Collaborating Authors

Ann Arbor

Manager, Data Science (Hybrid)


May Mobility is transforming cities through autonomous technology to create a safer, greener, more accessible world. Based in Ann Arbor, Michigan, May develops and deploys autonomous vehicles (AVs) powered by our innovative Multi-Policy Decision Making (MPDM) technology that literally reimagines the way AVs think. Our vehicles do more than just drive themselves - they provide value to communities, bridge public transit gaps and move people where they need to go safely, easily and with a lot more fun. We're building the world's best autonomy system to reimagine transit by minimizing congestion, expanding access and encouraging better land use in order to foster more green, vibrant and livable spaces. Since our founding in 2017, we've given more than 300,000 autonomy-enabled rides to real people around the globe.

Combining chest X-rays and EHR data using machine learning to diagnose acute respiratory failure Artificial Intelligence

When patients develop acute respiratory failure, accurately identifying the underlying etiology is essential for determining the best treatment, but it can be challenging to differentiate between common diagnoses in clinical practice. Machine learning models could improve medical diagnosis by augmenting clinical decision making and play a role in the diagnostic evaluation of patients with acute respiratory failure. While machine learning models have been developed to identify common findings on chest radiographs (e.g. pneumonia), augmenting these approaches by also analyzing clinically relevant data from the electronic health record (EHR) could aid in the diagnosis of acute respiratory failure. Machine learning models were trained to predict the cause of acute respiratory failure (pneumonia, heart failure, and/or COPD) using chest radiographs and EHR data from patients within an internal cohort using diagnoses based on physician chart review. Models were also tested on patients in an external cohort using discharge diagnosis codes. A model combining chest radiographs and EHR data outperformed models based on each modality alone for pneumonia and COPD. For pneumonia, the combined model AUROC was 0.79 (0.78-0.79), image model AUROC was 0.73 (0.72-0.75), and EHR model AUROC was 0.73 (0.70-0.76); for COPD, combined: 0.89 (0.83-0.91), image: 0.85 (0.77-0.89), and EHR: 0.80 (0.76-0.84); for heart failure, combined: 0.80 (0.77-0.84), image: 0.77 (0.71-0.81), and EHR: 0.80 (0.75-0.82). In the external cohort, performance was consistent for heart failure and COPD, but declined slightly for pneumonia. Overall, machine learning models combing chest radiographs and EHR data can accurately differentiate between common causes of acute respiratory failure. Further work is needed to determine whether these models could aid clinicians in the diagnosis of acute respiratory failure in clinical settings.

Welcome to the "Non-Service" AI Economy? – InsideSources


In American society, the service sector, which generally produces intangible rather than tangible goods, rules the U.S. economy and accounts for about 80 percent of the nation's gross domestic product. Service is in the eye of the beholder, and Americans have spoken loudly concerning their level of satisfaction with "service" in the U.S. economy. For example, the American Customer Satisfaction Index (ACSI) (comprising a cross-section of U.S. industries), based in the Ross School of Business, University of Michigan-Ann Arbor, tracks quarterly the overall satisfaction level of company performance by U.S. customers (ranking it from a low of 0 to a high of 100). Since recording a survey result of 77.0 in the third quarter of 2018, U.S. customer satisfaction has declined continuously to 73.6 in the first quarter of 2021, a ranking not surveyed by the ACSI since 2005. Interestingly, according to ACSI, "manufactured goods tend to score higher for customer satisfaction than do services.

The future of self-driving? Maybe less like Elon Musk and more like Domino's pizza robots


As companies like Tesla and its CEO Elon Musk come to Austin, Texas, the booming city and new tech hub has grown so much it has struggled to make good on its "keep it weird" motto. But since early June, when residents of the South Congress, Downtown, or Travis Heights neighborhoods order pizza from Southside Flying Pizza, their pies might arrive inside a three-wheeled robot -- the REV-1. But it is no full self-driving Tesla. About two dozen REV-1 vehicles now trundle down the roads of Austin and Ann Arbor, Michigan, where the company behind the robots -- Refraction AI -- first launched in 2019 in a bid to harness driverless technology in a new way. Autonomous vehicles, and their potential to disrupt the way people get around, have hovered on the horizon for years. But the technology hasn't matured as dramatically as early investors had hoped.

The future of self-driving?


As companies like Tesla and its CEO Elon Musk come to Austin, Texas, the booming city and new tech hub has grown so much it has struggled to make good on its "keep it weird" motto. But since early June, when residents of the South Congress, Downtown, or Travis Heights neighborhoods order pizza from Southside Flying Pizza, their pies might arrive inside a three-wheeled robot -- the REV-1. But it is no full self-driving Tesla. About two dozen REV-1 vehicles now trundle down the roads of Austin and Ann Arbor, Michigan, where the company behind the robots -- Refraction AI -- first launched in 2019 in a bid to harness driverless technology in a new way. Autonomous vehicles, and their potential to disrupt the way people get around, have hovered on the horizon for years. But the technology hasn't matured as dramatically as early investors had hoped.

The Pizza Delivery Guy Will Be a Robot at Many Campuses This Fall WSJD - Technology

Delivery company Grubhub Inc. plans to roll out food-delivering robots across U.S. college campuses from this fall, as automation grows in a sector turbocharged by the pandemic. Grubhub will deploy the suitcase-size rovers built by Russian tech company Yandex NV to some of the 250 colleges across the U.S. that Grubhub already operates in, the companies said Tuesday. The six-wheeled autonomous rovers have been tested in recent years on the snowy streets of Moscow, delivering food, groceries and documents. Since April, the robots have also been delivering orders from local restaurants in Ann Arbor, Mich., as part of a trial. The pandemic has boosted the food-delivery business, sparking interest from some companies to automate parts of their operations.

Coupa Acquires AI-Powered Supply Chain Design & Planning Leader LLamasoft for $1.5 billion - Supply Chain 24/7


Coupa Software (NASDAQ: COUP), a leader in Business Spend Management (BSM), announced that it has acquired LLamasoft, a leader in AI-powered supply chain design and planning for a purchase price of approximately $1.5 billion. Based in Ann Arbor, Mich., LLamasoft's technology is used by hundreds of enterprise customers, including brands such as Boeing, Danone S.A., Home Depot, and Nestle. The acquisition will strengthen Coupa's supply chain capabilities, enabling businesses to drive greater value through Business Spend Management. The events of this year continue to demonstrate the importance of supply chain agility, as companies work to more rapidly adapt to changing consumer preferences, economic conditions, and the political landscape. With demand uncertainty on one hand and supply volatility on the other, companies are in need of supply chain technology that can help them assess alternatives and balance trade-offs to achieve desired business results.

Open-Source Leg: The Quest to Create a DIY Bionic Limb


If you wanted to cover a large distance and had the world's best sprinters at your disposal, would you have them run against each other or work together in a relay? That, in essence, is the problem Elliott Rouse, a biomedical engineer and director of the Neurobionics Lab at the University of Michigan, Ann Arbor, has been grappling with for the best several years. Rouse, an engineer, is one of many working to develop a control system for bionic legs, artificial limbs that use various signals from the wearer to act and move like biological limbs. "Probably the biggest challenge to creating a robotic leg is the controller that's involved, telling them what to do," Rouse told Digital Trends. "Every time the wearer takes a step, a step needs to be initiated. And when they switch, the leg needs to know their activity has changed and move to accommodate that different activity. If it makes a mistake, the person could get very, very injured -- perhaps falling down some stairs, for example. There are talented people around the world studying these control challenges. They invest years of their time and hundreds of thousands of dollars building a robotic leg. It's the way things have been since this field began."

With to-do list checked off, U.S. physicists ask, 'What's next?


As U.S. particle physicists contemplate their future, they find themselves victims of their own surprising success. Seven years ago, the often fractious community hammered out its current research road map and rallied around it. Thanks to that unity—and generous budgets—the Department of Energy (DOE), the field's main U.S. sponsor, has already started on almost every project on the list. So this week, as U.S. particle physicists start to drum up new ideas for the next decade in a yearlong Snowmass process—named for the Colorado ski resort where such planning exercises once took place—they have no single big project to push for (or against). And in some subfields, the next steps seem far less obvious than they were 10 years ago. “We have to be much more open minded about what particle physics and fundamental physics are,” says Young-Kee Kim of the University of Chicago, chair of the American Physical Society's division of particles and fields, which is sponsoring the planning exercise. Ten years ago, the U.S. particle physics community was in disarray. The high-energy frontier had passed to CERN, the European particle physics laboratory near Geneva, where in 2012 the world's biggest atom smasher, the Large Hadron Collider (LHC), blasted out the long-sought Higgs boson, the last piece in particle physicists' standard model. Some physicists wanted the United States to build a huge experiment to fire elusive particles called neutrinos long distances through Earth to study how they “oscillate”—morph from one of their three types to another—as they zip along. Others wanted the country to help push for the next big collider. Those tensions came to a head during the last Snowmass effort in 2013, and the subsequent deliberations of the Particle Physics Project Prioritization Panel (P5), which wrote the road map. U.S. researchers agreed to build the neutrino experiment, but make it bigger and better by inviting international partners. They also decided to continue to participate fully in the LHC, and to pursue a variety of smaller projects at home (see table, below). The next collider would have to wait. Most important, DOE officials warned, the squabbling and backstabbing had to stop. In fact, physicists recall, the 2013 process had an informal motto: “Bickering scientists get nothing.” ![Figure][1] CREDIT: PARTICLE PHYSICS PROJECT PRIORITIZATION PANEL REPORT (2014) Physicists have just started to build the current plan's centerpiece. The Long-Baseline Neutrino Facility (LBNF) at Fermi National Accelerator Laboratory (Fermilab) in Illinois will shoot the particles through 1300 kilometers of rock to the Deep Underground Neutrino Experiment (DUNE) in South Dakota, a detector filled with 40,000 tons of frigid liquid argon. LBNF/DUNE, which should come online in 2026, aims to be the definitive study of neutrino oscillations and whether they differ between neutrinos and antineutrinos, which could help explain how the universe generated more matter than antimatter. “The angst in the neutrino community is a lot lower than it was last time,” says Kate Scholberg, a neutrino physicist at Duke University. “The DUNE program will be going on at least into the 2030s.” However, researchers are already thinking of upgrades to the $2.6 billion experiment, she notes. In other areas, the future looks less certain. The last time around, for example, scientists had a pretty clear path forward in their search for particles of dark matter—the so-far-unidentified stuff that appears to pervade the galaxies and bind them with its gravity. Researchers had built small underground detectors that searched for the signal of weakly interacting massive particles (WIMPs), the leading dark matter candidate, bumping into atomic nuclei. The obvious plan was to expand the detectors to the ton scale. Now, two multi-ton WIMP detectors are under construction. But so far WIMPs haven't shown up, and scaling up that technology further “is probably not going to work very well anymore,” says Marcelle Soares-Santos, a physicist at the University of Michigan, Ann Arbor. “So we need to think a little bit more out of the box.” Researchers are now contemplating a hunt for other types of dark matter particles, using new detectors that exploit quantum mechanical effects to achieve exquisite levels of sensitivity. A perennial question for the field is what the next great particle collider will be. The obvious need is for one that fires electrons into positrons to crank out copious Higgs bosons and study their properties in detail, says Meenakshi Narain, a physicist at Brown University. But possible designs vary. Physicists in Japan are discussing such a Higgs factory in the form of a 30-kilometer-long linear electron-positron collider. Meanwhile, CERN has begun a study of an 80- to 100-kilometer circular collider. China has plans for a similar circular collider. However, Vladimir Shiltsev, an accelerator physicist at Fermilab, says those aren't the only potential options. “The real picture is much murkier.” Snowmass organizers have received at least 16 different proposals for colliders, including one that would smash together muons—heavier, unstable cousins of electrons—and another that would use photons. Snowmass participants should consider all options, Shiltsev says. Joe Lykken, Fermilab's deputy director for research, suggests physicists could even push for DOE to support a massive experiment that has nothing to do with particles: a next-generation detector of gravitational waves, spacetime ripples set off when massive objects such as black holes collide. Their discovery in 2015 by the Laser Interferometer Gravitational-Wave Observatory (LIGO) opened a new window on the universe. LIGO consists of two L-shaped optical instruments with arms 4 kilometers long in Louisiana and Washington; it was built by the National Science Foundation. The next generation of ground-based detectors could be 10 times as big, and might better fit DOE, which specializes in scientific megaprojects, Lykken says. “It starts to sound like the kind of thing that the DOE would be interested in and maybe required for,” he says. During the coming year, Snowmass participants will air the more than 2000 ideas researchers have already proffered in two-page summaries. Then, a new P5 will formulate a new plan. Whatever ideas scientists come up with, to execute their new plan they'll have to maintain the harmony that in recent years has made their planning process an exemplar to other fields. “Being unified is the new norm for us,” quips Jim Siegrist, DOE's associate director for high energy physics. “So we have to continue to keep a lid on divisiveness and that'll be a challenge.” [1]: pending:yes

The Benefits of Autonomous Vehicles for Community-Based Trip Sharing Artificial Intelligence

This work reconsiders the concept of community-based trip sharing proposed by Hasan et al. (2018) that leverages the structure of commuting patterns and urban communities to optimize trip sharing. It aims at quantifying the benefits of autonomous vehicles for community-based trip sharing, compared to a car-pooling platform where vehicles are driven by their owners. In the considered problem, each rider specifies a desired arrival time for her inbound trip (commuting to work) and a departure time for her outbound trip (commuting back home). In addition, her commute time cannot deviate too much from the duration of a direct trip. Prior work motivated by reducing parking pressure and congestion in the city of Ann Arbor, Michigan, showed that a car-pooling platform for community-based trip sharing could reduce the number of vehicles by close to 60%. This paper studies the potential benefits of autonomous vehicles in further reducing the number of vehicles needed to serve all these commuting trips. It proposes a column-generation procedure that generates and assembles mini routes to serve inbound and outbound trips, using a lexicographic objective that first minimizes the required vehicle count and then the total travel distance. The optimization algorithm is evaluated on a large-scale, real-world dataset of commute trips from the city of Ann Arbor, Michigan. The results of the optimization show that it can leverage autonomous vehicles to reduce the daily vehicle usage by 92%, improving upon the results of the original Commute Trip Sharing Problem by 34%, while also reducing daily vehicle miles traveled by approximately 30%. These results demonstrate the significant potential of autonomous vehicles for the shared commuting of a community to a common work destination.