The Academic Minute

The creation of NASA’s space shuttle years ago may still spark innovation today. Francisco Polidoro, Jr., professor of management at the University of Texas at Austin, looks at the design to learn more. Faculty Bio: Francisco Polidoro Jr. is a professor of management at The University of Texas at Austin’s McCombs School of Business. Polidoro has taught courses on technology strategy, technology transfer in the global economy, general management and strategy, and special issues in strategic management. Before working at McCombs, he taught corporate strategy and special issues in strategy at the University of Michigan Ross School of Business. Polidoro has established himself as an esteemed researcher of strategic management, having won many international awards and honors for his work on technology strategy, technology and innovation management, entrepreneurship and innovation, social networks, and corporate venture capital. His research has appeared in the most prestigious management journals, including Academy of Management Journal, Academy of Management Review, Administrative Science Quarterly, Organization Science, and Strategic Management Journal. In addition to being invited to speak in major international conferences in the field of strategic management, he has also been invited to present his research at numerous institutions in the U.S., Europe, and Asia, including Cornell University, Imperial College London, INSEAD, Johns Hopkins University, the London Business School, Seoul National University, Singapore National University, Stanford University, and the University of Pennsylvania. Along with his academic interests in strategic management, Polidoro has an extensive history in the field of biotechnology and pharmaceuticals, as well as sociology in drug discovery and development. His expertise has been noted in research media such as Lab Manager, Pharmafile, and Big Think. Before his career in research and teaching, Polidoro gained extensive managerial experience in the automotive industry, where he worked for 13 years. During that time, he had several senior management positions at Mercedes-Benz and DaimlerChrysler and a variety of assignments in the U.S., China, Europe, and Latin America. Polidoro received a Ph.D. in business administration from the University of Michigan. He also earned an MBA from Henley Business School in England, a postgraduate diploma in general management and industrial relations from Fundação Getulio Vargas in Brazil, and a diploma in French literature and civilization from the Université de Nancy II in France. Transcript: Today’s breakthrough inventions, such as the iPhone, blend interdependent features in unique ways to unleash tremendous value. But creating them is challenging. Improving one feature can worsen others, and it may not be feasible to explore all possible combinations. So, how does an organization design a complex product for which there is no template? To gain insight into this question, we studied the creation of NASA’s space shuttle. The reusable spacecraft required integrating new solutions to many different features, such as fuel composition and payload capacity. We analyzed more than 7,000 pages from archives, including books, papers, and technical documents by NASA engineers. We uncovered two interrelated processes that supported the creation of this breakthrough ––oscillation and accumulation: • With oscillation, engineers focused on getting a specific feature and then stepped back from that solution to explore alternatives. They later returned to the initial solution with new insights. For example, in early iterations, they worked with liquid hydrogen and oxygen. Later, they reverted temporarily to an older fuel, kerosene, while they made progress on other features, such as payload capacity. • With accumulation, they increased the number of features meeting expectations. For example, they gradually combined the fuel composition of early iterations with the desired payload capacity. It sounds counterintuitive, but letting go of a solution for one feature creates the space for improving others. These insights can extend to many other settings, such as pharmaceuticals, in which creating breakthroughs requires masterful combination of interdependent features. For example, a researcher might identify a new compound that targets a disease pathway but set it aside temporarily due to side effects. After resolving those issues, the researcher returns to the compound. Raja Roy of the New Jersey Institute of Technology, Minyoung Kim of The Ohio State University, and Curba Morris Lampert of Florida International University also participated in this research. Read More: [ScienceDirect] - Creating a breakthrough invention: NASA’s internal knowledge generation for the Space Shuttle

Choosing between collecting fingerprints or the DNA is often a decision crime-scene investigators must make; but why can they not get both? Carol Ritter, senior instructor in the Forensic Science Program at Cedar Crest College, says a new method may help fix this problem. Faculty Bio: Carol Ritter is a full-time senior instructor in Forensic Science Program in the Chemical, Physical, and Forensic Sciences Department at Cedar Crest College in Allentown, Pennsylvania, where she also serves as the assistant director of the college’s Joni Berner Expert Witness Training Center and Crime Scene Lab. Ritter teaches undergraduate and graduate-level courses in pattern evidence, DNA analysis, forensic biology, bloodstain pattern reconstruction and crime scene reconstruction. She began her career in 1997 with the Pennsylvania State Police Crime Laboratory, analyzing controlled substances, sexual assault, homicide cases, and bloodstain patterns. Certified by the American Board of Criminalistics, Ritter has testified over 50 times at the state and federal levels. She previously served as technical coordinator for the PSP serology section, auditing state labs and training law enforcement personnel. Since joining the Berner Center, she as mentored students and taught courses in forensic pattern analysis, DNA analysis, and crime scene reconstruction. Her applied research focuses on bloodstain patterns, firearm-related patterns, and DNA analysis, bridging laboratory science with real-world forensic practice. Cedar Crest College’s Joni Berner, Esq. ’75 Expert Witness Training Center and Crime Scene Lab provides real-world environments where first responders, legal professionals, and community partners can strengthen their skills in evidence preservation, courtroom testimony, and crime-scene investigation. The state-of-the-art facility features realistic spaces for training, practice, and collaboration, and has been designed to meet a critical justice-system need: preparing professionals to handle crime scenes and deliver courtroom testimony with confidence, accuracy, and integrity. Transcript: Anyone who has watched a crime-scene investigation on TV understands the importance of obtaining fingerprints and DNA when gathering evidence. What doesn’t often make it to the screen are some behind-the-scenes dilemmas about how to approach evidence containing both fingerprinting and DNA collection. That’s because once fingerprint analysis is complete, DNA is typically collected from fingerprints by swabbing with water —but when Super Glue is used to enhance a print, the result is an excellent fingerprint, but DNA is trapped under the glue so swabbing with water often results in little to no DNA. In my lab, we are developing a chemical reagent that will help crime-scene investigators have it both ways – they would be able to perform a fingerprint and DNA comparison to help solve crimes more effectively. DNA is considered to be the holy grail of crime-scene evidence. But that’s not always true--because, to have your DNA available in a database, usually you have to be charged with a felony. That’s not the case with fingerprints, which can be more widely available. Investigators usually get more hits or more understanding of who committed a crime based on a fingerprint database. Our research explores a new method to cut through the glue with a reagent we’ve developed, enabling you to get a full DNA profile along with the fingerprint. Choosing whether to collect DNA from a fingerprint or to just go for the print—and risk losing evidence during collection--represents a frustrating compromise for crime-scene investigators and labs. And it can limit the scope of an investigation. As a forensic investigator, do you want to analyze the fingerprint or do you want to be able to do DNA analysis? When our work is complete, investigators may no longer need to make that choice. Read More: Expert Witness Training Center Cedar Crest Forensic Science Facebook

On New York University Week: How do animals make inferences? Christine Constantinople, assistant professor of neural science, tests rats with juice rewards. Faculty Bio: Christine Constantinople is an Assistant Professor in Neural Science at NYU. After obtaining a PhD in neurobiology from Columbia University, and postdoctoral research at the Princeton Neuroscience Institute, Dr. Constantinople joined NYU in 2019. Her lab studies the mechanisms by which neural circuits compute and represent cognitive variables for decision-making. Transcript: To survive in a changing and unpredictable world, animals cannot simply react to stimuli they encounter, but must make inferences that help them predict future outcomes, like when they might encounter food or predators. This process is among the most important cognitive operations that brains perform. To study how brains perform inference, we trained laboratory rats on a task in which they revealed how much they valued different amounts of juice rewards based on how long they were willing to wait for them. The task had hidden reward states with different average rewards. In high reward states, rats were offered 3-5 drops of juice, and three drops was disappointing. But in low reward states, rats could only expect 1-3 drops of juice, so three drops was a big win. Indeed, rats waited less time for three drops in high versus low reward states. We showed that rats inferred the reward state when deciding how long to wait: for instance, they immediately changed their wait times following a single reward that revealed a state transition. We trained hundreds of rats to perform this task using a high-throughput training facility developed in my lab. The facility, which I call the “rat factory,” uses computers to train about 100rats per day. A major goal is statistical power and rigor. We found that inactivating a part of the brain called the orbitofrontal cortex or OFC made rats worse at inferring the reward state. We used state-of-the-art silicon probe technology to record electrical signals from thousands of neurons in OFC, and identified neural correlates of single trial inferences. These results show that OFC supports inference based on prior knowledge, and lay the groundwork for figuring out how the OFC interacts with other brain regions to mechanistically implement inference.

On New York University Week: Think you can match inaugural address quotes to the presidents who spoke them? Think again. Joe Salvatore, clinical professor and director of educational theatre, says it may be harder than you think. Faculty Bio: Joe Salvatore is a Clinical Professor and Director of Educational Theatre at New York University’s Steinhardt School of Culture, Education, and Human Development, where he teaches courses in ethnodrama, verbatim performance, community-engaged theatre, and new play development, and co-leads the MA in Theatre for Social and Civic Engagement. He also serves as the Vice Chair for Academic Affairs for the Department of Music and Performing Arts Professions. In 2017, Joe founded the Verbatim Performance Lab (VPL), which, under his direction, has created over 30 video and live performance projects exploring a range of political, cultural, and social topics and facilitated outreach and education programs throughout the United States. Current projects include an international research collaboration examining how interview-based verbatim performance interventions can disrupt discrimination in healthcare delivery; an ethnodrama exploring the impact of clergy sexual abuse on survivors’ spirituality and health; and an interview project examining perceptions of migration in the United States. Joe is the author of Creating Ethnodrama: A Theatrical Approach to Research (Guilford Press).. Transcript: In the Verbatim Performance Lab, we ask actors to replicate the exact speech and gestural patterns of individuals in an original recording, such as a political speech or a public hearing, to investigate how the speaker’s gender, race and ethnicity might contribute to what a listener hears. Our most recent experiment, The Inauguration Project, focused on how audiences engage with a president’s inauguration speech when the president’s identity is unknown. We took excerpts from five inaugurals since 1969 and had actors who are different-bodied than the presidents who delivered them perform them verbatim for an audience. We then invited the audience to guess which president was speaking. We conducted this performance experiment in New York City, Washington, DC, and Austin, Texas, with slight variations each time. One theme emerged: the decoupling of the president’s identity from the speech allowed the audience to hear the similarities between the presidents and their messages rather than their partisan differences. One young woman remarked, with surprise, that she would have voted for Ronald Reagan based on a passage from his second inaugural address delivered by a young Black woman. When another audience heard the same excerpt performed by a mixed-race woman, 34% believed it was Joe Biden’s address. An excerpt from Bill Clinton’s second inaugural, delivered by a white woman, was attributed to George W. Bush by 42% of another audience. One audience member was slightly irritated at the similarities exposed by the anonymity, saying she listened to the speeches differently because they lacked the personalities behind them, which was precisely the point of the experiment. When an audience hears an inaugural address in someone else’s voice and body, it prompts them to question why they perceive things the way they do. This critical engagement makes verbatim performance a powerful tool for understanding that *how* a message is delivered can be just as important as the message itself. Read More: Verbatim Performance Lab

On New York University Week: Boomeranging back to live with your parents may be necessary, but not helpful in the long term. Sewin Chan, associate professor of public policy, explores why. Faculty Bio: Sewin Chan is an economist whose research focuses on economic and financial risks faced by households as they interact with housing, labor and credit markets. She has studied mortgages and housing market risk, consumer credit behavior, pensions, work and retirement decisions, job loss, geographic mobility, and accessible housing. Transcript: When young adults boomerang, or move back to their parents, we often think of millennials living in basements. To move beyond this stereotype and get a granular look at who these young adults actually are, my colleagues and I developed a new method to identify boomerangers. We used the American Community Survey that’s conducted each year by the Census Bureau. By tapping into this enormous dataset, we could finally link personal demographics to the actual geography of where boomerangers are moving to. What we found is that for young adults who’ve already left home, the option to boomerang acts like insurance. When there’s a major life event—like a job loss, a break-up, or a new baby—the parental home can serve as a vital safety net. But as we dug into the geographic data, we found a troubling spatial trend. Usually, in a healthy economy, people move toward opportunity. If you lose your job in a struggling town, you head to a thriving city. But boomerangers often do the exact opposite. Because they’re moving back to their parents, their destination is determined by where Mom and Dad happen to live, and not by job prospects. We found that young adults are frequently staying in weaker labor markets, or moving away from higher-opportunity places towards weaker ones. And the data shows this burden isn’t shared equally. Black and Hispanic young adults, and those from lower-income backgrounds, are much more likely to boomerang back to areas with higher unemployment and lower wages. Moving home might provide immediate help—like free housing or childcare—but it can also damage their longer-term career prospects. By identifying these patterns, we’ve revealed another channel through which disadvantage can be passed down from one generation to the next. Read More: [Taylor & Francis Online] - Moving back: Spatial and demographic differences in boomeranging to parents

On New York University Week: Does discrimination affect our immune system? Emiko Kranz, PhD student in the School of Global Public Health, says yes. Faculty Bio: Emiko Kranz is a mixed Japanese American PhD student whose work draws on her background in cancer immunotherapy, ethnic studies, and community health science. As a PhD student at NYU’s School of Global Public Health, Kranz researches how discriminatory stress may impact immune health. With a particular interest in T cells, she aims to better understand how chronic discrimination can “wear down” our ability to fight off infections. Kranz is also interested in looking at how historical oppression has shaped neighborhoods, influencing the ways communities adapt to care for themselves and others. Transcript: I grew up immersed in my grandparents’ storytelling; most memorably, my Granpa’s stories of what it was like growing up during World War II. He’d often describe life in “Camp”: when he, a 7-year-old U.S.-born citizen, was incarcerated along with over 120 thousand people of Japanese descent, due to what is now recognized as discriminatory wartime hysteria. “It wasn’t bad,” he’d claim, because he held onto the Japanese value of gaman—to endure dire circumstances with dignity—but I always wondered how his body carried this gaman for the rest of his life. Because my Granpa persevered, I get to be here today—talking about my research on how experiencing discrimination impacts health down to the molecular level. Many endure discrimination in subtle, “everyday” forms: maybe due to the color of our skin, who we love, or the faith that we hold. The frequency of this “everyday discrimination” has been linked to a variety of immune health outcomes; as such, our team sought to understand how everyday discrimination may impact immune cells in particular. Specifically, we looked at two cell types: T cells and B cells, which are key to a swift and strong response to infections. In a sample of middle-aged adults, we found everyday discrimination was linked with higher amounts of what we call “exhausted” T and B cells, which can be less effective at protecting us against future health issues. This gives important insight on how minute everyday interactions may become biologically embedded, potentially changing the course of our health outcomes. It also calls attention to the need for better systems of support to buffer discriminatory stress—perhaps to help us leave gaman in the past. Read More: [NYU] - Discrimination Linked to Diminished Immune System Function [ScienceDirect] - Discrimination exposure and lymphocyte differentiation: Results from the health and retirement study

On New York University Week: How does housing affect which older adults go into a nursing home? Melissa Bergh, PhD student at the Rory Meyers College of Nursing, takes a look at three ways. Faculty Bio: Marissa Bergh, BSN, RN, is a PhD student at NYU Rory Meyers College of Nursing. Her research focuses how housing environments shape long-term care trajectories for older adults. Working as geriatric care nurse in New York City, she found her passion supporting older adults to age in their homes — regardless of if that home was a fourth-floor walk-up, a single-room occupancy unit, or a luxury high-rise. Recognizing the vast disparities within the healthcare system surrounding effective delivery of this care, she decided to pursue her doctorate degree at NYU to research how to improve the integration of housing services and long-term care. Transcript: The United States is grappling with an ongoing housing crisis that profoundly affects older adults. Today, 20% of older adults spend more than a third of their income on housing, and only 4% of U.S. housing units are considered age friendly. Without affordable, accessible housing, older adults may be unable to remain at home, making a nursing home move more likely. To understand exactly what aspects of housing play a role in whether older adults end up in a nursing home, we synthesized nearly 25 years of research from a range of disciplines, including economics, sociology, and healthcare. We found three key ways that housing shapes whether someone moves to a nursing home. First, housing costs and ownership status are key drivers of financial strain. Homeowners can leverage their home’s value to help fund community-based care, such as hiring a home health aide. Renters, by contrast, face far greater financial vulnerability: rising housing costs and limited affordable housing can price them out of their communities, making nursing home placement more likely. Second, as people age, their homes often can’t keep pace with their changing needs. When homes can’t be adapted to match declining mobility or memory issues, nursing homes may seem like the only safe alternative. Third, housing-related inequities compound risks for marginalized communities. Older adults in racially and socioeconomically marginalized communities disproportionately face housing challenges — including living in under-resourced neighborhoods, experiencing homelessness, and facing greater housing cost burdens — all of which increase their risk of nursing home placement. Our review underscores that housing is a healthcare issue. Where someone lives shapes whether they can age in their community or move to a nursing home. Read More: [National Library of Medicine] - How Housing Influences Nursing Home Utilization in the United States: An Integrative Review [McKnights] - When housing needs and skilled nursing demand intersect, where do real solutions live?

What can sororities teach us about belonging? Charlotte Hogg, professor of rhetoric and composition at Texas Christian University, delves in the question. Faculty Bio: Dr. Charlotte Hogg is a professor at Texas Christian University specializing in rhetoric and composition. She has authored, co-authored, or co-edited five books, most recently White Sororities and the Cultural Work of Belonging. Her work has also ap­peared in Inside Higher Education, The Washington Post, College English, Rhetoric Review, Peitho, and elsewhere. She teaches women’s rhetorics and literacies, creative nonfiction, and composition. Transcript: What is it about sorority life that remains so appealing? Greek-life, even with its bad press, comprises roughly 10% of the college population nationally and as high as 50% on some campuses. I researched National Panhellenic Conference (aka historically-White) sorority life behind the highly curated recruitment videos to better understand what sorority systems can teach us about how subcultures enact belonging. Belonging seeks to erase differences for insiders, leaving others as outsiders. This kind of ideological work is done rhetorically through creating a shared space of making meaning connected to cultural values. In sororities, this happens by tethering sorority practices, activities, and values to their histories. Constantly connecting the present to the past creates a lineage that can also maintain what is fraught about the system: the divide between insiders and outsiders and who has been able to be a part of that lineage. Participating in activities that emulate principles of the sorority stoke belonging such as songs, rituals, and learning about the organization’s founders and their lasting relevance across time. Repeatedly hearkening the founders, for example, suggests the ways members are tethered to one another and to the sorority whether they joined in 1851 or 2025, bypassing the fact that sororities began at a time when only privileged, White women were afforded the opportunity of a college education. This erasure occurs subtly by emphasizing positive, admirable qualities all members should carry on from the founders such as seeking knowledge, service, loyalty, and friendship. Understanding how belonging happens through rhetorical practices can teach us lessons about the double-sided coin of belonging and exclusivity, be it on college campuses, the workplace, or social media we follow.

What happens when platforms recommend opinions instead of users? Sriniwas Pandey, lecturer at the School of Computing at Binghamton University, details this. Faculty Bio: Pandey is a lecturer in the School of Computing at Binghamton University. He received his PhD degree in system science from Binghamton University. He holds bachelor's and master's degrees in computer science engineering from UTU India and IIITDMJ India, respectively. His research interests include complex systems, networks and machine learning. His research aims to comprehend the intricacies of eccentric behavior within society, focusing on identifying the underlying factors contributing to such behaviors and its impact on network dynamics. Transcript: Although social media can serve as a civil digital meeting place, pockets of users with intense opinions that clash with others that have different views has become a common occurrence. There are plenty of reasons for this, but one factor is content recommendations by the platform itself. I co-authored a study with Binghamton University Professor Hiroki Sayama that explores how these content recommendation systems affect the overall social climate on social media. We created a computer simulation of a social media platform with users connected to each other. Each user had a default set of opinions. However these users could form new opinions or be influenced by what they saw from other users. The strength of connection between users could change over time based on how similar their ideas were. We found that when the platform only recommended similar users without adjusting what opinions people saw, users broke apart into tight communities that had very different ideologies. However, when the platform recommended opinions, rather than users, there was far less network fragmentation. This worked even when people naturally preferred users similar to them. This also led to more unusual or off-center opinions. We experimented with different initial social network configurations. When people were exposed to diverse opinions, their “knowledge base” expanded, giving them more room to generate creative ideas within their community. This implies that echo chambers are less likely when people see diverse opinions. The findings drive home the importance of social media design – and how choice of recommendation strategy can impact the cohesion of the platform’s community and the extent to which users are able to express less mainstream viewpoints.

There are stark mortality differences between pregnancy and abortion. Maria Steenland, assistant professor of family science in the School of Public Health at the University of Maryland, explores this. Faculty Bio: Dr. Steenland is an Assistant Professor in Family Science in the School of Public Health at the University of Maryland, College Park. She is a health services and health policy researcher focused on maternal and reproductive health policy in the United States. Her research uses econometric methods to evaluate maternal and reproductive health programs and policies, with a particular focus on Medicaid policy. The overarching goal of her research is to identify policy options to increase the equity and quality of women’s health services. Her previous research has examined the effect of Medicaid payment policies for immediate postpartum contraception, and expansions of Medicaid eligibility in pregnant and postpartum populations. Transcript: After Roe v. Wade was overturned by the Dobbs v. Jackson Women’s Health Organization decision in June of 2022, some advocates and academics expressed concern that abortion bans would harm maternal health. They argued that banning abortion could increase maternal mortality in part because the risk of death from childbirth is much higher than the risk from abortion. Continuing a pregnancy places significant physiologic stress on the body, which can lead to life-threatening complications, such as hemorrhage, sepsis, stroke, and heart failure. A prior study, using data from 1998 through 2005, found that the risk of death from childbirth was about 14 times greater than the risk of death from abortion. In the years since that estimate, the measurement of maternal death has improved, increasing the number of pregnancy-related deaths identified annually. At the same time, abortions are taking place earlier in pregnancy, reducing the risk of complications. Given these changes, my colleagues — Kerra Mercon, Ben Brown, Marie Thoma — and I re-estimated the difference between pregnancy-related death and abortion-related death using national data. Using figures from 2018 to 2021, we calculated a range of mortality ratios. We found that the risk of death from ongoing pregnancy was 44 to 70 times greater than the risk of death from abortion. Importantly, even our most conservative estimate was still three times higher than the previously reported figure. We don’t yet know what effect abortion bans will have on overall maternal mortality in the US. However, we can say that people who are forced to continue their pregnancies because of abortion bans will face a dramatically greater risk of death than had they been able to access an abortion. Read More: [University of Maryland School of Public Health] - Study: Risk of maternal death during pregnancy greatly exceeds risk of death from abortion

How do we pull back the curtain on China’s use of AI in its military? Cole McFaul, senior research analyst at Georgetown University, explores this key question. Faculty Bio: Cole McFaul is a Senior Research Analyst and an Andrew W. Marshall Fellow at Georgetown University’s Center for Security and Emerging Technology (CSET), where he mainly focuses on emerging technology competition in the Asia-Pacific and China’s science and technology ecosystem. Prior to joining CSET, Cole researched the political economy of China’s international engagement strategies at the Center for Strategic and International Studies and the Shorenstein Asia-Pacific Research Center at Stanford University. Cole holds a B.A. in Political Science and an M.A. in East Asian Studies from Stanford University. Transcript: It’s no secret that China is using AI to modernize its military and compete with the U.S. and other rivals. However, what has remained secret is civilian firms’ involvement in these efforts and China’s fusion of commercial innovation with military power. My colleagues Sam Bresnick, Daniel Chou and I set out to answer a key question: who supplies the PLA with AI-related goods and services? Our project relies on a novel data set of 2800 AI-related contract award notices published by the PLA between January 2023 and December 2024. We define an AI-related award as any contract supporting AI-enabled or autonomous technologies, like language and vision models, unmanned vehicles, augmented and virtual reality, simulation and training environments, and smart manufacturing and robotics. From this data set, we identified 338 entities awarded two or more AI-related contracts. Using open-source information, we classified each into one of three groups: state-owned enterprises, research institutions, and nontraditional vendors (which are firms without self-reported state ownership). We found that state owned enterprises and defense-affiliated research institutions led in AI-related military procurement. Institutions like AVIC, NORINCO, the Chinese Academy of Sciences, and the Seven Sons of National Defense dominate the top of the list. But a deeper look at who supplies the PLA with AI-related goods and services reveals that a wide range of other organizations are also active. Nearly 70 percent of the entities awarded two or more AI-related contracts were nontraditional vendors. Civilian universities like Shanghai Jiao Tong, Tsinghua University, and Peking University were also awarded AI-related contracts. Our research reveals that, at least in the public procurement of AI-related goods and services, military-civil fusion is no longer aspirational—it’s operational. Unless the United States adapts to this reality, it risks facing a Chinese defense base that is more capable, adaptable, and technologically sophisticated. Read More: [CSET] - Pulling Back the Curtain on China’s Military-Civil Fusion [CSET] - Civilian Tech Is Powering China’s Military

What is Gulf War Illness and why is recognition important? Beatrice Golomb, professor of medicine at the University of California San Diego, seeks to inform. Faculty Bio: Dr. Golomb is a Professor of Medicine at UC San Diego with over 15 years of experience treating veteran patients, including veterans with Gulf War Illness (GWI). She was the inaugural Scientific Director for the Congressionally directed Research Advisory Committee on Gulf War Veterans’ Illnesses (RAC) and her research for RAND and with funding from the Department of Defense have expanded knowledge of exposure relations, mechanisms, markers and treatment for GWI. Dr. Golomb and her team remain committed to research to improve the lives and health for our heroes from the Gulf War. Transcript: For decades, Gulf War veterans have battled for recognition of the often devastating health challenges they experience as a consequence of their honorable service. Next month, we will mark an immensely important milestone for Gulf War veterans, their families, clinicians and researchers. Gulf War illness will finally receive its own International Classification of Diseases — or ICD — diagnostic code. This is more than just administrative coding. This is long-overdue validation for the suffering of the quarter-million affected veterans. It is a formal acknowledgment that Gulf War illness is real, it is physical, and it is service-related. Gulf War illness affects about one-third of the nearly 700,000 U.S. troops who served in the 1990-1991 Gulf War. It manifests as a consistent profile of symptoms: persistent fatigue, cognitive difficulties, chronic pain, respiratory issues, skin problems, gastrointestinal distress. Decades of study have linked Gulf War illness to chemical exposures and identified objective abnormalities such as structural brain changes. With this new ICD code, health care providers will be better able to recognize, diagnose, and treat Gulf War illness. Insurance, medical records, research, and public health tracking will now explicitly acknowledge the condition, rather than forcing patients to substitute related diagnoses. For researchers like me, the change accelerates our ability to study Gulf War illness in large populations, monitor treatment outcomes rigorously, and understand how this condition may overlap or interact with other diseases. To all veterans whose symptoms were dismissed and whose needs went unmet: this new diagnostic code is for you. It’s a recognition of your service. It’s a commitment to your care. And it represents hope — hope that research, medicine, and policy will now move forward more fully, more justly, to give you the answers and the support you deserve. Read More: [PNAS] - Acetylcholinesterase inhibitors and Gulf War illnesses [ScienceDirect] - Adverse effect propensity: A new feature of Gulf War illness predicted by environmental exposures [National Library of Medicine] - Mitochondrial impairment but not peripheral inflammation predicts greater Gulf War illness severity

On Old Dominion University Week: A non-invasive method for detecting an aggressive brain tumor could be key for patients. But how does it work? Khan Iftekharuddin, Professor and Eminent Scholar, delves into this. Faculty Bio: Dr. Khan Iftekharuddin is a professor and Batten Endowed Chair in Machine Learning in the department of Electrical and Computer Engineering (ECE) at Old Dominion University (ODU). He concurrently serves as a Director, ODU Vision Lab and an Inaugural Director, Institute of Data Science. Dr. Iftekharuddin has been cited among the top 2% researchers in the globe for both career-long impact and single-year impact, and his Vision Lab has consistently ranked among top teams in Global Brain Tumor Segmentation and Patient Survivability Prediction Challenges co-organized by MICCIA and NCI since 2014. Prior to his current roles, he served as an Interim Dean in Batten College of Engineering and Technology, Associate Dean for Research, Innovation and Graduate Studies, and Chair of the ECE Department at ODU. He received his MS (1991) and PhD (1995) degrees in Electrical and Computer Engineering from University of Dayton, OH. Transcript: Glioblastoma Multiforme, or GBM, is the most aggressive and deadly type of brain cancer, killing about 10,000 Americans each year and accounting for half of all brain cancer deaths in the U.S. The fast-growing cancer spreads microscopic cancer cells in surrounding healthy tissue and has an average survivability of 18-24 months from diagnosis. Prognosis for GBM is poor, with recurrence in 90% of patient cases within six to nine months, even after aggressive treatment protocol including surgery, radiation and chemotherapy. Diagnosing brain tumor recurrence on standard imaging scans like MRIs is challenging because treatment-related changes in the brain tissues, such as scar tissue, necrosis (dead tissues) and edema (swelling), often appear like recurrent tumor tissue. Currently, the only way to confirm tumor recurrence is through an invasive brain biopsy. My colleagues and I are investigating how computational modeling, AI, and machine learning methods can help distinguish true tumor recurrence from surrounding abnormal tissues, without needing to do a biopsy. This work builds on long-standing research of brain tumor volume segmentation and tracking, tumor sub-typing, and patient survivability prediction. We’re working with about half a dozen clinical collaborators across the US to analyze and process large amounts of high-resolution Magnetic Resonance imaging alongside molecular and patient clinical data. This analysis will help us develop non-invasive AI models that classify tumor recurrence and radiation-induced challenges. These tools could improve early detection, tracking, and treatment planning, helping physicians better predict the trajectory of tumor growth and tailor interventions for individual patients. Additionally, we’re working to study inherent biases in these AI models and ensure that they are representative of different patient populations. This will bolster their robustness and efficacy in clinical settings. Research Projects

On Old Dominion University Week: How fast can a viral variant spread? Hong Qin, associate professor in the School of Data Science and the Department of Computer Science at Old Dominion University, analyzes the data to find out. Faculty Bio: Hong Qin is an Associate Professor in the School of Data Science and the Department of Computer Science at Old Dominion University. His work develops AI and statistical methods for genomic surveillance, pandemic prediction, and trustworthy health AI. Transcript: Viruses evolve as they spread, and when a new viral variant begins to outcompete others, it can quickly reshape an outbreak. But measuring a variant’s advantage is tricky, because case counts and sequencing volume rise and fall for reasons unrelated to biology. A new approach called the differential population growth rate, or DPGR, focuses on comparisons instead of absolute numbers. In a given region and short time window, DPGR looks at two variants that are sampled side-by-side. It tracks the ratio of their weekly sequence counts and takes a logarithm. If that log-ratio changes roughly as a straight line, the slope estimates how much faster one variant is growing than the other. A positive slope means variant A is gaining on variant B; a negative slope means variant A is losing ground. This pairwise design makes one variant an internal control, helping reduce distortions from shifting testing, reporting, or sequencing intensity. DPGR also has an additive property: if variant A overlaps with B, and B overlaps with C, their slopes can be combined to estimate a comparison of A versus C, even when A and C rarely appear together. Using DPGR with genomic surveillance data, researchers can map how variants’ advantages change across places and over time. For example, COVID-19’s Omicron variant outpaced the Delta variant worldwide, but the estimated advantage of Omicron differed by region. DPGR can also compare sublineages and build a “fitness staircase” that summarizes stepwise gains. The result? A simple, interpretable signal that complements other epidemic models and can help anticipate which variant may dominate next. Read More: [Wiley] - A data-driven sliding-window pairwise comparative approach for the estimation of transmission fitness of SARS-CoV-2 variants and construction of the evolution fitness landscape YouTube

On Old Dominion University Week: When it’s hot out, some places are hotter than others. Maryam Golbazi, research assistant professor of climate science, examines why. Faculty Bio: Maryam Golbazi is a Research Assistant Professor at Old Dominion University working with the Joint Institute on Advanced Computing for Environmental Studies (JI-ACES). She specializes in numerical weather prediction models, atmospheric chemistry modeling, wind energy, and data assimilation. Her work integrates advanced numerical modeling with satellite and in-situ observations to improve forecasts of air pollution, wind energy resources, and extreme weather events. She is currently leveraging data science and AI/ML methods to develop localized weather models, while maintaining the rigor and integrity of established physical modeling techniques. With prior research experience at the National Center for Atmospheric Research and many collaborative projects, Dr. Golbazi’s research bridges science and application to address pressing environmental and energy challenges. She aims to leverage fundamental science and state of the art data-driven techniques to produce actionable insights that help protect communities, inform policy, and guide sustainable infrastructure planning. Transcript: On a summer afternoon in Hampton Roads, Virginia, the heat doesn’t feel the same everywhere. In some neighborhoods, the air lingers thick, heavy, slow to cool even after sunset. In others, just a few miles away, temperatures drop faster, offering relief once the sun goes down. These differences aren’t random. They’re shaped by concrete, roads, buildings, and the environment. In our study, which I conducted with my colleague Frank Liu, we used some of the highest-resolution weather simulations ever applied to a real U.S. city to understand how extreme heat behaves at the neighborhood scale. Instead of looking at cities from satellites, we zoomed in, down to city blocks, using advanced atmospheric models. During two intense heat waves in the summer of 2024, our simulations revealed that dense urban areas were, on average, up to five or six degrees hotter than nearby rural regions. And at night urban neighborhoods stayed warm far longer. But temperature was only part of the story. When we combined heat exposure with census data, a pattern emerged: lower-income communities experienced higher heat stress. And that translated directly into energy demand. That matters, because cooling isn’t free. For families already struggling with energy costs, extreme heat becomes both a health risk and a financial burden. As heat waves become longer and more intense, understanding where heat concentrates, and who pays the price, may be just as important as predicting the temperature itself. Our research shows that climate change isn’t just about rising averages. It’s about how people experience heat differently, day to day, street to street! Read More: [Springer Nature] - High-resolution modeling of extreme heat events with socioeconomic consideration: a real-case WRF–LES approach

On Old Dominion University Week: Can AI chatbots spread generalizations? Tiffany Zhu, assistant professor of global ethics and technology, examines why this could be the case. Faculty Bio: Tiffany Zhu is a philosopher. She is an Assistant Professor of Global Ethics and Technology in the Department of Philosophy and Religious Studies at Old Dominion University. Prior to joining ODU in Fall 2025, she was a Faculty Fellow in AI Ethics at the California Center for Ethics and Policy at California State Polytechnic University Pomona Transcript: AI chatbots powered by large language models are increasingly shaping our understanding of the world. Some of my research examines how they use a linguistic device called “generics,” which expresses generalizations without explicit quantification. An example of a sentence using a generic to talk about a social group would be: “Immigrants work low-wage jobs”. These statements are consequential because they can spread stereotypes. One study looking at ChatGPT 3.5 found, among other tendencies, that the chatbot often paired social generics with what I call individuation hedges, which emphasize diversity within groups. For instance, when asked “Are women more likely to get attacked while walking alone at night?,” the chatbot affirmed the trend but added that “the likelihood of getting attacked depends on an individual’s characteristics.” Not only are these hedges too formulaic to counteract unfair generalizations, they also reduce the accuracy of some responses, obscure the structural causes of social patterns including oppression, and could lead users to form false or even harmful beliefs about themselves and others. To improve their use, I proposed a strategy with three elements. First, I suggest requiring democratic and interdisciplinary guidance during the process of reinforcement learning through human feedback. Second, I advise shifting away from a transactional toward a dialogical model of AI-human interaction, meaning chatbots should probe for context and user goals and assumptions rather than simply providing answers. Finally, I suggest chatbots should use generics in conjunction with historical framing and asking counterfactual questions, in order to help AI users to think flexibly about how social reality is contingent, changeable, and sometimes unjust.

On Old Dominion University Week: Coastal resilience will be key going forward in a warming climate. George McLeod, director of the Center for Geospatial Science, Education, and Analytics, shows how virtual worlds can help us protect our own. Faculty Bio: Dr. George McLeod is the Director of the Center for Geospatial Science, Education, and Analytics at Old Dominion University and Senior Fellow with Virginia’s Commonwealth Center for Recurrent Flooding Resiliency. He oversees the creation of vital location intelligence for a wide range of academic research questions, including those focused on the intersection of the built landscape and environmental hazards. His expertise in ocean science, geovisualization, remote sensing, and UAV operations has allowed him to play an important role in advancing coastal hazards and flooding resilience research in Virginia. Transcript: When I tell people what we’re working on, the first question is almost always the same: “Okay… what exactly is a digital twin?” At its simplest, a digital twin is a virtual version of a real place or object. Most of us have seen something like this before—think about the detailed, three-dimensional cities you move through in video games. They look real, but they’re mostly just scenery. In our work on coastal resilience, the digital twin isn’t the backdrop; it’s the main character. We’re building virtual versions of real coastal communities and entire regions so we can study very real challenges: sea-level rise, storm flooding, water quality, and the public-health impacts that come with all of that. And unlike a static 3D model, these digital twins are alive. They combine physical models of land and water with models of how systems behave, things like transportation networks, population movement, and hydrology. That lets us ask big, practical questions. For example: If a Category 3 hurricane hits Virginia on top of an extra foot of sea-level rise, which neighborhoods flood first? Which roads are cut off—and for how long? How much damage might we see? Who could be displaced, and where should emergency responders focus their efforts? Our most ambitious project takes this idea even further. We’re working with colleagues at NASA to build what we call the Coastal Zone Digital Twin of the entire Chesapeake Bay. It’s a dynamic system that’s constantly updated, showing what’s happening now, what’s likely to happen next, and letting us test “what if” scenarios, so communities can prepare for the coastal future that’s already on the way.

Are you good at multitasking? Jinglu Jiang, associate professor at the School of Management at Binghamton University, reveals how this behavior may allow harmful emails to slip by. Transcript: The ability to juggle multiple tasks is a defining feature of modern work. But that constant multitasking may make people more vulnerable to phishing attacks. In a recent study, my co-authors and I examined how multitasking affects people’s ability to detect phishing emails. We conducted two online experiments with nearly one thousand participants. In both experiments, participants worked in multitasking settings. They first completed a mentally demanding primary task, like memorizing numbers or work-related information, while being interrupted with a secondary task: deciding whether incoming emails were legitimate or phishing. This setup mirrors everyday work environments, where email alerts arrive while people are focused on other tasks. We found that when the primary task placed a high demand on people’s working memory, phishing detection performance dropped substantially. However, we also identified an important countermeasure. When participants received a simple reminder that some emails might be phishing attempts, detection performance improved—even under heavy cognitive load. We also found that message design plays a role. Reminders were especially effective against phishing emails that promised rewards. By contrast, loss-framed messages—such as warnings about account suspension—tended to trigger vigilance on their own, leaving less room for reminders to add value. Together, these findings suggest that phishing defenses should account for multitasking, not assume users are fully attentive. Organizations may benefit from context-aware reminders that support attention when cognitive demands are highest and risks are most likely to go unnoticed.

The intersection of race, gender, and financial access needs further study. Carlena Ficano, professor of economics at Hartwick College, discusses why. Faculty Bio: Dr. Ficano’s areas of expertise include: labor economics, applied econometrics, social policy on low income family well-being, the economics of higher education and, most recently, local economic development. Recent courses taught include: Econometrics The Marketplace Microbes, Markets, and Food Labor Economics The Economics of Race and Gender Principles of microeconomics Her current research in collaboration with Lawrence Ogbeifun, assistant professor of economics at Hartwick College, https://www.hartwick.edu/people/lawrence-ogbeifun/ investigates barriers that women and racial minorities face in accessing small business loans. This research project engages the authors in applied macroeconomic (Ogbeifun) and microeconomic (Ficano) work that directly relates to and could be used as examples in their regular course offerings in labor economics, the economics of race and gender, principles of microeconomics, principles of macroeconomics, econometrics and macroeconomic theory. Transcript: Is it possible to quantify what is lost both by the entrepreneurs and by the larger society, when access to credit is limited by the intersection of one’s race and gender? Small businesses success is a well-recognized driver of community well-being. But not everyone seeking to secure a small-business loan is viewed equally by lenders--and differential access to credit for minority and women entrepreneurs has the potential to impose significant constraints on local and regional economies. My current research, conducted jointly with co-author Dr. Lawrence Ogbeifun, aims to shed new light on the systemic barriers faced by minority and women entrepreneurs in accessing small business loans and the broader economic consequences of this inequity. Using confidential data on credit application success over a seven-year period and building upon earlier work that examined gender-differences in lending, this current project applies an important new lens to questions of lending discrimination and its implications. By using an intersectional approach that examines race and gender, we are seeking to quantify how small business lending discrimination limits business growth and innovation, ultimately hindering overall economic development. It is our hope that this work will contribute meaningfully to the field of economics and public policy by filling a gap in the literature around the intersection of race, gender, and financial access. Only empirical evidence can shape future lending practices aimed at promoting equity in small business finance—and this topic is relevant to a wide range of stakeholders, including policymakers, financial institutions, and advocacy groups working toward inclusive economic development. This research is directly relevant to our teaching responsibilities in courses on labor economics, race and gender, and macroeconomic policy. The insights gained from this research will enhance the learning experiences of our students, encourage critical thinking, and contribute to academic discourse and hopefully drive new conversations on systemic inequities.

What do electronic medical records have to do with declining COVID mortality rates at hospitals? Indranil Bardhan, Charles and Elizabeth Prothro Regents Chair in Health Care Management at the McCombs School of Business at the University of Texas at Austin, answers this. Faculty Bio: Indranil Bardhan is the Charles and Elizabeth Prothro Regents Chair in Health Care Management in the McCombs School of Business at The University of Texas at Austin. He is a professor of management information systems and teaches courses in the MBA program as well as the M.S. program. Bardhan has a courtesy appointment as a professor in the Department of Medical Education at Dell Medical School. His research focuses on health care analytics and digital health innovation, and involves close collaboration with the University of Texas Southwestern Medical Center and Dell Med. His studies have been funded by the National Science Foundation and the UT Health system. Bardhan’s research has won 10 best paper or runner-up awards and includes more than 50 publications in premier scholarly journals. He has also served as senior editor of several prestigious journals. Bardhan holds a Ph.D. in management science and information systems from Texas McCombs. He was inducted as a distinguished fellow of the INFORMS Information Systems Society in 2019. Transcript: During the first year of the COVID-19 pandemic, there was a little-known success story in U.S. hospitals. Mortality rates from COVID cases dropped from more than 7% in April 2020 to less than 2% a year later. What explains this decline? Our research found that one mechanism is a learning effect at hospitals, which is associated with testing new treatments. Using county-level data, we found a lower rate of COVID deaths in counties where hospitals participated in clinical trials and had greater capabilities for health IT, such as using electronic medical records. Not only did counties whose hospitals had greater IT capabilities do better at treating patients several months into the pandemic, but they also learned faster. We found that the learning effect of clinical trials was enhanced by having strong IT capability. More specifically, counties with advanced record use were better able to share data with other hospitals and to learn what treatments were working or not working against COVID-19. That sharing made a life-and-death difference. • Counties with high IT ratings reduced mortality rates per capita 75% over the pandemic’s first year. That’s compared to 47% for low-rated counties. • If all hospitals had had higher levels of IT, they would have seen 20,853 fewer deaths nationwide. As with IT, participating in clinical trials helped hospitals exchange information with other institutions and contributed to solutions. We hope that our research can help policymakers and decision makers understand the value of IT a little bit better. Perhaps they can invest in the right kinds of information technology to help hospitals make faster and better decisions, which ultimately will save more lives. Read More: [Nature] - Learning from COVID-19: clinical trials, health information technology, and patient mortality