Data Connects Us by uazresearch

DATA CONNECTS US

FALL 2023

UNIVERSITY OF ARIZONA LAND ACKNOWLEDGEMENT We respectfully acknowledge the University of Arizona is on the land and territories of Indige‑ nous peoples. Today, Arizona is home to 22 federally recognized tribes, with Tucson being home to the O’odham and the Yaqui. Committed

to diversity and inclusion, the university strives to build sustainable relationships with sovereign Native Nations and Indigenous communities through education offerings, partnerships, and community service.

A PUBLICATION OF THE UNIVERSITY OF ARIZONA OFFICE OF RESEARCH, INNOVATION & IMPACT AND INSTITUTE FOR COMPUTATION & DATA-ENABLED INSIGHT Producers Stephanie Doster, Kim Patten, Lisa Romero Designer & Illustrator Edmundo Canto Writing & Editorial Team Emily Litvack, Mariana Calvo-Llanos, Kristina Makansi, Michael Pisetsky, Craig Reck, Eric Van Meter

Original Reporting Some of the articles in this magazine are based on original reporting by Rosemary Brandt, Logan Burtch-Buus, Anna Christensen, Mikayla Mace Kelley, Susan McGinley, Kimberly Nichols, and Niranjana Rajalakshmi.

Questions & Feedback datainsight@arizona.edu

C ontents

03 INNOVATION Data Acumen 3 · Superfast Computing 4 · Digital Twins 5 · 3D Modeling 6 · Volumetric Video 7 · Measuring Innovation 8 · Wearable Tech 9

10 HEALTH Health Devices 10 · Alzheimer’s Disease 11 · Drug Development 12 · Detecting Autism 13 · Heart Health 14 · All of Us 15 · Mental Illness 16 · One Health 17 · Tech in Nursing 18

19 EDUCATION Medical Training 19 · Probing ChatGPT 20 · College Recruiting 22 · AI in Education 23

24 ENVIRONMENT Measuring Wind 24 · Modeling Waterways 25 · Biosphere 2 Drought 26 · Shrinking Forests 27 · CO2 Models & Mitigation 28 · Climate Moms 30 · NEPAccess 32

33 SOCIETY Political Violence 33 · Indigenous Data 34 · Endangered Languages 35 · AI in Art 36 · Service Dogs 37 · Healthful Buildings 38 · AI & Architecture 39

40 INDUSTRY AI & Labor 40 · Safer Mines 41 · Data-Driven Agriculture 42 · AI in Cybersecurity 43

44 SPACE Junk in Space 44 · Giant Magellan Telescope 45 · Black Holes 46

O pening

L etter

A MESSAGE FROM ELLIOTT CHEU INTERIM SENIOR VICE PRESIDENT OF RESEARCH & INNOVATION Today’s advanced technologies are not simply tools – they are powerful forces reshaping the way we work, learn and interact with the world. Data science, machine learning and generative AI are evolving at lightning speed, expanding what is possible in education, research, industry and how we live our lives. University of Arizona scientists are leveraging data and advanced computation to create powerful new models to predict changes in water systems across the U.S. and to study black holes in the farthest reaches of space. They are revisiting landmark health studies to reveal findings hidden within the existing data, while mining current data to improve outcomes in medi‑ cine, social justice and more. I am inspired by our faculty, graduate students and undergraduates taking on food security, workforce safety, disappearing languages and public policy. I continue to be moved by their work to ensure our wounded veterans receive support, and I am hopeful for pharmaceuticals shown to reverse symptoms of Alzheimer’s disease for the first time in history. And yet, we know that with new tech‑ nologies come important ethical ques‑ tions and responsibilities that we must address as a society. The Expert Per‑ spectives interviews throughout this magazine feature forward-thinking re‑ searchers and educators tackling tough questions about the limitations and po‑ tential pitfalls of data and computation. The risks and challenges are great, as are the rewards and outcomes. I

invite you to learn more about how the University of Arizona is harness‑ ing the power of data and AI for the benefit of our students, our state and society at large.

ELLIOTT CHEU

Interim Senior Vice President of Research & Innovation

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Q&A with Arthur “Barney” Maccabe, executive director of the Institute for Computation & Data-Enabled Insight (ICDI).

NEXT-GE N E DUCATION

BUILDING DATA ACUMEN Machine learning and data science are rapidly changing what industry can accomplish if workers have the right skills and tools. Do universities have a responsibility there? This has always been a challenge in society when new technologies emerge. The important thing is to meet people where they are. We can offer certificates and applied master’s degrees as we’ve historically done, but that’s not a solution for everyone. We also need more innovative models, like services we’re building in ICDI, where people in industry can bring their own data and we work with them to extract information and teach them skills, including AI technologies, in the process. The university’s Data Science Institute, led by Nirav Merchant, already offers a “bring your data” capability for faculty and graduate students on campus – our

research workforce. It’s all about building “data acumen” for people who are already experts in their fields, especially with the rapid pace of technology advancement. What about for students in K–12 or transferring from community colleges? Do universities have a role to play there? It’s absolutely critical that we look at who’s potentially coming into a univer‑ sity and what preparation they need. That’s part of what the Data Sciences Academy, led by Joe Watkins, does. DSA works with K–14 programs across the state, rethinking how we’ve his‑ torically done things. We learn calculus to know things about objects in motion and limits and optimization. Today, data science enables discovery in the same way. So should we still teach algebra in ninth grade? Or should we teach data and have algebra emerge from that in a more natural way? I think that’s the kind of discussion that needs to be had.

I nnovation

T R A N SIT IO NING BEY O ND TRAN SISTORS

COMPUTING AT THE SPEED OF LIGHT Mohammed Hassan, associate professor at the University of Arizona College of Science, leads inter‑ national research demonstrating a way to register the on/off switching of laser signals at speeds on the scale of attoseconds, or quintillionths of a second. The breakthrough paves the way for previously unattainable data transfers, including ultra-distance communications, such as from Earth into deep space. Nearly all computers and electronics in use today still rely on semiconductor-based transistors, a 1940s innovation that translates electrical signals into “on or off” binary data. Advancing computing power has overwhelmingly focused on increasing the rate of signaling, achieving speeds at the scale of trillionths of a second in today’s most advanced systems.

However, the electricity in these systems creates heat in various ways, requiring cooling strategies like fans or liquid cooling systems and establishing a theoretical ceiling to performance. At some point, the energy required for cooling the system exceeds the energy it can support. As an alternative to electricity, alloptical signaling offers a way around the heat problem. It also enables data transfer a million times faster. But regis‑ tering the signals to translate them into binary data was an unsolved challenge. The research team devised a way to log the on/off state of laser signals at unprecedented speeds with fused silica. This special form of silicon dioxide can change from being reflective to being nearly transparent – correspond‑ ing to the on/off state of computing data – almost instantaneously.

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3 D & S O M U C H M O RE

NEXT-GEN DESIGN & MODELING CONVERGE IN DIGITAL TWINS Digital twins are interactive, 3D virtual representations used in engineering, construction and manu‑ facturing. One example: modeling an entire aircraft down to the electromagnetic fields generated by its various components before procuring a single rivet. They offer unprecedented data tools for safety plan‑ ning, energy management and more.

Combining satellite photos, digital room designs, georeferencing, ma‑ chine learning and AI, a team at the University of Arizona College of Applied Science & Technology have now completed the first phase of a digital twin of main campus: all 330 buildings, as well as streets and outdoor spaces, all to precise scale. In their full expression, digital twins don’t simply reproduce exact dimen‑ sions, they perfectly simulate physical properties like vibration, heating and cooling, materials expansion and contraction, light, air flow and more. They provide next-generation fidelity down to scale recreation of literal nuts and bolts, plus the ability to integrate data sources for a functional operating model that can be shared across teams. Those applications will aid renova‑ tions and new construction on campus, but the tool’s ultimate value extends much further: modeling heating, cooling, stormwater runoff and Wi-Fi reception, optimizing human and ve‑ hicle traffic during events, predicting power consumption, planning for emergency scenarios and more.

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DE PTH PE R C EPT I O N

TURNING 2D IMAGES INTO 3D MODELS Invented at the University of Arizona, “sparse holography” uses artificial in‑ telligence to create 3D virtual models. The technology has applications for any situation in which 360-degree perspectives are critical but can’t be obtained by direct measurement or imaging, including studying living tissue or helping autonomous vehicles inter‑ pret the world around them. Developed and named by David Brady, professor in the Wyant College of Optical Sciences, sparse holography begins with two-dimensional holo‑ grams, created by passing lasers over an object. Computers then read interfer‑ ence patterns in the light and translate that data into flat images with limited depth information when viewed from different angles. Sparse holography transforms those 2D holograms into 3D models that can be printed into physical objects or manipulated on screen for views from any perspective. Its novel engineering offers views of features smaller than a human hair from across an area the size of a football field. The innovation is already improving diagnostic capabili‑ ties of commonly used technologies like X-ray systems. A decade ago, Brady’s research drove another breakthrough in optical sciences: the world’s first gigapixel camera, with applications now ranging from high-stakes surveillance to medical imaging, and astronomy to

documenting cultural artifacts in precise detail. For reference, a gigapixel is a billion pixels – a thousand times the resolution of the megapixel cameras ubiquitous in today’s smartphones. Learn More

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VOLUME TRIC V I D EO

USHERING IN THE FUTURE OF EDUCATION As technologies for virtual connection continue to evolve, the University of Arizona is using volu‑ metric video to capture a person’s exact likeness, speech and movements in 3D video that can be recorded or broadcast live. Bryan Carter, director of the university’s Center for Digital Humanities, says this technology will transform education, merging 3D video with real-time, two-way audio between a projected person and an audience. With the science around volumetric video in its earliest days, the university will study its potential in education throughout fall 2023. Students par‑ ticipating in the research will “bring” volumetric video of an instructor into their live environments on handheld screens or with special glasses. As the instructor discusses objects in their surroundings, researchers will use sensors, eye-tracking and other technologies to log the students’ attention, focus and distraction, then

analyze that data against measured learning out‑ comes. Surveys will also help the team better understand student thoughts and attitudes about their education experience. Demand for distance learning skyrocketed during the pandemic. According to the National Center for Education Statistics, 75 percent of college students were taking at least one course online in fall 2020, and research projects signifi‑ cant growth for online education. Online meetings also saw pandemic-driven growth. The video conference platform Zoom, for example, surged from 10 million to 300 million daily users from December 2019 to April 2020. Carter notes that virtual connection becoming part of everyday school and work is driving innovation: “We’ve all begun to experi‑ ence the limitations of ‘presence’ associated with video conferencing or even virtual reality.”

I nnovation

DE CLIN IN G DI S R UPT I O N

REFINEMENT VS. REVELATION IN SCIENCE What was the last scientific breakthrough that really shook things up? If one doesn’t spring to mind, you’re not alone. New research finds that even as the volume of published science continues to swell, the work behind it is less and less disruptive. This discovery raises an alarm: while we urgent‑ ly need innovation in fields from medicine to food security, the systems that reward today’s scientific endeavors may be making trailblazing less likely. Professor Erin Leahey in the University of Arizona College of Social and Behavioral Sciences joined researchers from the University of Minnesota to gather data on 49 million patents and scientific papers over the past 60 years, and used com‑ putational techniques to analyze their citation patterns. Their goal was to understand how

papers and patents influence knowledge flows. They found that scientists are working with increasingly narrow slices of knowledge in a trend toward incrementally consolidating earlier work – better versions of earlier equations, for example, versus the revelation of DNA’s double-helix. To promote more disruptive science, research‑ ers suggest that universities focus more on quality over quantity and consider ways to allow scholars time to keep up with expanding knowledge fron‑ tiers. They also suggest funding agencies invest more in awards that support careers, not just projects, enabling scholars to produce higherimpact work. Learn More

The Rho Ophiuchi cloud complex, imaged by the James Webb Space Telescope, is among many disruptive discoveries that have “transformed humanity’s view of the cosmos” per NASA administrator Bill Nelson. NASA, ESA, CSA, STScI, Klaus Pontoppidan (STScI)

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Q&A with Janet Meiling Roveda, professor in the College of Engineering and director of the Center to Stream Healthcare in Place, sponsored by the National Science Foundation. Davis de Dios Media

LIVE STRE AMIN G

HEALTH & WEARABLE TECH What are some of the challenges in bringing health care wearables to market? The materials used are in direct contact with skin and some devices emit radiation in various wavelengths – so even something like a wristband device needs to undergo studies and clinical trials for FDA approval. That can mean significant development and refine‑ ment time between early science, prototypes and a medical-grade product. Also, for startups and mid-size companies, releasing a product without a large user base is risky. We need insurance companies to get involved and cover these products. Government involvement, both federal and state, could also lower the risk for innovation and provide greater incentive to reach the market sooner.

Will wearables dramatically expand the volume of health data available to researchers, broadly speaking? When it comes to data, one thing peo‑ ple often overlook is ownership. For example, in the U.S., hospitals usually claim ownership of patient data, requiring researchers to navigate various silos to access it. One of the countries that has managed to break down those silos very success‑ fully is Israel. There, it’s much easier for researchers to access data for studies, even data from multiple sources. They use blockchain technology to create smart contracts, so with one signature, patients grant access to their de-iden‑ tified data for research. And because the data is stored across decentralized, interconnected servers, they’re not dependent on a single pro‑ vider, thus decreasing vulnerability to security breaches. Unfortunately, we’re not anywhere close to this level of data accessibility in the United States.

H ealth

WE AR IT WE LL

DATA, DEVICES & HEALTH Data is key to modern medicine, and everyday wearables offer the promise of more comprehensive data for better health outcomes. Unfortunately, due to multiple points of failure – including device removal, jiggling and loss of contact or loss of power – Fitbits and smartwatches still don’t come close to delivering clinical-grade data. Led by Philipp Gutruf, assistant professor in the College of Engineering, the Gutruf Lab is overcoming these deficiencies with “biosymbiotic” wear‑ ables:custom-printed/perfect-fit mesh wearables that provide 24/7, high-fideli‑ ty sensing anywhere on the body. They get power and send data wirelessly, disappear under clothing and are so lightweight that users can forget they’re there. The lab is also creating implantables that open avenues for gathering data and delivering interventions. A col‑ laboration with University of Arizona

orthopedic surgeons created the world’s first battery-free, digital, in‑ ternal sensors to capture data on bone health. The devices have integrated LEDs to deliver therapeutic optical stimulation, all powered wirelessly. The research team also co-invented a next-gen pacemaker. In lieu of elec‑ trical leads attached with screws or hooks, a soft, wireless, battery-free “glove” unfolds around the heart. Placement is less invasive, and rather than shocking the heart – including its pain receptors – it issues precisely targeted light that only stimulates neu‑ rons that trigger contraction. Ultimately, the scientists see these two domains converging: seamlessly integrated wearable and implantable devices – wireless, fully automated and always on – that merge sensing and stimulation to deliver unprecedented insight into health and provide closedloop, personalized interventions.

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A N E W HO PE

GENE MODELING REVEALS MOST PROMISING ALZHEIMER’S INTERVENTIONS TO DATE University of Arizona researchers have, for the first time, shown that big data and advanced computing could unlock a way to treat and even prevent Alzheimer’s disease. Next step: human trials funded by the National Institutes of Health. With more than 2,000 samples from a national database of brain tissue from patients who had Alzheimer’s, Rui Chang, associate professor at the College of Medicine – Tucson, created an algorithm that integrates today’s vast but disconnected knowledge of genetics and molecular processes to create a brain model unlike any other. It allows scientists to see how gene changes at one point in time trigger divergent chain reactions and down‑ stream effects. The tool revealed 19 genes that, when undergoing changes upstream, lead to downstream amyloid plaques and tau tangles in the brain – hallmark symptoms of Alzheimer’s. Collabora‑ tors at Harvard University confirmed that effect in the lab, validating the genes as treatment targets. Chang then used 3D computer models to rapidly identify 3,000 fed‑ erally approved drug compounds that “fit” those target genes, not unlike the way keys fit certain locks. Clinical trials will now offer the first tests to discover if any of those compounds

could prove the key that not only improves Alzhei‑ mer’s symptoms, but actually prevents the disease.

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Associate professor Rui Chang is embarking on clinical trials for new ways to treat Alzheimer’s disease. Kris Hanning/University of Arizona Health Sciences

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DIGITAL DIS C O V ER Y

TRANSFORMING DRUG DEVELOPMENT Pharmaceutical makers have historically used a highcost, hands-on discovery process – one that makes return on investment likely only for conditions that affect millions of people. As such, they’ve invested little in tackling rare diseases and conditions that mainly affect impoverished populations. For example, the World Health Organization lists Chagas disease and dengue among 20 Neglected Tropical Diseases, so named for being “almost absent from the global health agenda.”

Now University of Arizona researchers, led by Travis Wheeler, associate pro‑ fessor at the Coit College of Pharmacy, are bringing machine learning to drug development to not only accelerate discovery – rapid solutions for the next pandemic, for example – but also slash costs, making research on neglected diseases more financially viable. At the heart of this game-changing approach is a simple reality: every living cell creates and relies on proteins, which are chains of molecules assem‑ bled in ways that give each class of proteins a unique shape. Drug molecules also have signature shapes and can only affect proteins they fit, like blocks in the game of Tetris. For example, one drug for cystic fibrosis works by linking to a key protein in a way that prevents it from misfolding out of its innate, healthy state. Researchers are applying machine learning to hundreds of thousands of these configurations and building al‑ gorithms that swiftly scan billions of ways that drug molecules could fit to proteins in human biology. The tools enable scientists to study vastly larger pools of potential drug molecules, making discovery faster and cheaper and increasing the likeli‑ hood of spotting a “needle in the hay‑ stack” molecule that might otherwise be overlooked.

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AUGME N TE D EXPER T I S E

MACHINE LEARNING COULD IMPROVE AUTISM OUTCOMES An interdisciplinary team of researchers is developing ways for non-expert clinicians to iden‑ tify children at risk for autism spectrum disorder (ASD), which affects 1 in 54 children in the U.S. Led by Gondy Leroy, professor at the University of Arizona Eller College of Management, the team combines expertise in information science, pedi‑ atric medicine and public health. The researchers are using machine learning to mark the electronic health records of children at high risk for autism based on observed behaviors that align with a medical diagnosis of ASD.

Early diagnosis and treatment drives the best long-term outcomes for children ultimately diag‑ nosed with ASD, a condition that today is often identified late or missed entirely in childhood. Tools from the research, funded by a $1.5 million health information technology grant from the National Institute of Mental Health, will drive earlier referrals for expert evaluation and will be especially valuable in smaller towns and rural areas, which are home to fewer physicians, especially those with specialized training for early detection of ASD. Learn More

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DATA RE V I S I T ED

NEW ANALYSIS OF LANDMARK STUDY REVEALS A VERY DIFFERENT FINDING Research on the use and health impact of omega-3s is controversial. Study designs, patient populations and the types of supplements used vary widely, and researchers have found inconsistent results. The 2019 VITAL study of nearly 26,000 U.S. residents concluded that supplementation with omega-3s had no effect on cardiovascular disease or cancer. But research led by Floyd “Ski” Chilton, director of the University of Arizona Center for Precision Nutrition & Wellness, used the study’s raw data to zoom in on the effectiveness of omega-3s specific to the 5,106 African American subjects. High-resolution statistical analysis sug‑ gested that omega-3s did, in fact, reduce myocardial infarction (heart attacks) in African Americans who have elevated cardiovascular risk factors and who don’t eat high amounts of fish – an effect not observed in European Americans. Researchers then applied AI to iden‑ tify two large, equal-number subsets from among the VITAL participants, clinically matched in all ways except that one group was entirely of African ancestry, the other, European. Preliminary results from this study also suggest that African Americans,

but not European Americans, have a reduced risk of myocardial infarction in response to omega-3s. Chilton has been at the vanguard of science study‑ ing the interaction between diet and ancestral-based genetic variation. Earlier work showed that selective pressures on the African continent 80,000 years ago altered fatty acid metabolism – an imbalance that supplementation with omega-3s can help rebalance in populations with African ancestry.

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ALL OF US

THE MOST INCLUSIVE LARGESCALE MEDICAL RESEARCH PROJECT IN HISTORY Funded by a six-year, $72 million grant from the National Institutes of Health (NIH), University of Arizona scientists are bringing advanced computing to a colossal new dataset to expand what we know about how biology, environment and behavior influence health. The NIH All of Us Research Program had provided data on more than 409,000 participants as of spring 2023. Records include medical histories, survey responses about family and lifestyle, census infor‑ mation and a trove of genetic data, including whole genome sequences for more than 245,000 individuals.

National Institutes of Health (NIH)

Just one whole genome contains about 3 billion base pairs of DNA. “We’re then overlaying data on ancestry, environmental factors, social determinants of health,” said Jason Karnes, director of scientific programs for All of Us UArizonaBanner. “The dimensionality of the data is outstripping human cognitive capacity, so we’re working on sever‑ al projects using machine learning to look at combinatorial effects.” Initial data is already producing important insights, in part due to the unprecedented diversity of partici‑ pants, Karnes said. In one study, topline analyses first replicated years of research showing strong associations between disease and self-reported race/ethnicity. But when adjusted for social determinants of health such as socioeconomic status, race and ethnicity became less predictive. There’s certainly much more work to be done,” Karnes said, “but pre‑ liminary results suggest that social determinants of health have much more of an impact on disease risk than do genetics, and that health disparities are driven by systemic inequities in terms of access to care and other sociocultural factors.”

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PRE CISION PS YC HI AT R Y

REVOLUTIONIZING TREATMENT FOR MENTAL ILLNESS Much of the reporting around precision medicine focuses on diseases that wreak bodily harm, like cancer or autoimmune disorders. Research led by Ayman Fanous at the University of Arizona College of Medicine – Phoenix is advancing precision psychiatry, an emerging field developing personalized treatments for mental illnesses. Like other areas of precision medicine, precision psy‑ chiatry builds treatments around a growing understand‑

Ayman Fanous, chair of the Department of Psychiatry at the University of Arizona College of Medicine – Phoenix. Dr. Fanous previously served as chief of Psychiatric Genetics at the Washington Veterans Affairs Medical Center. University of Arizona College of Medicine – Phoenix Media Production

ing of how genes, environmental factors, individual profiles for metabolites and proteins and other factors interact to cause depression, delusions, compulsive behaviors and other symptoms. The work draws on international collaborations sampling genes from hundreds of thousands of patients, with data from the Million Veteran Program, a research initiative of the U.S. Department of Veterans Affairs, proving especially helpful. Veterans data offers samples from African Americans, Asians, Latinos and Native Americans in numbers histori‑ cally lacking in medical research. This diversity helps investigators and minority populations alike, since these groups are the most likely to suffer health dis‑ parities and are underrepresented in other large-scale biological data sets. AI provides critical assistance in pro‑ cessing the abundance of data. Re‑ search on genetic drivers alone means detecting which among more than three billion DNA base-pairs might cause certain proteins or cells to malfunction. While cystic fibrosis, for example, arises from a single gene mutation, risk for schizophrenia or bipolar disorder is influenced by hundreds of genes.

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ON E H E ALTH

TRACING HEALTH DRIVERS THROUGH THE WEB OF LIFE Recognizing that the well-being of people, animals and the environment is linked, the University of Arizona is uniting expertise from across disciplines in the One Health Research Initiative (OHRI). Using AI to help analyze complex data sets, researchers aim to ensure safe food and water and advance global health security for more than 2 billion people in expanding arid regions around the world. Heat and drought make Arizona a valuable test case, says Frank von Hippel, OHRI lead and a pro‑ fessor at the Zuckerman College of Public Health. Collaborating with CNRS, the National Centre for Scientific Research in France and the largest fundamental science agency in Europe, UArizona scientists are learning how climate change im‑ pacts air and water pollution. Drought

has made Lake Powell a prime target for such research. The reservoir spans Arizona and Utah, is a freshwater re‑ source for multiple states and recently reached an historic low – just 40% of capacity. As its water diminishes, compounds considered safe when well diluted become increasingly concentrated. Sediment no longer underwater exposes materials that can become airborne pollutants. Further, as supplies dry up, potential limits on water use in agricul‑ ture raises troubling questions for food security. The research team will use advanced sensors to track the dispersion of pol‑ lutants and contaminants in air, water and food systems across the region, and will use AI to analyze the complex data sets that result.

Low water levels in Lake Powell may be increasing environmental toxins.

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Q&A with Janine Hinton, director of the Steele Innovative Learning Center in the University of Arizona College of Nursing.

HEALTH CARE H ORIZ ON S

NURSING & TECHNOLOGY What are some ways that simulations, AI and other advanced technologies are making a difference in nursing? There’s a landmark study by the National Council of State Boards of Nursing that showed up to 50% of nursing clinicals could be done in simulations without any difference on performance outcomes. I think there’s also tremendous value in utilizing sim‑ ulation for continuing education. We did a birthing mannequin refresher program for paramedics, and we recently collaborated with educators from the Donor Network of Arizona to explore how simulation can help ensure every possible organ remains un‑ damaged and available for transplant. Some research is exploring how AI could help guide care decisions based on past outcomes. What

do we need to be thinking about as that proceeds? Say you have two patients with a very similar diagnosis. For one patient, the insurance provider says we can use all the latest and greatest medica‑ tions. For the other, only older, generic drugs. If those constraints aren’t part of the data input, how does AI process those outcomes? The other big issues, I think, are social determinants of health – things like poverty, prior access to care, stress. Are algorithms accounting for all of that? Again, consider two patients with the same diagnosis, but one is healthier, likely because of those social determi‑ nants. If you’re asking AI which one will benefit from an expensive interven‑ tion based on predicted survivability, is that ethical? Pre-existing challeng‑ es shouldn’t cause a continuation in health care disparities and inequities.

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BE T TE R ME D I C I N E

SIMULATED STRESS, REAL EXPERTISE Under the leadership of Allan Hamilton, professor in the University of Arizona College of Medicine – Tucson, the Arizona Simulation Technology & Education Center (ASTEC) is producing doctors and nurses trained to excel in real-world care. Simulations develop basic skills but also ensure practice in uncommon procedures. And because they in‑ clude data on trainees’ physiological states – saliva cortisol levels (a stress indicator) and sensors to measure heart rates and breathing – trainers can manipulate conditions to amp up tension to roughly 95% of real-world, complex surgeries. For example, students confronted with bleeding computerized manne‑ quins not only learn to perform emer‑ gency cricothyroidotomies (a neck incision to allow air into the trachea), they train to do it blindfolded. Mastering procedures under pressure helps physicians later handle real-world strain with relative ease. ASTEC also improves social and interprofessional skills. From tracking eye movements to recording speech, algorithms analyze how students talk to patients and how they interact with colleagues. Trainers can thus assess bedside manner and performance as part of a team. Hamilton can see a future in which AIs accompany providers throughout their education and careers, offering guidance to continually improve

performance. At the same time, he stresses that technology must never replace humanity. “The art of medicine lies in the human element,” he says. “We should always remember that the ultimate goal of medicine is to care for and love our fellow human beings, and that cannot be replaced by AI.”

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Kris Hanning/University of Arizona Health Sciences

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K E E P ING U P W IT H AI

STUDENT RESEARCHERS PROBE THE VULNERABILITIES OF CHATGPT AI tools are fast becoming pervasive in medicine and other high-stakes fields, far outpacing workforce preparedness education. Marvin Slepian, director of the Arizona Center for Accelerated Biomedical Inno‑ vation, is responding with experiential learning that’s cracking open the black box of AI while immersing students in its uses.

In one experiment, student teams rely on different resources for information: ChatGPT, Google and library collections. “The hypothesis is that the mechanism of retrieval – the detailed prompts of ChatGPT versus the simple query structures of Google versus sweat-ofthe-brow library research – will gener‑ ate different results,” Slepian says. “But what do those results have in common, what gets left out and how do they compare in terms of accuracy?” Another experiment investigates the risk of AI amplifying misinforma‑ tion. Students probe the thresholds at which repeatedly feeding ChatGPT wrong information gets it to return falsehoods as fact, outputs dubbed “hallucinations” in the world of AI. Slepian’s students are forerunners in defining the parameters of those vul‑ nerabilities while also proposing ways to make AI outputs more reliable, e.g., by annotating with sources or enabling crowd-sourced metadata about quali‑ ty and accuracy. “We have to be fast and agile,” Slepian says. “We can’t wait years to put together a grant to study these things.” As a physician-researcher, he sees incredible promise in AI tools but notes they’re being adopted at breakneck speed, despite being still poorly under‑ stood. “We need guidelines for these technologies, and we need them now.”

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Undergraduates Jordan Rodriguez and Katelyn Rohrer are among Slepian’s researchers who presented new data on ChatGPT at the Biomedical Engineering Society conference in October 2023. My initial impressions of generative AI tools were a touch catastrophic. I remember feeling despair that, with simple prompting, they could replicate all the work I’m doing in college more easily and, in many cases, more competently. After those impressions faded, I learned how to integrate ChatGPT into my toolset. I learned how powerful it could be – not in replacing my work but to explain a new concept or algorithm or programming language in a concise way. I now firmly believe that these tools can be used to greatly expand people’s ability to learn and am incredibly excited about their potential. They democratize individualized education. My biggest concern is the potential for reliance in a new generation of knowledge workers. A new generation will have to resist the urge to offload all their work and learning to generative models. Jordan Rodriguez Computer Science & Spanish Undergraduate

Gregor Orbino for The University of Arizona/Arizona Board of Regents

I remember when the Chat GPT 3.5 model came out, it seemed to be almost magical. However, the more I use the model, the more I can see the patterns in its responses. It becomes apparent that it works on certain templates based on whatever it’s asked. ChatGPT is very proficient in finding specific information and answering niche questions, but when using it, you need to maintain a certain skepticism about the responses since it does “hallucinate” several answers. Our project is still in its preliminary stages, but we tested whether ChatGPT could correctly affirm true statements and correctly reject false ones. We found that the model accurately affirmed true information ~91% of the time and accurately rejected misinformation ~83% of the time. Katelyn Rohrer Computer Science & Cybersecurity Undergraduate

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STRUCTURA L R A C I S M

BIG DATA IN COLLEGE RECRUITING Lists of prospective students have become fundamental in college recruiting. University of Arizona research has exposed how the data and tools that produce these lists routinely marginalize already underrepresented populations.

Karina Salazar, assistant professor in the College of Education, partnering with Ozan Jaquette at UCLA, obtained the details of 830 list orders from 14 public universities, including which filters vendors used to match students to those orders. They also analyzed the resulting roster of more than 3.6 million then-prospective students. While methodologies vary slightly from one list vendor to the next, all offer ways to sort students not simply on grades or test scores but also on factors such as how many other stu‑ dents from their high school or zip code historically attended college – criteria highly correlated with race and household income. The studies found that these filters dramatically reduced diversity in resulting lists. They also found that associated travel for recruiting over‑ whelmingly focused on higher-income, mostly white communities. Salazar notes that university staff often outsource list purchases. They may have little understanding of how hired consultants and list vendors translate their expressed goals into the lists they get back. The underlying complication, she argues, is that structural racism persists in college recruiting precisely because lists and filters viewed as “normal” systematically benefit populations that already dominate higher education.

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Q&A with Sandiway Fong, director of the University of Arizona Human Language Technology Program.

U ND ERSTAN DIN G TH E ASSIGN ME N T

NAVIGATING AI IN EDUCATION Many professors now ask students to sign an agreement not to use tools like ChatGPT for schoolwork. Is that appropriate? Naturally students want to be able to look up con‑ cepts and class material. They’ve been doing that already, with Google. It’s just that AI is much quicker. It’s a question-answering system, which has actually been a holy grail of the search engine industry for many, many years. Should schools pursue ways to recognize when students use AI? Recently, seven U.S. companies in AI made an agree‑ ment with the government to explore a watermarking system, so people can know when content is generated

by AI. But students understand that. They’re not afraid to cut and paste and change a word or two to get around that. We have to be realistic. This genie is out of the bottle. I think it would be very naive of us to expect students not to use it, even if you make them promise not to. Given that, how should educators respond to this technology? In my courses, I tell students, ‘Use anything you like. I don’t care whether that’s an extraterrestrial, an AI, your friend…’ Because my assignments are structured so that students have to think. I think that should be the ultimate criterion, which puts the onus on professors to design work that can’t be accomplished with copy and paste. And I think this is a poten‑ tial upside: professors could do bet‑ ter, and students could actually end up learning more.

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WIN DS OF C HA N G E

ALGORITHMS REVEAL “HIDDEN” WEATHER University of Arizona researchers have devised a way to measure wind using machine learning and algorithms – an innovation that could help predict extreme weather events and save lives in some of Earth’s most populous areas. The novel approach pulls water vapor information from National Oceanic and Atmospheric Administra‑

tion (NOAA) satellites. Scientists have long used NOAA raw data for modeling but now can process it with algorithms not available a decade ago. This approach, led by Xubin Zeng, professor in the College of Science, overcomes limitations that have compromised models for predicting hurricanes, tracking airborne pollut‑ ants and more. Deducing wind data from cloud movements, for example, offers very limited information for certain layers of atmosphere. Similarly, vast areas of the planet, including oceans, have few or no surface stations for pulling measure‑ ments from balloon-borne sensors. Wind is a major player on the atmo‑ spheric stage, moving not just water vapor but also aerosols like dust and sea salt. It also drives the formation of clouds and precipitation, including extreme weather. The innovation provides critical data for improving a range of climate and weather models and has given scien‑ tists the first comprehensive picture of winds across the tropics and midlati‑ tudes – areas that are home to roughly 90% of Earth’s human population. The team is now developing a sat‑ ellite concept optimized for wind research based on this breakthrough and preceding research from the past five years.

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WATE R, WATE R E V ER YWHER E

POWERFUL PREDICTION FOR CHANGING WATERWAYS In response to climate change and increasingly frequent natural disasters, University of Arizona scientists are creating powerful tools that use AI to predict changes across the nation’s web of water systems. Coastal storm surge can flood freshwater systems with saltwater. Heavy rains overwhelm waterways. Heat and drought dry up streams, con‑ centrate pollutants in evaporating reservoirs and make environments dangerously vulnerable to fire. Weather can impact water systems in many ways – often with far-reaching effects – but sophis‑ ticated models are used mainly by researchers because they require extensive expertise. Tools that civil and federal professionals typically use for decision making rely on assumptions and over‑ simplifications from past events. Laura Condon, assistant professor in the College of Science, is bridging that gap between widespread

need and deep expertise. Funded by $5 million from the National Science Foundation, HydroGEN will offer the most comprehensive yet accessible predictive model of the nation’s watersheds. Named for its ability to generate hydrologic scenarios, HydroGEN uses machine learning and AI to forecast variables like stream flow, soil moisture and groundwater. Computation and data storage for the platform are powered by CyVerse, a 15-year, $117 million investment by the NSF housed in UArizona’s Data Science Institute. Available publicly online once completed, the system will give community leaders, resource managers and policymakers easy-to-use, sci‑ entifically rigorous tools for decisions on water use for agriculture, managing wildfires and other critical needs.

E nvironment

T H I R S T FO R K NO W LED GE

EXPERIMENTS FOR A HOTTER, DRIER WORLD In an unprecedented experiment, University of Arizona scientists subjected the 30-acre, enclosed rainforest at Biosphere 2 to a three-month drought before reintroducing healthy moisture levels. The resulting data is revealing how plants and soil-based microbes responded – knowledge that could prove critical for adapting to a hotter, drier world. The newest branch of research, led by associate professor Malak Tfaily in the College of Agriculture, Life and Environmental Sciences, reveals what hap‑ pens underground during and following drought. Throughout the experiment, sensors documented changes in the forest’s rhizosphere – the zone sur‑ rounding roots, where complex interactions with soil microbes occur. Data showed notable drops in the levels of organic compounds that plants pumped out from their roots.

The rainforest at Biosphere 2. University of Arizona

Microbes in soil depend on these root exudations for energy. Reducing them can change the composition and activ‑ ity of microbial communities, altering how they cycle nutrients. Research also showed that the effects of drought on soil microbes varied with the plant species associat‑ ed with those microbial communities. The finding suggests that depending on the composition of the ecosystem, drought-tolerance strategies can in‑ corporate different interventions to produce different targeted effects below ground. Finally, soil microbes in drought con‑ ditions released more volatile organic compounds (VOCs) and released them later in the day. Researchers theorize this microbial response might increase the potential for cloud cover in realworld conditions. Ambient air is hotter later in the day and therefore more likely to carry VOCs high enough to seed cloud formation.

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F UTURE F O R ES T S

A SHRINKING ROLE FOR FORESTS IN CLIMATE CONTROL Researchers at the University of Arizona Laboratory of Tree-Ring Research found that rising temperatures are stunting tree growth, a phe‑ nomenon with troubling implications for global warming and climate resilience planning. Forests remove carbon dioxide (CO2) – the major driver of global warming – from the air, helping to reduce its accumulation in the atmosphere. The study used a first-ever fusion of data from tree rings and the U.S. Forest Service census: surveys every 10 years of a forest’s soil quality, its number of trees and tree diameters. The resulting model forecasts that Arizona’s pon‑ derosa pine forests will see tree growth declines of 56% to 91%.

Higher temperatures stress trees’ watertransport systems, especially in taller trees, de‑ creasing growth and increasing vulnerability to drought. The study also found that trees in overly dense stands have a stronger negative response to heat. Thinning in those areas and revising fire suppression policies could help alleviate overall forest stress. Because smaller trees have less capacity for CO2 uptake, the study signals yet another setback in the fight against global warming. Modeling sug‑ gests that forests in warming areas around the world can also expect growth declines. Learn More

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BRIDGIN G DI S C I PL I N ES

RESEARCH AT THE NEXUS OF GENOMIC & ECOSYSTEM SCIENCES The University of Arizona BRIDGES* program examines how genes drive impacts that span from individual organisms to entire ecosystems. Funded by the National Science Foundation, BRIDGES engages outstanding graduate students in projects that trace cascading effects of DNA from biology to ecology to better understand

how wild and agricultural systems function and respond to change. For Changpeng Fan and Sabrina Wilson, the program supports research that explores role of plants and soil microbes in the carbon cycle, both key players in strategies for curb‑ ing global warming.

E nvironment

Changpeng Fan, a PhD student in hy‑ drometeorology, is using deep learning technologies to create algorithms that can predict microorganism populations in soil and how much organic matter those microbes decompose. Because soil plays a key role in the way that carbon cycles through Earth systems, properly accounting for its impact improves our ability to predict levels of carbon dioxide in the atmosphere, which is key to modeling climate change. Historically, studying these microbe communities has relied on physically collecting soil samples and manually analyzing all of the genetic material they hold. It’s a labor-intensive under‑ taking constrained by time, money and geographic accessibility. As an alternative to that process, Fan is training algorithms on real-world soil samples. As this machine learning advances, AI will be able to increasingly predict the functional composition of soil microbe communities and their impact on carbon sequestration based purely on inputs such as geolocation, local weather and other factors. Sabrina Wilson, a master’s student in atmospheric science and ecosystem genomics, is working to bring new data inputs to the Community Land Model (CLM 5.0), a tool developed by the National Center for Atmospheric Re‑ search to model Earth systems. Her research explores how biochar, a soil amendment used in agriculture, could mitigate climate change through its positive effects on soil health and greenhouse gas emissions. Biochar is a win-win-win in environ‑ mental science. Created by applying intense heat and pressure to forest trimmings or plant waste left over from harvests, it helps soil hold nutrients, retain water and nurture microbes, increasing crop yields.

Biochar, used to amend crop soil, could play a role in reducing greenhouse gasses in the atmosphere. Creative Commons

It also reduces carbon dioxide in the atmosphere by making plants better at storing carbon and by us‑ ing up waste that would otherwise decompose or be burned, releasing greenhouse gasses. Wilson collected data on a range of biochar ap‑ plications and their effects on greenhouse gas‑ ses and soil. Using AI and machine learning tools at Sandia National Laboratory in New Mexico, she’s now optimizing the relevant parameters in CLM 5.0 and validating the predictive accuracy of those ad‑ justments across environments around the world. * Building Resources for InterDisciplinary training in Genomic and Ecosystem Sciences

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CLIMATE MO MS

WORKING FOR THE FUTURE OUR KIDS DESERVE University of Arizona climate scientists Joellen Russell and Beth Tellman are self-described “climate moms” – mothers gathering and using climate data to inform decision making today and preserve a bet‑ ter world for their kids and all future generations. As mothers who also happen to be climate sci‑ entists, they study the Earth and human impact on its ecosystems. Dedicated to preserving the planet for the future, they know that to solve the

Joellen Russell and Beth Tellman at SXSW. Joe C. Klug

problem of climate change, science must be de‑ mystified and everyday people around the world must demand solutions that preserve the planet for all our kids. “Why do we do it?” asks Russell. “We’re moms – this is what we do. We want our little ones to have the safe, secure, prosperous future that they de‑ serve and that we grew up with.”

E nvironment

Research by Tellman, assistant professor in the College of Social and Behavioral Sciences, is saving lives and helping to protect economies by reveal‑ ing how global warming is dramatically increasing flooding. Using satellites, she captures data from flood events around the world and uses machine learning to not only reconstruct past incidents, but also develop algorithms that predict flooding and its destruction with increasing accuracy. Since flooding sets back progress in many de‑ veloping countries, the research has far-reaching social and economic value. She recently pro‑ duced a 20-year history of flooding in Bangladesh and trained local stakeholders to use her algo‑ rithms to create their own flood maps. Better flood data can also help with kitchen-ta‑ ble economics, potentially reigning in insurance costs and helping families get financial support. A new collaboration using Tellman’s flood maps is helping households prove flood damage to secure FEMA disaster aid.

Research by Russell, a professor in the College of Science, also spans the world, expanding our understanding of the relationship between global warming and our oceans, which both store and release the greenhouse gas carbon dioxide (CO2). Since 2014, Russell and her team have been building the world’s largest dataset on conditions of the Southern Ocean, using sensor-laden robots that float more than half a mile below the frigid water’s surface. Her studies show that increasingly intense westerly winds – belts of winds that are key to large-scale weather patterns in both the northern and southern hemispheres – are driving a massive outgassing of carbon dioxide from deep ocean waters that have held it for nearly a millennium. That additional CO2 in the atmosphere increases atmospheric warming, which further intensifies wind in a vicious cycle with dangerous effects on global weather systems.

E nvironment

OPE N G OVER N MEN T

UNLOCKING 50+ YEARS OF ENVIRONMENTAL DATA The National Environmental Policy Act (NEPA) requires impact assessments before work pro‑ ceeds on federally funded projects like highway construction, oil exploration and more. The results of the assessments inform decision makers of possible harm to environmental considerations like animal populations and watersheds. Since NEPA’s implementation in 1970, the find‑ ings from those studies have been stored in hard-to-access archives, placing a half century of knowledge – thousands of information-rich documents – beyond the reach of the public and modern data science. Now the NEPAccess project, led by the University of Arizona, is unlocking data embed‑ ded in environmental impact statements (EIS) and environmental analyses required by NEPA. The goal is to create a more efficient, transparent and accountable tool for science-based, demo‑ cratic environmental governance. Using machine learning, the project is turning unstructured documents into structured, search‑

able data in a model that can also be adapted for transforming other troves of government docu‑ ments into information ripe for meta-analyses. Devised by a team of data scientists, environ‑ mental researchers and scholars in public policy and law, the process begins with manually pull‑ ing content to train machine-learning algorithms, enabling automated natural language processing to take over the work of knowledge extraction. The searchable NEPAccess repository launched in late 2021, and today provides free, full-text and filtered searching of EISs with continued work to provide new functionality in response to stake‑ holder input and user-experience data. For the first time, researchers around the world can apply AI, machine learning, georeferencing and other computing tools to study NEPA information across agencies, action types, regions and sectors.

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S ociety

WAT C H & LEARN

TRACKING THE GROWING THREAT OF POLITICAL VIOLENCE IN REAL TIME In an era of growing social and political dissent, University of Arizona scientists have created an AI tool that can help governments and law enforcement more precisely forecast and prepare for violent unrest. Investigations of domestic terrorism in the U.S. have more than quadrupled in a decade, according to the Government Accountability Office, with anti-au‑ thority extremism being the second-largest category of incidents. U.S. Capitol Police report that in recent

years, threats against federal lawmak‑ ers have risen 400%. Government and researchers have long worked to track political unrest online to inform policy and security. But the process has been labor- and time-intensive, requiring human moni‑ toring of voluminous information. ConfliBERT (a portmanteau of “con‑ flict” and “BERT,” an AI created by Google) was developed through a $1.5 million National Science Foundation grant. Co-led by Javier Osorio, assis‑ tant professor in the College of Social and Behavioral Sciences, researchers trained the AI on materials curated for conflict and violence content, sourced from international agencies, mainstream media, the United Nations and U.S. foreign relations. ConfliBERT far outperforms general natural language processing tools in spotting and analyzing red flags for conflict and violence, with applications for both predicting unrest and learning from past incidents. The researchers also developed a novel training method using less than 34 GB of data. That model and ConfliBERT’s publicly available code dramatically lower barriers for com‑ munities around the world where developing similar tools would be cost-prohibitive.

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S C I E NC E W IT H C ARE

INDIGENOUS DATA SOVEREIGNTY Stephanie Russo Carroll, a University of Arizona expert in Indigenous data sovereignty, led development of protocols for Indigenous data collection, use and management now adopted by UNESCO, the govern‑ ments of Australia and New Zealand and other entities. Denice Ross, deputy U.S. chief technology officer for tech capacity and a UArizona alumna, has also connected with Carroll about ways to implement the CARE principles within the federal government. When completing her doctoral dissertation on health programs run by six Native American tribes, Carroll struggled to find a single area that she could compare across tribes. The information available described only deficits and shortcomings, a distortion she attributes to the

fact that state and federal agencies, not tribes, defined what mattered and determined what data was collected. Much of Carroll’s work now focuses on influencing institutions to follow emerging policies for Indigenous data sovereignty and governance: the rights of Indigenous Peoples to con‑ trol the creation, stewardship and use of data about themselves, their lands and their cultures, often abbreviated IDSov and IDGov. Carroll helped create the CARE Principles for Indigenous Data Gov‑ ernance, which provide guidance to ensure collective benefit from re‑ search; authority seated in Indigenous communities; responsible data use, storage and access; and ethics front and center, always.

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Stephanie Russo Carroll is associate director of the Native Nations Institute, director of the Collaboratory for Indigenous Data Governance and an associate professor in the Mel and Enid Zuckerman College of Public Health at UArizona. Carroll is Dene/Ahtna, a citizen of the village of Kluti-Kaah in Alaska. Kris Hanning/University of Arizona Health Sciences

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H E R I TAGE AS D ATA

TECHNOLOGY BRINGS NEW TOOLS TO REVITALIZING ENDANGERED LANGUAGES University of Arizona scholars are collaborating with Indigenous commu‑ nities to fortify endangered languages. Combining data science, machine learning and other technologies, the work offers a model for safeguarding cultural heritage and creates tools for self-directed language work in communities around the world. One project supports revitalizing the ancestral language of the Schit‑ su’umsh, also known as the Coeur d’Alene tribe. The last elder who grew up speaking it as their first language died in 2018. Linguists in the College of Social and Behavioral Sciences, working with the university’s American Indian Language Development Institute, have created a web application that is being trained on Coeur d’Alene words as a path to mastering the language more broadly. With machine learning, the ap‑ plication will eventually be able to independently build its command of the language by scanning texts to learn more advanced grammar and syntax. Those scanned materials also become part of a master archive, opening doors to a trove of potential research that relies on databases of digitized text. A separate project in partnership with the Tohono O’odham Community College in Sells, Arizona, uses similar technologies to develop natural lan‑

guage voice recognition for the O’odham language. One goal of the work is to develop algorithms so fully trained that they’ll be able to provide automated transcriptions of tribal audio archives, such as de‑ cades of recorded community meetings and events.

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V I R T U AL U NREALIT Y

BLURRING FACT & FICTION By summer 2023, deepfake ads and social media posts aimed at the 2024 U.S. election were already rampant: Hilary Clinton lauding a Republican; Donald Trump hugging Anthony Fauci; 30 seconds of night‑ marish what-if images generated wholly by AI. “We live according to a certain realism, rather than a reality,” says artist Marcos Serafim, assistant professor in the College of Fine Arts. Today more than ever, technology shapes our beliefs about “truth.” Serafim explores that dependency in some works by using AI to generate footage of interviews. They’re

fake, but presented in contexts we accept as inherently true, blurring lines between what is and isn’t real. His art also challenges our trust in data. In the audiovisual installation Membrana Semipermeable, Serafim explores racism and violence in the HIV/AIDS crisis along the US-Mexico border. His algorithms take public data on gender, ethnicity and more, and transpose it into false but photorealis‑ tic content, challenging the idea of an intrinsic link from data to truth. Ultimately, Serafim sees art as one of many paths to knowledge and wants to raise awareness of how computers can manipulate and disinform. “There is danger in the technologies that I’m utilizing,” Serafim says, “and only two things stand in the way of us being con‑ trolled by it: awareness and literacy.”

Work by UArizona assistant professor Marcos Serafim on display in 2022 at the Fosdick-Nelson Gallery, School of Art & Design, Alfred University. Colette Chermak/Alfred University

S ociety

TOP DOG S

THE SCIENCE OF SERVICE University of Arizona scientists at the College of Veterinary Medicine are using multimodal data and machine learning to validate and improve the many ways people benefit from trained service dogs. Work led by associate dean of research Maggie O’Haire examines the impact of service dogs for veterans with post-traumatic stress disorder (PTSD). The dogs’ training is designed to instill a sense of safety, and self-report survey research shows it works. O’Haire is validating those find‑ ings with objective data from veterans with PTSD – some with and some without service dogs. Wrist and collar wearables track sleep, activity and proximity between veteran-canine pairs, and saliva samples from both provide data on stress hormone levels. Participants without dogs also provide saliva samples and wearables data. Com‑ bined, the data paints pictures of daily life for veterans with and without service dogs. Raising and training a service dog takes con‑ siderable time and money. Costs range from

$50,000 to $70,000 per dog, and only about half ultimately achieve certification. Research by associate professor Evan MacLean aims to raise that rate and lower costs by identifying biological and behavioral markers correlated with success‑ ful canine trainees. MacLean gathers data from cognitive tests, ob‑ served actions, genetic analyses and other bio‑ logical traits. Machine learning integrates those datasets across a growing roster of subjects to identify patterns that predict which puppies will be the best candidates for service training. Ultimately, he hopes to also categorize can‑ didates by aptitudes. Matching strengths to specific needs – sniffing out explosives vs. assisting someone with a seizure disorder, for example – helps optimize health and safety outcomes at both ends of the lead.

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WE LLN E SS BY D ES I G N

BUILDING FOR BETTER HEALTH Adults in industrialized nations spend roughly 90% of their lives indoors. Those environments affect us as individuals – patient recoveries in hos‑ pitals, for example – and as societies, e.g., impact‑ ing productivity and, by extension, economies. Altaf Engineer, associate professor in the University of Arizona College of Architecture, Planning and Landscape Architecture, is work‑ ing on several projects to make indoor environ‑ ments healthier, for both crisis response and everyday well-being. Research to create “smart buildings” is developing networks driven by data in which ventilation and other systems automatically respond to environmental conditions in real time. This became even more important during the pandemic, which highlighted a critical health management gap: lack of systems that simultaneously monitor and improve air quality.

Another project explores how ambient light and color in an environment can influence per‑ ceived temperature – people may feel warmer around reds and oranges, for example. Study participants are equipped with various sensors worn on their wrists, head, chest – and even in their shoes – to monitor light exposure, activity, sleep quality, heart rate variability (an indicator of stress) and more. At the same time, stand-alone sensors monitor their environmental parameters, such as air quality, temperature and humidity. Algorithms analyze the continuously amassing data to reveal how environmental factors drive changes in heart rate, stress response and other physiological responses – knowledge that can inform healthier renovations and shape new principles for evidence-based, healthful building design.

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P erspective

Q&A with Jonathan Bean, co-director of the Institute for Energy Solutions, part of the Arizona Institute for Resilience.

P OWE R & CON TROL

AI IN ARCHITECTURE What role does advanced computing play in architecture today? One area is dynamic energy modeling for buildings: an hour-by-hour analysis of all the heat that’s coming in or going out through windows or walls. There’s a lot of utility for that, but I was trained as a social scientist, so I also have a different perspective. There’s this idea that if we could just control every‑ thing to the nth degree, a lot of our problems would be solved. If we could control everyone’s water heater, we could manage everything in a sustainable way – the master-controlled smart city. That’s the vision that keeps getting funded, despite ample evidence that it’s not an approach guaranteed

to work. And many people don’t want the government to control anything in their homes. What’s interesting to me is that we keep ignoring the part of the equation we know how to solve — using pas‑ sive energy conservation strategies in new and existing buildings, like con‑ struction with thermal mass materials that use less energy in the first place. Given that, where do you see value for advanced technologies? The reality is we need to understand so many scenarios: different uses, cli‑ mates, building codes. So I’m excited about tools that use AI to say, “OK, you’re designing a house or remodel‑ ing a lab space – here are some things you can do that will optimize energy conservation and production for this particular scenario.” That’s where I think assistive technologies are really unexplored and exciting.

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P erspective

Q&A with George Hammond, director of the UArizona Economic and Business Research Center in the Eller College of Management.

T H E F UTURE OF WORK

INDUSTRY, AI & LABOR What industries will AI most disrupt? It’s middle-skill occupations – interpreters, telemar‑ keters, legal assistants. Those kinds of jobs. Cargo and freight – drivers, loaders, warehousing – are al‑ ready pretty automated and will get even more so. It’s not so much that these jobs will be eliminated en‑ tirely. But we’re all going to have that digital assistant that is helping in some way to do our job. Do we need laws to protect jobs? There are legitimate questions about how we should legislate AI, but another angle to consider is that with a declining labor force, we’re going to need it in order to maintain our standard of living.

The Baby Boomer generation is retir‑ ing and also passing away. At the same time, we’ve seen a significant decline in birth rates. One of the implica‑ tions is slower growth in the prime working-age labor force. What economic issues are flying under the radar? There’s way too much focus on job losses without allowing for the possibility of new occupations springing up. If people lose a middle-skilled job, can they upskill? There will also be whole new occupations that we can’t predict today. And what gets missed is that we need this kind of technological change to deal with demographic shifts to keep our standard of living growing. So, yes, there are big issues that we need to think about. We need to be clear-eyed about what can go wrong while also seeing the potential for a better future.

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IN DUSTRIAL-STRE N G T H I N N O VAT I O N

SMARTER MACHINES, SAFER MINES As one of the most technologically advanced industries today, mining and mineral extraction require highly skilled workers. The University of Arizona is prioritizing the health and safety of these workers, using AI and machine learning to reduce accidents and inju‑ ries in underground mines. Nathalie Risso, assistant professor in the College of Engineering, led development of an algorithm to visually scan worksites for issues with PPE compliance. To train the algorithm, a campus-wide invitation rapidly popu‑ lated a database of 2,000+ photos of students in mining gear, ensuring diversity representative of multinational mining workforces. Researchers have also created an app that allows anyone – even those without safety expertise – to use the cameras in smartphones to determine risks for collapse and falling rock. Users capture video or photos that an algorithm analyzes in real time. UAri‑ zona’s San Xavier Underground Mining Laboratory provided visuals to train the algorithm, and the app will crowd‑ source more data from ongoing use, continually refining its sensitivity to hazardous conditions. A third project uses machine learning to train autonomous drones and robots for rescue operations. The devices can assess injuries, detect hazards like toxic gasses and discover safe exit paths. Above-ground vehicles work in con‑ cert with the AI bots, overcoming

potential connectivity issues and processing data on the spot. The innovations have use in any high-risk emergency (think earthquakes or nuclear plant malfunctions) as well as non-crisis scenarios, potentially including extraterrestrial mining operations.

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SMART AGRICULTURE

BIG DATA FOR GROWING CONCERNS In a state ranked among the nation’s top growers for lettuces, melons and several vegetables, University of Arizona scientists are developing plants adapted to thrive in our rapidly warm‑ ing world. Among them, Duke Pauli, associate professor in the College of Agriculture, Life and Environmental Sciences, is using AI and the world’s largest agricultural robot to study how crops respond to heat and drought. As test crops grow, a continuously moving scanner creates high-res‑ olution, 3D images of each plant. Sensors also track information such as chlorophyll properties, a proxy for plant stress levels. The data totals as much as 10 TB a day. Algorithms process data from each plant in relation to its unique genetic

makeup. Analyzing hundreds of thou‑ sands of specimens, AI can identify which genes drive desired traits to per‑ form best in various climate conditions. The Pauli Lab also developed a tool that replicates each crop digitally in virtual reality. Scientists can intuitively access troves of data and explore any plant in 3D over its lifespan and in its original spatial context. The research is powered by CyVerse, which is housed in UArizona’s Data Sci‑ ence Institute. This high-performance computing collaboration with the National Science Foundation provides data storage and tools for more than 100,000 researchers around the world.

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3D, data-rich reproductions of plants help UArizona scientists discover crops’ top performers. Emmanuel Miguel Gonzalez/School of Plant Sciences

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P erspective

Q&A with Dalal Alharthi, assistant professor in the College of Applied Science and Technology and former corporate cybersecurity engineer.

DIGITAL VIG IL AN CE

is how to strike the balance be‑ tween human and AI remediation.

AI EFFECTS IN CYBERSECURITY

Cybersecurity is always an arms race. Has AI introduced new threats? Certainly, AI has indeed introduced novel threats, not only by enabling more sophisticated and automated cy‑ berattacks, but also by creating con‑ vincingly deceptive phishing emails.

What are some of the ways AI is changing how enterprises approach cybersecurity? Today, AI is used mainly for detection. Companies are using AI-powered threat detection tools to search the internet for files that may have accidentally been made publicly accessible, leaked data and employees creating random logins using their company email and password. AI can deliver daily reports on those types of security issues.

But from both a researcher perspec‑ tive and a practitioner perspective, the more I learn about this field, the more I know that there is no way that we can be 100% secure.

One of the hot issues in industry, however, is how to use AI to automate the remediation of vulnerabilities. Having AI automatically fix security issues it detects is fast and cost efficient, but it can also break other functionalities in the process. The challenge

Social engineering – manipulating or deceiving someone – will always be the way to gain unauthorized access, so I think it’s really “the human firewall” we need to work on more. Even with the most advanced technologies, if we do not invest in employee awareness, we will never be secure.

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CISLUN AR S EC UR I T Y

MITIGATING THE DANGERS OF JUNK IN SPACE Debris from space missions has accumulated around and between the Earth and our moon since the 1950s. University of Arizona scientists have developed telescopes and algorithms to find, identify and track that junk to help protect lives and avert equipment damage in future missions. More than 100 lunar missions are planned for the coming decade. Junk in cislunar space – the zone that extends about 2.66 million miles from Earth – creates collision risks for those missions. Unimpeded by the moon’s thin atmosphere, fall‑ ing junk could also disastrously smash planned lunar bases. Despite these dangers, no group or organization has consistently tracked objects near the moon. For starters, the vastness of cislunar space defies simple description. Imagine looking for a lentil suspended in a swimming pool 30 times the size

of Earth – not a precise analogy, but not far off. Moonlight further complicates the task. “Spot‑ ting objects during a full moon is like trying to find a firefly’s faint glow next to a bright searchlight,” says UArizona Space4 Center Director Vishnu Reddy, who co-leads the project with Robert Furfaro, professor in the College of Engineering. The team developed telescopes to suppress stray light. Other technologies capture the wave‑ lengths of light bouncing off junk. Algorithms trained in the physics of space drift link that in‑ formation with other data to ultimately identify objects, deduce their origins and calculate their most likely ongoing trajectories.

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Artist’s rendering of the interior of a reflecting telescope at night. Giant Magellan Telescope – GMTO Corporation

OPTICAL IN NO VAT I O N

A FASTER PATH TO LARGE-SCALE MIRRORS With computer modeling and an innovative polishing process, the University of Arizona is creating mirrors for the Giant Magellan Tele‑ scope (GMT) with remarkable speed and accu‑ racy. When operational in 2029, GMT will be the most powerful optical telescope on Earth, able to detect Earth-like planets orbiting distant stars. Reflecting telescopes deliver views of the uni‑ verse using mirrors to gather light. While emerging technologies are expanding the ways scientists can process and analyze that light, one inalterable fact of optics remains: the larger and smoother the mirrors, the more powerful the scope. Driven by that principle, a team of UArizona scientists is creating GMT’s seven mirrors at nano‑ meter-level accuracy. Together, the mirrors will form a light-collecting area of nearly 3,700 square feet, producing images at 10 times the resolution of the Hubble Space Telescope.

As part of the project, UArizona faculty invented a technology to achieve unprece‑ dented exactness and efficiency. A balloon-like structure filled with a special fluid maintains uniform pressure and a precision fit between mirrors and polishing tools while simulation and modeling algorithms continually optimize the robotic polishing process. The resulting surfaces have smoothness devia‑ tions of less than 20 nanometers – the equivalent of a flat area the size of the U.S. with no buildings, peaks or valleys greater than 2 millimeters. In addition to optical views piercing farther into space than ever before, the team is also deliver‑ ing giant time and cost savings, trimming a process that once took nearly a year to just over 90 days.

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A CE LE STIAL COLLE CTION

BLACK HOLES BY THE MILLIONS The University of Arizona created the world’s largest library of black hole simulations to better identify and understand these phenomena. The library offers millions of simula‑ tions built on variations in how plasma around a black hole interacts with its magnetic fields. Creating them required 80 million CPU hours of crunching equa‑ tions for how matter and energy behave in extreme gravity – the equivalent to 2,000 laptops running at full tilt for a year, but condensed into two months. Black holes are arguably one of the most compelling celestial objects for inspiring future scientists, and their study expands our understanding of everyday physics and our own cosmic backyard.

Led by Chi-Kwan Chan, associate astronomer at UArizona’s Steward Ob‑ servatory, researchers used the library to learn more about Sagittarius A*, the black hole at the center of our Milky Way galaxy. Based on the closest match in the simulation library, Sagittarius A* is likely spinning and its accretion disk – the condensed gas, dust and plasma swirling around it – generates a magnetic field. That information combined with 2019 findings about the black hole at the center of the Messier 87 galaxy – the first to be directly imaged – provides further support for Einstein’s paradigmshifting theory from 1905, that spacetime is flexible and warped by the intense mass and energy pulled into black holes.

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Images created by UArizona’s Chi-kwan Chan and University of Illinois’ Ben Prather as part of Event Horizon Telescope Collaboration.

THE UNIVERSITY OF ARIZONA Established in 1885 and synonymous with academic and research excellence, the Carnegierecognized R1 University of Arizona is the state’s land grant institution. Ranked among the nation’s top 50 public universities by U.S. News & World Report and #16 for the employability of its grad‑ uates, UArizona is in the top 4% of all universities – both public and private – with more than $824 million in research and development activity in

FY22, as ranked by the National Science Founda‑ tion. The university is ranked #1 in astronomy and astrophysics, #2 in the U.S. in water resources, #5 in NASA-funded activity, and #6 in the phys‑ ical sciences. As a member of the Association of American Universities, the 71 leading U.S. public and private research universities, UArizona is recognized for advancing interdisciplinary schol‑ arship and entrepreneurial partnerships.

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Exact digital recreations of heads of lettuce grown at the University of Arizona Maricopa Agricultural Center, a 2,100-acre research farm near Phoenix. The UArizona Pauli Lab developed a tool that replicates crops in virtual reality, enabling scientists anywhere in the world to intuitively access troves of data and explore each plant in precise, high-resolution, 3D detail over its entire lifespan and in its original spatial context. Story on page 42.

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