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February 22, 2019

BROWSE NEWS RESULTS

48 Results | Page 1 | 2 | 3 | 4 | Last | Next
 
StartupExchange
February 1, 2019

ClimaCell: Doing weather differently—from observation to forecasting with virtual sensors

Rei Goffer is co-founder and Chief Strategy Officer of ClimaCell, the MIT-connected startup using a software-based approach to accurately predict the weather on a global scale.
Traditional weather forecasts are generated by dedicated sensors including satellites, weather stations, and radar. Aside from being expensive to deploy, these standard approaches come with significant blind spots. Satellites can’t detect weather under cloud cover, radar miss ground-level weather, and weather stations only cover small areas—all of which leads to massive gaps in public weather data—the same information used by every weather company. And while radar has improved in the last 50 years, there are no new instruments, and no new layers of data—until now.

ClimaCell tackles the age-old issues surrounding weather forecasting from a unique perspective. “Rather than deploying more sensors, which would cost billions of dollars, we repurpose existing technologies to generate weather data,” says Rei Goffer, co-founder and Chief Strategy Officer of ClimaCell. “Our DNA is different from other weather companies,” he says. “We aren’t strictly a group of meteorologists looking for incremental change. We’re interested in disrupting the entire forecasting chain at every point.”


Rei Goffer
Cofounder & Chief Strategy Officer,
Climacell


This end-to-end disruption begins with the fact that ClimaCell is the only weather company using software-based sensing. “We have millions of sensing points that no one else has,” says Goffer. Their software-based approach allows them to observe the environment in a more complete manner, which means they can generate more robust, accurate data, which in turn leads to pinpoint-accurate weather predictions at a micro level in real time. “We can literally predict and deliver real-time forecasts that differentiate the weather on one street to the next,” says Goffer.

They do this by partnering with wireless and cable companies, tapping into millions of wireless signals and satellite networks that are weather sensitive in order to extract data from them. They also use millions of other weather sensors, including IOT devices, to deliver information missed by traditional weather sources. By combining data gathered from over 500 million new sensing points with data from 12,000 traditional sensing points, Goffer and his team have created the most powerful weather engine the world has ever seen. Their proprietary weather models then translate these stores of new data into the most accurate weather forecasts available.

Being the only company in the world using a software-based approach to the weather means a better understanding of what is going on in real time, better modeling, and better products. It also means ClimaCell has had no shortage of interest across a variety of industries. To date, they can count JetBlue, Ford Smart Mobility, and National Grid as investors and partners. “In addition to our unique approach to the weather, our partnerships are what make us special,” says Goffer. We have very strong partners working with us to develop solutions to their industry problems with the intention of taking those solutions to market.”

While the developed world suffers from gaps in weather data, the developing world is almost completely devoid of traditional weather sensors. “Places like India, Brazil, Africa, and Southeast Asia are lagging decades behind what we see here in the US or Western Europe in terms of weather sensors,” says Goffer.

The agriculture industry in particular feels the impact of this disparity. According to the United Nations Food and Agriculture Organization, more than 60 percent of the world’s population depends on agriculture for survival, with the majority of those people located in the developing world. As reported by World Bank, growth in the agriculture sector is two to four times more effective in raising incomes among the poorest compared to other sectors.

ClimaCell intends to have a major impact on agriculture-driven economic growth, food security, and poverty reduction. “The ability to bring cutting-edge water forecasts into developing places, providing emergency alerts, bringing better flood predictions, better data to farmers. This is what keeps us excited.”




Goffer points out that ClimaCell’s technology will play a significant role in improving uptake of crop insurance in developing countries. In most developed countries, close to 100% of farmers are covered by crop insurance, meaning that if a US farmer’s wheat crop is destroyed due to the weather, the farmer won’t lose their livelihood. In the developing world, uptake of crop insurance hovers around 15 percent, in large part due to a lack of historical weather data. “Providing crop insurance in the developing world is very complicated. Insurance is always based on data; the more you know about the history of the weather, the history of crops, what is actually happening in real time, the lower the premiums and the more farmers you attract.”

However, in the agriculture-reliant developing world, insurance companies offer crop insurance at unaffordable rates, if they offer it at all. “If you can move from having 1,000 sensing points in a country like India—which is more or less what the government has—to having 100,000 sensing points—which is what we at ClimaCell have—the impact can be tremendous,” says Goffer. This means helping millions of farmers, impacting food security, and opening the door to precision agriculture, among other things.

Goffer and his co-founders struck upon the idea for ClimaCell while completing their MBA’s at MIT Sloan and Harvard Business School. “Being accepted into MIT’s Legatum Fellowship was especially helpful as we looked to launch and scale our fledgling company,” says Goffer. “We received endless support from faculty at Sloan and other departments, and we’re still in very close contact with many of them today.

Two years ago, ClimaCell was three friends with a PowerPoint presentation. Today they are backed by US $70 million in Post-Series B funding, with a team of 100 people who are experts in their fields. “Today we’re working on partnerships with some of the largest companies in the world: in the tech space, in energy, and mobility as well, not to mention some big governmental institutions. That’s our way forward, that’s our path to growth and scalability,” says Goffer.

As a select member of STEX25, Goffer understands what it means to be industry ready. “We’ve been serving customers since day one,” he says. “Having greater access to ILP industry members is hugely important for us. I look at the list of companies engaged with MIT and it’s fair to say that ClimaCell can have a tremendous impact on all of these companies, all of these industries. After all, the weather touches everything.”



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
January 31, 2019

twoXAR: Discovering the cure for cancer with artificial intelligence

Andrew A. Radin and Andrew M. Radin are co-founders of twoXAR, the startup with an AI-driven drug discovery platform that is significantly faster, more affordable, and more accurate than traditional approaches.
Since being named to the inaugural STEX25 class in 2017, the aptly named twoXAR—serendipitously, both co-founders are named Andrew Radin (hence, two times AR)—has moved from proof of concept to commercialization, signing a number of important deals, closing their series A funding with SoftBank, and developing a substantial portfolio of discovery-stage disease programs across a variety of therapeutic areas.

Typical drug discovery relies on a linear and stepwise approach that often iterates on several steps, which compounds the time, cost, and failure rate to develop a new drug. This process involves first understanding pathogenesis (how the disease works), selecting a target protein to modulate, and then creating a bioassay to test molecules against the target protein. With that in place, researchers test thousands of drug candidates in High-throughput screening to find hits that they then use medicinal chemistry to turn into drug candidates. It’s often represented that this process takes about 5 years.


Andrew A. Radin and Andrew M. Radin
Co-founders, twoXAR


The pharmaceutical industry has faced declining R&D productivity for many years. Recent estimates indicate that the total cost to develop a new drug is ~$2 billion and can take 10-15 years with


Andrew M. likes to make the point that the technology, while not irrelevant, is the conduit not the end goal: “The goal is efficient drug discovery; AI-driven technology is what allows us to be the most efficient in discovering new drug candidates,” he says. But clearly, the technology is key. Which is where Andrew A. comes in. “If you bring a computer scientist to the table to solve drug discovery problems, a computer scientist can reimagine whether some of the steps are even necessary, and possibly replace them,” he says.

While twoXAR is not the first company to apply computer science to drug discovery, the team is certainly approaching it from a different perspective. Andrew A. notes that while researching how computer science was being used in drug discovery, he noticed that the software approaches he came across fell into two categories: either someone had created data that no one else had access to or someone had created a shrewd algorithm capable of extracting “hidden” information from available data sources. Regardless, both approaches suffered from the same problem: an inordinate number of false positives. “My thought was, rather than going after single data sources, why not combine multiple data sources from many different disciplines, many different angles, different lenses, and combine them to get clearer picture of what is real and what is a false positive,” says Andrew A.

The result has been an influx of industry suitors interested in discussing the possibilities of efficiency change for their own drug discovery programs. “Since being named to STEX25, we’ve benefitted tremendously from the support offered by the Industrial Liaison Program,” says Andrew M. “In conjunction with MIT STEX and ILP we’ve had the opportunity to meet biopharma partners across the world, including Korea, Japan, the UK, and of course, here in Boston.”

“Our ideal partner is a company looking to bring innovation to their drug development systems, which means a company culture that is open to external partnerships and overall flexibility with regard to the drug discovery process as a whole,” says Andrew A. “Our technology allows us to rapidly identify potential new therapies and validate them quickly, which requires our partners to think differently about the process and be able to go from selecting a disease of interest to in vivo efficacy in a just few months.”


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
January 31, 2019

Interpretable AI: Taking the guesswork out of Artificial Intelligence

Co-founders of Interpretable AI, Jack Dunn and Daisy Zhuo build fully explainable AI solutions that deliver state-of-the-art performance.
Co-founding Partners at Interpretable AI, Jack Dunn and Daisy Zhuo believe that in order to unlock the full potential of artificial intelligence, its decisions must be fully explainable and understood by all relevant stakeholders. This means humans working with machines, not blindly following them. Perhaps more importantly, this means humans trusting machines and the decisions they make.

The dominant approaches for generating interpretable models like decision trees were developed in the 80s, when computing power was limited, and have not evolved over time to better exploit increases in computation power. However, Interpretable AI technologies leverage advances in modern optimization to revisit these issues from a fresh perspective, providing much better performance than what has come to be expected from interpretable models.


Daisy Zhuo & Jack Dunn
Co-founding Partners, Interpretable AI


“Traditionally, practitioners have had to choose between models that are interpretable and models that have good performance,” says Dunn. “Our core product, Optimal Decision Trees, is able to produce a simple decision path that humans can follow. It mimics the human decision-making process while maintaining the same level of performance as deep learning systems.”

Interpretable AI has recently secured a partnership that will allow them to bring their technology to a number of large retailers in the US and abroad. One area of retail that is already seeing the impact of artificial intelligence is assortment planning (i.e., considering financial objectives while determining which products will be on offer where and when). Dunn and Zhuo aim to disrupt that space in a whole new way.

Zhuo explains: “Our technology will allow retailers to really get into the data and understand what drives sales, how to prepare for new product releases, how to stock them, where to ship them, and really how to harness the power of AI without wondering whether they can trust what it’s telling them.”

Their products are based on years of research under the guidance of their Co-founding Partner, Dimitris Bertisimas, who is also Co-director of the Operations Research Center at MIT, where Dunn and Zhuo received their PhDs. Both cite the Institute’s mission as an important aspect to their desire to take cutting edge research out into the world, rethinking and improving the status quo.

“Being at MIT, this whole culture of working together with industry on real problems helped us to speed up our methodological development to have an impact in the world—it’s why we’re driven to make sure that the work we do in the lab can create value in the world,” says Dunn. “Through our consulting engagements in healthcare and insurance, we saw a real need for interpretability and scalability,” says Zhuo. “That’s why we designed our method from the ground up to fit this particular need.”

In the medical field, their technology has already been embraced by two of the world’s leading institutions. Their “risk-calculator,” which helps make quick decisions about whether or not a patient needs surgery, or what type of surgery to perform, is being used on a daily basis in the emergency room at Massachusetts General Hospital.

It works like this: Let’s say a doctor wants to predict a patient's risk of post-surgery acute renal failure. The doctor answers a handful of questions prompted by Dunn and Zhuo’s technology. For example, Is the patient's creatinine level below 2.5mg/dl? Is the patient currently on dialysis? Is the patient currently on mechanical ventilation? Based on the responses, the model calculates an accurate prediction of risk of acute renal failure after surgery. In addition, thanks to the transparent nature of Predictive AI’s Optimal Decision Trees, the doctor can see the medical rationale and data behind the prediction.

The team at Interpretable AI have also worked hand-in-hand with oncologists at Dana Farber to develop a “cancer mortality predictor.” The technology takes into account the variables, values, and logic required to determine the best treatment path for patients. The transparent nature of its decision-making feature allows oncologists to verify their intuition while providing patients with a clearer understanding of their options, thereby empowering both stakeholders in a difficult process often fraught with anxiety. Interpretable AI are working to have this new product in a number of large cancer hospitals throughout the US.




Unlike other approaches to interpretable AI, which use a post-hoc, guessing game approach to explainability, the models at Interpretable AI are built from the ground up to be interpretable from the beginning. Zhuo uses the example of a risk scoring system in banking. A practitioner will train a deep neural network that might predict a person has an 80 percent chance of default, at which point, after seeing the percentage, the practitioner will make an attempt at explainability.

“These approaches are local in nature, only considering how small changes to the person’s characteristics affect the prediction,” says Zhuo. “Whereas with our Optimal Decision Trees approach, you can also see globally why a particular person falls into a particular group, and why the decision was made to segment people in that manner,” says Zhuo.

Today, 15 percent of enterprises are using AI, but 31 percent are expected to incorporate it in the next year. With their powerful combination of thoroughly interpretable, high performance algorithms that are utterly unique to the market, Dunn and Zhuo are intent on taking their technology to industries with high regulatory requirements that may previously have been hesitant to use artificial intelligence: the banking and insurance industries, for example.

“It’s not enough for a bank to tell an applicant that their black box method has denied them a loan,” says Dunn. “But our fully explainable methods can output the series of variables and decisions that led to the loan being denied, while still giving the bank the ability to harness the increased predictive power of AI.” This not only helps banks give better risk estimates but also provides the consumer with a never-before-seen level of transparency regarding the decision-making process. Dunn and Zhuo believe their current collaborations will establish a more transparent, equitable process, thereby leading to greater customer engagement and eventually, long-term industry growth.



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
January 31, 2019

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: CATALOG, CompanionMx, DUST Identity, Inkbit, Osaro, RightHand Robotics, and ThruWave.
MIT Startup Exchange is pleased to announce the addition of seven companies to its roster of STEX25 startups. New additions to the program include: CATALOG (using DNA to store digital data), CompanionMx (mobile monitoring platform for mental health), DUST Identity (unclonable identity layer using nanodiamonds), Inkbit (multi-polymer 3D printing), Osaro (AI for unstructured data), RightHand Robotics (imaging AI for robotics), and ThruWave (millimeter wave sensors that can see through containers).

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.

“For startups, the benefits of being invited to join STEX25 are very real,” states Marcus Dahllof, Program Director, MIT Startup Exchange. “Access to potential customers is a key challenge startups face. For decades, the ILP has connected industry to MIT. Startup Exchange is able to exploit that deep knowledge and expertise to make targeted introductions with high success rates. The most recent additions showcase both the innovative spirit as well as the breadth of technologies covered across MIT.”

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
December 19, 2018

iQ3Connect: Virtual reality at every engineer’s fingertips

Cloud service and inexpensive headsets bring VR to the desktop for everyday work and global collaborations.
For engineers working on immensely complex projects such as jet engines or cars, the potential payoffs of virtual reality (VR) are strikingly obvious. VR workspaces have been employed for many years to allow engineering teams explore and troubleshoot their creations along with other key experts in their organizations or partner firms.

To date, this approach has required physically gathering all these professionals in extremely expensive and difficult-to-operate VR rooms. But IQ3Connect, an MIT Startup Exchange STEX25 company, now offers a desktop alternative.


Ali Merchant
President & CEO, iQ3Connect


“Our technology powers a high-performance immersive 3D workspace that companies can use for product engineering and training,” says Ali Merchant, co-founder and CEO of the Woburn, Mass. firm. “Instead of using a 2D collaboration tool like PowerPoint or WebEx, you can simply enter a 3D space along with other members of your global team, with the click of a button, without leaving your desktop.”

The IQ3Connect platform is delivered via private or public cloud and is designed to work on any engineering-strength desktop and VR headset. “Going forward, these collaborative VR workspaces will come to every engineer’s desktop,” Merchant predicts.

Sharing the engineering workspace
Merchant earned his doctorate in aeronautical and aerospace engineering at MIT, where his research focused on designing and analyzing complex turbomachinery components used in aircraft engines.

“About two years ago when low-cost cost virtual reality devices like the Oculus Rift started coming on the market, we saw an opportunity to take that technology platform, developed at MIT over 10-plus years of research, and use it as a foundation to build the IQ3 collaboration platform,” he says.

Working with Yunus Shah, who has held leadership roles at the well-established simulation software firm ANSYS, Merchant focused his startup on the goal of allowing engineers at their desktops to engage via VR with their exact 3D designs.

One dramatic benefit of the VR environment is the ability to understand designs at the same scale seen in the real world. “For example, if you look at an engine on your laptop screen you really can't tell how big it is,” he says. “But in a VR system you can see it at its actual scale. You can actually walk into the nacelle of an engine and then go in between the blades and understand the complexity in a way that is not possible on any 2D screen. That capability completely transforms the way engineers can work and collaborate.”

IQ3 brings this environment to teams; colleagues invited from anywhere on the globe can enter a VR meeting just as they would a normal web conference. “I can work directly on the 3D geometry and engage with my team members, identify problems, make changes to the design, mark it up and essentially complete a design review,” Merchant says. “I can do that much more efficiently and quickly in a VR space, and reduce the number of errors that might occur with conventional 2D tools.”

“Everyone can freely look at the 3D geometry and be completely free to move around in the 3D space, just like you would if you were in a physical factory floor walking around a product,” he adds. “You can work in parallel in this 3D space and then bring everybody together in a meeting. It's a much more productive environment.”

Extending the virtual team
Engineering collaborations can grow as needed in this shared VR space, Merchant emphasizes.

“Typically in a larger product design project, a cross-functional team has to validate the initial prototype design and raise aspects of that design, all the way from the way the parts fit together to how they can be accessed and how they can be manufactured,” he says. “All these questions have to be answered by different teams.”

The IQ3 platform can bring all these teams together in an immersive real-time 3D environment, so that they can work much more efficiently and quickly to resolve problems. “If you can improve the quality of your engineering and solve these problems early in the design process, you can save a lot of time and money,” Merchant points out.

These extended teams can reach well outside the organization itself, incorporating critical suppliers as well. “If communication barriers with suppliers can be reduced, then there's a huge cost savings on both sides,” he says.




Easing adoption
Bringing transformative tools such as VR into an organization is always a challenge, and IQ3 aims to ease the transformation in several ways.

One major ingredient is to deliver high-performance software that works agnostically with hardware and software that is standard in the engineering world.

“We can work with almost any hardware that's out there, so you can use a mix of devices that best suits your company's requirements,” he says. Even engineers who lack VR equipment altogether can still participate in the IQ3 workspace via browser.

Adhering to the latest web standards for delivering VR “gives us a huge advantage over other VR software,” he says. “Working through a browser gives us a lot of flexibility in terms of how IQ3 can be used within an organization. And if you need to bring your customers or suppliers into an IQ3 meeting, all they need is a browser and a headset, and our software does not require any installation.”

The IQ3 platform software also enforces security for the critical engineering data sets brought into the VR sessions, which remain stored in the cloud.

Perhaps most importantly, the company focuses on providing bottom-line benefits for customers. “In our initial meetings with companies, people got very excited,” Merchant recalls. “There was an initial Wow factor, which died off. Then the questions were, what’s the business case and where’s the return on investment?”

“We’ve been fortunate to develop relationships with customers, really understand their business needs and problems, and shape our technology to actually solve those engineering problems,” he says.

In one automotive company, for instance, the previous use of physical prototypes to assess certain problems with parts sometimes resulted in costly errors. “They were able to use IQ3 to solve such problems upfront before they commit to manufacturing, and they're able to do this in a collaborative space,” he says. “They can bring the relevant engineers or even their suppliers and their customers to engage to solve the problem.”

IQ3 is also finding applications in very different engineering fields. One is biomedical engineering, where drug developers can use VR to “walk around” biological molecules. Another is in offshore petroleum. The company has a partnership with 3D at Depth, which uses lidar (surveying based on laser pulses) to map out undersea oil drilling structures. 3D at Depth’s customers can use the IQ3 platform to collaboratively assess the condition of their undersea equipment, which would be a daunting task without combining 3D lidar data with VR, Merchant says.

Sometimes, IQ3 also addresses another type of business need that has nothing immediate to do with product engineering: business data visualization, tapping into the vast data sets organizations are collecting on sales or other lifeblood operating information.

“A large 3D canvas is the best way to visualize these large corporate data sets,” he says. “We can bring the data sets into iQ3 and let participants analyze the data freely in our 3D space.”



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
December 7, 2018

Distilled Analytics: Solving identity problems in financial services with data driven AI

David Shrier is founder and CEO of Distilled Analytics, an MIT spinout that aims to improve financial inclusion and tackle widespread structural issues within the financial services industry.
As CEO of Distilled Analytics, David Shrier is using machine learning, biometrics, and AI technology to solve some of the biggest problems in the financial services industry. It’s what brought him to the Institute, and it’s what led him to found Distilled Analytics in 2017. Specifically, he started the MIT spinout to address the fact that 3.5 billion people around the world currently have limited or no access to the financial system.

Financial inclusion is a key enabler for many of the United Nations Sustainable Development goals and is essential in supporting overall economic growth. In fact, a report by the McKinsey Global Institute suggests adoption of digital finance could promote financial inclusion, benefiting billions of people, and adding $3.7 trillion to emerging economies alone.

But how do you include someone, if you don’t know who they are? Having pinpointed digital identity as essential to unlocking financial inclusion, Shrier and Distilled Analytics are using an AI-driven data science called Social Physics to solve structural problems around identity in financial services.


David Shrier
Founder & CEO, Distilled Analytics


Invented and developed at MIT by Shrier’s co-founder at Distilled Analytics, Professor Alex Pentland, Social Physics is a new way of understanding human behavior based on the analysis of big data. It forms the basis for everything Distilled Analytics does, including their core technology, Predictive Identity, which is delivered in a software as a service model. Shrier says, “We’re now able to identify people to create better identity profiles and credit models, help governments identify citizens properly to better deliver services to them, as well as predict what people are going to do with their money, and help financial institutions lend to them with confidence.”

For example, Distilled Analytics is looking to use better identity profiles to solve the $330 billion dollar per year false-decline problem that plagues the financial services industry. Here in the US, your credit card works approximately 99 percent of the time. However, once you leave the country, the approval rates for a valid transaction decrease drastically. Shrier cites a country like South Africa, where the approval rate for legitimate customer transactions hovers around 27 percent. Why? Because of an inability to verify identity.

“We’ve had conversations with the leading credit card networks in the world and they, like we, think false decline is a major problem. It’s important for driving payments and inclusion, and it’s important for helping prosperity in emerging economies as well as helping the economy in developed markets.” Distilled Analytics believe they can take card-not-present false declines from 12 percent to near zero percent in developed markets and from as much as 73 percent to near zero percent in emerging markets. And major credit card companies agree. In fact, some of the largest credit card companies and financial institutions around the world are turning to Shrier and his team for solutions to their structural financial services problems around identity.

“We want to be the operating system of identity, credit, fraud and financial services globally,” says Shrier. “That is what success looks like for us.” At present they are in final negotiations with a top-two credit card network, with a $120 billion insurance company, and are looking to partner with governments interested in incorporating their analytics into their electronic identity systems and financial institutions with $5 million to $50 million assets under management.

Shrier refers to what they do as Biometrics 3.0. If Biometrics 1.0 is a fingerprint or facial scan, and Biometrics 2.0 is basic behavioral recognition, Biometrics 3.0 is a much more complex system. “Biometrics 3.0 is a combination of techniques that includes behavioral, physiological, geospatial temporal analysis, and other biometrics in an adaptive Bayesnet. It’s a very complex artificial intelligence system where the different models of behavior talk to each other.”

With a more robust, stronger identity, Distilled Analytics builds apps on their platform, including a predictive credit app that is 30 percent to 50 percent more accurate than existing credit scores obtained from traditional consumer credit reporting agencies. They’ve also built fraud prediction apps that identify where fraud is happening today as well as predicting where fraud might happen three months into the future.

A glance at the Distilled Analytics founding team bio page reads like a who’s who of heavy hitters specializing in FinTech, big data, and quantitative analytics. The experience housed under one roof is formidable.




Aside from developing a new social science and being a serial entrepreneur, co-founder Alex Pentland is the founding faculty director of the MIT Connection Science Research Initiative. He is also a global thought leader on big data, AI, and digital privacy who currently advises AT&T and the UN Secretary General. Forbes has called him one of the “seven most powerful data scientists in the world.”

Co-founder Alex Lipton was Managing Director of Bank of America Merrill Lynch for 10 years, and is widely regarded as the foremost authority on quantitative analytics. He is a Connection Science Fellow at MIT Media Lab and Visiting Professor of Financial Engineering at EPFL.

For his part, Shrier has developed $8.5 billion of growth opportunities with C-suite executives for Dun & Bradstreet, Wolters Kluwer, Ernst & Young, GE, The Walt Disney Company, AOL Verizon, and Starwood, as well as private equity and VC funds. He councils the government of Dubai on blockchain activity and established the world’s first FinTech and blockchain programs at MIT and Oxford.

Now that Distilled Analytics has been named a STEX25 industry-ready startup, Shrier takes a moment to reflect on the importance of a program like ILP: “As a lecturing and Managing Director at MIT, ILP was a tremendous collaborator, getting some of our best ideas into the hands of member companies. And It’s a fantastic resource for global 1,000 companies looking for the great innovation of the future. Now, as the CEO of a STEX25 company, I’m thrilled to have ILP as a partner to help navigate the dialogue with those large, complex organizations.”



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
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
November 20, 2018

PathAI: Diagnosing cancer with Artificial Intelligence

Using deep learning and computer vision technology for faster, more accurate disease diagnosis with Aditya Khosla, co-founder and CTO of PathAI.
Pathologists play a vital role in diagnosing deadly diseases like cancer. Unfortunately, they don’t always get it right. In fact, studies suggest that in the realm of pathology the incidence of diagnostic error is in the range of 10-15%. If we consider that in a single day a pathologist working in a clinical setting examine close to 300 slides, each of which contains a small piece of tissue holding approximately 50,000 cells, it’s no surprise that mistakes are made. “A machine on the other hand, can look at every single pixel in an image and churn through that tirelessly. That’s what machines are good for: looking over a lot of data,” says Aditya Khosla, CTO of PathAI.

Co-founded in 2016 by Khosla and CEO Andy Beck, PathAI utilizes computer vision and deep learning to identify and diagnose cancer. More specifically, PathAI applies convolutional neural networks (CNNs) to the realm of pathology. CNNs, considered perhaps the most significant breakthrough in deep learning, are particularly adept at image recognition. In fact, CNNs excel at recognizing patterns in images without needing to be trained to recognize components that define an image.


Aditya Khosla
Cofounder & CTO, PathAI


Khosla, who holds a PhD in machine learning and computer vision from MIT, makes it simple for us: “Instead of specifying if a cancer cell is larger or has a different shape compared to normal cells, we provide the computer with example images of cells with cancer and cells without cancer, and the computer learns to do a superb job of identifying these cells. That is what we have built at PathAI to improve diagnoses and save lives in the process.”

Khosla and Beck joined forces to win the Camelyon16 Challenge, the goal of which was to identify metastatic cancer in lymph nodes. Having been given training data (i.e., slides from patients with cancer and without cancer), their goal was to train a model to identify cancer in new slides. Competing against their machine were human pathologists. While the human pathologists had an error rate of 3.5 percent in competition setting, Khosla and Beck’s fully automated system had an error rate of just .6 percent.

Perhaps more telling was that when the same test was given to pathologists in a clinical setting—meaning less time and attention for each slide, among other things—the average error rate for human pathologists increased to 15 percent, whereas Khosla and Beck’s machine maintained a consistent error rate of .6 percent. “That was a very significant advance compared to systems that had existed before,” says Khosla. “Since that time, our goal has been to put a pathologist together with our machine to bring that error rate down to zero. Because what we really want at PathAI, is to get the right diagnosis for every patient every single time.”

Winning the Camelyon Challenge so handily turned the heads of two major players in healthcare: Bristol-Meyers Squibb and Philips. “They saw how promising the technology was and really wanted to try it out on their data,” says Khosla. “Since that time, we’ve seen that we can get results that are even more reliable and more robust. We’re also starting to develop systems much more quickly than we could before.”




Khosla notes that while the PathAI system is built from scratch, his PhD research at MIT directly impacts his work today. On the other hand, Andy Beck has an MD from Brown Medical School and a PhD in Biomedical Informatics from Stanford University, where he developed one of the first machine-learning based systems for cancer pathology. Says Khosla, “Andy has a deep knowledge of medicine and machine learning. In large part, it is due to his unique ability to combine these disciplines that we’ve been able to build something that is both tractable from a machine learning perspective as well as useful from a medical perspective.”

Today PathAI has a team of nearly 40 people, and that number is set to grow by the end of the year. On the tech side, it’s a team filled with scientists from top institutions. On the software engineering side, PathAI’s ranks are swelled with former members of the best tech companies, including Google and Microsoft. It’s a team that has allowed Khosla and Beck to deliver promising results to partners at a rapid rate.

PathAI typically collaborates with pharmaceutical companies, diagnostic labs, and academic medical centers. Much of the work they do with pharmaceutical companies entails developing systems for automating reads of pathology data. In the field of immune-oncology, PathAI has proven extremely effective at assisting companies working with biomarkers to identify new drug pathways, while improving patient outcomes and helping to improve the design for future clinical trials.

For diagnostic labs, PathAI designs decision support tools to help make pathologists more accurate and efficient by providing systems that help diagnose diseases. PathAI’s system highlights the different areas on a slide where it thinks there might be cancer, which directs the attention of pathologists and reduces the amount of time spent reviewing a particular case. “One of our biggest goals is to have a huge impact on patients who are actively suffering from diseases,” says Khosla. “This effect will come through our decision support tools developed for diagnostic labs and companion diagnostics developed for pharmaceutical companies.”

Interestingly, PathAI might be at the forefront of revolutionizing pathology in more ways than one. According to Khosla, the majority of pathology isn’t digital, and this is mostly due to the fact that storing digital data is more expensive than storing physical data. “The role that we hope to play, is essentially to give pathology labs a better reason to digitize their data,” he says.

By improving disease diagnoses and saving lives in the process, Khosla and PathAI have developed a reason to spur the process of digitization in the realm of pathology.



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.”



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