Rice University's new Bachelor of Science in AI will be one of only a few in the country. Photo via Getty Images.

Rice University announced this month that it plans to introduce a Bachelor of Science in AI in the fall 2025 semester.

The new degree program will be part of the university's department of computer science in the George R. Brown School of Engineering and Computing and is one of only a few like it in the country. It aims to focus on "responsible and interdisciplinary approaches to AI," according to a news release from the university.

“We are in a moment of rapid transformation driven by AI, and Rice is committed to preparing students not just to participate in that future but to shape it responsibly,” Amy Dittmar, the Howard R. Hughes Provost and executive vice president for academic affairs, said in the release. “This new major builds on our strengths in computing and education and is a vital part of our broader vision to lead in ethical AI and deliver real-world solutions across health, sustainability and resilient communities.”

John Greiner, an assistant teaching professor of computer science in Rice's online Master of Computer Science program, will serve as the new program's director. Vicente Ordóñez-Román, an associate professor of computer science, was also instrumental in developing and approving the new major.

Until now, Rice students could study AI through elective courses and an advanced degree. The new bachelor's degree program opens up deeper learning opportunities to undergrads by blending traditional engineering and math requirements with other courses on ethics and philosophy as they relate to AI.

“With the major, we’re really setting out a curriculum that makes sense as a whole,” Greiner said in the release. “We are not simply taking a collection of courses that have been created already and putting a new wrapper around them. We’re actually creating a brand new curriculum. Most of the required courses are brand new courses designed for this major.”

Students in the program will also benefit from resources through Rice’s growing AI ecosystem, like the Ken Kennedy Institute, which focuses on AI solutions and ethical AI. The university also opened its new AI-focused "innovation factory," Rice Nexus, earlier this year.

“We have been building expertise in artificial intelligence,” Ordóñez-Román added in the release. “There are people working here on natural language processing, information retrieval systems for machine learning, more theoretical machine learning, quantum machine learning. We have a lot of expertise in these areas, and I think we’re trying to leverage that strength we’re building.”

The new Rice Nexus is partnering with Google Public Sector and Non Sibi Ventures to support high-potential AI-focused startups. Image via Rice University.

Google teams up with Rice University to launch AI-focused accelerator

eyes on AI

Google Public Sector is teaming up with Rice University to drive early-stage artificial intelligence innovation and commercialization via the new Rice AI Venture Accelerator, or RAVA.

RAVA will use Google Cloud technology and work with venture capital firm Non Sibi Ventures to connect high-potential AI-focused startups with public and private sector organizations. The incubator will be led by Rice Nexus, which launched earlier this year in the Ion District as an AI-focused "innovation factory.”

“Google Public Sector is proud to partner with a leading institution like Rice University to launch the Rice AI Venture Accelerator,” Reymund Dumlao, director of state and local government and education at Google Public Sector, said in a news release. “By providing access to Google Cloud’s cutting-edge AI, secure cloud infrastructure and expertise, we’re enabling the next generation of AI pioneers to develop solutions that address critical challenges across industries and within the public sector. This unique partnership between education and industry will give participants access to cutting-edge research, leading technologists, specialized resources and a collaborative academic ecosystem, fostering an environment for rapid innovation and growth.”

Participants will have access to Google Public Sector’s AI leadership as well as experts from Rice’s Ken Kennedy Institute, which focuses on AI and computing research. It will be led by Sanjoy Paul, Rice Nexus’ inaugural executive director. Paul previously worked at Accenture LLC as a managing director of technology and is a lecturer in Rice's Department of Computer Science.

Rice Nexus will serve as the physical hub for RAVA, but the program will support AI startups from across the U.S., as part of Rice’s Momentous strategic plan, according to the university.

“This hub enables AI startups to go beyond building minimum viable products that meet industry privacy standards by utilizing the latest AI technologies from Google Cloud,” Paul said in the news release. “Our goal is to maximize the return on investment for our corporate partners, driving meaningful innovation that will have lasting impact on their industries.”

The 10,000-square-foot Rice Nexus space currently serves as home base for several startups with ties to Rice, including Solidec, BeOne Sports and others. Read more about the new incubation space here.

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

Houston-area researchers score $1.5M grant to develop storm response tech platform

fresh funding

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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Texas Space Commission doles out $5.8 million to Houston companies

On A Mission

Two Houston-area companies have landed more than $5.8 million in funding from the Texas Space Commission.

The commission granted up to $5.5 million to Houston-based Axiom Space and up to $347,196 to Conroe-based FluxWorks.

The two-year-old commission previously awarded $95.3 million to 14 projects. A little over $34 million remains in the commission-managed Space Exploration and Aeronautics Research Fund.

Axiom Space, a commercial spaceflight company, said the new funding will go toward the development of its orbital data center capabilities. By the end of this year, Axiom plans to launch two free-flying nodes in low-Earth orbit to support its orbital data center operations. More nodes are set to go online in the coming years.

“Axiom Space is actively evaluating how our [orbital data center] architecture can enhance critical U.S. capabilities, including the proposed Golden Dome missile defense architecture,” Jason Aspiotis, global director of in-space data and security at Axiom, said in a news release. “In this context, real-time, around-the-clock availability, secure orbital processing, and AI-driven autonomy are vital for ensuring mission success.”

Founded in 2021, FluxWorks provides magnetic gear technology that was developed at Texas A&M University.

In 2024, FluxWorks was one of two startups to receive the Technology in Space Prize, funded by Boeing and the Center for the Advancement of Science in Space (CASIS), which manages the International Space Station National Laboratory.

FluxWorks is testing the performance of magnetic gear in microgravity environments, such as the International Space Station.

“Gearboxes aim to reduce the mass of motors required in a variety of applications; however, the lubricant needed to make them work properly is not designed for use in extreme environments like space,” according to a 2024 news release about the Technology in Space Prize. “Magnetic gears do not require lubricant, making them an appealing alternative.”

The Texas Space Commission granted $25 million to Houston aerospace companies Starlab Space and Intuitive Machines earlier this year. Read more here.

3 Houston startups named most innovative in Texas by LexisNexis

report card

Three Houston companies claimed spots on LexisNexis's 10 Most Innovative Startups in Texas report, with two working in the geothermal energy space.

Sage Geosystems claimed the No. 3 spot on the list, and Fervo Energy followed closely behind at No. 5. Fintech unicorn HighRadius rounded out the list of Houston companies at No. 8.

LexisNexis Intellectual Property Solutions compiled the report. It was based on each company's Patent Asset Index, a proprietary metric from LexisNexis that identifies the strength and value of each company’s patent assets based on factors such as patent quality, geographic scope and size of the portfolio.

Houston tied with Austin, each with three companies represented on the list. Caris Life Sciences, a biotechnology company based in Dallas, claimed the top spot with a Patent Asset Index more than 5 times that of its next competitor, Apptronik, an Austin-based AI-powered humanoid robotics company.

“Texas has always been fertile ground for bold entrepreneurs, and these innovative startups carry that tradition forward with strong businesses based on outstanding patent assets,” Marco Richter, senior director of IP analytics and strategy for LexisNexis Intellectual Property Solutions, said in a release. “These companies have proven their innovation by creating the most valuable patent portfolios in a state that’s known for game-changing inventions and cutting-edge technologies.We are pleased to recognize Texas’ most innovative startups for turning their ideas into patented innovations and look forward to watching them scale, disrupt, and thrive on the foundation they’ve laid today.”

This year's list reflects a range in location and industry. Here's the full list of LexisNexis' 10 Most Innovative Startups in Texas, ranked by patent portfolios.

  1. Caris (Dallas)
  2. Apptronik (Austin)
  3. Sage Geosystems (Houston)
  4. HiddenLayer (Austin)
  5. Fervo Energy (Houston)
  6. Plus One Robotics (San Antonio)
  7. Diligent Robotics (Austin)
  8. HighRadius (Houston)
  9. LTK (Dallas)
  10. Eagle Eye Networks (Austin)

Sage Geosystems has partnered on major geothermal projects with the United States Department of Defense's Defense Innovation Unit, the U.S. Air Force and Meta Platforms. Sage's 3-megawatt commercial EarthStore geothermal energy storage facility in Christine, Texas, was expected to be completed by the end of last year.

Fervo Energy fully contracted its flagship 500 MW geothermal development, Cape Station, this spring. Cape Station is currently one of the world’s largest enhanced geothermal systems (EGS) developments, and the station will begin to deliver electricity to the grid in 2026. The company was recently named North American Company of the Year by research and consulting firm Cleantech Group and came in at No. 6 on Time magazine and Statista’s list of America’s Top GreenTech Companies of 2025. It's now considered a unicorn, meaning its valuation as a private company has surpassed $1 billion.

Meanwhile, HighRadius announced earlier this year that it plans to release a fully autonomous finance platform for the "office of the CFO" by 2027. The company reached unicorn status in 2020.

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This article originally appeared on Energy Capital HTX.

UH student earns prestigious award for cancer vaccine research

up-and-comer

Cole Woody, a biology major in the College of Natural Sciences and Mathematics at the University of Houston, has been awarded a Barry Goldwater Scholarship, becoming the first sophomore in UH history to earn the prestigious prize for research in natural sciences, mathematics and engineering.

Woody was recognized for his research on developing potential cancer vaccines through chimeric RNAs. The work specifically investigates how a vaccine can more aggressively target cancers.

Woody developed the MHCole Pipeline, a bioinformatic tool that predicts peptide-HLA binding affinities with nearly 100 percent improvement in data processing efficiency. The MHCole Pipeline aims to find cancer-specific targets and develop personalized vaccines. Woody is also a junior research associate at the UH Sequencing Core and works in Dr. Steven Hsesheng Lin’s lab at MD Anderson Cancer Center.

“Cole’s work ethic and dedication are unmatched,” Preethi Gunaratne, director of the UH Sequencing Core and professor of Biology & Biochemistry at NSM, said in a news release. “He consistently worked 60 to 70 hours a week, committing himself to learning new techniques and coding the MHCole pipeline.”

Woody plans to earn his MD-PhD and has been accepted into the Harvard/MIT MD-PhD Early Access to Research Training (HEART) program. According to UH, recipients of the Goldwater Scholarship often go on to win various nationally prestigious awards.

"Cole’s ability to independently design and implement such a transformative tool at such an early stage in his career demonstrates his exceptional technical acumen and creative problem-solving skills, which should go a long way towards a promising career in immuno-oncology,” Gunaratne added in the release.