جست‌وجوی استاد و پژوهشگر

Marta Ariasمشاهده پروفایل

دانشیار

• Machine Learning
• Data Mining
• Design and Analysis of Algorithms
• +۳ مورد دیگر

Marta Arias is an Associate Professor in the Computer Science Department at Universitat Politècnica de Catalunya (UPC), Barcelona, and a member of the LARCA research group. She has held academic positions since 2007, including roles at Columbia University and the University of Edinburgh. Her research focuses on Machine Learning, Data Mining, Algorithm Design, and Logic in Computer Science. She teaches advanced courses in Machine Learning, Information Retrieval, and Complex Networks at undergraduate and master's levels, including the Erasmus Mundus program in Data Mining and Knowledge Management. Educations: PhD in Computer Science from Tufts University (2000-2004), Postgraduate Studies at University of Edinburgh (1999-2000), B.Sc. in Computer Science from UPC (1992-1997). Professional experience includes software engineering and research roles in New York and Barcelona. Research highlights include developing algorithms for real-time ranking systems, Twitter-based financial forecasting, and causal network analysis. Notable contributions include the 'Best Paper Award' at AIPESW@ECAI 2020 and a 3rd place award in football performance research. Her work spans theoretical foundations (e.g., Horn clause learning) and applied domains like cybersecurity, healthcare analytics, and energy systems. Teaching responsibilities include labs and lectures on Machine Learning, Information Retrieval, and Programming, with active involvement in curriculum design and pedagogical innovation. Ongoing projects include enterprise risk analysis, synthetic data generation, and network modeling.

Elnaz Kabirمشاهده پروفایل

استادیار

• Resilient Energy Systems
• Clean Energy Transition
• Power-Outage Management
• +۳ مورد دیگر

Elnaz Kabir is an Assistant Professor at Texas A&M University’s Department of Engineering Technology & Industrial Distribution (ETID), located within the College of Engineering. Her research focuses on developing data-driven frameworks to enhance critical infrastructure resilience, particularly in power systems. She holds a Ph.D. in Industrial and Operations Engineering from the University of Michigan, an M.Sc. in Statistics, and a B.Sc. in Industrial Engineering from K. N. Toosi University of Technology. Education: Ph.D., Industrial & Operations Engineering, University of Michigan – 2021 M.Sc., Statistics, University of Michigan – 2017 B.Sc., Industrial Engineering, K. N. Toosi University of Technology – 2011 Research Interests: Kabir’s work emphasizes resilient energy systems, clean energy transition, and disaster operations management. She employs predictive analytics, machine learning, and stochastic optimization to address challenges like power outage management and energy system decarbonization. Her projects often involve collaboration with industry partners such as American Electric Power and Southern Company. Key Contributions: Her research spans outage prediction frameworks, climate-technological grid vulnerability, and supply chain optimization. Notable achievements include a 2016 Best Poster Award from the Society for Risk Analysis for her work on tree failures during storms. Teaching: Kabir teaches Analytics for Distribution Operations (IDIS 450) and previously taught Decision Analysis (IOE 460) at the University of Michigan. She is actively involved in mentoring students and fostering interdisciplinary collaborations.

Lynn A. Darbyمشاهده پروفایل

استاد

• Exercise Physiology
• Weight Management
• Athletic Performance
• +۳ مورد دیگر

Lynn A. Darby, Ph.D., is a Professor and Kinesiology Program Coordinator at Bowling Green State University's School of Applied Human Development. She holds advanced degrees in Exercise Physiology from The Ohio State University and completed a post-doctoral fellowship in Physiology at St. Louis University. Her research focuses on functional movement testing for athletes, older adults, and golfers, alongside exercise interventions for weight loss and well-being. Dr. Darby is certified by the American College of Sports Medicine and holds specialized certifications in Golf Fitness and Functional Movement Systems. Her teaching spans undergraduate courses like Exercise Testing and Prescription, and graduate courses such as Physiology of Aging and Research Methods. Her work frequently explores the biomechanical and physiological impacts of exercise, particularly in high-impact sports and senior populations. Recent studies highlight functional movement screening for injury prediction, mood regulation through exercise, and the efficacy of weight loss programs. Dr. Darby’s contributions include over 40 peer-reviewed articles, emphasizing practical applications of exercise science in athletics and aging populations. Her research trends underscore interdisciplinary approaches, blending biomechanics, psychology, and clinical outcomes to optimize human performance and health.

Brent Foxمشاهده پروفایل

استاد

• Pharmacy Informatics
• Health Information Technology
• Medication Management
• +۳ مورد دیگر

Dr. Brent Fox is a Professor at Auburn University's Harrison College of Pharmacy, where he also serves as Special Assistant to the Dean and Director of Student Affairs . He earned all his degrees (BS, PharmD, PhD) at Auburn and has been an integral member of the faculty since 2006. His work spans academic leadership, teaching, and research in pharmacy informatics. Education : B.S. in Pharmacy, PharmD, and Ph.D. in Pharmaceutical Sciences from Auburn University. Dr. Fox's research focuses on pharmacy informatics education and health information technology (HIT) in clinical practice, particularly medication management. He pioneered work on integrating informatics into pharmacy curricula and explored mHealth applications to support health outcomes. His recent work addresses the opioid epidemic through prescription drug monitoring programs (PDMPs) and machine learning for adverse drug events. His scholarly contributions include over 300 presentations/posters, two published informatics texts, and collaborative research projects funded by NIH and FDA. Key funded initiatives include machine learning for adverse drug event detection and comparative surveillance of generic drugs. Scientific Awards : ASHP Distinguished Service Award (2012), Young Alumni Achievement Award (2012), Excellence in Outreach Award (2017), ASHP Fellow. Dr. Fox has advised six PhD students and three Master's candidates while teaching courses in pharmacy foundations, practice management, research methods, and graduate seminars. His leadership extends to founding the Auburn University Center for Opioid Research, Education, and Outreach and chairing the ASHP Section of Pharmacy Informatics and Technology.

• Data Analytics
• Distributed Systems
• Query Optimization
• +۵ مورد دیگر

Jorge-Arnulfo Quiané-Ruiz is a researcher at Qatar Computing Research Institute , with affiliations at institutions like Technical University of Berlin and University of Nantes (PhD, 2008). His work focuses on data analytics , distributed systems , and query optimization , particularly in cross-platform environments and trusted execution frameworks . Research Interests : Data analytics across heterogeneous platforms Secure and compliant data processing in trusted environments Efficient query allocation and optimization Scalable algorithms for big data and streaming graphs Machine learning in data management Table extraction and data cleaning Publications : Recent work includes benchmarks for TEE-based joins and cross-database query frameworks (2023). Explores space-efficient graph algorithms and ML-driven revenue optimization (2022). Developed RHEEMix , a cost-based optimizer for cross-platform systems (2018). Collaborations : Regularly partners with Volker Markl and Zoi Kaoudi on unified analytics frameworks. Contributed to projects like Apache Wayang and AgoRA .

• Business Process Management
• Process Mining
• Business Process Simulation
• +۴ مورد دیگر

Marlon Dumas is a leading researcher in business process management and process mining at the University of Tartu, Estonia. With over 467 publications spanning from 1997 to 2025, his work has significantly advanced methodologies in business process analysis, simulation, and optimization. His research bridges theoretical foundations with practical applications, developing tools and frameworks that enable organizations to analyze and optimize operational processes. Dumas's primary research interests include business process management, process mining, business process simulation, prescriptive process monitoring, and data-aware business processes. He has pioneered methods for modeling resource availability, activity delays, and waiting times in business processes. His work on prescriptive process monitoring addresses critical challenges such as resource constraints, uncertainty in predictions, and causal effect estimation for interventions. Recent publications reveal a strong trend toward integrating artificial intelligence with business process management, particularly exploring the application of large language models to process optimization, monitoring, and redesign tasks. His research demonstrates consistent innovation, with publications appearing in top venues including Information Systems, Data & Knowledge Engineering, and the International Conference on Business Process Management. Dumas has developed several influential tools including SIMOD for automated discovery of business process simulation models, Optimos for simulation-driven process optimization, and Kairos for prescriptive monitoring. His collaborative network is extensive, featuring frequent co-authorship with prominent researchers including Marcello La Rosa, Luciano García-Bañuelos, Fabrizio Maria Maggi, and Wil M. P. van der Aalst. His work on privacy-preserving process mining, particularly regarding differentially private release of event logs, addresses critical challenges in applying process mining techniques while maintaining data privacy and compliance with regulations like GDPR. Dumas's research continues to push boundaries, with recent work exploring the integration of large language models with business process management systems, suggesting an ongoing commitment to advancing the field through innovative applications of emerging technologies.

Dace Reihmaneمشاهده پروفایل

دانشیار

• Sports Science
• Exercise Physiology
• Biochemistry
• +۳ مورد دیگر

Associate Professor Dace Reihmane is a prominent academic staff member in the Department of Human Physiology and Biochemistry at Rīga Stradiņš University (RSU), where she serves as the leader of the study course 'Medical Biochemistry.' She also holds the position of Acting Lead Researcher at the Laboratory of Sports and Nutrition Research. Dr. Reihmane maintains her office at RSU's Main Building (Room A-606) located at 16 Dzirciema iela, Rīga, LV-1007. Dr. Reihmane's research focuses primarily on sports science and exercise physiology, with special emphasis on exercise-induced changes in various biomarkers through invasive human studies. Her work bridges biochemistry, physical activity, and child health, making significant contributions to understanding how exercise affects physiological processes in young populations. Her research portfolio demonstrates a clear trajectory toward advancing sports science methodology and application in Latvia. Dr. Reihmane leads several major research initiatives including the national research program 'Innovations, methodologies and recommendations for the development and management of the sports sector in Latvia' (VPP-IZM-Sports-2023/1-0001), the EXPOSE research grant, and the TCReX project. She previously directed the University of Latvia's collaboration with the Latvian Olympic Committee on the Physical Activity and Children's Overall Health Study (PACH study). As a member of the scientific committee of the European College of Sport Science, Dr. Reihmane contributes to the broader international sports science community. Her methodological expertise was enhanced through participation in the Interdisciplinary IT Learning program at the University at Buffalo, State University of New York, where she gained advanced data analysis skills applicable to her research in sports science. Dr. Reihmane's work shows increasing emphasis on developing research methods and knowledge bases for health indicators in children and youth involved in sports, with particular attention to how these relate to training and competition processes. Her leadership in national research programs indicates her growing influence in shaping sports science policy and practice in Latvia.

• Nonlinear Analysis
• Partial Differential Equations
• Mathematical Physics
• +۳ مورد دیگر

Yanyan Li is a Distinguished Professor of Mathematics at Rutgers, The State University of New Jersey, affiliated with the Department of Mathematics. His research focuses on nonlinear analysis, partial differential equations (PDEs), and their applications to fluid dynamics, geometric analysis, and conformal geometry. He has contributed extensively to topics including Navier-Stokes equations, Monge-Ampère equations, and the Nirenberg problem. His work bridges theoretical mathematics and applied problems, such as conductivity in composite materials and microRNA's role in glioma cell behavior. Recent research emphasizes singularity analysis in PDEs and the development of comparison principles for degenerate elliptic equations. Notable contributions include studies on compactness theorems for conformal metrics and the classification of axisymmetric solutions to Navier-Stokes equations. Prof. Li’s articles frequently address boundary value problems, geometric PDEs, and the interplay between nonlinear operators and physical phenomena. His methodologies span variational principles, viscosity solutions, and geometric techniques, reflecting a synthesis of analytical rigor and interdisciplinary application. While no specific awards are listed here, his tenure as a Distinguished Professor underscores his scholarly impact. He advises students in mathematical analysis and related fields, though explicit student listings are not provided in the source text.

Christina Lindemannمشاهده پروفایل

پژوهشگر ارشد

• Psychiatry
• Addiction Medicine
• Health Services Research
• +۳ مورد دیگر

Christina Lindemann is a Research Fellow and Project Coordinator at the University Medical Center Hamburg-Eppendorf (UKE) , affiliated with the Department of Medical Psychology within the Faculty of Medicine . Her work focuses on addiction medicine, healthcare services research, and epidemiology of substance use disorders. She leads projects on alcohol and benzodiazepine dependence, guideline implementation, and healthcare utilization patterns. Her research emphasizes improving diagnostic and treatment practices for alcohol-related disorders, particularly through evidence-based screening tools and policy interventions. Lindemann collaborates with national and international teams to analyze large healthcare datasets, contributing to studies on substance use prevention, injury risk reduction in ADHD populations, and healthcare system optimization. Her work bridges clinical practice and public health, addressing gaps in patient care and policy. Lindemann has coordinated initiatives such as the Hamburger Netzwerk für Versorgungsforschung, evaluating healthcare innovations and their practical implementation. She frequently publishes in journals like Frontiers in Psychiatry , Sucht , and Journal of Clinical Psychopharmacology , focusing on methodological rigor and real-world applicability of findings.

GNEKPE Christianمشاهده پروفایل

دانشیار

• Innovation Strategy
• Technology Management
• Circular Economy
• +۴ مورد دیگر

Dr. GNEKPE Christian is an Associate Professor in the Department of Strategy, Entrepreneurship and Innovation at Toulouse Business School. His work bridges strategic management, technology innovation, and sustainability, with a focus on intellectual property strategies, circular economy adoption, and AI applications in sustainable infrastructure. He holds a PhD in Management and has extensive international research collaborations. Research Interests: His core areas include innovation strategy, technology management in cross-border contexts, and leveraging AI for energy efficiency. He explores how firms balance internal/external knowledge sourcing in biopharmaceutical and circular economy sectors, and the role of patent portfolios in shaping M&A equity stakes. Publications: Recent work (2023-2024) highlights AI-driven building retrofit predictions, regulatory influences on circular innovations, and SME leadership adaptations. Earlier studies (2016-2018) analyzed patent strategies' impact on global acquisitions and the dark side of inter-organizational relationships. Awards: Not listed in available materials. Advising & Grants: No specific advisees or grant details provided in current data. His research is typically funded through institutional and collaborative grants.

• Cardiovascular Diseases
• Comparative Effectiveness Research
• Data Science
• +۳ مورد دیگر

Dr. Chungsoo Kim is a Research Fellow at Yale School of Medicine, affiliated with the Section of Cardiovascular Medicine and the Yale/YNHH Center for Outcomes Research and Evaluation (CORE). He holds a PharmD from Ajou University College of Pharmacy and a PhD in Biomedical Informatics from Ajou University Graduate School of Medicine. His research focuses on leveraging real-world data and AI methodologies to improve healthcare outcomes, including drug safety evaluations, comparative effectiveness analyses, and clinical decision-support systems. His work spans cardiovascular diseases, pharmacotherapy optimization, and public health informatics. Dr. Kim collaborates internationally on observational studies involving multi-country databases to address gaps in medication use, disparities in treatment uptake, and post-acute sequelae of diseases like COVID-19. Education: PharmD, Ajou University College of Pharmacy, 2019 PhD in Biomedical Informatics, Ajou University Graduate School of Medicine, 2024 Research Interests: Dr. Kim's work emphasizes real-world evidence generation through large-scale health data. Key areas include: Drug safety and comparative effectiveness using EHRs and claims data AI-driven predictive modeling for healthcare decision-making Pharmacoepidemiology and regulatory science Health disparities in medication access and outcomes Post-acute care and long-term outcomes of infectious diseases Research Trends in Articles: His publications highlight a focus on translational research using observational data to address critical clinical questions, such as optimizing antihyperglycemic therapies, evaluating cardiovascular risks of medications, and analyzing disparities in obesity treatment. Recent work includes methodological advancements in phenotypic prediction and multi-outcome modeling. Labs/Teams: Active contributor to the Center for Outcomes Research and Evaluation (CORE) and collaborator with the Observational Health Data Sciences and Informatics (OHDSI) global network.

Konstantinos Katsikopoulosمشاهده پروفایل

استاد

• Human decision making
• AI decision making
• Transparency
• +۳ مورد دیگر

Professor Konstantinos Katsikopoulos is a Professor of Behavioural Science at the University of Southampton. His work integrates behavioral science with analytical methodologies to address decision-making under radical uncertainty. He leads research on transparent AI models, simple heuristics, and behavioral operations, with applications in security, finance, and public health. His group has developed tools for critical challenges such as threat detection in Afghanistan, UK banking regulation, and influenza prediction surpassing big data approaches. He holds editorial roles at journals including Journal of Mathematical Psychology and Perspectives on Psychological Science , chairs The OR Society's Behavioral OR group, and co-organizes interdisciplinary Winter Schools on Bounded Rationality. Funded by NASA, German Science Foundation, and EU Horizon Europe, his research spans decision theory, cognitive operations, and metascience. He is author of Classification in the Wild and a forthcoming Palgrave book on cognitive operations. Teaching includes Behavioral Operations, Management Analysis, and postgraduate research methods. Awards include the US Psychonomics Society Fellowship and multiple best paper awards. His current projects emphasize transparent decision models, disaster risk communication, and heuristic applications in healthcare and climate policy.

Tom Johnstenمشاهده پروفایل

استاد

• Data Mining Algorithms
• Computational Biology
• Machine Learning Applications

Dr. Tom Johnsten is a Professor in the Computer Science Department at the University of South Alabama, part of the School of Computing. He holds a Ph.D. in Computer Science from the University of Louisiana at Lafayette (1998), an M.S. in Computer Science from Arizona State University (1992), and a B.S. in Mathematics from Oklahoma State University (1987). His research focuses on developing data mining algorithms, computational biology, and applying machine learning technologies to interdisciplinary challenges. His work bridges computational methods with biological systems, cybersecurity, and environmental data analysis. Notable contributions include advancements in DNA repair mechanisms, anomaly detection in data streams, and prescriptive maintenance frameworks. He has taught courses such as Digital Logic and Computer Architecture, Operating Systems, Programming Language Theory, and Data Mining. His office is located in Shelby Hall, Room 1119, with regular office hours on Tuesdays and Thursdays. Dr. Johnsten’s publications reflect a strong emphasis on cybersecurity, bioinformatics, and data-driven solutions. He has explored topics like mitochondrial DNA repair, robotic system security, and synthetic signal peptide design. While no scientific awards are explicitly listed, his extensive publication record highlights sustained academic contribution. Teaching and advising roles include mentoring students through courses like Research Methodologies and Database Design. His work integrates theoretical computer science with practical applications in diverse fields.

John Turnerمشاهده پروفایل

دانشیار

• Operations Research
• Optimization
• Revenue Management
• +۷ مورد دیگر

John Turner is an Associate Professor of Operations and Decision Technologies at the Paul Merage School of Business, University of California, Irvine. His research applies rigorous optimization methods to real-world problems in advertising, healthcare, sports, and environmental policy. He teaches management science and operations courses, notably in the Master of Business Analytics program. PhD, Operations Research, Tepper School of Business, Carnegie Mellon University MSc, Operations Research, Tepper School of Business, Carnegie Mellon University B.Math. Honors Operations Research Co-op, University of Waterloo John Turner’s research spans several high-impact domains. He specializes in revenue management, large-scale optimization, online advertising allocation (particularly guaranteed targeted display advertising), sports scheduling under disruptions, healthcare logistics (including trauma center and air ambulance location), and environmental policy. His methodological contributions include deep Benders cuts and Gini-based ad allocation. His work integrates predictive and prescriptive analytics to solve complex decision problems. His most recent publications (2022–2024) reflect a strong trend in methodological innovation in optimization (e.g., deepest cuts in Benders decomposition) and novel applications in sports analytics and advertising. The research consistently appears in top journals like Operations Research and Manufacturing & Services Operations Management , indicating sustained scholarly impact in operations research and management science. INFORMS George B. Dantzig Dissertation Award (2011) Yahoo! Faculty Research & Engagement Scholar (2012) INFORMS William Pierskalla Best Paper Award in Health Care Management Science (2014) Lave-Weil Prize for Best Working Paper on Problem Solving (2012) Junior Faculty Research Award, Merage School (2017) Multiple CORCL Awards (2011, 2017, 2018, 2019, 2021, 2023) Turner has served as an Associate Editor for Operations Research and has received consistent teaching awards, especially for his instruction in the core Management Science course for the Master of Business Analytics program. While specific grant details are not listed, his CORCL awards and industry collaborations (e.g., with a large retailer on carrier selection) suggest active research funding and practical engagement. His doctoral thesis on ad slotting and pricing laid the foundation for his later work in digital advertising. John Turner leads research in optimization-based decision technologies, with active projects in sports analytics, advertising, and healthcare logistics. His lab or research group focuses on developing algorithms and models that bridge theory and practice, often in collaboration with industry partners. Future work appears to center on dynamic systems, contextual bandits, and further refinement of decomposition methods.

Elizabeth Williamsمشاهده پروفایل

پژوهشگر

• Operational Research
• Healthcare Analytics
• Mathematical Modeling
• +۵ مورد دیگر

Dr Elizabeth Williams is a part-time Researcher at Cardiff University's School of Mathematics , embedded within the Cardiff and Vale Health Board to improve healthcare service efficiency. With a PhD in Mathematics (Cardiff, 2023) and MMath (University of South Wales, 2019), she specializes in Operational Research applied to healthcare systems. Education : MMath Mathematics (Distinction), University of South Wales (2019) PhD Mathematics, Cardiff University (2023) Honours : OR Society Doctoral Award (2023) SIAM Student Chapter Certificate (2022) IMA Student Prize (2019) Her research combines mathematical models and algorithms to optimize services like end-of-life care , cardiology capacity , and emergency department flow . Recent work explores virtual ward planning and AI integration in healthcare resource allocation. Key publications span 2020-2025 , focusing on frail elderly care , operational efficiency , and predictive analytics . She contributes to journals like Journal of the Operational Research Society and conferences including IEEE ICHI . Scientific Awards : OR Society Doctoral Award (2023) SIAM Recognition (2022) IMA Prize (2019) OR Society Undergraduate Award (2019) As a lecturer , she teaches modules like MAT031 Further Operational Research and MAT009 Healthcare Modelling . She also serves on committees such as the OR Society Early Career Researcher Network and OR66 Conference Organizing Team .