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

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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

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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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Axiom Space-tested cancer drug advances to clinical trials

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A cancer-fighting drug tested aboard several Axiom Space missions is moving forward to clinical trials.

Rebecsinib, which targets a cancer cloning and immune evasion gene, ADAR1, has received FDA approval to enter clinical trials under active Investigational New Drug (IND) status, according to a news release. The drug was tested aboard Axiom Mission 2 (Ax-2) and Axiom Mission 3 (Ax-3). It was developed by Aspera Biomedicine, led by Dr. Catriona Jamieson, director of the UC San Diego Sanford Stem Cell Institute (SSCI).

The San Diego-based Aspera team and Houston-based Axiom partnered to allow Rebecsinib to be tested in microgravity. Tumors have been shown to grow more rapidly in microgravity and even mimic how aggressive cancers can develop in patients.

“In terms of tumor growth, we see a doubling in growth of these little mini-tumors in just 10 days,” Jamieson explained in the release.

Rebecsinib took part in the patient-derived tumor organoid testing aboard the International Space Station. Similar testing is planned to continue on Axiom Station, the company's commercial space station that's currently under development.

Additionally, the drug will be tested aboard Ax-4 under its active IND status, which was targeted to launch June 25.

“We anticipate that this monumental mission will inform the expanded development of the first ADAR1 inhibitory cancer stem cell targeting drug for a broad array of cancers," Jamieson added.

According to Axiom, the milestone represents the potential for commercial space collaborations.

“We’re proud to work with Aspera Biomedicines and the UC San Diego Sanford Stem Cell Institute, as together we have achieved a historic milestone, and we’re even more excited for what’s to come,” Tejpaul Bhatia, the new CEO of Axiom Space, said in the release. “This is how we crack the code of the space economy – uniting public and private partners to turn microgravity into a launchpad for breakthroughs.”

Chevron enters the lithium market with major Texas land acquisition

to market

Chevron U.S.A., a subsidiary of Houston-based energy company Chevron, has taken its first big step toward establishing a commercial-scale lithium business.

Chevron acquired leaseholds totaling about 125,000 acres in Northeast Texas and southwest Arkansas from TerraVolta Resources and East Texas Natural Resources. The acreage contains a high amount of lithium, which Chevron plans to extract from brines produced from the subsurface.

Lithium-ion batteries are used in an array of technologies, such as smartwatches, e-bikes, pacemakers, and batteries for electric vehicles, according to Chevron. The International Energy Agency estimates lithium demand could grow more than 400 percent by 2040.

“This acquisition represents a strategic investment to support energy manufacturing and expand U.S.-based critical mineral supplies,” Jeff Gustavson, president of Chevron New Energies, said in a news release. “Establishing domestic and resilient lithium supply chains is essential not only to maintaining U.S. energy leadership but also to meeting the growing demand from customers.”

Rania Yacoub, corporate business development manager at Chevron New Energies, said that amid heightening demand, lithium is “one of the world’s most sought-after natural resources.”

“Chevron is looking to help meet that demand and drive U.S. energy competitiveness by sourcing lithium domestically,” Yacoub said.

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This article originally appeared on EnergyCapital.