Houston research breakthrough could pave way for next-gen superconductors

Quantum Breakthrough

Researchers from Rice University say their recent findings could revolutionize power grids, making energy transmission more efficient. Getty Images

A study from researchers at Rice University, published in Nature Communications, could lead to future advances in superconductors with the potential to transform energy use.

The study revealed that electrons in strange metals, which exhibit unusual resistance to electricity and behave strangely at low temperatures, become more entangled at a specific tipping point, shedding new light on these materials.

A team led by Rice’s Qimiao Si, the Harry C. and Olga K. Wiess Professor of Physics and Astronomy, used quantum Fisher information (QFI), a concept from quantum metrology, to measure how electron interactions evolve under extreme conditions. The research team also included Rice’s Yuan Fang, Yiming Wang, Mounica Mahankali and Lei Chen along with Haoyu Hu of the Donostia International Physics Center and Silke Paschen of the Vienna University of Technology. Their work showed that the quantum phenomenon of electron entanglement peaks at a quantum critical point, which is the transition between two states of matter.

“Our findings reveal that strange metals exhibit a unique entanglement pattern, which offers a new lens to understand their exotic behavior,” Si said in a news release. “By leveraging quantum information theory, we are uncovering deep quantum correlations that were previously inaccessible.”

The researchers examined a theoretical framework known as the Kondo lattice, which explains how magnetic moments interact with surrounding electrons. At a critical transition point, these interactions intensify to the extent that the quasiparticles—key to understanding electrical behavior—disappear. Using QFI, the team traced this loss of quasiparticles to the growing entanglement of electron spins, which peaks precisely at the quantum critical point.

In terms of future use, the materials share a close connection with high-temperature superconductors, which have the potential to transmit electricity without energy loss, according to the researchers. By unblocking their properties, researchers believe this could revolutionize power grids and make energy transmission more efficient.

The team also found that quantum information tools can be applied to other “exotic materials” and quantum technologies.

“By integrating quantum information science with condensed matter physics, we are pivoting in a new direction in materials research,” Si said in the release.

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This article originally appeared on our sister site, EnergyCapitalHTX.com.

Researchers created a mathematical model that helps transplant centers make decisions about when to move forward with a matching donor and when to wait. This work can potentially help decision making in other industries. Photo via Getty Images

Houston researchers build life-saving decision making mathematical model with big potential

houston voices

To wait, or not to wait? That is the question — or at least it might be, if you need a kidney transplant.

Nearly 89,000 Americans with chronic kidney disease are on a waitlist for a new organ, and an estimated 13 people die each day while awaiting a transplant. But there are real costs to matching patients with the first donor that becomes available, just as there are equally real costs to having them wait in hopes of finding a better one.

Recently, Rice Business professor Süleyman Kerimov and colleagues at Stanford University and Northwestern University developed a mathematical model that helps clarify when it's best to match patients to donors as quickly as possible and when it's best to wait.

Their findings, which appear in two papers published in Management Science and Operations Research, respectively, could help optimize all manner of matching markets in which participants seek to connect with potential partners based on mutual compatibility — a sprawling category that encompasses everything from e-commerce platforms to labor markets that match employees with employers.

Kerimov and his colleagues focused on programs that match live kidney donors with people who need transplants. Live donors typically volunteer to give one of their kidneys to a loved one. But biological differences between a donor and their intended recipient can render the pair incompatible.

Kidney exchange programs solve this problem by swapping donors amongst different patient-donor pairs, choreographing a kind of kidney-transplant square dance aimed at finding a compatible partner for every willing donor.

In countries such as Canada and the Netherlands, kidney-matching programs perform a batch of matches every few months (called periodic policies). American programs, meanwhile, tend to perform daily matches (called greedy policies). Both models seek to produce the greatest number of high-quality transplants possible, but they each have advantages and disadvantages.

Less frequent matches in a periodic policy allow more patient-donor pairs to accumulate in the kidney exchange network, creating potential for better matches over time. But this approach risks making some patients sicker as they wait for a better match that might never appear.

Arranging feasible matches as soon as they become available in a greedy policy avoids that predicament. But it means passing up the opportunity to make a potentially better match that could represent the possibility of a longer, healthier life.

Balancing these trade-offs is tricky. There is no way of predicting precisely when a patient-donor pair with a particular set of characteristics will show up at the kidney-exchange network. And in the world of organ transplants, there are no do-overs.

Kerimov and his colleagues have constructed a mathematical model that represents a simplified version of a kidney exchange network.

Within the model, the researchers could dictate which patient-donor pairs could be matched with one another. They can also assign different values to individual matches based on the number of life years they provide. And they can establish the probability that various kinds of patient-donor pairs with particular characteristics might arrive at the network and queue up for a transplant at any given time.

Having set those parameters, the researchers applied different matching policies and compared the results. As it turns out, the answer to whether one should wait or not is: It depends.

To determine which policies generated the best outcomes — i.e., performing matches either daily or periodically — the researchers calculated the difference between the total value in life years that could possibly be generated within the network and the amount generated by a specific policy at a particular point in time. The goal was to keep that number, evocatively dubbed "all-time regret," as small as possible over both the short and long term.

In their first paper, Kerimov and his team explored a complex network in which donor kidneys could be swapped amongst three or more patient-donor pairs. When such multiway matches were possible, the cost of applying a daily-match policy turned out to be onerous. Using all available matches as quickly as possible eliminated the chance of later performing potentially higher-value matches.

Instead, the researchers found they could minimize regret by applying a periodic policy that required waiting for a certain number of patient-donor pairs to arrive before attempting to match them. The model even allowed the team to calculate precisely how long to wait between matchmaking sessions to get the best possible results.

In their second paper, however, the team looked at a simpler network in which kidneys could only be swapped between two donor-patient pairs. Here, their findings contradicted the first: Applying a daily-match policy minimized regret; a periodic matching process yielded no benefit whatsoever.

To their surprise, the researchers discovered they could design a foolproof algorithm for making two-way matches in simple networks. The algorithm employed a ranked list of possible match types; and the researchers found that no matter how many patient-donor pairs of various kinds randomly arrived at the network, the best choice was always simply to perform the highest-ranked match on the list.

In future research, Kerimov hopes to refine the model by feeding it data on real patient-donor pairs that have participated in actual kidney exchange programs. This would allow him to create a more realistic network, more accurately calculate the likelihood that particular kinds of patient-donor pairs will show up, and assign values to matches based not only on life years but also on rarity and difficulty. (Certain blood types and antibody profiles, for example, are rarer or more difficult to match than others.)

But Kerimov already suspects that in a real-world situation, the wisest course of action will be to alternate between periodic and greedy policies as circumstances dictate. In a simple region within a kidney exchange network that only allows for two-way matches, pursuing a greedy policy that involves taking the first match that appears on a fixed menu of options would be the best choice. In a more complex region that allows three-way matches, however, pursuing a periodic matching policy that involves waiting to make rarer and more difficult matches would ultimately offer more patients more years of healthy life.

The benefits of choosing flexibly between greedy and periodic policies should hold for any kind of matching market that can be represented by a network with simpler and more complex regions, such as a logistics system that matches online orders to delivery trucks or a carpooling system that matches passengers with drivers across different parts of a city.

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This article originally ran on Rice Business Wisdom and was based on research from Süleyman Kerimov, an assistant professor of management – operations management in the Jones Graduate School of Business at Rice University.

This Halloween, consider your office costume contest or luncheon social a productive part of the day. Socialization in the office has been linked to greater creativity, according to a Rice University researcher. Getty Images

Rice University research finds that office socializing can be a pathway to innovation and creativity

Houston voices

Innovation is a team sport. We know that creative workplaces represent a series of social networks, each brimming with useful ideas and expertise. And there is clearly a link between innovation within a firm and the colleagues and friends with whom employees hobnob off duty.

But how exactly does that alchemy happen? What's the relationship between creativity and the hive of direct and indirect contacts in an employee's cell phone?

A recent study by Jing Zhou of the business school, Giles Hirst of Australian National University, Daan Van Knippenberg of Erasmus University, Eric Quintane of the University of Los Andes and Cherrie Zhu of Monash University sheds new light on this. Mapping the social networks that underlie a creative workplace, the researchers showed that employee creativity rises when social networks are more diverse.

The researchers started with the premise that direct links in a network are offshoots of larger networks. The more diverse these indirect networks are, the researchers found, the more likely that innovative concepts will appear in a company's intellectual landscape.

The most efficient resources for gathering novel perspectives are networks made up of two-step "non-redundant ties"—that is, people you may not interact with directly, but with whom your direct ties do interact. These contacts are effectively the raw material employees use to come up with new ideas and ways of working. But why are these indirect networks so important? They diversify the thinking of the group, Zhou and her colleagues argue. Because these networks include individuals who are not necessarily linked, they lower the chances of groupthink or stale ideas.

To test their hypothesis, the researchers looked at the social networks of a large, state-owned pharmacy corporation in the People's Republic of China. Examining 11 divisions, each with roughly 25 sales representatives, the team studied creativity among the sales representatives. Evenly divided between men and women, the representatives were, on average, 35 years of age with approximately 10 years' of experience. Some had developed networks so large that they reached beyond the corporation's geographic territory.

The representatives' creativity manifested itself in a range of forms: new ways to promote products, strategies to cross-sell products, ideas for connecting with hard-to-access sales targets and plans for boosting client sales. The ideas included making products more visible in retail outlets and personalizing product launches to push customers to specific distributors. Because this kind of inventiveness is critical to gaining an edge, it's one of the most important tools in pharmaceutical marketing.

The researchers devised a matrix that matched sales metrics and managers' creativity rankings to the types of social networks the representatives had. The map showed clearly that a two-step, indirect network with few redundancies correlated to individual creativity. When networks were further removed than this, employee creativity was unchanged.

The implication: Firms should attend closely to the kind of social networks their workers cultivate. Not only that, it's possible to teach employees how to design networks for maximum efficiency. Persuading employees to make that effort might be another matter. Luckily, possible incentives abound, from bonuses to the satisfactions of a varied network to the simple pleasure of a more ample expense account. Executives just need to get creative in making their case.

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This story originally ran on Rice Business Wisdom.

Jing Zhou is the Mary Gibbs Jones Professor of Management and Psychology in Organizational Behavior at the Jones Graduate School of Business of Rice University.

In a recent study, a Rice Business professor found that board members actually need incentives — both short- and long-term — to act in stakeholders' best interests. Getty Images

Rice University research finds executive board members are driven by incentives

Houston voices

If you're a stockholder, you may envision your investment helmed by a benevolent, all-knowing board of directors, sitting around a long finely-grained wooden table, drinking coffee, their heads buried in PowerPoint charts as they labor to plot the best course for the company. Too often, however, you can't take for granted that a company's board will steer it wisely.

Companies choose directors because they offer rich and varied experience in the business world. Many who serve on boards, moreover, are CEOs of other corporations, or have headed big companies in the past. As of October 2018, for example, six of the 11 directors on Walmart's board and eight of 13 on AT&T's board hold CEO or CFO positions in other firms. So it's easy to assume that board members will act in the best interests of stockholders.

But in a recent study, Rice Business professor Shiva Sivaramakrishnan found that board members actually need incentives — both short- and long-term — to act in stakeholders' best interests.

Corporations usually compensate board members with stock options, grants, equity stakes, meeting fees, and cash retainers. How important is such compensation, and what sort of incentives do board members need to perform in the very best interests of a company? Sivaramakrishnan joined co-author George Drymiotes to trace how compensation impacts various aspects of board performance.

Recent literature in corporate governance has already stressed the need to give boards of directors explicit incentives in order to safeguard shareholder welfare. Some observers have even proposed requiring outside board members to hold substantial equity interests. The National Association of Corporate Directors, for example, recommended that boards pay their directors solely with cash or stock, with equity representing a substantial portion of the total, up to 100 percent.

To the extent that directors hold stock in a company, their actions are likely influenced by a variety of long-and short-term incentives. And while the literature has focused mainly on the useful long-term impact of equity awards, the consequences of short-term incentives haven't been as clear. Moreover, according to surveys, most directors view advising as their primary role. But this role also has received little attention.

To scrutinize these issues, the scholars used a simple model, which assumes the board of directors perform three roles: contracting, monitoring and consulting. The board contracts with management to provide productive input that improves a firm's performance. By monitoring management, the board improves the quality of the information conveyed to managers. By serving in a consulting role, the board makes managers more productive, which, in turn, means higher expected firm output.

This model allowed the scholars to better understand the relationship between the board of directors and the company's managers, as well as with shareholders. The former was particularly important to take into account, because conflict between a board and managers is typically unobservable and can be costly.

The results were surprising. Without short-term incentives, the researchers found, boards did not effectively fulfill their multiple roles. Long-term inducements could make a difference, they found, but only in some aspects of board performance.

While board members were better advisors when given long-term motivations, short-term incentives were better motivators for performing well in their other corporate governance roles, according to the research, which tied specific aspects of board compensation to particular board functions.

Restricted equity awards provided the necessary long-term incentives to improve the efficacy of the board's advisory role, the scholars found, but only the short-term incentives, awarding an unrestricted share or a bonus based on short-term performance, motivated conscientious monitoring.

The scholars also examined managerial misconduct. Board monitoring, they concluded, lowered the cost of preventing such wrongdoing — but only if the board had strong short-term incentives in place.

Even at the highest rungs of the corporate ladder, in other words, short-term self-interest is the greatest motivator. Maybe it's not surprising. In the corporate world, acting for one's own benefit is a given — so stockholders need to look more closely at those at the very top. Like everyone else, board directors need occasional brass rings within easy reach to do their best.

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This story originally ran on Rice Business Wisdom.

Shiva Sivaramakrishnan is the Henry Gardiner Symonds Professor in Accounting at the Jesse H. Jones Graduate School of Business at Rice University.

Keeping on track with trends is crucial to growing and developing a relationship with your customers, these Rice University researchers found. Getty Images

Rice researcher delves into the importance of trendspotting in consumer behavior

Houston voices

Every business wants to read consumers' minds: what they love, what they hate. Even more, businesses crave to know about mass trends before they're visible to the naked eye.

In the past, analysts searching for trends needed to pore over a vast range of sources for marketplace indicators. The internet and social media have changed that: marketers now have access to an avalanche of real-time indicators, laden with details about the wishes hidden within customers' hearts and minds. With services such as Trendistic (which tracks individual Twitter terms), Google Insights for Search and BlogPulse, modern marketers are even privy to the real-time conversations surrounding consumers' desires.

Now, imagine being able to analyze all this data across large panels of time – then distilling it so well that you could identify marketing trends quickly, accurately and quantitatively.

Rice Business professor Wagner A. Kamakura and Rex Y. Du of the University of Houston set out to create a model that makes this possible. Because both quantitative and qualitative trendspotting are exploratory endeavors, Kamakura notes, both types of research can yield results that are broad but also inaccurate. To remedy this, Kamakura and Du devised a new model for quickly and accurately refining market data into trend patterns.

Kamakura and Du's model entails taking five simple steps to analyze gathered data using a quantitative method. By following this process of refining the data tens or hundreds of times, then isolating the information into specific seasonal and non-seasonal trends or dynamic trends, researchers can generate steady trend patterns across time panels.

Here's the process:

  • First, gather individual indicators by assembling data from different sources, with the understanding that the information is interconnected. It's crucial to select the data methodically, rather than making random choices, in order to avoid subjectively preselecting irrelevant indicators and blocking out relevant ones. Done sloppily, this first step can generate misleading information.
  • Distill the data into a few common factors. The raw data might include inaccuracies, which must be filtered out to lower the risk of overreacting or noting erroneous indicators.
  • Interpret and identify common trends by understanding the causes of spikes or dips in consumer behavior. It's key to separate non-cyclical and cyclical changes, because exterior events such as holidays or weather can alter behavior.
  • Compare your analysis with previously identified trends and other variables to establish their validity and generate insights. Looking at past performance through the filter of new insights can offer managers important guidance.
  • Project the trend lines you've identified using historical tracking data and their modeling framework. These trend lines can then be extrapolated into near-future projections, allowing managers to better position themselves and be proactive trying to reverse unfavorable trends and leverage positive ones.

It's important to bear in mind that the indicators used for quantitative trendspotting are prone to random and systematic errors, Kamakura writes. The model he devised, however, can filter these errors because it keeps them from appearing across different series of time panels. The result: better ability to identify genuine movements and general trends, free from the influence of seasonal events and from random error.

It goes without saying that the information and persuasiveness offered by the internet are inevitably attended by noise. For marketers, this means that without filtering, some trends show spikes for temporary items – mere viral jolts that can skew market research.

Kamakura and Du's model helps sidestep this problem by blending available historical data analysis, large time panels and movements while avoiding errors common to more traditional methods. For managers longing to glimpse the next big thing, this analytical model can reveal emerging consumer movements with clarity – just as they're becoming the future.

(For the mathematically inclined, and those comfortable with Excel macros and Add-Ins, who want to try trendspotting on their own tracking data, Kamakura's Analytical Tools for Excel (KATE) can be downloaded for free at http://wak2.web.rice.edu/bio/Kamakura_Analytic_Tools.html.)

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This article originally appeared on Rice Business Wisdom.

Wagner A. Kamakura is Jesse H. Jones Professor of Marketing at Jones Graduate School of Business at Rice University.

As more and more offices have remote workers, managers need to know how to measure virtual employee success. Getty Images

Rice University research calls for more ways to measure virtual employee success

Houston Voices

Managers are always hunting for ways to measure performance. They need to know what's succeeding and what's not so they can make adjustments and improve a work team's output. This has led to countless research that looks at ways to measure and boost employee performance. Indeed, one recent study showed there were more than 130 models and frameworks for measuring team performance in the workplace.

But how we do business has been changing in the last two decades. Communication technology and information sharing increasingly has decentralized the workforce. More and more people are working remotely. Consider telecommuters, online messenger services such as Slack and customer service call centers routing their calls across the world. What forces determine how these virtual teams function?

In a recent study, Rice Business professor Utpal Dholakia and colleagues René Algesheimer of the University of Zurich and Călin Gurău of GSCM-Montpellier Business School looked closely at what motivates remote teams and how to measure what they do. They began with a standard input-mediator-output-input model (IMOI) to measure team characteristics such as size, tenure, communication, strategic consensus and intentions. Then they dove further, including expected team performance, actual team performance and past team performance into the equations. Finally, they analyzed the influence of motivational (desire to perform) and rational (shared goals) dimensions.

To conduct the research, Dholakia, Algesheimer and Gurău analyzed professional computer gaming teams, reasoning that such teams work together in highly competitive environments. The gamers' lack of organizational context, meanwhile, eliminated any bias that could be linked to traditional institutional structures such as culture and goals. There was a downside, however: the gaming teams didn't fully replicate the situation of virtual teams in business organizations.

Still, by choosing the European Electronic Sports League (ESL) the researchers were able to pick from more than half a million teams that play in excess of 4 million matches a year. In the end, 606 teams participated in the study by answering a questionnaire in the course of a year. The teams all had stable structures and specific objectives, strategies and training, just like virtual work teams. Data was also collected from the ESL database and included in the model.

The findings: most studies do not consider expected and actual team performance in their calculations. This is important because research shows a strong link between expectation and performance. Including both sets of results can help managers choose the right steps to enhance team strategy and effectiveness. (The study did not analyze issues such as trust, training, conflict resolution or leadership, areas Dholakia recommends for further research).

The framework devised by Dholakia and his colleagues gives researchers a more precise way to analyze remote or international teamwork. It also could help guide managers in examining a team's cultural diversity, and how that might affect output. In a time when the workplace is growing ever less tangible, Dholakia's model is a sturdy tool to measure what's happening out there.

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This article originally appeared on Rice Business Wisdom.

Utpal Dholakia is the George R. Brown Professor of Marketing at Jones Graduate School of Business at Rice University.

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Texas falls to bottom of national list for AI-related job openings

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For all the hoopla over AI in the American workforce, Texas’ share of AI-related job openings falls short of every state except Pennsylvania and Florida.

A study by Unit4, a provider of cloud-based enterprise resource planning (ERP) software for businesses, puts Texas at No. 49 among the states with the highest share of AI-focused jobs. Just 9.39 percent of Texas job postings examined by Unit4 mentioned AI.

Behind Texas are No. 49 Pennsylvania (9.24 percent of jobs related to AI) and No. 50 Florida (9.04 percent). One spot ahead of Texas, at No. 47, is California (9.56 percent).

Unit4 notes that Texas’ and Florida’s low rankings show “AI hiring concentration isn’t necessarily tied to population size or GDP.”

“For years, California, Texas, and New York dominated tech hiring, but that’s changing fast. High living costs, remote work culture, and the democratization of AI tools mean smaller states can now compete,” Unit4 spokesperson Mark Baars said in a release.

The No. 1 state is Wyoming, where 20.38 percent of job openings were related to AI. The Cowboy State was followed by Vermont at No. 2 (20.34 percent) and Rhode Island at No. 3 (19.74 percent).

“A company in Wyoming can hire an AI engineer from anywhere, and startups in Vermont can build powerful AI systems without being based in Silicon Valley,” Baars added.

The study analyzed LinkedIn job postings across all 50 states to determine which ones were leading in AI employment. Unit4 came up with percentages by dividing the total number of job postings in a state by the total number of AI-related job postings.

Experts suggest that while states like Texas, California and Florida “have a vast number of total job postings, the sheer volume of non-AI jobs dilutes their AI concentration ratio,” according to Unit4. “Moreover, many major tech firms headquartered in California are outsourcing AI roles to smaller, more affordable markets, creating a redistribution of AI employment opportunities.”

Houston energy trailblazer Fervo closes $462 million Series E

Fresh Funds

Houston-based geothermal energy company Fervo Energy has closed an oversubscribed $462 million series E funding round, led by new investor B Capital.

“Fervo is setting the pace for the next era of clean, affordable, and reliable power in the U.S.,” Jeff Johnson, general partner at B Capital, said in a news release.

“With surging demand from AI and electrification, the grid urgently needs scalable, always-on solutions, and we believe enhanced geothermal energy is uniquely positioned to deliver. We’re proud to support a team with the technical leadership, commercial traction, and leading execution capabilities to bring the world’s largest next-generation geothermal project online and make 24/7 carbon-free power a reality.”

The financing reflects “strong market confidence in Fervo’s opportunity to make geothermal energy a cornerstone of the 24/7 carbon-free power future,” according to the company. The round also included participation from Google, a longtime Fervo Partner, and other new and returning investors like Devon Energy, Mitsui & Co., Ltd., Mitsubishi Heavy Industries and Centaurus Capital. Centaurus Capital also recently committed $75 million in preferred equity to support the construction of Cape Station Phase I, Fervo noted in the release.

The latest funding will support the continued buildout of Fervo’s Utah-based Cape Station development, which is slated to start delivering 100 MW of clean power to the grid beginning in 2026. Cape Station is expected to be the world's largest next-generation geothermal development, according to Fervo. The development of several other projects will also be included in the new round of funding.

“This funding sharpens our path from breakthrough technology to large-scale deployment at Cape Station and beyond,” Tim Latimer, CEO and co-founder of Fervo, added in the news release. “We’re building the clean, firm power fleet the next decade requires, and we’re doing it now.”

Fervo recently won Scaleup of the Year at the 2025 Houston Innovation Awards, and previously raised $205.6 million in capital to help finance the Cape Station earlier this year. The company fully contracted the project's capacity with the addition of a major power purchase agreement from Shell this spring. Fervo’s valuation has been estimated at $1.4 billion and includes investments and support from Bill Gates.

“This new investment makes one thing clear: the time for geothermal is now,” Latimer added in a LinkedIn post. “The world desperately needs new power sources, and with geothermal, that power is clean and reliable. We are ready to meet the moment, and thrilled to have so many great partners on board.”

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This article originally appeared on EnergyCapitalHTX.com.

Baylor center receives $10M NIH grant to continue rare disease research

NIH funding

Baylor College of Medicine’s Center for Precision Medicine Models received a $10 million, five-year grant from the National Institutes of Health last month that will allow it to continue its work studying rare genetic diseases.

The Center for Precision Medicine Models creates customized cell, fly and mouse models that mimic specific genetic variations found in patients, helping scientists to better understand how genetic changes cause disease and explore potential treatments.

The center was originally funded by an NIH grant, and its models have contributed to the discovery of several new rare disease genes and new symptoms caused by known disease genes. It hosts an online portal that allows physicians, families and advocacy groups to nominate genetic variants or rare diseases that need further investigation or new treatments.

Since its founding in 2020, it has received 156 disease/variant nominations, accepted 63 for modeling and produced more than 200 precision models, according to Baylor.

The center plans to use the latest round of funding to bring together more experts in rare disease research, animal modeling and bioinformatics, and to expand its focus and model more complex diseases.

Dr. Jason Heaney, associate professor in the Department of Molecular and Human Genetics at BCM, serves as the lead principal investigator of the center.

“The Department of Molecular and Human Genetics is uniquely equipped to bring together the diverse expertise needed to connect clinical human genetics, animal research and advanced bioinformatics tools,” Heaney added in the release. “This integration allows us to drive personalized medicine forward using precision animal models and to turn those discoveries into better care for patients.”

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