Tag: Taipei Medical University

In busy emergency departments, every minute and every medical resource matters. An AI-driven prediction model developed by Taipei Medical University may help physicians determine more efficiently whether patients need CT scans, supporting faster decision-making, reducing unnecessary imaging, and improving hospital resource use.

The model learns from real-world emergency department records, including mixed languages notes, abbreviations, and fragmented symptom descriptions. By turning unstructured medical language into practical decision support, the system demonstrates how artificial intelligence can assist frontline clinicians in complex and fast-paced care settings.

The study was led by Professor Yung-Chun Chang from the Graduate Institute of Data Science at Taipei Medical University (TMU), in collaboration with Dr. Ting-Yun Huang from the Emergency Department of Shuang-Ho Hospital.

Addressing Emergency Department Overcrowding

Emergency department overcrowding remains a critical global healthcare challenge affecting treatment efficiency, patient flow, and medical safety. Computed tomography (CT) imaging is essential for diagnosing serious and potentially life-threatening conditions, but previous studies have estimated that 20–40% of CT scans may be unnecessary. Overuse of CT imaging can increase healthcare costs, place pressure on hospital resources, and expose patients to avoidable radiation.

To address this issue, the research team developed an AI-based predictive system designed to support early clinical decision-making and optimize medical resource allocation.

Turning Clinical Language into AI Decision Support

A key innovation of this study lies in its ability to tackle the complexities of real-world emergency departments. Clinical records in Taiwanese hospitals often include mixed Chinese–English terminology, domain-specific abbreviations, and institution-specific expressions. In emergency settings, these notes are often brief, fragmented, and highly unstructured due to time constraints and urgent clinical workflows.

To overcome these challenges, the research team developed a comprehensive clinical language engineering pipeline capable of processing multilingual and highly heterogeneous medical text. The system performs automated language normalization, terminology standardization, correction of inconsistent clinical expressions, and semantic representation learning from noisy narrative data

This enables the model to extract meaningful diagnostic information from incomplete or irregular documentation and improves its robustness in real-world clinical settings.

The proposed framework integrates large-scale clinical text processing, multimodal feature fusion, prompt-based feature representation, and data augmentation using large language models. Unlike conventional prediction methods that rely mainly on structured numerical data or laboratory results, the system leverages unstructured clinical narrative text—such as chief complaints, symptom descriptions, medical history, and pain severity—as the primary source of predictive information. This design allows the model to capture subtle clinical patterns and contextual relationships often overlooked by traditional machine learning approaches.

Using 165,391 emergency department records from Shuang-Ho Hospital, the model demonstrated strong predictive performance, achieving an AUROC of 0.88. It showed high reliability in identifying patients who were unlikely to require CT imaging and outperformed traditional machine learning methods as well as existing biomedical language models.

The system was also developed with practical clinical implementation in mind. After training, the model can operate on standard hospital computing infrastructure and has the potential to be integrated with electronic health record systems, enabling real-time clinical decision support in diverse healthcare environments.

Advancing AI-Enabled Healthcare Innovation

This research highlights the importance of interdisciplinary collaboration between AI engineers and frontline clinicians. It also demonstrates the transformative potential of large language models in healthcare system optimization.

By addressing the complexities of multilingual and unstructured clinical data, the study provides a foundation for future AI-driven medical decision support systems. The research was supported by Taiwan’s National Science and Technology Council and published in Engineering Applications of Artificial Intelligence, an international journal with a 2024 impact factor of 8.0 and a ranking in the top 2.5% of the Engineering, Multidisciplinary category. These achievements reflect TMU’s growing impact in AI-enabled healthcare innovation and its commitment to advancing precision medicine and intelligent healthcare.

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• Original Article: Enhancing the Effectiveness of Emergency Department Computed Tomography Scans Using Pre-Trained Language Models
• Author Profile: Yung-Chun Chang, Professor, Graduate Institute of Data Science, College of Management

Major depressive disorder (MDD) remains one of the world’s most disabling conditions, yet finding an effective antidepressant treatment often still relies on a prolong trial-and-error process. A research team lead by Professor Syu-Jyun Peng has developed an EEG-based, machine-learning drug-response prediction system designed to help clinicians identify whether a patient is likely to respond to medication within the first weeks of treatment.

The project, “AI EEG Drug-Response Prediction System: Longitudinal Tracking and Personalized Recommendations for Depression Treatment,” received the 2025 IIP International Inventor Prize – International Innovation and Invention Elite Award, highlighting its potential contribution to precision psychiatry and personalized mental healthcare.

Turning Early EEG Signals into Actionable Predictions

Although antidepressants are widely used, patients often need to try multiple medications before finding an effective treatment. This delay can prolong symptoms, increase relapse risk, and place additional burden on patients, families, and healthcare systems.

Associate Professor Peng’s team developed the system based on a key insight: brain network activity may begin to change soon after medication starts, before clinical improvement becomes clearly visible. By capturing these early neurophysiological changes through electroencephalography, or EEG, the system aims to provide objective biomarkers that can support earlier and more individualized treatment decisions.

The study collected EEG recordings from 77 patients with major depressive disorder at baseline (Week 0) and after one week of medication (Week 1). These data were then used to predict treatment response at Weeks 4, 6, and 8 through machine-learning models combining EEG-derived features and clinical variables.

A Practical Pipeline for Real-World Clinics

The system was designed with real-world clinical application in mind. EEG is non-invasive, relatively affordable, and suitable for repeated monitoring, making it a promising tool for psychiatric care.

In the research pipeline, EEG signals are preprocessed and divided into canonical frequency bands (delta, theta, alpha, and beta) and converted into interpretable features.

The team focused on two complementary domains:

1. Power-based metrics (absolute and relative power), capturing spectral characteristics of brain activity.
2. Functional connectivity and phase synchronization metrics—including PLV, PLI, and wPLI, which measure how different brain regions coordinate with one another.

The team also computed a change index, comparing Week 1 and Week 0 EEG patterns, to identify early brain changes that may predict later treatment outcomes.

Promising Prediction Performance

The system achieved strong predictive results, with accuracy reaching 83.1% for Week 4, 73.3% for Week 6, and 80.0% for Week 8 treatment response.

Functional connectivity and phase synchronization features consistently emerged as major contributors to predictive performance. These findings support the idea that early changes in brain network dynamics may serve as meaningful biomarkers for antidepressant response.

From Research Prototype to Precision Psychiatry

The “heart” of this project was never just algorithm tuning—it was learning to build something that clinicians can confidently trust. Early on, EEG data can be complex and affected by artifacts, individual differences, and recording conditions. To address these challenges, the team refined preprocessing methods, strengthened artifact handling, and selected interpretable features with clinical relevance.

Following recognition at the 2025 IIP International Inventor Prize, the project will move toward larger-scale validation and clinical translation. Future development will focus on multi-site studies, clinician-friendly decision-support interface, multimodal data integration, and broader healthcare applications.

By helping clinicians identify likely treatment responders earlier, the system has the potential to reduce ineffective medication trials, shorten time to remission, and support more personalized depression care.

Advancing Personalized Mental Healthcare

This award-winning AI EEG drug-response prediction system represents an important step toward precision psychiatry. By combining EEG, machine learning, and longitudinal treatment tracking, Associate Professor Peng’s team aims to transform early brain responses into actionable clinical insights, which is helping patients move more quickly toward effective care.

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Original Article: Predicting the longitudinal efficacy of medication for depression using electroencephalography and machine learning

Keywords: Depression; Electroencephalography; Functional connectivity; Machine learning

Author Profile: Syu-Jyun Peng, Professor, In-Service Master Program in Artificial Intelligence in Medicine, College of Medicine

What if one of the most fatal cardiovascular emergencies could be stopped before it even begins?

A research team from Taipei Medical University (TMU) is redefining the future of cardiovascular care. In a breakthrough study published in the Journal of Nanobiotechnology, TMU researchers have introduced a new concept in cardiovascular medicine – “Biological Structural Intervention,” a pathology-tailored nanotherapy that targets the cellular roots of aortic weakening, shifting care from passive “watchful waiting” to a proactive prevention of aortic dissection (AD).

Ending the Era of “Watchful Waiting”

Aortic dissection is a life-threatening emergency where the inner layer of the aorta tears, often leading to rapid deterioration or sudden death. Currently, patients at risk are managed through hemodynamic control—lowering blood pressure to reduce stress on the wall. However, this “hemodynamic holding” does not address the underlying biological degradation, leaving many patients at risk of late-stage complications.

“Our goal was to move beyond simply managing pressure,” says Professor Chun-Che Shih, Vice Superintendent of TMU Wan-Fang Hospital. “We have developed a way to actively intervene in the biological structure of the aorta, reinforcing it at the cellular level before a rupture happens”.

The Innovation: A Biological Shield

The TMU team developed “triple-responsive” nanoparticles (MPCR NPs) that act as a precision biological shield. Rather than circulating broadly like traditional systemic drugs, these nanoparticles target Galectin-3 (Gal-3)—a protein that serves as a persistent “homing beacon” for inflammation and structural weakening.

Key features of this Biological Structural Intervention include:

• Precision Targeting: The nanoparticles achieve a selective accumulation in diseased aortic tissue, ensuring therapeutic action is concentrated exactly where the wall is failing.
• Triple-Responsive Activation: The therapy remains dormant until it senses the specific acidic pH, enzymatic activity, and oxidative stress found at the site of aortic damage.
• Multimodal Repair: Once activated, the system releases a combination of Nitric Oxide (NO) and Resveratrol (RES) to stabilize vascular muscle cells, restore the protective vessel lining, and block the enzymatic destruction of the aortic wall.

Transforming the Future of Cardiovascular Care

This research establishes a new paradigm: Active Prevention. By reinforcing the aorta’s structural integrity biologically, this technology bridges the dangerous clinical gap between daily blood pressure pills and invasive emergency surgery.

“This platform demonstrates how nanomedicine can move from simply delivering drugs to actively shaping the biological environment,” explains Professor Fwu-Long Mi. “It provides a scalable strategy for the preemptive treatment of complex vascular disorders”.

The findings mark a significant step toward a future where “precision prevention” replaces “emergency reaction,” potentially saving lives by ensuring the aorta never reaches a breaking point.

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Original Article:

Pathology-tailored nanotherapy via Galectin-3-targeted and triple-responsive nanoparticles enables multimodal therapy against aortic dissection

As high-temperature cooking methods such as roasting, grilling, and frying become increasingly common in modern diets, food scientists are paying closer attention to heat-induced contaminants that may pose health risk. While glycidyl esters (GEs) and 3-monochloropropanediol esters (3-MCPDEs) have long been associated with refined edible oils, emerging research suggests these compounds may also form directly in meat during cooking. To better understand how cooking conditions affect contaminant formation, researchers from Taipei Medical University (TMU) and the University of California, Davis (UC Davis) conducted a pioneering study investigating the relationship between heating conditions, fat composition, and lipid oxidation in meat products.

A research team led by Associate Professor Wei-Ju Lee from the School of Food Safety, College of Nutrition at TMU, in collaboration with Professor Selina C. Wang of UC Davis, has published new findings on the formation of heat-induced contaminants in meat. Their study, “Effects of oven heating on the formation of glycidyl esters and 3-monochloropropanediol esters in various meats”, has been published in the international journal Food Chemistry.

Previous studies have shown that GEs and 3-MCPDE are commonly found in refined vegetable oils and processed foods containing such oils. These compounds are considered potential carcinogenic contaminants and are primarily formed during the high-temperature deodorization stage of edible oil refining. In recent years, researchers have also discovered that endogenous fats in foods may generate GEs and 3-MCPDEs during cooking and thermal processing. Meat products, in particular, have attracted growing attention; however, the mechanism underlying the formation of these contaminants and the factors influencing their production have remained unclear.

This study is the first to establish quantitative relationships among fat composition, lipid oxidation, and contaminant formation under different heating conditions using real meat products as research samples. By employing actual meat specimens, the study overcomes the limitations of previous studies that focused on vegetable oil models and fills gaps in the literature. The research examined four commonly consumed meats with varying fat contents (pork loin, pork belly, beef belly, and chicken thigh), which were heated in a high-temperature oven to evaluate the effects of different cooking temperatures (150–300°C) and durations (10–30 minutes) on the formation of GEs and 3-MCPDEs.

The results demonstrated that contaminant concentration increased with both heating temperature and cooking duration, reaching peak levels after heating at 300°C for 30 minutes. At the same time, the meat samples experienced substantial moisture loss. The researchers also found positive correlations between fat content, lipid oxidation indicators, and the concentrations of GEs and 3-MCPDEs. In high-fat meat products, the two contaminants were also highly correlated.

These findings confirm that fat content and lipid oxidation are key factors promoting GEs and 3-MCPDEs during the thermal processing of meat products. The study provides important scientific evidence for improving meat preparation safety and offers valuable insights for the development of safer cooking practices.

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• Original Article: Effects of oven heating on the formation of glycidyl esters and 3-monochloropropanediol esters in various meats
• Keywords: Moisture content, Fat content, Fatty acid composition, Acid value, Total polar compound, Totox value
• Author Profile: Wei-Ju Lee, Associate Professor, School of Food Safety, College of Nutrition

Diabetes and obesity remain major global health challenges, and researchers are increasingly looking beyond peripheral organs such as the pancreas, liver, muscle, and adipose tissue to better understand how metabolic diseases develop and progress. The brain, particularly the hypothalamus, has emerged as a key regulatory center for energy balance, blood glucose control, and systemic metabolism. However, the neural mechanisms linking brain signaling to metabolic dysfunction are still not completely clear.

A recent study led by Assistant Professor Ya-Tin Lin at the Graduate Institute of Metabolism and Obesity Sciences, College of Nutrition, Taipei Medical University (TMU), provides new insight into this field. Her research identifies a hypothalamic neuropeptide pathway that may contribute to central insulin resistance and systemic metabolic imbalance, offering a new perspective on the relationship between the brain and metabolic disease.

Hypothalamic arcuate nucleus NPFFR2 signaling impairs central insulin sensitivity and exacerbates diabetes-related metabolic dysregulation in mice, suggesting that its activation exerts a negative modulatory effect on centrally mediated metabolic parameters.

With these research findings, Assistant Professor Lin received 2^nd Prize in the 9^th (2025) Professor Juei-Hsiung Tsai Excellent Research Award. The award-winning study, “Hypothalamic NPFFR2 attenuates central insulin signaling and its knockout diminishes metabolic dysfunction in mouse models of diabetes mellitus,” was published in the international journal Clinical Nutrition (2024 Impact factor 7.4; ranked in the top 7.1% in NUTRITION & DIETETICS).

The study focuses on neuropeptide FF (NPFF) and its receptor, neuropeptide FF receptor 2 (NPFFR2), in the hypothalamus. The findings show that this neuropeptide system plays an important role in regulating the sensitivity of neuronal insulin signaling pathways. When NPFFR2 is abnormally activated in the arcuate nucleus of the hypothalamus, central insulin signaling may be impaired, worsening insulin resistance in the diabetic brain and negatively affecting systemic glucose and lipid metabolism.

Using mouse models of diabetes, together with molecular, cellular, and physiological approaches, the research team demonstrated that deletion of NPFFR2 attenuated both central and peripheral metabolic dysfunction. These findings suggest that NPFFR2 may be a key factor in the progression of metabolic diseases such as diabetes and obesity.

The research also highlights the active role of the brain in metabolic regulation. Rather than merely passively receiving information about the body’s metabolic state, the brain serves as a central hub that helps coordinate energy balance and blood glucose control. Understanding how neural signals influence peripheral metabolism may therefore open new directions for the prevention and treatment of chronic metabolic disease.

Looking ahead, the NPFFR2 pathway may serve as a potential target for future drug development or nutritional intervention strategies. By improving central insulin resistance, such approaches could create new opportunities for managing diabetes, obesity, and related metabolic disorders.

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• Original Article: Hypothalamic NPFFR2 attenuates central insulin signaling and its knockout diminishes metabolic dysfunction in mouse models of diabetes mellitus
• Keywords: Central insulin resistance; Diabetes mellitus; Hypothalamic arcuate nucleus; Metabolic symptoms; NPFFR2; Neuropeptide FF
• Author Profile: Ya-Tin Lin, Assistant Professor, Graduate Institute of Metabolism and Obesity Sciences, College of Nutrition

From a trained pharmacist to a former drug reviewer at the U.S. Food and Drug Administration (FDA), and ultimately a leader in innovative drug development, Jane Ching-Liu Jan has dedicated more than two decades to advancing Taiwan’s biotechnology industry on the global stage.

Jane Ching-Liu Jan, Distinguished Alumna of Taipei Medical University, Chairperson of PharmaEssentia

During her tenure at the FDA, Jane was directly involved in drug evaluation and regulatory review, gaining extensive insight into global regulatory standards and clinical assessment processes. This experience later became a critical foundation for establishing PharmaEssentia and shaping its international regulatory and research strategies.

One of the company’s most significant achievements was the U.S. FDA approval of Ropeginterferon alfa-2b, the world’s first long-acting interferon of its kind for the treatment of polycythemia vera (PV). This milestone not only marked a major breakthrough for the company, but also demonstrated Taiwan’s growing capabilities in innovative drug research and global regulatory advancement.

The journey of new drug development was not without challenges. Regulatory complexities, international arbitration, and market uncertainties tested both leadership and organizational resilience. Throughout these challenges, Jane and her team remained committed to scientific rigor, long-term value creation, and patient-centered innovation.

Today, her professional journey stands as a compelling example of how expertise, global perspective, and sustained commitment can enable Taiwan’s biotechnology sector to engage confidently with the international community and contribute meaningfully to global health innovation.

For more information, please refer to the original Mandarin article:

https://bookzone.cwgv.com.tw/article/34665

Dean Nai-Wen Kuo of the College of Management at Taipei Medical University (TMU) has been named a recipient of the 2025 Distinguished Alumni Award by Johns Hopkins University (JHU), recognising his contributions to healthcare leadership, public health, and international collaboration.

The honour, one of JHU’s highest alumni distinctions, was presented during the university’s 150th anniversary celebrations in Baltimore in October 2025. It recognises individuals whose work demonstrates sustained professional excellence, public service, and global impact.

Founded in 1876, Johns Hopkins University is widely regarded as the first modern research university in the United States and continues to rank among the world’s leading institutions. In the 2026 Times Higher Education World University Rankings, it is placed 16th globally.

A graduate of JHU’s public health doctoral programme, Dean Kuo has built a career that spans academic leadership, healthcare management, and international health cooperation. He previously served as Deputy Superintendent (Administration), Taipei Medical University Hospital, and Deputy CEO of the Taipei City Hospital Administration Center, where he led system-wide initiatives in quality improvement and financial management.

At TMU, he has played a key role in advancing international engagement. During his tenure as Dean of the Office of Global Engagement (2011–2016), he expanded the university’s global academic network and established a number of cross-border collaboration and exchange programmes. He later served as Dean of the College of Public Health, strengthening partnerships with leading institutions including JHU, Yale University, and the National University of Singapore, while contributing to national health policy and research initiatives.

His work also extends to broader public health and social impact efforts, including international collaborations on epidemic preparedness and tuberculosis control in Southeast Asia, as well as nationwide initiatives in Taiwan to improve healthcare accessibility.

Reflecting on the recognition, Dean Kuo described the award as deeply meaningful, noting that his experience at Johns Hopkins shaped his professional path and inspired his long-term commitment to improving health and advancing social justice.

The recognition underscores not only his individual achievements but also the longstanding academic and public health ties between TMU and Johns Hopkins University, highlighting TMU’s continued engagement in global health, education, and research collaboration.

Summary

A recent study led by researchers at Taipei Medical University(TMU) examines how trust affects AI use in nutrition education, particularly the actual use of AI chatbots among dietetic students. The findings suggest that students are more likely to integrate AI tools into their learning when they perceive them as reliable and useful, highlighting the importance of building digital literacy and critical thinking skills alongside technical training in healthcare education.

AI in Nutrition Education: Why Trust Matters for Future Dietitians

As artificial intelligence becomes increasingly embedded in healthcare, universities face a new challenge: preparing future professionals not only to use AI tools, but to use them wisely. For students in nutrition and dietetics, this means learning how to engage with AI chatbots and digital assistants that can provide dietary information, support clinical reasoning, and streamline learning tasks.

At Taipei Medical University, researchers set out to understand a key factor behind students’ willingness to adopt these technologies: trust. While AI tools such as chatbots are widely available, not all students choose to use them in their studies or future practice. Understanding what drives real-world adoption is crucial for designing effective digital health education.

Trust as a driver of AI adoption

The study explored how dietetic students perceive and use AI chatbots as virtual nutrition assistants. Rather than focusing only on whether students had access to AI tools, the research examined the psychological and behavioral factors that shape actual usage.

The results showed that trust plays a central role. Students who believed that AI tools were reliable and helpful were significantly more likely to incorporate them into their learning. In contrast, students who were uncertain about the accuracy or appropriateness of AI-generated information tended to use these tools less, even when they were readily available.

This finding suggests that simply introducing AI into classrooms is not enough. How students perceive these tools—and whether they feel confident evaluating AI-generated information—can strongly influence whether AI becomes a meaningful part of their learning process.

Structural model showing key factors associated with students’ actual use of ChatGPT for learning and nutrition-related tasks.

Beyond technical skills: building critical digital literacy

The research highlights an important implication for medical and health professions education. Training future healthcare professionals to work with AI requires more than teaching them how to operate digital tools. Students must also develop the ability to critically assess AI outputs, understand limitations and potential biases, and integrate algorithmic suggestions with professional judgment.

In nutrition education, where advice can directly affect patient health, this balance is especially important. AI chatbots may offer rapid access to information, but students still need to evaluate whether recommendations are evidence-based and appropriate for individual patients.

Implications for AI-enabled healthcare education

As AI-supported systems become more common in hospitals and clinics, students who are comfortable working with digital tools—and who understand both their value and their limitations—will be better prepared for future practice. The study suggests that universities should place greater emphasis on fostering trust through transparent AI use, clear guidance on ethical and safe applications, and training that strengthens students’ confidence in navigating AI-supported environments.

By addressing both the technical and human dimensions of AI adoption, institutions like Taipei Medical University aim to cultivate healthcare professionals who can work alongside intelligent systems while maintaining professional responsibility and patient-centered care.

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Original Article: Trust Predicts Actual Use of AI Chatbot as a Virtual Nutrition Assistant Among Dietetic Students in Taiwan: A Path Analysis

Original Article: Trust Predicts Actual Use of AI Chatbot as a Virtual Nutrition Assistant Among Dietetic Students in Taiwan: A Path Analysis

The Global Business Fast-Track program launched by Taipei Medical University’s College of Management creates new international study pathways for students.

Taipei Medical University (TMU) continues to expand its global academic partnerships as its College of Management establishes dual degree collaborations with three internationally recognized business schools: Johns Hopkins Carey Business School, the Gies College of Business at the University of Illinois Urbana-Champaign (UIUC), and the Paul Merage School of Business at the University of California, Irvine (UC Irvine).

All three institutions are ranked among the world’s top universities in the 2026 Times Higher Education World University Rankings, with Johns Hopkins University ranked 16th, UIUC 41st, and UC Irvine 97th. With these agreements, the TMU College of Management becomes the first and currently the only management school in Taiwan to hold dual degree partnerships with three business schools from universities ranked within the global top 100.

The initiative began in 2025, when TMU signed its first dual degree agreement with UIUC’s Gies College of Business, enabling bachelor’s–master’s and master’s–master’s study pathways. The university later expanded its international network through additional agreements with UC Irvine’s Paul Merage School of Business and Johns Hopkins Carey Business School, creating broader opportunities for international academic exchange.

According to Dean Nai-Wen Kuo of the TMU College of Management, the partnerships aim to connect TMU students with leading global business education while expanding international career opportunities. Many of the partner programs are designated STEM programs in the United States, allowing international graduates to remain in the country for up to three years of Optional Practical Training (OPT) after graduation.

The dual degree collaborations also provide TMU students with a dedicated fast-track admission pathway to partner schools. Because of the established institutional partnerships, applicants from TMU are reviewed through a streamlined process rather than competing within the general global applicant pool. In some cases, admission decisions may be issued in as little as two weeks after application submission.

Recruitment brochure for the 2026 dual degree program between UIUC Gies College of Business and the TMU College of Management.

In addition, partner institutions offer several application benefits for TMU students. GRE and GMAT requirements and application fees are waived, and some partner schools also waive recommendation letter requirements. Scholarships may also be offered based on applicants’ academic qualifications.

Because the agreements are established at the school-to-school level, TMU students may apply to multiple graduate programs within each partner business school. This arrangement provides students with greater flexibility to pursue different areas of specialization and expands opportunities for interdisciplinary study.

Dean Kuo noted that global partners recognize TMU’s strengths in areas such as innovation and entrepreneurship, healthcare data analytics, research capacity, and smart healthcare management. By building partnerships with leading international business schools, TMU aims to provide students with broader interdisciplinary learning opportunities and stronger global career prospects.

Dean Ian Williamson of the UC Irvine Paul Merage School of Business and Dean Nai-Wen Kuo of the TMU College of Management at Taipei Medical University Hospital.