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Tobias Benjamin MüllerView profile

Professor

• Health Economics
• Health Policy
• Hospital Quality Assessment
• +3 more

Prof. Dr. Tobias Benjamin Müller serves as a Professor at the Bern University of Applied Sciences (BFH) within the School of Health Professions and the Institute of Health Economics and Health Policy. His academic profile demonstrates significant leadership in health economics research with direct policy implications for the Swiss healthcare system. His educational foundation includes a PhD in Health Economics from the University of Lucerne (2013-2017), participation in the International Doctoral Program in Health Economics and Policy at the Swiss School of Public Health, and a Master of Science in Economics from the University of Bern (2011-2013). Müller's research program focuses on critical healthcare efficiency questions, particularly examining hospital quality variation, low-value care prevalence, and physician decision-making processes. His methodology combines rigorous econometric analysis with emerging machine learning techniques to extract meaningful insights from complex healthcare datasets. Current projects investigate whether Swiss hospitals provide consistent quality to patients, the extent of unnecessary care in outpatient settings, and how personalized information affects medical decisions. His publication record shows a clear trajectory toward increasingly sophisticated analytical approaches, with recent work (2022-2025) emphasizing machine learning applications for risk adjustment in hospital comparisons and behavioral economics frameworks for understanding health plan choices. The consistent funding from organizations like SNSF underscores the policy relevance of his research. As project leader for multiple significant initiatives including Hospital Comparison in the Swiss Inpatient Sector and Low-Value Care in Outpatient Care, Müller directs research teams investigating fundamental questions about healthcare value and efficiency. His work directly contributes to the United Nations Sustainable Development Goals related to good health and well-being, with practical applications for healthcare policymakers seeking to optimize resource allocation.

Rui ZhangView profile

Associate Professor

• Quantitative methods
• Prescriptive analytics techniques
• Revenue management
• +5 more

Rui Zhang is an Associate Professor in the Strategy, Entrepreneurship, and Operations division at Leeds School of Business, University of Colorado Boulder. He currently serves as the Faculty Director of the Master's Program in Business Analytics and previously directed the Ph.D. Program in Operations. He is also an Associate Editor for INFORMS Journal on Computing and Networks . Dr. Zhang's research focuses on quantitative methods, particularly prescriptive analytics techniques. His work spans three main application areas: revenue management problems, last-mile delivery optimization, and influence maximization on social networks. Methodologically, he employs integer programming, network optimization, and approximate dynamic programming to develop innovative solutions to complex operational challenges. His research portfolio shows a clear progression from fundamental network optimization problems to increasingly complex applications in business analytics. Recent work emphasizes autonomous vehicle-assisted delivery systems and sophisticated influence maximization models with practical constraints like latency requirements. His publications consistently appear in top-tier operations research journals including Operations Research , Manufacturing & Service Operations Management , and multiple INFORMS publications. Runner-up for the 2022 INFORMS Computing Society (ICS) Prize Multiple Best Paper awards As Faculty Director of the MS in Business Analytics program, Dr. Zhang oversees curriculum development and student mentorship in this rapidly growing field. His Erdős number is 3 (Paul Erdős → Daniel J. Kleitman → Bruce L. Golden → Rui Zhang), reflecting connections to foundational mathematical research. His work bridges theoretical optimization with practical business applications across multiple domains including e-commerce logistics, social media marketing, and revenue management systems.

Jennifer LaneView profile

Lecturer

• Epidemiology
• Real world data
• Big data science
• +3 more

Jennifer Lane serves as a Clinical Lecturer in Trauma and Orthopaedic Surgery within the Centre for Bone and Joint Health at Queen Mary University of London's Blizard Institute, Faculty of Medicine and Dentistry. Her work bridges academic research and clinical practice, focusing on large-scale epidemiological studies of musculoskeletal conditions using real-world data from national healthcare systems. Her educational foundation includes: Undergraduate medicine at Exeter College, Oxford Masters of Surgical Education at Imperial College London (graduated with distinction) PhD in Musculoskeletal Sciences at the University of Oxford Dr. Lane's research program centers on epidemiology, real-world data science, and federated network analyses applied to hand and wrist surgery. She has established herself as a leading investigator in the epidemiology of common hand conditions, with findings directly influencing NHS clinical guidelines. Her methodological expertise in big data analytics enables innovative approaches to studying trauma patterns and surgical outcomes across diverse populations. Analysis of her recent publications (2022-2025) reveals a consistent focus on leveraging national databases for population-level surgical research, particularly examining hand trauma, hip fracture management, and the intersection of metabolic surgery with peripheral nerve conditions. Her work demonstrates sophisticated application of observational health data science to address pressing clinical questions in orthopaedics. Her scientific recognition includes: NIHR Academic Clinical Fellowship Versus Arthritis Clinical Research Fellowship MRC Doctoral Training Fellowship As an educator, Dr. Lane lectures for Oxford University's Masters in Surgical Science & Practice and DPhil in Musculoskeletal Sciences programs. Her research is supported by competitive fellowships that funded her doctoral work on hand condition epidemiology. During the COVID-19 pandemic, she contributed as a core member of an international research consortium whose findings directly influenced national treatment policies in Europe and the United States. She operates within the Centre for Bone and Joint Health's collaborative research environment at the Blizard Institute, while maintaining active involvement in international research networks focused on real-world evidence generation in surgical care.

• Internet of Things
• Business Process Management
• Model-Driven Development
• +7 more

Prof. Estefanía Serral Asensio is an Associate Professor at the Faculty of Economics and Business (FEB) at KU Leuven , with a primary affiliation to the Information Systems Engineering Research Group (LIRIS) in Brussels. She holds a highly international and interdisciplinary academic profile, having previously served as an Assistant Professor at Eindhoven University of Technology (2018), led the Semantic Knowledge Representation and Integration research group at the Technical University of Vienna (2012–2014), and contributed to the ProS Research Center at the Technical University of Valencia (until 2012). PhD in Computer Science (2011) Master in Software Engineering, Formal Methods, and Information Systems (2008) 5-year Bachelor in Computer Science (2006) Her research focuses on Internet of Things (IoT) , Business Process Management , and context-adaptive systems , with methodological expertise in Model-Driven Development , Conceptual Modeling , and ubiquitous systems . Key projects include Novel Process Mining Techniques for Discovering IoT-enhanced Business Processes (2022–2025), Novel Sustainability-Driven IoT Prescriptive Analytics for Improving Irrigation Practices in Fruit Trees (2021–2024), and foundational work on Runtime Evolution of IoT Processes (2018–2020). Her publications span top-tier venues like CAiSE , ER , SOSYM , and Internet of Things Journal . She teaches courses in ICT Strategy and Architecture , ICT Management , and Research Methodologies in Business Information Systems Engineering , contributing to academic programs at KU Leuven.

• International Business
• Technology Innovation Management
• Social Network Analysis
• +3 more

Marianna Marra is an Associate Professor in International Business and Innovation at the University of Sussex Business School. She holds a PhD from Aston University and is a Fellow of the Royal Society of Arts and the Higher Education Academy. Her research focuses on multinational knowledge networks, institutional contexts impacting MNEs, and social network analysis. She leads roles such as Director of MSc Entrepreneurship & Innovation and chairs the British Academy of Management's International Business/International Management SIG. Key achievements include grants like the 2021 Research Excellence Award and editorial roles in journals including Journal of Management Studies and International Business Review . Marianna actively supervises doctoral students exploring international business, innovation, and quantitative methods. Education: PhD from Aston University (UK), PhD from University of Salerno (Italy), visiting scholar at Rutgers University (US). Research Interests: Multinational knowledge networks, institutional analysis, social network methods, and technology innovation management. Her work bridges international business theory and practice, analyzing how institutions and networks shape MNE strategies. Recent grants include seed funding from the University of Sussex and the Society for the Advancement of Management Studies. She has organized major events like the 2023 BAM Conference and the EIBA Summer School on Research Methods in International Business. Awards: Excellence Education Award (2019), Research Excellence Awards for Emerging Scholar (2021). Grants: Over 10 grants since 2018, including BA/Leverhulme Small Research Grant (2020). Marianna contributes to academic communities through editorial boards and conference leadership, emphasizing sustainability and disruptive innovation in business practices.

Sina Moghadas KhorasaniView profile

Assistant Professor

• Innovation Management
• Information Design
• Supply Chain Management
• +5 more

Sina Moghadas Khorasani is an Assistant Professor of Operations Management and Quantitative Methods at Vanderbilt University's Owen Graduate School of Management. He holds a Ph.D. in Operations Management from the University of Utah (2019) and completed a postdoc at UC San Diego's Rady School of Management. His research focuses on innovation management, supply chain optimization, and the application of AI/business analytics to competitive environments. Education: Ph.D., Operations Management, University of Utah, 2019 M.Eng., Engineering Management, University Putra Malaysia, 2013 B.Sc., Civil Engineering, Sharif University of Technology, 2011 Research emphasizes dynamic contests, information design in innovation, and strategic competition. Notable work includes optimizing feedback mechanisms in contests and analyzing impacts of consumer cooperative mergers. Teaching evaluations consistently exceed department averages, with courses like Advanced Business Analytics (Python) and Operations Management. Awards include the 2018 Doctoral Teaching Excellence Award and recognition for his contest-screening research from INFORMS.

Shakiba EnayatiView profile

Assistant Professor

• Operations Research
• Healthcare Systems Optimization
• Supply Chain Management
• +3 more

Shakiba Enayati is an Assistant Professor at the University of Missouri-St. Louis (UMSL), specializing in Supply Chain and Analytics. She holds a Ph.D. in Operations Research from North Carolina State University and prior degrees in Industrial Engineering. Her research focuses on stochastic and dynamic systems modeling, particularly in healthcare, supply chain, and service systems, with notable contributions to vaccine distribution strategies and opioid crisis management. She previously served as an Assistant Professor at the State University of New York, Plattsburgh (2017–2020) and held roles at North Carolina State University, including Graduate Research Assistant and Instructor. Her educational background includes a B.S. (2004–2009), M.S. (2009–2012), and Ph.D. (2012–2016) in Industrial Engineering and Operations Research. Her work emphasizes optimizing complex systems to address real-world challenges, such as equitable vaccine distribution and improving emergency medical services. Research Interests: Analytic modeling and optimization of stochastic systems applied to healthcare logistics, supply chain resilience, and public health emergencies. Her recent work includes drone-based vaccine delivery systems and analyzing disparities in pandemic interventions. Enayati teaches courses in Business Analytics, Supply Chain Management, and Prescriptive Analytics. Publications span 2024 to 2017, addressing topics like ambulance service optimization, hybrid procurement strategies, and multi-modal logistics. Her research often bridges theoretical models with practical implementation to enhance decision-making in dynamic environments.

• Medical Statistics
• Public Health Interventions
• Health Economics
• +8 more

Jim Lewsey is a Professor of Medical Statistics at the University of Glasgow, affiliated with the School of Health and Wellbeing and the Health Economics & Health Technology Assessment (HEHTA) research group. He specializes in advanced statistical methodologies, including interrupted time series analysis, survival analysis, and causal inference techniques. His research focuses on evaluating public health interventions, alcohol policy, and decision analytic modeling for healthcare resource allocation. Prof. Lewsey serves on key committees such as the NIHR Public Health Research Programme Committee and previously the Chief Scientist Office Health Improvement Committee. He leads the Analysis of Linked Health Data (ALDA) program and co-leads the SHW Data Science research theme, emphasizing cutting-edge methods like synthetic controls and instrumental variable analysis. His work spans epidemiological studies on cardiovascular outcomes, diabetes treatments, and health inequalities, with a strong emphasis on translating statistical rigor into actionable public health strategies. Notable contributions include studies on the impact of alcohol minimum unit pricing, socioeconomic determinants of health, and the evaluation of natural experiments in policy contexts. Prof. Lewsey’s expertise in health economics and technology assessment informs evidence-based policy decisions, particularly in Scotland and the UK. Key achievements include his Chartered Statistician status (awarded and revalidated by the Royal Statistical Society) and contributions to over 185 publications. His research often addresses complex real-world challenges, such as optimizing healthcare resource allocation and reducing disparities in access to treatments. Current projects include evaluating policy impacts on alcohol-related harm and advancing methodologies for analyzing linked health datasets.

Bruce D. JohnsonView profile

Professor

• Cardiovascular Medicine
• Respiratory Physiology
• Exercise Physiology
• +4 more

Bruce D. Johnson, Ph.D., is a Professor of Medicine and Physiology at Mayo Clinic, holding appointments in the Department of Cardiovascular Medicine, Division of Preventive, Occupational, and Aerospace Medicine, and the Department of Physiology & Biomedical Engineering. His research focuses on heart-lung interactions in clinical and extreme environments, including chronic heart failure, exercise physiology, and wearable technology development. He leads the Mayo Clinic's Energy Balance Core Laboratory and has conducted field studies in Antarctica, high-altitude mountains, and with unique populations such as breath-hold divers and military pilots. Education: Ph.D. in Respiratory Physiology and Biodynamics from the University of Wisconsin, Madison; Fellowships in Anesthesia Research and Preventive Medicine. Research Interests: Human performance limits, environmental physiology, disease detection via noninvasive methods, and health technology innovation. His work integrates clinical research with industry partnerships to advance wearable devices for chronic disease monitoring. Notable projects include studies on high-altitude physiology funded by NSF and DOD collaborations. Grants & Projects: NIH-funded studies on pulmonary limitations in heart failure, NSF projects on Antarctic expeditions, and DOD-supported research on cardiac resynchronization therapy optimization. Dr. Johnson's 335+ peer-reviewed publications span cardiovascular medicine, respiratory physiology, and environmental health. He has received multiple NIH fellowships and awards for exceptional research contributions.

Colin G. WalshView profile

Associate Professor

• Predictive Analytics
• Clinical Informatics
• Machine Learning
• +7 more

Dr. Colin G. Walsh is an Associate Professor in the Department of Biomedical Informatics, Medicine, and Psychiatry at Vanderbilt University School of Medicine. He is a practicing internist and clinical informatician with a research focus on predictive analytics, machine learning, and natural language processing applied to mental health, suicide risk, and value-based care. He leads the Walsh Lab, mentoring trainees from diverse backgrounds. Education: Undergraduate: Mechanical Engineering, Princeton University Medical Degree: University of Chicago Residency & Chief Residency: Internal Medicine, Columbia University Medical Center Fellowship: Biomedical Informatics, Columbia University (NLM-funded) Dr. Walsh's research centers on developing clinically grounded predictive models using structured and unstructured clinical data. His work includes machine learning for mental health, utilization optimization, and value-based healthcare analytics. He focuses on translating data science into actionable clinical tools for suicide screening, risk prediction, and quality improvement. His recent publications demonstrate a strong trend in applying NLP and machine learning to EHR data for suicide risk prediction, bipolar disorder modeling, and healthcare utilization. His work spans high-impact journals in psychiatry, informatics, and genetics, often involving multi-site collaborations and real-world validation. Scientific Awards and Honors: Fellow, American College of Medical Informatics (FACMI) Fellow, American Medical Informatics Association (FAMIA) Fellow, International Academy of Health Sciences Informatics (FIAHSI) Dr. Walsh actively mentors trainees and leads projects involving NLP, predictive modeling, and clinical decision support. His lab focuses on ethical AI, algorithmic bias, and stakeholder engagement in model development. He has no indication of part-time status, retirement, or former staff designation.

• Biostatistics
• Statistical Methodology
• Epidemiology
• +6 more

Hegang Chen, PhD, serves as Professor in the Department of Epidemiology and Public Health at the University of Maryland School of Medicine. He is a lead statistician for the General Clinical Research Center (GCRC) and member of the Hormone Responsive Cancers Program within the Marlene and Stewart Greenebaum Cancer Center, with extensive collaborative experience in clinical trials and epidemiological research since joining in 2002. His academic credentials include: Ph.D. in Statistics from the University of Illinois M.S. in Mathematics from the University of Mississippi Dr. Chen's research centers on statistical methodology development—including optimal experimental design, generalized mixed linear models, and machine learning applications for molecular biology and real-time clinical decision support (e.g., predicting blood transfusion needs)—and biomedical collaborations spanning cancer, infectious diseases, trauma, public health, and pharmacogenomics. His work has been published in premier journals like Nature, JAMA, and Annals of Statistics. Analysis of his recent publications (2020-2025) reveals a dominant focus on predictive analytics for traumatic brain injury outcomes, blood transfusion prediction, and lung cancer diagnosis using real-time physiological monitoring and machine learning. This trend demonstrates a translational shift toward operationalizing statistical methods in critical care decision support systems. No scientific awards were mentioned in the provided text. While no specific advisees are listed, Dr. Chen's leadership in the GCRC and extensive collaborative network across trauma, oncology, and global health indicate active mentorship within multidisciplinary teams. His grant involvement is evidenced by NIH-funded GCRC work and high-impact publications in clinical domains. Dr. Chen maintains key affiliations with the General Clinical Research Center as lead statistician and the Hormone Responsive Cancers Program, where he integrates advanced statistical methodologies into translational cancer research and clinical trial design.

• Causal Inference
• Substance Use Disorders
• Public Health
• +3 more

Kara Rudolph is an Assistant Professor of Epidemiology at the Mailman School of Public Health, Columbia University. Her research focuses on causal inference methods to address substance use disorders, public health policy, and environmental health impacts, with affiliations to the Health Analytics Center. Role: Assistant Professor (Epidemiology) University: Columbia University School: Mailman School of Public Health Affiliation: Health Analytics Her work spans causal inference, substance use disorders, and health policy, emphasizing methodological rigor and real-world applications. Recent publications analyze: Effects of policies (e.g., housing vouchers, cannabis laws, opioid regulations) Environmental exposures (e.g., air pollution, unconventional gas development) Methodological advancements (e.g., Riesz representers, SWIGs, mediation analysis) Health disparities (e.g., racial/ethnic differences, underrepresentation in trials) She actively collaborates on Medicaid patient outcomes and longitudinal studies of complex exposures. Her email is kr2854@cumc.columbia.edu.

Bianca Maria ColosimoView profile

Professor

• Metal Additive Manufacturing
• Statistical Process Control
• Image Data Analysis
• +3 more

Prof. Bianca Maria Colosimo is a faculty member at Politecnico di Milano in the Department of Mechanical Engineering. Her research focuses on in-situ sensing and monitoring of metal additive manufacturing (AM) processes, particularly Laser Powder Bed Fusion. She develops statistical data mining techniques for defect detection and process control, integrating big data streams into quality assurance systems. Her work aims to advance smart AM technologies through real-time monitoring and digital twin methodologies. Key research interests include: Metal Additive Manufacturing Statistical Process Control Image Data Analysis Laser Powder Bed Fusion In-situ Monitoring Smart Manufacturing Systems Publications highlight her contributions to layerwise imaging , plume signature analysis , and spatial statistical modeling for AM processes. Contact: Department of Mechanical Engineering, Politecnico di Milano.

Dana MukamelView profile

Professor

• Healthcare Policy
• Geriatrics
• Mental Health
• +4 more

Dana Mukamel is a Professor of Medicine at the University of California, Irvine, with an affiliation in the Department of Population Health & Disease Prevention within the School of Public Health. Her research focuses on healthcare policy, geriatrics, mental health, and chronic disease management, particularly advanced chronic kidney disease (CKD) and cancer care. She explores topics such as Medicare Part D cost disparities, nursing home staffing and quality, digital mental health interventions, and rural-urban healthcare gaps. Dana's recent publications highlight trends in digital health adoption during the pandemic, staffing instability in nursing homes, and CKD treatment outcomes. Her work often intersects with health equity, examining disparities in rural and minority populations, and innovates through technology-driven care coordination models for seniors. She contributes to policy evaluations, including the Medicare Prescription Payment Plan, Certificate of Need regulations, and Medicaid waiver programs. Her projects frequently involve multi-site collaborations and stakeholder engagement, especially with peer perspectives in mental health interventions and county-level healthcare systems.

Raik StolletzView profile

Professor

• Operations Management
• Business Analytics
• Quantitative Decision Analysis
• +8 more

Prof. Dr. Raik Stolletz is a Professor of Production Management at the Business School of the University of Mannheim, leading the Chair of Production Management within the Area Operations Management. His research focuses on business analytics, quantitative decision support for manufacturing and service systems, and stochastic modeling for operations optimization. Area Operations Management, Business School, University of Mannheim Research Interests : Stolletz specializes in predictive and prescriptive business analytics, with applications in Industry 4.0, airport operations, automotive production, workforce planning, and distribution center management. His work emphasizes tools for decision-making in deterministic and stochastic environments. Research Trends : His recent publications address time-dependent queueing systems, buffer allocation in stochastic flow lines, assembly line balancing, and revenue management in production. Keywords span Operations Management, Supply Chain Analytics, and Industrial Engineering, with sub-fields including Stochastic Optimization, Dynamic Scheduling, and Service Systems. Grants & Collaborations : Projects are funded by industry partners, the European Union, and the German Science Foundation (DFG), focusing on automotive production networks, airport logistics, and manufacturing system optimization.