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June 20, 2019

BROWSE NEWS RESULTS

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StartupExchange
May 23, 2019

MIT Startup Exchange announces Spring 2019 additions to its accelerator, STEX25

MIT Startup Exchange is pleased to announce the addition of seven companies to its roster of STEX25 startups. New additions to the program include: Arbor Bio, BlinkAI, Cellino Biotech, Nara Logics, Secure AI Labs, Silverthread, Top Flight Technologies, Via Separations, and Zapata Computing.
MIT Startup Exchange is pleased to announce the addition of nine companies to its roster of STEX25 startups for its Spring 2019 cohort:

Arbor Bio (protein discovery for improving human health and sustainability)
BlinkAI (transformative AI solutions from sensor information)
Cellino Biotech (engineering cell therapies at light speed)
Nara Logics (synaptic intelligence for better decisions)
Secure AI Labs (secure analytics across healthcare)
Silverthread (improving software health)
Top Flight Technologies (hybrid energy power systems)
Via Separations (molecular filtration for industrial processes)
Zapata Computing (software solutions for quantum computing)

“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 run by MIT Startup Exchange featuring 25 "industry-ready" startups. MIT Startup Exchange adds startups to STEX25 on a roughly quarterly basis, from among more than 1,700 MIT-connected startups. STEX25 startups receive promotion, travel, and advisory support, and are prioritized for meetings by the MIT Industrial Liaison Program’s (ILP) industry liaisons. STEX25 companies collaborate with ILP corporate members through targeted Startup Exchange workshops and showcases, exhibits at ILP conferences, and other events tailored towards industry.

“The selection process for STEX25 is increasingly competitive. We are fortunate to have strong nominators across MIT; from programs such as the Venture Mentoring Service, as well as from affiliate programs, such as The Engine. That said, more and more nominations are coming from founders of STEX25 graduates/alumni, as they have seen the incredible benefits of our program,” said MIT Startup Exchange Program Director, Marcus Dahllöf.

Dahllöf continued: “In our most recent cohort, we see strong representation of AI and tough tech startups, which might have a long product roadmap ahead. The commonality is a deep passion for solving problems that are hard, and where enterprise customers or partners can play a key role in commercialization. MIT faculty and PhDs continue to be well-represented within these startups.”



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 19, 2019

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

David Shrier is founder and CEO of Distilled Identity, an MIT spinout that aims to improve financial inclusion and tackle widespread structural issues within the financial services industry.
As CEO of Distilled Identity, 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 Identity 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 Identity are using an AI-driven data science called Social Physics to solve structural problems around identity in financial services.


David Shrier
Founder & CEO, Distilled Identity


Invented and developed at MIT by Shrier’s co-founder at Distilled Identity, 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 Identity 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 Identity 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 Identity 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 Identity 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 Identity 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 Identity 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
March 26, 2019

Procter & Gamble-Nara Logics partnership leads to breakthrough for Olay Skincare


Jana Eggers
CEO
Nara Logics

When Procter & Gamble (P&G) wanted to innovate in a crowded market, they turned to a nimble tech startup in the MIT ecosystem: Nara Logics. The result was an Edison Award for Innovation and a Constellation Group Supernova Award for Digital Transformation. The collaboration also led to a doubling of Olay’s ecommerce conversion rates, and gave the brand a platform to engage millennial consumers. Importantly, it helped reduce a key pain point for skincare shoppers—confusion at the shelf—by helping them choose products tailored to their individual needs.

In 2015, P&G was seeking fresh ways to develop its latest product in a skincare market dominated by increasingly tech-savvy customers expecting ever-greater levels of personalization in their shopping experiences.

“We went looking for the best and the brightest,” says Damon Frost, P&G Beauty Chief Information Officer (CIO). “We needed a team capable of truly complementing Olay Skin Advisor with a state-of-the-art recommendation engine.” The decision to engage the services of Nara Logics would prove fortuitous in P&G’s quest to harness the potential of data-driven marketing powered by cutting-edge artificial intelligence.

“You’ve probably heard that AI is a black box,” says Nara Logics CEO Jana Eggers. “Well, ours isn’t. This is one cutting-edge aspect of our technology.” Eggers refers to this as providing “The Why’s” or the reasons behind the recommendations. Nara Logics’ Synaptic Intelligence Platform, inspired by recent research in biological neural networks, quickly merges disparate, siloed data to provide recommendations for decisions that are easy to understand for enterprise and consumer alike.

In fact, this ability to provide “The Why’s” was noted time and again by P&G customers during the pre-launch testing phase as an essential aspect to their appreciation for Olay Skin Advisor. On the strength of a 90 percent approval rate for recommendations and products, it was no surprise that P&G moved from proof of concept to a worldwide launch in such a short period of time.

“P&G is really well known for their consumer testing, so to have them validate our platform’s ability to provide hyper-personalization, and then see such a successful global rollout—that’s big for us,” says Eggers. To date, Olay Skin Advisor is available in countries throughout Asia, Europe, and North America and has amassed over 5 million visits from women around the world.

While Nara Logics’ AI platform is supporting Olay Skin Advisor around the world, Eggers insists that maintaining offices in Cambridge, MA and being members of the MIT innovation ecosystem is an important aspect of how Nara Logics operates. “We’re proud to be affiliated with MIT Startup Exchange and the MIT innovation community in general. Our exposure to Fortune 500 companies through the Startup Exchange has been extremely beneficial.”

“If you’re invited to be one of the companies that gives a presentation to industry members of the MIT Industrial Liaison Program [ILP], it’s always a relevant conversation. ILP programs and events are high quality and productive for us,” says Eggers.

For their part, ILP member P&G sought out a recommendation engine and emerged with a groundbreaking partnership. “After a global search to identify a company leading in their field and a perfect match for this project, it’s no surprise that we wound up partnering with a startup in the MIT innovation pipeline,” says Kevin McCarthy, P&G Associate Director, Global Business Development/Startup Innovation.

Now that P&G and Nara Logics have teamed up to bring the world the first successful application of AI to skincare personalization, they’re expanding on their partnership, initiating new innovation projects together, which means that other personalization platforms like Olay Labs and Gillette’s Shave Advisor are benefitting from the Nara Logics platform.


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
February 28, 2019

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
February 14, 2019

BMF Material Technology, Inc.: 3D precision manufacturing

Nicholas Fang is cofounder and Chief Scientist of BMF Material Technology Inc., a startup focused on the design, development, and production of Microscale 3D printing systems and manufacturing of miniature precision components for industrial applications.
Guided by co-founder and Chief Scientist Nick Fang, BMF Material is poised to revolutionize the 3D printing industry. In 2016, Fang was prototyping ideas at the MIT Nano Optoelectronics & 3D Nano Production Technology Laboratory, which he founded and directs. The idea was to produce high-precision plastic miniature components with a shorter turn-around time and at a lower cost than traditional methods. It was a visit to his lab from Dr. Xiaoning He, now BMF’s CEO, that allowed Fang to recognize the true potential for his technology to become a commercially viable solution capable of disrupting an entire industry. “Dr. He suggested that there are myriad precision use cases for nano/micro 3D printing, including cardiac stents, endoscope lenses, and specific electrical connectors,” says Fang.


Nicholas Fang
Cofounder & Chief Scientist,
BMF Material Technology


Complex, microscopic devices are all around us, in cell phones, cameras, and medical equipment. These structures, including micro adaptors, miniscule connectors, and tiny springs, are essential components for tools used across a variety of industries. For example, there is tremendous demand for complex 3D micro/nano structures in areas that include precision optics, biomedicine, tissue engineering, new energy, HD display, microfluidic components, micro/nano optical components, micro/nano sensors, micro/nano electronics, bio-chips, and optoelectronics.

However, production of these high-precision components can be time consuming and expensive, resulting in significant bottlenecks from prototype to production. At present, CNC machining and micromolding are the standard manufacturing processes. CNC machining is a process whereby pre-programmed computer software dictates the movement of factory tools and machinery. Micromolding entails building a cavity to match the shape of the part required.
Once a prototype is established, injection molding is used to replicate the structure. These methods cost approximately ten thousand dollars per mold with a lead time of up to eight weeks, which is why BMF Materials represents such an attractive prospect to OEM manufacturers.

Fang and his team have entered the manufacturing arena printing plastic parts more rapidly and in a more cost-effective manner than either CNC machining or micromolding. What’s more, BMF’s methods are more effective than traditional stereolithography (SLA), which is widely considered to be the gold standard of 3D printing. Using their nanoArch series printers, BMF Technology Inc., produces plastic parts with 3D micron tolerances that are ten times more accurate and many times faster than conventional 3D SLA processes.

Their printers are the first commercialized high-resolution, multi-material 3D micro-fabrication equipment based on Projection Micro Litho Stereo Exposure (PuLSE) technology, which yields miniature components of exceedingly high resolution and high tolerance, far beyond the scope of traditional or low-resolution 3D printers. All of this thanks to their advanced optical engine design and precision process control.

The optical industry is one area that is in constant need of high-precision, personalized solutions that BMF is capable of meeting. Fang cites eyeglasses, contact lenses, and surgical tools as examples of devices containing complex geometries and engineering-specific materials. As a result, BMF have teamed up with the largest ophthalmic hospital in China, Beijing Tongren Hospital, to produce low-cost personalized contact lenses. Despite the fact that no two human eyes are the same, traditional lenses are made in mass quantities from factory-molded, semi-finished blanks. The advent of personalized free form lenses as designed by BMF and Beijing Tongren could improve the vision of eyeglass wearers while significantly impacting the optical lens industry.

Though still an early stage company, BMF’s innovative technology has received considerable support from interested parties. Shortly after spinning out of MIT in 2016, they received an angel round of investment of RMB 27 million from Green Pine Capital Partners and Mia Capital Partners. In 2017, Shenzhen Capital Group led the series A round investment of RMB 60 million. 2017 also saw BMF receive the 11th Zero2IPO Ventures 50 Awards New Seed award, and they were lauded as “Annual Innovation Company” in the 2017 Shenzhen Innovation List.




“We’re currently focused on industrial areas that require fast, high-value design and product adaptation,” says Fang. Most recently, Johnson & Johnson placed an order for BMF’s nanoArch 3D high-resolution printing system. In addition, BMF’s tech helps power Masdar Institute of Science and Technology, Abu Dhabi, UAE, and National Laboratory of Solid State Microstructures, Nanjing University. They have also developed strategic partnerships with well- regarded Chinese companies like SonoScape and Goertek.

“We believe precision additive manufacturing is capable of addressing some key geometrical and material challenges faced by subtractive machining,” says Fang. “In the future, we expect that micro and nano 3D printing will solve the problems surrounding the evaluation of early stage products and the validation of designs that require precision internal geometries, complex interconnected channels, as well as side openings.”

Fang notes his relationship with MIT ILP as unique and beneficial to the work he does both in and out of the lab. “When I was a researcher, I was very grateful ILP provided a platform for me to interact with industry from different domains and sectors. Now, as a member of STEX25, I recognize the value of a partner like ILP from a different perspective, as a program that can broker high-value introductions to industry members.”

At present, the MIT spinout intent on breaking the bottleneck in high-precision nano 3D printing has offices in Boston, Hong Kong, and Shenzhen. The team also has more than 20 years of scientific research and engineering experience behind them, not to mention a peerless R&D group, including Senior Scientist and MIT Department of Engineering lecturer Dr. William Plummer. “In the near future, with our focus on high-resolution and high-precision additive manufacturing tools and services, together with our industry partners, we aim to overcome the current challenges of producing complex components such as fiber optical connectors and adapters for medical surgical tools with micron tolerances.”



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
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

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
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
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.