy hinges on readily having usable data. For both a company and its equipment manufacturers, data analytics would let them know exactly what they’re delivering, and, rather than trying to sell an input, businesses could guarantee an outcome. That ability hasn’t existed and it’s one area that Ramsøy says gives Arundo an advantage. “Think of us as the Android of the industrial internet,” he says. “That is what really makes me excited.”

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.
StartupExchange
July 16, 2018

Humatics: Microlocation tech aims to improve human/robot interaction

David Mindell, MIT Professor in Aerospace Engineering and the History of Technology, is the author of five books, including the recent “Our Robots, Ourselves.” He’s also a pilot and an expert on automated underwater subs who has collaborated with Titanic discoverer Robert Ballard and others in more than 25 underwater explorations. In 2015, he launched a Cambridge, Mass. startup called Humatics that is developing a microlocation system for positioning people and objects down to the millimeter scale.
“Our mission is to revolutionize the way we locate, navigate, and collaborate between people and machines,” says Mindell, CEO of the 25-person company. The Humatics technology was not developed at MIT, but “we build on work from MIT,” says Mindell. The company has also benefited from MIT’s various venture startup services, and has been selected as member of MIT ILP’s elite STEX25 (Startup Exchange 25) accelerator program.

Humatics, which counts Ballard and Apollo 15 astronaut and MIT graduate Dave Scott among its scientific advisors, is initially targeting its technology at industrial automation. The primary mission is to streamline the choreography of robots and humans working together in factories. The application has more in common with deep-sea exploration than you might expect.

David Mindell
Founder & CEO
Humatics


“Humatics incorporates lessons we have learned in robotics and automation in extreme environments, such as the deep ocean, outer space, aviation, and some military environments,” explains Mindell. “Our microlocation technology is based on work I did in the 1990s on sonar for high precision navigation of robots in deep ocean. From a navigation standpoint, factories have a lot in common with the deep ocean. There’s no GPS, a lot of noise, and only a few fixed beacons to work from.”

For years, Mindell has sought to translate sonar technologies into new terrestrial applications, but only in the last few years has it become feasible, driven in part by developments in automotive radar. “We’re driving the Moore’s Law for radar,” says Mindell. “Costs are coming down to the point where small, short-range radar can be brought to bear on robotics and autonomous systems.”

The RF-based Humatics technology is unlike traditional sonar, radar, or LIDAR, in that it doesn’t do “blobology on reflective echoes,” says Mindell. “However, it uses a lot of the same core technologies. Unlike GPS, it can work indoors, and is not limited to an uncertainty range of three to nine meters,” says Mindell. “Almost all robotics and human work happens within a much smaller circle.”

Humatics technology won’t replace all the sensor systems in a factory, says Mindell. Yet, he adds that “Right now there’s no solution for millimeter scale precise positioning in the harsh environment of manufacturing where there are strange lighting conditions and lots of stuff moving around.”

The Humatics technology, differs from most sensor and machine vision solutions used with robotics in that it doesn’t perform blobology, which Mindell describes as “looking at a big dot cloud of echoes and try to apply different algorithms to get 80 percent certainty.” Instead, the microlocation system can pinpoint multiple transponders within a 30-meter range, each of which has a unique digital tag.

“You might wear a wristband, or have safety equipment with built-in transponders, or they can be attached to robots, workpieces, or engine parts,” says Mindell. “We know exactly what and where each transponder is and how far it is from other transponders -- not just how far an object is from your hand, but how far it is from a particularly point on your hand.”




Uncaging the robots
Millimeter-scale precision is essential for the growing efforts to free robots from their cages and let them work with people. “The robotics industry is transitioning,” says Mindell. “Now the innovations are not so much in the robots as in the applications, use cases, and environments that allow them to work in human settings. Most robots are still inflexible clockwork mechanisms doing repetitive tasks. We’re interested in making robots more flexible and collaborative, and less walled off from humans. Right now, we still have a very stand-off relationship with most robots, and for good reason – they can kill you. That’s changing, but we have a long way to go in improving communications, mutual awareness, and safety.”

To improve safety and productivity in a mixed workspace, Humatics has developed spatial technology algorithms that work with the microlocation network to track mobile humans and robots. “Our Spatial Intelligence Platform ™ tracks and analyzes how people and machines and parts move through the factory,” says Mindell. “We can then gather the tremendously rich information from those motion paths, and compare it to a larger database.”

Customers can augment the Humatics platform with visualization, machine learning and analytics tools to help compare daily motion paths against historical patterns. The resulting lessons can be used to provide feedback to workers to enable continuous improvement by emulating the motions of the best workers. “Our system helps to explore the choreography of people working with machines,” says Mindell.

Beyond the factory: automobiles and drones
Humatics is already looking to other applications, including driverless cars. The microlocation technology could be used to identify the location of other vehicles, pedestrians, and infrastructure more precisely than what is capable with GPS, LIDAR, or vision systems.

“Our technology enables relative positioning info with a greater reliability and accuracy than is possible now with blobology based sensors,” says Mindell. “For example, bicycles are very hard to recognize at night or in the snow and rain, but our system is immune to those conditions.”

The catch is that all the other cars, bicycles, pedestrians, and traffic infrastructure would also need transponders. Yet, the hardware is simple enough that it should be able to affordably scale to the level of today’s RFID technology.

Drones are another possibility, especially when operating in tight urban environments or interior spaces where precise positioning is crucial. “To be active in populated areas, drones need to be in very precise relationships to the people and things around them,” says Mindell.

Mindell says that when he pilots a plane, he uses GPS about 99.99 percent of the time. However, the FAA requires a ground-based navigation system backup. “It’s crazy to think we won’t have drones flying through airspace that don’t have a similar requirement,” he says. “When you’re close to buildings, you still want to be operating in direct relationship with things. Our microlocation technology can provide short-lived, very precise navigational interactions.”

The myth of full autonomy
Some of the guiding principles behind Humatics were laid out by Mindell in his 2015 book, “Our Robots, Ourselves: Robotics and the Myths of Autonomy,” which argued that the drive to create fully autonomous robots risks missing out on the greater potential of robotics. “For autonomous systems to be useful, they need to situate themselves in human environments,” says Mindell. “The highest form of technology is that which gives you exactly the right level of automation you need at the time.”

“The more sophisticated companies are designing cars that are your collaborator and friend, that can learn from your driving habits, work with the environment, and draw answers from the cloud,” he says. “These robots bring new levels of decision making embedded within human context. Those relationships should be built into the core of autonomous systems.”

Even if the automobile industry moves to a fully self-driving experience instead of an ADAS interaction, the cars won’t be as autonomous as advertised, says Mindell. “The driver will still pick the destination and possibly change it en route.”

People also tend to overlook the hidden human inputs baked into a car’s programming. “Autonomous car projects draw on huge databases of human drivers,” says Mindell. “There are thousands of human inputs and assumptions from programmers about what constitutes a pedestrian and how fast the car is moving. Even in a driverless car, there’s always a wrapper of human control. All these systems are networks of people and machines.”

The Humatics technology may well provide one essential piece of the puzzle for connecting the robot and human worlds. “The boundaries of autonomous, remote, and manual control are blurring,” says Mindell. “We’re building the navigational envelopes that allow those robots to work with precision, safety, and collaboration in human environments. The integration is where the action is.”



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.

StartupExchange
May 29, 2018

TVision Insights: The leader in eyes on screen attention measurement

Yan Liu, cofounder and CEO of TVision Insights, is pioneering the way brands, agencies, TV networks, and OTT platforms determine the true value of their video content and advertising.
TVision Insights is the data analytics company redefining audience measurement. They were recently named to the Advertising Research Foundation’s Innovation A-List, the ARF’s top award to innovative startups in advertising. But when they started in 2014, while cofounder and CEO Yan Liu was pursuing his MBA at MIT Sloan, it was just Liu and a few PowerPoints, as he tells it. In four short years they’ve grown into a sizable company of 45 full-time employees serving top brands and agencies in the TV and media industries, including ABC/Disney, NBC, and The Weather Channel. While Liu is proud of the rapid growth, he is well aware that innovation is a constant process. “Last year, we improved how we operate to leverage the latest deep learning computation technology,” he says. Integrating cutting-edge approaches to AI and machine learning into their core technology goes hand-in-hand with TVision’s mission: “The end goal is to offer the highest quality, unique data to every stakeholder to help them make better decisions so the entire ecosystem will be more effective. Using our platform is a win for content providers, brands, agencies and ultimately, the consumer.” Yan Liu,
Cofounder and CEO,
TVision Insights In 2017, television advertisement spending totaled $205B, with the U.S. market alone accounting for $72B or 38% of global TV ad expenditures. But in an increasingly fractured media landscape, where the advent of digital is just one aspect of the equation informing media consumption, deeper insight into ad placement is essential. Yet, despite the size of the market and the shifting nature of consumer habits, our tools for gauging these behaviors haven’t changed much in close to 40 years.

Liu is well aware of the disparity: “The only data widely available are traditional TV ratings,” he says, “which are used to determine the pricing for ad slots, which ads to run and when—basically, all the important decisions in a massive industry are largely being made using an outdated model.” Namely, the Nielsen ratings, which capture whether or not a television is on and what show or ad is on the channel but not actual user engagement. Nielsen’s traditional people-meter technology does a fantastic job of collecting what is on the TV screen, but it isn’t capable of understanding if people are actually paying attention and what in particular they are engaging with. TVision takes audience measurement several steps further, introducing state-of-the-art technology that collects exactly what is going on in front of the screen.

Their computation software can be easily integrated into the graphic processing unit of any web camera. Once installed, their AI technique tracks how many people are watching, their attention level, even their emotions, all in real time. This is what Liu refers to as the special ingredient of TVision Insight’s technology: eyes-on-screen, passive data collection that accurately identifies viewing patterns in a way the world has never seen before. But what about privacy concerns? “Being transparent and maintaining audience privacy is an essential part of how we operate,” says Liu. Every TVision user voluntarily opts in and is compensated on a monthly basis. “We tell all panelists how the data will be used, do not store any images or videos, and all of the information gathered is processed on the local device in the living room.” In other words, the process is anonymous and personally identifiable information never leaves the home device.


TVision’s second-by-second innovations are targeted at three client segments. For the brand marketer, TVision delivers the ability to understand which commercial is the most effective in terms of audience engagement, without the cost and hassle of traditional focus groups. Media planners also benefit from TVision’s endeavors, as Liu explains: “At the end of the day, media planners and buyers are really buying audience attention,” says Liu. “We can overlay that information on top of Nielsen ratings to help them make better decisions to increase their ROI.” Finally, TV networks and OTT content providers like Hulu, Netflix and Amazon can utilize TVision’s data to sell commercials at higher prices because the quality and accuracy of the data provided by Liu and his team allows them to justify the value of the inventory. For example, while a late-night show might have a low rating, attention level may be quite high, which means the inventory can be sold at a higher price. Additionally, that second-by-second data that pinpoints increased attention level could be used by networks to understand what causes viewers to pay attention and how to maintain high-attention viewership or replicate the success of a particular show by implementing similar strategies into another show.

While Tvision is a relatively small-scale startup at this stage, their continued success and overwhelmingly positive feedback from consumers means that Liu and his team are looking to build on the breadth of their current partnerships. He identifies three industry categories for collaboration. Brands and agencies interested in purchasing data fall into the first group. But in addition to being a media measurement company, TVision is also a deep learning AI company, which means they are interested in collaborations with hardware companies or anyone with a significant interest in AI and its unique applications.

Finally, Liu mentions his interest in connecting with international media research firms who want to bring TVision technology abroad. “Today there are 76 countries around the world, all using the same technology to measure TV ratings. We want to build on our success and expand beyond the U.S. market into Europe, Asia and the rest of the world.” As consumer behavior continues to change, Liu and TVision are confident we’ve reached a point where the industry must embrace a new set of standards for understanding audience engagement. “We want to innovate the entire field,” says Liu. “As a relatively small MIT startup we might not be able to change everything, but we’re confident we can play a critical role by offering unique high-quality data to help inform better decision making, thereby making the entire ecosystem more effective.”

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.
StartupExchange
May 16, 2018

figur8: Digitizing 3-D body movements for everyone

Nan-Wei Gong and figur8 are spurring the growth of wearable technology within the sports medicine and digital health sectors, where they aim to commercialize digitized 3-D body movement technologies.
Nan-Wei Gong is an MIT research affiliate and engineer with a penchant for turning wearable technologies into viable tech startups. Her most recent venture, figur8, Inc., is an MIT E14 company that straddles the sports medicine and digital health sectors with the aim of taking digitized 3-D body movement technologies out of the lab and into the hands of everyday users. She holds a PhD and MS in Media Arts and Sciences from MIT and a MS in Materials Science and Engineering from National Tsing Hua University in Taiwan. During her time at the MIT Media Lab, she co-founded and was hardware engineering lead at her first startup, 3dim Tech Inc., an MIT spinoff that designed and developed gesture control and 3-D sensing software. Nan-Wei Gong,
Cofounder and CEO,
figur8, Inc. In 2013, 3dim won the grand prize at the MIT $100K Entrepreneurship Competition, followed by a successful exit in 2014. She is also the founder and CEO of Circular2, Inc., a consulting firm specializing in wearable computing, hardware system design and manufacturing. Other notable successes include her stint as R&D Lead of Project Jacquard, which saw Google partnering with Levis to develop everyday wearable textile technologies. Her fluency in both the engineering and industry aspects of wearable sensor technologies means she knows what she’s talking about when she says of her latest venture: “We’re certain that figur8 is uniquely positioned to become the low-cost, easy to use, hardware platform capable of democratizing 3-D body movements for everyone.”

The growth of the wearable technology market shows no sign of slowing down, though it is evolving beyond the typical wrist-worn devices most consumers are familiar with. Nan-Wei Gong and figur8 are at the forefront of this transition. “We go beyond the one point of measurement utilized by products like Fitbit and translate that into a modular platform allowing users to take measurements from any part of the body.” One might recall the brightly colored strips of kinesiology tape that first came to prominence during the 2008 Olympics and have since become ubiquitous in the realm of professional sports. “It’s a form factor that is widely accepted in sports medicine,” says Gong. “At figur8, we take that form factor and make it smart through our movement platform, or what we call a Kit.”

To do form analysis, existing platforms rely heavily on a room full of cameras. Gong’s unique vision, actualized in the form of figur8, minimizes the technology so it’s wearable and tracks the movement and 3-D contour of the human body. The individual sensors used to track the movement of a user’s back muscles or the laxity of their knees, for example, are composed into a network of sensors which then transmit signals through Bluetooth or a smartphone, allowing users to receive specific suggestions for improving their body movements. Whether you’re interested in improving your golf swing or your gait as a runner, figur8 would help you reach your goal.


They’ve been working with hospitals and sports science doctors since the early stages of development—including their Director of Sports Science Donna Scarborough, former Director of Sports and Analytics Lab at MGH—and the entire system is built to be HIPAA compliant, meaning that data gathered is treated as medical records, with all the requisite privacy protocols that entails. This year they’ll be rolling out their developer SDK’s and KPI’s and they already have several early adopters, primarily research groups, interested in using the figur8 hardware platform for analysis and studies in computer interaction design, gaming design and sports training. “We want to be the platform that is focused on content management to allow everyone to create, download and upload content using our kits,” she says. “We see the potential for movement data to become something interesting, exciting and valuable, potentially even to be traded as a commodity.”

As Gong and her team engage new clients, they are particularly interested in working with industry partners that rely on camera-based models for movement analyzation and want to take this outside of the lab. They’re also looking to partner with groups that have may have never used this type of technology but have a part of their system or business that relies on the movements of people. “Imagine you have a factory with workers of different skill sets. Our platform can be used to analyze and improve craftsmanship or even the fatigue and stress levels of workers.” The implications are fascinating. figur8’s platform could, in theory, be used to help determine who is best fit to do a particular type of job or how much break time is necessary for the body to recover and function at an optimal level.

She credits much of the figur8 ethos of innovation and experimentation to her ties with the Media Lab. “I’m interested in bringing my expertise to other fields,” says Gong, echoing the Institute’s emphasis on eliminating silos and effectively engaging specialists with disparate backgrounds to solve the pressing challenges of our time. “At figur8, we are not just about engineering ideas and engineering solutions,” she says. Rather, they’ve collaborated with doctors, physical therapists, sports scientists and athletes to create a product based on specific needs. Her choice of team members reflects a similar mindset. figur8 boasts a group of top engineers who also have experience bringing products from prototype to production, including co-founder and CTO Tim Ren, Hardware Lead Marius Gailius and Software Lead Keith Desrosiers. “I want figur8 to be driven by collaboration between experts,” says Gong. “This includes bringing people like our Business Specialist, Yi-Yun Chao and our Design Lead Mian Wei together to ensure that figur8 is peerless, not just in terms of hardware, devices and engineering, but in every aspect of what we do.”

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.
StartupExchange
April 27, 2018

MIT Corporate Relations and MIT Startup Exchange announce STEX25 additions

MIT Startup Exchange is pleased to announce the addition of seven companies to its roster of STEX25 startups. New additions to the program include: Asimov, Feature Labs, Form Energy, Formlabs, Legit, Liquid Piston, and PathAI.
MIT Startup Exchange is pleased to announce the addition of seven companies to its roster of STEX25 startups. New additions to the program include: Asimov (programmable living cells), Feature Labs (data science automation), Form Energy (renewable energy storage solution), Formlabs (3D printing of polymers), Legit (AI to improve R&D process), Liquid Piston (high efficiency combustion engine), and PathAI (pathology AI).

“STEX25 startups exhibit the high-caliber talent and cutting-edge technology that are hallmarks of MIT, and feedback from industry partners is that MIT Startup Exchange is one of the most effective filters for emerging tech startups,” said Executive Director of MIT Corporate Relations Karl Koster. “We continue to see strong interest from our corporate ILP members resulting in advanced discussions and multiple partnerships.”


STEX25 is a startup accelerator
within MIT Startup Exchange
featuring 25 "industry-ready" startups.

MIT Startup Exchange is adding startups to STEX25 on a roughly quarterly basis, from among more than 1,600 startups in the database. STEX25 startups receive promotion, travel, and advisory support and are prioritized for meetings by the MIT Industrial Liaison Program’s (ILP) industry liaisons.

“STEX25 is playing a formative role in accelerating MIT-connected startups by enabling founders to have high level conversations with the right corporate people at the right time,” said MIT Startup Exchange Program Director Marcus Dahllöf. “Even those startups that are relatively advanced, with well-defined product markets and established sales processes, are finding tremendous value in our program, and are very actively involved. This speaks to the quality of our corporate ILP connections and the quality of our events.”

STEX25 is adding tailored services to further collaboration with ILP corporate members, including targeted Startup Exchange workshops and showcases, exhibits at ILP conferences, and other events tailored towards industry.

The broad group of STEX25 startups represent a number of important fields, including artificial intelligence, automation, data analytics, energy, healthcare, internet of things (IoT), life science, advanced manufacturing, machine learning, materials, nanotech, sensors, and more.

To learn more about STEX25 and MIT Startup Exchange, visit http://ilp.mit.edu/stex25.jsp.




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.

StartupExchange
April 23, 2018

Catalia Health: Innovation at the intersection of healthcare and technology

Aging is fast becoming one the most significant social transformations of the 21st century and Cory Kidd, founder and CEO of Catalia Health, is motivated to face the challenges that are bound to come.
Cory Kidd has been working at the intersection of healthcare and technology for more than 20 years, including his time as a masters and PhD student at the MIT Media Lab. It’s a period he refers to as the basis of his current work as founder and CEO of Catalia Health. He’s spent significant time working to solve one of the big healthcare issues of our time: With a rapidly aging population, the extent of chronic conditions has become more and more prevalent. According to the United Nations, aging is fast becoming one the most significant social transformations of the 21st century. In fact, it’s estimated that there are more than 960 million people, or 13 percent of the population, aged 60 or older around the globe, with a growth rate of 3 percent per year, making this age group the fastest growing of all. By 2030 the projected number of older persons is expected to reach 1.4 billion. The fact that we are living longer, in part due to improved healthcare, means that people are dealing with healthcare issues we simply didn’t have to face in years prior. Cory Kidd and Catalia Health are facing these challenges head on. Cory Kidd,
Founder and CEO of Catalia Health

With far-reaching implications across social sectors and industries, Kidd says it’s no surprise that much of what we hear and read on the subject is focused on the economic and delivery aspects of healthcare. But on an individual level, one of the key challenges that patients face is simply how to properly manage illnesses on their own on a day-to-day basis. Kidd addresses the issue: “At Catalia Health, one of our main concerns is really trying to understand the challenges patients are facing when it comes to sticking with therapy.” Kidd has found that the greatest personal issues for patients aren’t about remembering or forgetting to do something—taking medication, for example. “Rather, the challenges tend to focus around symptoms, side effect management, and psychological issues that are common for people dealing with a chronic disease,” says Kidd. The advent of new technologies, including Catalia’s robot healthcare coach that has garnered significant media attention, may just solve these problems.

With Kidd at the helm, Catalia Health is delivering a care management system to patients. “We’re not selling a piece of hardware or software. Rather, we are providing a service to help engage patients,” says Kidd. To understand what he means, we have to first understand the status quo. At present, healthcare facilities either send someone to a patient’s home or, much more commonly, a nurse calls a patient a few times a month. And of course, these days there are more devices and apps on the market than ever before; most of them involve glowing, beeping devices that serve as reminders and have screens that patients must navigate, as with any other application.

At Catalia Health, however, the interface is unique: each patient is provided with a small robot named Mabu that can be sat on the kitchen counter or coffee table. “There are very specific reasons we use this type of interface,” says Kidd. “And the reason we use Mabu the robot has a little to do with technology but quite a bit more to do with psychology.” While most people spend an inordinate amount of time communicating via phone and computer screens, the simple fact is that human beings are more engaged during face-to-face conversations. Not only do we pay more attention and find ourselves more involved, but it turns out that in-person conversations function to provide an essential sense of credibility. In fact, Kidd, during his time as a researcher at MIT, explored this very phenomenon and found that the importance of credibility and trustworthiness provided by face-to-face interactions carried over into the world of technology. “In other words, when we put a robot in front of a patient that can literally make eye contact with them,” this leads to the aforementioned psychological effects of credibility associated with a person-to-person, or in this case person-to-robot, interaction.


In terms of technology, Kidd points out that Mabu, the interactive voice-enabled robot interface, functions in much the same way as many of the devices that we are familiar with today—it allows for back-and-forth conversation in a similar way to Amazon Echo, Siri or Google Home. What’s really happening behind the scenes is that Catalia Health’s proprietary machine learning algorithms are generating conversations tailored to each individual patient. “We’re building models in the background,” says Kidd, “medically, psychologically and biographically about each patient, and we’re using our AI algorithms to create a conversation for that patient instantaneously.” Catalia Health then gathers the data, maintaining HIPAA compliance throughout, and reports to the doctor, care manager nurse, or pharmacist. “While the technology that makes Mabu tick is complex, the interface to the patients is as simple as a conversation,” Kidd assures us.

It’s an exciting time for the San Francisco-based startup with deep MIT roots. Catalia Health is currently in the process of launching Mabu to hundreds of patients in early 2018. And they’re going out at scale. Most of their partners are hospital systems and large pharmaceutical companies delivering healthcare management programs. “Right now, we are rolling out our heart failure product with Kaiser Permanente in California, which is particularly exciting,” says Kidd. And while Catalia Health’s current clients are based in the U.S., they are in talks with customers and partners around the world. Though he’s understandably hesitant to share details at this time, we can expect public announcements over the course of 2018, as Catalia Health starts rolling out to patients and clients at scale. And the world is taking notice. Kidd was recently named Entrepreneur of the Week by Longevity Network, and the traction gained by Catalia Health is evidenced by spotlights from media heavyweights including Wired and the New York Times.

For Kidd, becoming a part of STEX25 is particularly gratifying. “It’s been a lot of fun for me as an MIT alum to witness the evolution of an already robust Institute ecosystem develop around entrepreneurialism and innovation. I experienced it during my time at MIT, and it’s grown tremendously, so to be invited to participate in STEX25 is amazing.” While Catalia already has a host of important commercial clients in the healthcare domain, teaming up with MIT ILP provides an opportunity for greater outreach to even more potential industry partners.

As we move through 2018 propelled by the latest innovations, Kidd takes time to reflect: “I’ve been in this field for more than 20 years. The practical applications coming to the fore in just the past two to three years have been astounding. It’s an incredibly exciting time as the crossover between technology companies and healthcare companies becomes more prominent.” Given that these are Kidd’s fields of interest, the cross-pollination occurring is particularly intriguing. “For Catalia Health,” says Kidd, “we’re inspired by the prospect of helping more people around the world than ever before.”

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.
StartupExchange
March 26, 2018

TetraScience: research modernization for the digital age

Siping “Spin” Wang is co-founder and CTO of TetraScience, the data focused startup that connects existing lab instruments to a single cloud platform where researchers can manage experiments and easily access data.
Despite the myriad number of scientific advances that have occurred in the past decade alone, many laboratory processes, particularly as they apply to data collection and sharing, remain outdated to the point of archaic, often stifling collaboration and potentially delaying scientific advances. Researchers spend an inordinate amount of time observing experiments in person, taking measurements on their instruments before copying the information by hand into lab notebooks and entering it into spreadsheets and electronic notebooks to share with other scientists. Compounding the tedium and potential for human error is the fact that access to data is hindered by a lack of uniformity among a wide range of manufacturers and instruments using disparate languages, formats and systems. Spin Wang recognized these laboratory pain points while working as a researcher at the MIT Department of Electrical Engineering and Computer Science. Siping Wang,
Cofounder and CTO,
TetraScience, Inc. His collaborators at the time, former Harvard post-docs and now TetraScience co-founders Alok Tayi (CEO) and Salvatore Savo (COO), were experiencing the same frustrations. If you ask Spin for his definition of innovation, he’ll tell you it’s not about creating something new for the sake of it. Rather, for Spin Wang and his TetraScience colleagues, innovation means identifying a particular industry need and solving that problem. It sounds simple, but it forms the basis for the TetraScience vision and their success: “My co-founders and I recognized a need for research modernization, did the necessary market research and set about applying current technologies—Internet-of-Things, cloud computing, containerization, virtualization—into building this data integration platform to improve the lives of scientists.”

The TetraScience data integration platform features three main components. The first aspect is what they refer to as a TetraScience Link, or the modules and integrations to which researchers connect their equipment, which in turn connects to the cloud. “We’re providing a single repository for your data—not the raw data or a screenshot of your instrument—actual clean, well structured, searchable data and metadata,” says Spin. The second component is a pipeline that facilitates data transfer from one system to another. Spin touches on the fact that such a transfer requires the researcher to facilitate the data flow and most likely perform some type of customized logic. “Our data pipeline allows researchers to orchestrate a sequence of steps and logic to perform data integration in a flexible and configurable way.” Finally, Spin Wang and TetraScience are revolutionizing the field with data integration that is instrument and manufacturer agnostic: “There are a tremendous number of vendors in this ecosystem,” says Spin. These vendors use a variety of interfaces, formats, and even philosophies that inform their software and hardware design. TetraScience data integration gets everyone on the same page, so to speak, allowing any software to communicate with any system in a consistent and vendor agnostic manner.

In addition to their recent induction into MIT’s STEX25 accelerator, TetraScience are recipients of the prestigious Digital Science Catalyst Grant Program Award, former participants in the much-lauded Y Combinator accelerator (2015) as well as counting Founder Collective, Dorm Room Fund, First Round and Floodgate as investors. They’re already partnered with industry leaders in the pharmaceutical and biotechnology fields as well as counting several of the most well-respected research and academic institutions and scientific instrument manufacturers as clients. Spin points out that while their current focus is on enterprise pharmaceutical industry and growth stage biotechnology companies, the ideal partner has less to do with size or scale than mindset: “The 21st century is about data, regardless of industry,” says Spin. “However, life science industries are facing data challenges sooner than many others, simply based on the amount of data being generated.” He continues, “Our ideal clients care about data quality, data hygiene, compliance and traceability. They want to focus on visualization and analytics.” The TetraScience team know first-hand the frustrations of wasted time and inaccessibility of important data. They’re eliminating these headaches and are intent on working with organizations looking for flexible, scalable solutions to their problems and inefficiencies.


A recent study by the Tufts Center for the Study of Drug Development revealed that the sheer volume of data collected in clinical trials is not only posing technical and integration challenges to data management staff but is also leading to longer development times. On average it takes more than a decade and costs over $2B to develop and gain market approval for new drugs. Wang and TetraScience are looking to make a major dent in those numbers. Spin’s message to ILP member companies and the world at large: “TetraScience provides a product-driven, scalable, commercially supported data integration platform helping its partners to acquire data from a variety of data sources in the floor above or on the other side of the world, regardless of instruments (HPLC, protein purification and etc.), your contract organizations (CRO/CMO/CDMO) or your software systems (ELN/LIMS).”

The founders and current team are deeply involved in the life sciences and drug discovery. They truly understand the domain and are capable of leveraging data and technology to modernize research and ease the path to scientific discovery. By remaining vendor agnostic, with the ability to connect anyone’s system to the cloud, monitoring and sharing data from anywhere will soon become the new norm if TetraScience have anything to say about it. Call it the newest great advancement in research modernization or the great equalizer for data sharing. Regardless, with Alok Tayi, Salvatore Savo and Spin Wang driving the TetraScience data integration platform, lab research is finally entering the modern age, and the future of research collaboration and drug discovery looks very bright as a result.

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.