OpenSafe.AI, a new platform that utilizes AI, data, and hazard and resilience models to support storm response decision makers, has secured an NSF grant. Photo by Eric Turnquist

Researchers from Rice University have secured a $1.5 million grant from the National Science Foundation to continue their work on improving safety and resiliency of coastal communities plagued by flooding and hazardous weather.

The Rice team of engineers and collaborators includes Jamie Padgett, Ben Hu, and Avantika Gori along with David Retchless at Texas A&M University at Galveston. The researchers are working in collaboration with the Severe Storm Prediction, Education and Evacuation from Disasters (SSPEED) Center and the Ken Kennedy Institute at Rice and A&M-Galveston’s Institute for a Disaster Resilient Texas.

Together, the team is developing and hopes to deploy “Open-Source Situational Awareness Framework for Equitable Multi-Hazard Impact Sensing using Responsible AI,” or OpenSafe.AI, a new platform that utilizes AI, data, and hazard and resilience models "to provide timely, reliable and equitable insights to emergency response organizations and communities before, during and after tropical cyclones and coastal storm events," reads a news release from Rice.

“Our goal with this project is to enable communities to better prepare for and navigate severe weather by providing better estimates of what is actually happening or might happen within the next hours or days,” Padgett, Rice’s Stanley C. Moore Professor in Engineering and chair of the Department of Civil and Environmental Engineering, says in the release. “OpenSafe.AI will take into account multiple hazards such as high-speed winds, storm surge and compound flooding and forecast their potential impact on the built environment such as transportation infrastructure performance or hazardous material spills triggered by severe storms.”

OpenSafe.AI platform will be developed to support decision makers before, during, and after a storm.

“By combining cutting-edge AI with a deep understanding of the needs of emergency responders, we aim to provide accurate, real-time information that will enable better decision-making in the face of disasters,” adds Hu, associate professor of computer science at Rice.

In the long term, OpenSafe.AI hopes to explore how the system can be applied to and scaled in other regions in need of equitable resilience to climate-driven hazards.

“Our goal is not only to develop a powerful tool for emergency response agencies along the coast but to ensure that all communities ⎯ especially the ones most vulnerable to storm-induced damage ⎯ can rely on this technology to better respond to and recover from the devastating effects of coastal storms,” adds Gori, assistant professor of civil and environmental engineering at Rice.

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This article originally ran on EnergyCapital.

Angela Wilkins joins the Houston Innovators Podcast to discuss the intersection of data and health care. Photo courtesy

Houston data scientist joins medical device startup amid AI evolution in the sector

HOUSTON INNOVATORS PODCAST EPISODE 241

When most people hear about Houston startup Starling Medical, they might think about how much potential the medical device company has in the field of urinalysis diagnostics. But that's not quite where Angela Wilkins's head went.

Wilkins explains on the Houston Innovators Podcast that when she met the company's co-founders, Hannah McKenney and Drew Hendricks, she recognized them as very promising startup leaders taking action on a real health care problem. Starling's device can collect urine and run diagnostics right from a patient's toilet.

"It was one of those things where I just thought, 'They're going to get a bunch of data soon,'" Wilkins says. "The opportunity is just there, and I was really excited to come on and build their AI platform and the way they are going to look at data."

For about a year, Wilkins supported the startup as an adviser. Now, she's working more hands on as chief data officer as the company grows.



Wilkins, who serves as a mentor and adviser for several startups, has a 20-year career in Houston across all sides of the innovation equation, working first at Baylor College of Medicine before co-founding Mercury Data Science — now OmniScience. Most recently she served as executive director of the Ken Kennedy Institute at Rice University.

This variety in her resume makes her super connective — a benefit to all the startups she works with, she explains. The decision to transition to a startup team means she gets to work hands on in building a technology — while bringing in her experience from other institutions.

"I think I've really learned how to partner with those institutions," she says on the show. "I've really learned how to make those bridges, and that's a big challenge that startups face."

"When we talk about the Houston innovation ecosystem, it's something we should be doing better at because we have so many startups and so many places that would like to use better technology to solve problems," she continues.

Wilkins has data and artificial intelligence on the mind in everything she does, and she even serves on a committee at the state level to learn and provide feedback on how Texas should be regulating AI.

"At the end of the day, the mission is to put together a report and strategy on how we think Texas should think about AI," she explains. "It's beyond just using an algorithm, they need infrastructure."

Colorado is the first state to pass legislation surrounding AI, and Wilkins says all eyes are on how execution of that new law will go.

"We should have technology that can be double checked to make sure we're applying it in a way that's fair across all demographics. It's obvious that we should do that — it's just very hard," she says.

"Better and personalized healthcare through AI is still a hugely challenging problem that will take an army of scientists and engineers." Photo via UH.edu

Houston expert explains health care's inequity problem

guest column

We are currently in the midst of what some have called the "wild west" of AI. Though healthcare is one of the most heavily regulated sectors, the regulation of AI in this space is still in its infancy. The rules are being written as we speak. We are playing catch-up by learning how to reap the benefits these technologies offer while minimizing any potential harms once they've already been deployed.

AI systems in healthcare exacerbate existing inequities. We've seen this play out into real-world consequences from racial bias in the American justice system and credit scoring, to gender bias in resume screening applications. Programs that are designed to bring machine "objectivity" and ease to our systems end up reproducing and upholding biases with no means of accountability.

The algorithm itself is seldom the problem. It is often the data used to program the technology that merits concern. But this is about far more than ethics and fairness. Building AI tools that take account of the whole picture of healthcare is fundamental to creating solutions that work.

The Algorithm is Only as Good as the Data

By nature of our own human systems, datasets are almost always partial and rarely ever fair. As Linda Nordling comments in a Nature article, A fairer way forward for AI in healthcare, "this revolution hinges on the data that are available for these tools to learn from, and those data mirror the unequal health system we see today."

Take, for example, the finding that Black people in US emergency rooms are 40 percent less likely to receive pain medication than are white people, and Hispanic patients are 25 percent less likely. Now, imagine the dataset these findings are based on is used to train an algorithm for an AI tool that would be used to help nurses determine if they should administer pain relief medication. These racial disparities would be reproduced and the implicit biases that uphold them would remain unquestioned, and worse, become automated.

We can attempt to improve these biases by removing the data we believe causes the bias in training, but there will still be hidden patterns that correlate with demographic data. An algorithm cannot take in the nuances of the full picture, it can only learn from patterns in the data it is presented with.

Bias Creep

Data bias creeps into healthcare in unexpected ways. Consider the fact that animal models used in laboratories across the world to discover and test new pain medications are almost entirely male. As a result, many medications, including pain medication, are not optimized for females. So, it makes sense that even common pain medications like ibuprofen and naproxen have been proven to be more effective in men than women and that women tend to experience worse side effects from pain medication than men do.

In reality, male rodents aren't perfect test subjects either. Studies have also shown that both female and male rodents' responses to pain levels differ depending on the sex of the human researcher present. The stress response elicited in rodents to the olfactory presence of a sole male researcher is enough to alter their responses to pain.

While this example may seem to be a departure from AI, it is in fact deeply connected — the current treatment choices we have access to were implicitly biased before the treatments ever made it to clinical trials. The challenge of AI equity is not a purely technical problem, but a very human one that begins with the choices that we make as scientists.

Unequal Data Leads to Unequal Benefits

In order for all of society to enjoy the many benefits that AI systems can bring to healthcare, all of society must be equally represented in the data used to train these systems. While this may sound straightforward, it's a tall order to fill.

Data from some populations don't always make it into training datasets. This can happen for a number of reasons. Some data may not be as accessible or it may not even be collected at all due to existing systemic challenges, such as a lack of access to digital technology or simply being deemed unimportant. Predictive models are created by categorizing data in a meaningful way. But because there's generally less of it, "minority" data tends to be an outlier in datasets and is often wiped out as spurious in order to create a cleaner model.

Data source matters because this detail unquestionably affects the outcome and interpretation of healthcare models. In sub-Saharan Africa, young women are diagnosed with breast cancer at a significantly higher rate. This reveals the need for AI tools and healthcare models tailored to this demographic group, as opposed to AI tools used to detect breast cancer that are only trained on mammograms from the Global North. Likewise, a growing body of work suggests that algorithms used to detect skin cancer tend to be less accurate for Black patients because they are trained mostly on images of light-skinned patients. The list goes on.

We are creating tools and systems that have the potential to revolutionize the healthcare sector, but the benefits of these developments will only reach those represented in the data.

So, what can be done?

Part of the challenge in getting bias out of data is that high volume, diverse and representative datasets are not easy to access. Training datasets that are publicly available tend to be extremely narrow, low-volume, and homogenous—they only capture a partial picture of society. At the same time, a wealth of diverse health data is captured every day in many healthcare settings, but data privacy laws make accessing these more voluminous and diverse datasets difficult.

Data protection is of course vital. Big Tech and governments do not have the best track record when it comes to the responsible use of data. However, if transparency, education, and consent for the sharing of medical data was more purposefully regulated, far more diverse and high-volume data sets could contribute to fairer representation across AI systems and result in better, more accurate results for AI-driven healthcare tools.

But data sharing and access is not a complete fix to healthcare's AI problem. Better and personalized healthcare through AI is still a hugely challenging problem that will take an army of scientists and engineers. At the end of the day, we want to teach our algorithms to make good choices but we are still figuring out what good choices should look like for ourselves.

AI presents the opportunity to bring greater personalization to healthcare, but it equally presents the risk of entrenching existing inequalities. We have the opportunity in front of us to take a considered approach to data collection, regulation, and use that will provide a fuller and fairer picture and enable the next steps for AI in healthcare.

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Angela Wilkins is the executive director of the Ken Kennedy Institute at Rice University.

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2 Houston space tech cos. celebrate major tech milestones

big wins

Two Houston aerospace companies — Intuitive Machines and Venus Aerospace — have reached testing milestones for equipment they’re developing.

Intuitive Machines recently completed the first round of “human in the loop” testing for its Moon RACER (Reusable Autonomous Crewed Exploration Rover) lunar terrain vehicle. The company conducted the test at NASA’s Johnson Space Center.

RACER is one of three lunar terrain vehicles being considered by NASA for the space agency’s Artemis initiative, which will send astronauts to the moon.

NASA says human-in-the-loop testing can reveal design flaws and technical problems, and can lead to cost-efficient improvements. In addition, it can elevate the design process from 2D to 3D modeling.

Intuitive Machines says the testing “proved invaluable.” NASA astronauts served as test subjects who provided feedback about the Moon RACER’s functionality.

The Moon RACER, featuring a rechargeable electric battery and a robotic arm, will be able to accommodate two astronauts and more than 880 pounds of cargo. It’s being designed to pull a trailer loaded with more than 1,760 pounds of cargo.

Another Houston company, Venus Aerospace, recently achieved ignition of its VDR2 rocket engine. The engine, being developed in tandem with Ohio-based Velontra — which aims to produce hypersonic planes — combines the functions of a rotating detonation rocket engine with those of a ramjet.

A rotating detonation rocket engine, which isn’t equipped with moving parts, rapidly burns fuel via a supersonic detonation wave, according to the Air Force Research Laboratory. In turn, the engine delivers high performance in a small volume, the lab says. This savings in volume can offer range, speed, and affordability benefits compared with ramjets, rockets, and gas turbines.

A ramjet is a type of “air breathing” jet engine that does not include a rotary engine, according to the SKYbrary electronic database. Instead, it uses the forward motion of the engine to compress incoming air.

A ramjet can’t function at zero airspeed, so it can’t power an aircraft during all phases of flight, according to SKYbrary. Therefore, it must be paired with another kind of propulsion, such as a rotating detonation rocket engine, to enable acceleration at a speed where the ramjet can produce thrust.

“With this successful test and ignition, Venus Aerospace has demonstrated the exceptional ability to start a [ramjet] at takeoff speed, which is revolutionary,” the company says.

Venus Aerospace plans further testing of its engine in 2025.

Venus Aerospace, recently achieved ignition of its VDR2 rocket engine. Photo courtesy of Venus Aerospace

METRO rolls out electric shuttles for downtown Houston commuters

on a roll

The innovative METRO microtransit program will be expanding to the downtown area, the Metropolitan Transit Authority of Harris County announced on Monday.

“Microtransit is a proven solution to get more people where they need to go safely and efficiently,” Houston Mayor John Whitmire said in a statement. “Connected communities are safer communities, and bringing microtransit to Houston builds on my promise for smart, fiscally-sound infrastructure growth.”

The program started in June 2023 when the city’s nonprofit Evolve Houston partnered with the for-profit Ryde company to offer free shuttle service to residents of Second and Third Ward. The shuttles are all-electric and take riders to bus stops, medical buildings, and grocery stores. Essentially, it works as a traditional ride-share service but focuses on multiple passengers in areas where bus access may involve hazards or other obstacles. Riders access the system through the Ride Circuit app.

So far, the microtransit system has made a positive impact in the wards according to METRO. This has led to the current expansion into the downtown area. The system is not designed to replace the standard bus service, but to help riders navigate to it through areas where bus service is more difficult.

“Integrating microtransit into METRO’s public transit system demonstrates a commitment to finding innovative solutions that meet our customers where they are,” said METRO Board Chair Elizabeth Gonzalez Brock. “This on-demand service provides a flexible, easier way to reach METRO buses and rail lines and will grow ridership by solving the first- and last-mile challenges that have hindered people’s ability to choose METRO.”

The City of Houston approved a renewal of the microtransit program in July, authorizing Evolve Houston to spend $1.3 million on it. Some, like council member Letitia Plummer, have questioned whether microtransit is really the future for METRO as the service cuts lines such as the University Corridor.

However, the microtransit system serves clear and longstanding needs in Houston. Getting to and from bus stops in the city with its long blocks, spread-out communities, and fickle pedestrian ways can be difficult, especially for poor or disabled riders. While the bus and rail work fine for longer distances, shorter ones can be underserved.

Even in places like downtown where stops are plentiful, movement between them can still involve walks of a mile or more, and may not serve for short trips.

“Our microtransit service is a game-changer for connecting people, and we are thrilled to launch it in downtown Houston,” said Evolve executive director Casey Brown. “The all-electric, on-demand service complements METRO’s existing fixed-route systems while offering a new solution for short trips. This launch marks an important milestone for our service, and we look forward to introducing additional zones in the new year — improving access to public transit and local destinations.”

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This article originally ran on CultureMap.