جست‌وجوی استاد و پژوهشگر

Milad Ghasrikhouzaniمشاهده پروفایل

مدرس ارشد

• Travel Behaviour
• Transport Modelling
• Behavioural Modelling
• +۸ مورد دیگر

Milad Ghasrikhouzani serves as a Senior Lecturer at UNSW Canberra within the School of Engineering and Technology. His academic position focuses specifically on Travel Behaviour and Transport Modelling, where he contributes to both teaching and research activities in transportation systems. Dr. Ghasrikhouzani specializes in behavioral modeling, discrete choice analysis, and consumer preference research related to transport innovation adoption. His research examines how individuals and communities respond to emerging mobility solutions including autonomous vehicles (AVs), electric vehicles (EVs), and other new transport technologies. A distinctive aspect of his work investigates the role of social influence in facilitating behavioral shifts and accelerating adoption of innovations in the transport sector. His research framework considers both individual decision-making processes and broader social/systemic factors that shape travel behavior in evolving mobility landscapes. Analysis of Dr. Ghasrikhouzani's recent publication record (2022-2025) reveals consistent research output across multiple high-impact transportation journals. His work spans diverse subfields including sustainable transport consumption, demand-responsive transport systems, behavioral modeling of transportation choices, and the psychological drivers of mobility decisions. A notable trend shows increasing focus on integrated transport systems, particularly examining micromobility-public transport integration, trust in navigation technologies, and the impact of neighborhood characteristics on travel behavior. His research methodology frequently employs advanced statistical techniques including survival analysis, hybrid choice modeling, and discrete choice experiments. Dr. Ghasrikhouzani has secured research funding from significant government and industry partners including the National Housing Research Program through the Australian Housing and Urban Research Institute (AHURI), the ACT Government, the NSW Department of Premier and Cabinet, and the NSW Office of Environment and Heritage. These partnerships demonstrate the practical relevance and policy impact of his research in transportation planning and urban development contexts.

Casiano Alberto Manrique De Lara Peñateمشاهده پروفایل

استاد

• Tourism Economics
• Maritime Transport Economics
• Applied Economics
• +۷ مورد دیگر

Casiano Alberto Manrique De Lara Peñate is a Professor in the Department of Applied Economic Analysis at the Universidad de Las Palmas de Gran Canaria, School of Economics Business and Tourism. His academic work spans multiple research groups including GIR Economy Trade and Maritime Transport, Institute of Tourism and Sustainable Economic Development, and GIR Quantitative Finance and Computational Finance. His research interests focus on Tourism Economics , Maritime Transport Economics , and Applied Economic Modeling . Professor Manrique has developed innovative approaches to social accounting matrices, value-added accounting, and tax fraud detection using network analysis. His work bridges theoretical economics with practical applications in tourism, transportation, and business accounting. Professor Manrique's recent publications demonstrate consistent high-impact research in top-tier journals across tourism economics, operations research, and economic modeling. His work shows particular strength in applying quantitative methods to solve complex economic problems in tourism and maritime sectors. Among his notable contributions are: Methodologies for measuring input inefficiencies in the hotel industry Models for understanding fraud in consumption taxes using evolutionary game theory Multi-criteria frameworks for fleet deployment considering environmental impacts Innovative approaches to value-added accounting and business accounting matrices Professor Manrique maintains active research collaborations across Europe, particularly with institutions in Sweden and Germany, focusing on maritime transport optimization and tax compliance systems. His work combines rigorous economic theory with practical applications that address contemporary challenges in tourism management, sustainable transportation, and financial accountability.

Belma Turanمشاهده پروفایل

پژوهشگر

• City Logistics
• Vehicle routing problems
• Stochastic optimization
• +۱ مورد دیگر

Belma Turan is a University Assistant (post doc) at the Institute of Transport Economics and Logistics, Vienna University of Economics and Business (WU), where she has conducted research since 2018. Her work centers on optimizing urban freight systems through advanced computational methods. Her academic credentials include: PhD in Logistics and Operations Management, University of Vienna (2011-2016) Master of Business Administration, University of Vienna (2009-2011) Bachelor of Business Administration, University of Graz (2005-2009) Dr. Turan specializes in City Logistics, Vehicle Routing Problems, Stochastic Optimization, and Metaheuristics. She develops algorithmic solutions for complex urban freight challenges, particularly focusing on stochastic elements in routing and sustainability transitions. Her methodology combines theoretical optimization frameworks with real-world case studies in Vienna's urban environment. Analysis of her 7 publications (2017-2024) reveals a consistent trajectory toward sustainable urban mobility. Early work established foundations in vehicle synchronization and routing algorithms, while recent research (2022-2024) directly addresses European sustainability initiatives, electric scooter fleet management, and cargo bike logistics. Her publications demonstrate increasing interdisciplinary engagement with environmental policy and practical implementation of optimization models. No major scientific awards are publicly documented for Dr. Turan. She has secured substantial research funding through 5 projects between 2018-2023: Sustainability Initiatives (2022-2023): Deputy project lead on European urban freight sustainability Electric Scooters in Vienna (2021-2022): Principal researcher on e-scooter fleet optimization INNS'PAKET (2020-2021): Co-lead on innovative parcel delivery systems MUKE (2020-2021): Critical success factors analysis in freight logistics Sustainable Distribution System (2018-2021): Vienna-focused two-tier delivery model Dr. Turan operates within WU's Institute of Transport Economics and Logistics research ecosystem, collaborating closely with Professors Vera Hemmelmayr and Thomas Wakolbinger. Her team specializes in computational logistics, with active projects spanning optimization algorithms, sustainable mobility transitions, and urban freight policy implementation.

Natalia Chernovaمشاهده پروفایل

دانشیار

• Queueing Theory
• Probability Theory
• Mathematical Statistics
• +۳ مورد دیگر

Natalia Chernova is an Associate Professor at Novosibirsk State University, affiliated with the Department of Probability Theory and Mathematical Statistics since 2001. She earned her PhD in 1996 with a dissertation on "Ergodic theorems for complex stochastic service models" under S.G. Foss. Her academic career at NSU includes roles as assistant (1990–1997), senior lecturer (1997–2001), and associate professor since 2001. She has taught courses in probability theory and mathematical statistics across multiple faculties, including Mathematics, Economics, Natural Sciences, and Philosophy. Her research focuses on Ergodicity of queueing systems with complex structures Polling systems Non-fully accessible systems with multiple devices and call types Monotonicity properties and comparison theorems for queueing systems She has authored or co-authored numerous publications in journals such as Siberian Mathematical Journal , Problems of Information Transmission , and Operations Research Letters , alongside textbooks and educational materials. Her recent work (2012–2013) investigates stability regions of polling systems dependent on service time distributions and adaptive mechanisms. She has collaborated with scholars like Sergey Foss and Bara Kim, contributing to theoretical advancements in stochastic models and queueing networks.

Maximilian Schifferمشاهده پروفایل

دانشیار

• Operations Research
• Management Science
• Machine Learning
• +۷ مورد دیگر

Prof. Dr. Maximilian Schiffer is an Associate Professor at the Professorship of Business Analytics & Intelligent Systems , TUM School of Management , Technical University of Munich. He holds a Ph.D. in Operations Research from RWTH Aachen University (2017) and has previously served as a visiting postdoctoral scholar at Stanford University and a postdoctoral scholar at RWTH Aachen. Education: Ph.D. in Operations Research, RWTH Aachen University (2017) Maximilian’s research focuses on developing operations research and prescriptive analytics methods to address societal mobility and transportation challenges, including electric vehicles , autonomous mobility-on-demand systems , and multi-modal transportation networks . His interdisciplinary work bridges robotics , computer science , and transport engineering . Recent publications highlight advancements in autonomous fleet control , electric vehicle charging infrastructure , and collusion detection in dynamic pricing . Scientific Awards: INFORMS TSL Dissertation Prize GOR Doctoral Dissertation Prize Best Paper Award, IEEE Conference on Intelligent Transportation Systems Maximilian is an associate member of the GERAD research group and serves on the editorial board of OR Spectrum and the TSL Dissertation Prize committee . His lab, the Professorship of Business Analytics & Intelligent Systems , trains students in machine learning, optimization, and future mobility systems.

Pia Ammannمشاهده پروفایل

پژوهشگر

• Mathematical Modeling
• Optimization
• Transportation and Logistics
• +۲ مورد دیگر

Pia Ammann, M.Sc., is a research associate and doctoral candidate at the Chair of Operations Management at Technische Universität München (TUM) since 2019. She is associated with the RTG 'Advanced Optimization in a Networked Economy' (AdONE) and holds the GOR Master’s Thesis Prize 2020 and the Heureka Student Award 2019. B.Sc. and M.Sc. in Management and Technology at TUM (2012–2019) Study abroad at National Taiwan University (2016) Scholar of the Academic Training Program (Dieter Schwarz Stiftung) since 2021 Her research focuses on mathematical modeling and optimization in transportation and logistics, particularly addressing vehicle routing and scheduling challenges. She has explored synchronization constraints, automated truck platooning, and real-world multi-attribute problems using advanced heuristics like Adaptive Large Neighborhood Search. Her publications span journals like Transportation Research Part B: Methodological, arXiv working papers, and conference contributions at events such as Operations Research (2023), VeRoLog (2022), and the International Conference on Operations Research (2021). She is currently on parental leave until March 2025. GOR Master’s Thesis Prize (2020) Heureka Student Award (2019)

Issmaïl El Hallaouiمشاهده پروفایل

استاد

• Operations Research
• Optimization
• Mathematical Modeling
• +۵ مورد دیگر

Issmaïl El Hallaoui is a Full Professor in the Department of Mathematical and Industrial Engineering at Polytechnique Montréal. His research focuses on operations research, optimization, and mathematical modeling, with applications in transportation, vehicle routing, and software development. He is affiliated with the Institute for Data Valorization (IVADO) and the Research Group in Decision Analysis (GERAD). Operations Research and Management Science Combinatorial Optimization Online Optimization Vehicle Scheduling Transportation Logistics Software Development With over 116 publications, his work spans 2025 INFORMS Journal on Applied Analytics, 2024 Transportation Research Part E, and 2023 European Journal of Operational Research. His recent projects include supply chain optimization for OCP, real-time dial-a-ride problem solving, and energy system modeling via game theory. Dr. El Hallaoui has supervised 11 PhD and 3 Master's students, including Bani, A., Er Raqabi, E. M., and Tahir, A. His teaching portfolio includes courses on Integer Programming (MTH6404) and Real-time Optimization (MTH6601).

Ollivier Hyrienمشاهده پروفایل

استاد

• Statistical methods for bioassays
• Machine learning
• Dynamic modeling
• +۷ مورد دیگر

Ollivier Hyrien is an Affiliate Professor of Biostatistics at Fred Hutchinson Cancer Center, where he also holds professorships in the Biostatistics, Bioinformatics and Epidemiology Program within the Vaccine and Infectious Disease Division, and the Herbold Computational Biology Program within the Public Health Sciences Division. He is additionally a Member of the Translational Data Science Integrated Research Center (TDS IRC) at Fred Hutch. Dr. Hyrien earned his PhD in Statistics from the University of Rennes 1 in Rennes, France in 2001. His academic journey has led him to become a prominent figure in biostatistics with a focus on applying advanced statistical methods to complex biological problems. Dr. Hyrien is a biostatistician who applies advanced technologies such as machine learning and dynamic modeling to address problems in laboratory experiments and clinical studies. His research focuses on statistical methods for flow cytometry, single-cell sequencing and other bioassays. His lab specializes in developing and applying statistical methods, computational tools, and mathematical models to study the immune system and other complex biological systems, such as the microbiome, the hematopoietic system, and the central nervous system, using high-dimensional, high-throughput data. A core focus of his current research is supporting the development of vaccines and passive immunization strategies against diseases caused by pathogens such as the Human Immunodeficiency Virus (HIV), malaria, SARS-CoV-2, herpes simplex virus (HSV), as well as treatments against cancer. One exciting area of Dr. Hyrien's research is the study of the evolution of the B-cell repertoire. Understanding this process involves solving how B cells, through somatic hypermutation (SHM) and clonal selection, adapt to recognize foreign antigens and neutralize pathogens. This work involves using machine learning and statistical tools as well as stochastic processes (e.g., branching processes) to quantify and predict how SHM accumulate over time in the immunoglobulin genes and how these mutations impact the structure and binding properties of the receptors and antibodies produced by B cells. Dr. Hyrien's recent publications demonstrate a strong focus on HIV vaccine development, statistical methods for bioassays, and immune response modeling. His work spans multiple disciplines including biostatistics, immunology, virology, and computational biology, with particular emphasis on HIV broadly neutralizing antibodies, vaccine efficacy trials, and statistical modeling of biological processes. His research often involves large collaborative efforts across multiple institutions, reflecting the interdisciplinary nature of modern biomedical research. The Hyrien Lab is highly collaborative, working closely with numerous research groups across Fred Hutch and other institutions. Dr. Hyrien's work frequently involves statistical analysis for major vaccine trials, including those for HIV and COVID-19, where his expertise in biostatistics and mathematical modeling contributes significantly to understanding immune responses and vaccine efficacy.

• Mathematical Optimization
• Mixed Integer Programming
• Parallel Computing
• +۴ مورد دیگر

Dr. Yuji Shinano is a Researcher at the Zuse Institute Berlin (ZIB) in the Department of Applied Algorithmic Intelligence Methods within the Mathematical Algorithmic Intelligence division. His work focuses on mathematical optimization, particularly the development and parallelization of algorithms for solving complex optimization problems. His research interests span Mathematical Optimization, Mixed Integer Programming, Parallel Computing, Energy Systems Optimization, and Combinatorial Optimization. He has made significant contributions to the SCIP Optimization Suite, a prominent framework for solving mixed integer programming problems. His work often involves applying these optimization techniques to practical problems in energy system design and transportation networks. Dr. Shinano's publication record shows a consistent research trajectory from 2012 through 2025, with particular emphasis on parallel implementations of optimization algorithms, energy system modeling, and challenging combinatorial problems like the Quadratic Assignment Problem. His recent work (2023-2025) demonstrates continued innovation in optimization methodology, with publications on QUBO solving, parallel branch-and-bound techniques, and the latest SCIP Optimization Suite release. Among his notable projects are HPO-NAVI, BEAM-ME, and work on Linear, Integer, and Constraint Programming. His collaborations span multiple institutions, with frequent co-authorship with researchers like Thorsten Koch and Ryohei Yokoyama, particularly on energy system optimization projects. Dr. Shinano has contributed to both theoretical advances in optimization methodology and practical implementations that solve real-world problems in energy systems and transportation networks. His work bridges the gap between theoretical optimization and practical applications requiring high-performance computing solutions.

Enrique Ruiz Zunigaمشاهده پروفایل

دانشیار

• Production Engineering
• Logistics Systems
• Robotics
• +۷ مورد دیگر

Enrique Ruiz Zuniga serves as Associate Professor in Production Engineering at the University of Skövde's School of Engineering Science in Sweden, while simultaneously holding a JSPS research fellowship at Kyoto University's Systems Design Laboratory and collaborating with Japan Manned Space Systems Corporation (JAMSS). His career bridges academic research and industrial applications across Europe and Asia, focusing on optimizing complex manufacturing and logistics systems through advanced computational methods. University of Skövde, School of Engineering Science JSPS Research Fellow, Kyoto University Japan Manned Space Systems Corporation (JAMSS) collaborator Dr. Ruiz Zuniga's educational background includes a B.Eng. in industrial engineering from the University of Malaga, Spain, a BSc in automation engineering from the University of Skövde, Sweden, followed by an MSc in industrial informatics and a 2020 PhD in informatics from the University of Skövde, completed in partnership with Xylem Water Solutions Manufacturing. His doctoral research focused on facility layout design using simulation-based optimization methodologies. His primary research interests encompass the design, verification, and improvement of logistics, robotics, and complex production systems, with methodological expertise in Lean Production, Discrete-Event Simulation, System Dynamics, Simulation-Based Optimization, and the Functional Resonance Analysis Method. Dr. Ruiz Zuniga's work demonstrates a consistent focus on international collaboration and practical implementation of theoretical models in real-world industrial settings across healthcare and manufacturing sectors. Analysis of his publication record reveals an evolution from foundational work in facility layout design toward more recent explorations of AI integration, human-centered design, and resilient production systems. His research shows increasing sophistication in combining simulation approaches with functional analysis methods, with a growing emphasis on human factors and system resilience in complex production environments. REFUSE (2023-2026): Resource efficient use of reconfigurable machining systems Dynamic SALSA (2023-2024): AI scheduling for assembly and logistics systems Envisioned world problems (2021-2023): Functional approaches for system design Emergency Department Modeling (2012-2016): Healthcare production systems Dr. Ruiz Zuniga has coordinated international engineering programs in Industrial Engineering, Product Design Engineering, and Mechanical Engineering (all 60 credits), while teaching courses including Introduction to Lean Philosophy, Methods Engineering, Mechatronics/Electronics, and Production and Logistic Simulation. His work demonstrates a strong commitment to bridging theoretical research with practical industrial applications in production engineering through international collaboration and methodological innovation.

Zuzana Čičkováمشاهده پروفایل

دانشیار

• Operations Research
• Game Theory
• Econometrics
• +۲ مورد دیگر

Zuzana Čičková is an Associate Professor at the Department of Operations Research and Econometrics, Faculty of Economic Informatics, University of Economics in Bratislava. Her work bridges computational intelligence, game theory, and economic modeling, with a focus on spatial competition, logistics, and market dynamics. Academic Rank: Associate Professor Department: Operations Research and Econometrics University: University of Economics in Bratislava Her research spans game theory, vehicle routing, and econometric modeling. Recent projects include multi-criteria game theory applications in spatial allocation (2022), analysis of Arctic shipping routes (2022), and optimization of recycling networks under VEGA grants. She has contributed to algorithm design (SOMA/DE) and market concentration indicators like the Herfindahl-Hirschman Index. Her publications reflect expertise in computational intelligence for economic problems, including vehicle routing with time windows , spatial competition models , and data-driven decision support systems . While no explicit awards are listed, her leadership in VEGA projects (2017-2025) and international collaborations with institutions like Bifröst University and Óbuda University underscores her academic impact. 31 Web of Science/Scopus publications with 98 citations 8 invited lectures at international/national conferences PhD in Econometrics and Operations Research (2005) Teaching since 2001, advising 38 master’s theses She has undertaken international mobilities in Iceland (2014, 2022), Germany (2007), Czech Republic (2022), and Spain (2022), focusing on research and pedagogical innovation. Her current projects include VEGA 1/0115/23 on cooperative game theory in international relations (2023-2025).

• Combinatorial Optimisation
• Vehicle Routing Problems
• Exact Algorithms
• +۲ مورد دیگر

Dr. Thu Dang is a Lecturer in Management Science at Lancaster University, specializing in Combinatorial Optimization Problems. She is affiliated with the Department of Management Science and participates in the Data Science Institute, Lancaster Intelligent, Robotic and Autonomous Systems Centre (LIRA), and the STOR-i Centre for Doctoral Training. Her research focuses on both exact methods for solving problems to proven optimality and heuristic methods for real-life problems, particularly Vehicle Routing Problems. She completed her PhD with the STOR-i CDT, working on exact algorithms and heuristic methods for large-scale and complex Vehicle Routing Problems under the supervision of Prof. Adam Letchford and Dr. Burak Boyaci at Lancaster University in collaboration with Routeware, Inc. Dr. Dang's publication record shows consistent contributions to combinatorial optimization, with recent work spanning theoretical aspects of flowshop scheduling to practical applications in school bus routing and vehicle routing on road networks. Her research increasingly incorporates hybrid approaches combining traditional optimization techniques with reinforcement learning. She has received professional recognition through the YoungWomen4OR Prize and the Kingman Prize. Dr. Dang supervises PhD students Sebastian Caceres Gelvez (researching Optimization) and Lauren Durrell (affiliated with STOR-i Centre for Doctoral Training). She actively participates in academic conferences including the Operational Research Society's annual conference, the International Symposium on Combinatorial Optimization, and the Conference on Optimization, Modeling, Simulation and Analytics (COMOSA 2025). Her research group involvement includes the LIRA Centre's Fundamentals Optimisation team and the STOR-i Centre for Doctoral Training, reflecting her commitment to both theoretical and applied optimization research.

Banu Ulusoy-Dereliمشاهده پروفایل

پژوهشگر ارشد

• Vehicle Routing
• Urban Logistics
• Transportation Systems
• +۳ مورد دیگر

Banu Ulusoy-Dereli serves as a Research Fellow at the Technical University of Munich (TUM) School of Management within the Professorship of Business Analytics & Intelligent Systems led by Prof. Dr. Maximilian Schiffer. Her research develops optimization algorithms for urban transportation systems with direct industry applications. Education: Bachelor of Science in Industrial Engineering, Bilkent University Master's in Industrial Engineering, Koç University Research Focus: Specializing in Vehicle Routing and Urban Logistics , Banu employs Mixed Integer Programming and Metaheuristics to solve complex transportation challenges. Her work bridges theoretical optimization with real-world city logistics implementation, emphasizing scalable algorithmic solutions for large-scale problems. Publication Trends: Recent work centers on two-echelon location routing systems, integrating mobile depots and time synchronization constraints. Her publications demonstrate evolving expertise in adaptive large neighborhood search techniques for city logistics, reflecting strong alignment between theoretical methodology development and practical transportation applications. Scientific Awards: No awards documented in available sources. Advising & Grants: Current information indicates no formal student advising roles or externally funded research grants. Research Environment: Actively contributes to TUM's Professorship of Business Analytics & Intelligent Systems team, collaborating on intelligent transportation systems research within Munich's academic ecosystem while maintaining industry connections from prior professional experience.

Jordan T Gebhardtمشاهده پروفایل

دانشیار

• Swine Production Medicine
• Feed Safety
• Livestock Nutrition
• +۵ مورد دیگر

Jordan T. Gebhardt serves as an Associate Professor in Swine Production at Kansas State University's College of Veterinary Medicine, Department of Diagnostic Medicine/Pathobiology. With dual doctoral credentials (DVM and PhD) from Kansas State University complemented by a BS from Michigan State University, Dr. Gebhardt brings comprehensive expertise to swine production medicine and feed safety research. His educational background includes: BS (2014), Michigan State University DVM (2019), Kansas State University PhD (2020), Kansas State University Dr. Gebhardt's research program addresses critical challenges facing the swine industry, with particular emphasis on practical production medicine, feed safety, and nutritional strategies. His work investigates how the feed supply chain serves as a potential route for disease transmission, with specific focus on porcine epidemic diarrhea virus (PEDV) and African swine fever virus (ASFV). Through collaborations with swine nutritionists, feed scientists, and veterinarians, he develops practical solutions for swine producers and feed manufacturers. Analysis of his publication record reveals a strong focus on swine health management, feed biosecurity, and production optimization. His research spans viral transmission pathways, feed additive efficacy, nutritional interventions, and mortality reduction strategies in commercial swine operations. The collaborative nature of his work is evident through partnerships with the Kansas State Applied Swine Nutrition team and industry stakeholders. As an educator, Dr. Gebhardt teaches the graduate-level course DMP830 (Quantitative Analysis in Food Production Veterinary Medicine), serves as advisor for the KSU CVM Student Chapter of the American Association of Swine Veterinarians, and mentors veterinary and graduate students. His research activities are supported through collaborations with the university's swine research initiatives and industry partnerships focused on practical problem-solving in swine production. Dr. Gebhardt maintains an active research laboratory in 307 Coles Hall, working with a collaborative group investigating feed safety and biosecurity solutions for the swine industry. His team's work contributes to the university's Feed Safety Resources initiative, providing science-based guidance to producers and feed manufacturers.

• Information Systems
• Data Science
• Machine Learning
• +۵ مورد دیگر

Vaibhav Krishna is a Postdoctoral Associate in the Department of Social and Behavioral Sciences at the Yale School of Public Health, where his research bridges information systems, data science, and public health challenges. His work focuses on sustainable agriculture optimization, crisis informatics, and knowledge-sharing dynamics in digital communities. Education: PhD in Information Systems from ETH Zurich (2022) MS in Statistics from ETH Zurich (2017) BTech in Material Science from Indian Institute of Technology Bombay (2009) Dr. Krishna's research integrates advanced computational methods to address complex public health problems. He develops quantitative frameworks for sustainable food systems that balance environmental, nutritional, and economic constraints, with specific applications in India. His crisis informatics work analyzes real-time sensemaking during pandemics using language models, while his social computing research examines motivation dynamics in community question-answering platforms through graph-based recommendation systems. His publication record reveals three dominant research trajectories: sustainable agriculture optimization (40% of recent work), crisis-informed knowledge systems (35%), and machine learning for social computing (25%). Methodologically, he combines optimization algorithms, temporal graph analysis, and natural language processing to tackle sparse data challenges in real-world settings. His interdisciplinary approach consistently translates technical innovations into actionable public health insights.