جست‌وجوی استاد و پژوهشگر

Hwanhee Hongمشاهده پروفایل

دانشیار

• Bayesian Statistical Methods
• Comparative Effectiveness Research
• Network Meta-Analysis
• +۵ مورد دیگر

Hwanhee Hong is an Associate Professor in the Department of Biostatistics and Bioinformatics at Duke University School of Medicine and a member of the Duke Clinical Research Institute. Affiliated with the Biostatistics, Epidemiology, and Research Design (BERD) Methods Core and the B&B Faculty, Dr. Hong maintains an active research program focused on statistical methodology development for clinical applications. Dr. Hong's educational background includes a Ph.D. in Biostatistics from the University of Minnesota (2013), an M.S. in Biostatistics from Harvard University (2010), and a B.S. in Statistics from Chung-Ang University, Korea (2008). Prior to joining Duke, they completed a postdoctoral fellowship at Johns Hopkins Bloomberg School of Public Health under Dr. Elizabeth A. Stuart. Research interests center on Bayesian statistical methods for comparative effectiveness research, network meta-analysis, causal inference, measurement error correction, and data integration across multiple sources. Their work emphasizes adaptive information borrowing to address clinical and public health questions, particularly in developing flexible Bayesian modeling frameworks that synthesize diverse data streams for evidence generation. Analysis of recent publications reveals consistent methodological focus on network meta-analysis techniques, causal inference with error-prone covariates, generalizability assessment, and time-to-event analysis. The research spans applications in pediatrics, cardiology, obesity treatment, and vaccine effectiveness, demonstrating strong translational impact across medical domains. Dr. Hong actively collaborates with biostatisticians, epidemiologists, clinicians, and health policymakers through multiple funded projects, though no formal awards or fellowships are documented in the provided materials. Participant, Faculty Success Program, National Center for Faculty Development & Diversity (2023) Participant, Faculty Curriculum on Anti-Racism, Duke Office of Faculty Advancement (2021) Teaching responsibilities include BIOSTAT 719: Generalized Linear Models. Current grant funding spans eight major projects totaling over $20 million, primarily from NIH and PCORI, addressing pediatric care coordination, mental health interventions, obesity treatment, diabetes management, and advanced biostatistical methodology development. Dr. Hong's laboratory work focuses on computational approaches for data integration, maintaining active collaborations with the Duke Clinical Research Institute and multiple external institutions including Johns Hopkins University and North Carolina State University.

• Infectious Disease Epidemiology
• Vector Borne Diseases
• Malaria Research
• +۶ مورد دیگر

Dr. Caroline Buckee is a Professor of Epidemiology at the Harvard T.H. Chan School of Public Health, where she joined as an Assistant Professor in summer 2010 and was promoted to Professor in 2021. She served as Associate Director of the Center for Communicable Disease Dynamics from 2013-2023. Dr. Buckee co-founded and co-directs Crisis Ready (crisisready.io), a joint platform between Harvard's Data Science Initiative and Direct Relief that supports data-driven responses to public health emergencies. She also co-leads the South Asia Climate and Health Research Cluster supported by Harvard's Salata Institute for Climate and Sustainability. Dr. Buckee's research spans infectious disease epidemiology and ecology with a focus on vector-borne diseases including malaria and dengue. Her work examines human mobility and the impact of labor migration on epidemic spread, as well as the intersection of climate risks and human health. Her research group actively supports National Malaria Control Programs in the global south to improve surveillance and analytical approaches. Specific projects include studying the impact of gold-mining on malaria transmission in the Amazon region and investigating how extreme heat affects poor working women in India, with the goal of developing community-led interventions. Dr. Buckee's recent publications demonstrate a strong focus on integrating genomic data with epidemiological modeling, particularly for malaria parasites. Her work addresses critical gaps in understanding spatial disease dynamics, the impact of human mobility on outbreaks, and the development of privacy-preserving methods for using mobile phone data in public health. She has made significant contributions to understanding how environmental factors, including climate change and resource extraction, influence disease transmission patterns. Dr. Buckee has received NIH funding including the R35GM124715 grant for "New approaches to measuring and containing the spatial spread of human pathogens" and R21GM100207 for "An alignment free network approach to analyzing highly recombinant malaria parasites." Her research has been widely cited and has influenced public health responses to infectious disease outbreaks globally. Through Crisis Ready, Dr. Buckee's team works at the intersection of data science and public health emergency response, developing tools and approaches to improve real-time decision-making during crises. Her group maintains strong international collaborations, particularly with researchers and public health officials in malaria-endemic regions of South America and South Asia.

• Artificial Intelligence
• Educational Technology
• Business Process Management
• +۵ مورد دیگر

Darko Etinger is an Associate Professor and currently serves as the Dean of the Faculty of Informatics at Juraj Dobrila University of Pula in Croatia. His academic career spans over two decades with significant contributions to the fields of information systems, educational technology, and artificial intelligence. He teaches undergraduate courses including Artificial Intelligence, Business Information Systems, Business Process Management, ICT Fundamentals, Information Systems, Introduction to Artificial Intelligence, and Multimedia Systems. At the graduate level, he teaches Development of IT Solutions, IT Management, Modelling and Simulation, and Project Management. He also supervises doctoral research in Management of Information Technologies in Education. Dr. Etinger's research interests span several key areas in computer science and education technology. He has made significant contributions to understanding how information and communication technologies can support children with special educational needs, as evidenced by his 2024 and 2025 books on the topic. His work also focuses on business process management, educational data mining, and the application of artificial intelligence in educational contexts. His 2024 book 'Introduction to R and RStudio' demonstrates his commitment to data science education. Analysis of his recent publications shows a strong focus on practical applications of technology in education and business. His work spans educational robotics, learning management systems analysis, business process modeling, and the application of large language models in healthcare. He has a particular interest in how technology can be made accessible and beneficial for diverse learner populations, including those with special needs. His research often takes a human-centered approach, examining not just the technology itself but how it's adopted and used by end users. His 2025 bibliometric analysis of metaverse security demonstrates his ability to tackle emerging technological challenges. Associate Professor at Faculty of Informatics, Juraj Dobrila University of Pula Current Dean of Faculty of Informatics Author of multiple publications on educational technology and information systems Supervisor for numerous graduate theses on educational technology topics Active participant in EDIH Adria project as evidenced by 2025 publications Researcher in educational robotics implementation in Croatian schools Dr. Etinger has advised numerous students completing their theses, with a focus on educational technology applications. His research has been published in various international conferences and journals including IEEE Engineering Management Review, Procedia Computer Science, and System Dynamics Review. His current research appears to be heavily focused on the EDIH Adria project, which is a European Digital Innovation Hub initiative focusing on technology transfer and business innovation. He maintains an active research program with publications spanning from 2003 to the present, demonstrating sustained scholarly contribution to his fields of expertise.

Andrea Rusnockمشاهده پروفایل

استاد

• History of Vaccination
• Medical History
• Public Health
• +۳ مورد دیگر

Andrea Rusnock is a Professor of History at the University of Rhode Island. Her research focuses on the history of vaccination, disease and public health, and the history of quantification during the Enlightenment. Ph.D., Princeton University (1990) M.A., Princeton University (1985) B.A., Brown University (1982) Her research explores intersections of medical history, political arithmetic, and biopolitics. Key themes include: Development of statistical methods in public health Global spread of vaccination practices Medical governance and institutional finance Quantification of population and disease Recent publications analyze smallpox vaccination policies, biopolitical frameworks, and the role of accounting in medical philanthropy. Her work has been supported by major grants from the National Endowment for the Humanities, the Wellcome Trust, and the National Library of Medicine. National Endowment for the Humanities Fellowship American Council of Learned Societies Fellowship National Library of Medicine Grant Wellcome Trust Fellowship Rusnock serves as Vice Chair of the Institutional Review Board and contributes to the Health Professions Advisory Committee. As editor of Osiris (2009–2017), she advanced interdisciplinary scholarship in the history of science.

• Information Hiding
• Steganography
• Steganalysis
• +۴ مورد دیگر

Andrew D. Ker is Professor and Associate Professor of Computer Science at the University of Oxford's Department of Computer Science, and Tutorial Fellow in Computer Science at University College, Oxford since 2009. He received his BA in Mathematics & Computer Science (1994-1997) and DPhil in Computer Science (1997-2000) from Oxford, followed by academic appointments including Junior Research Fellow (2000-2003), Special Supernumerary Fellow (2003-2009), and Royal Society University Research Fellow (2003-2011). His research focuses on information hiding, particularly steganography (covert communication in digital media) and steganalysis (detection of hidden data), with additional interests in digital media forensics and programming language semantics. His foundational work includes the mathematical formalization of the 'square root law of steganographic capacity'. Recent publications explore practical implementations in social media platforms, GPU-accelerated steganalysis, and linguistic steganography techniques. He has received multiple scientific awards including Best Paper Awards at ACM Workshops on Information Hiding & Multimedia Security, SPIE conferences, and the International Workshop on Digital Watermarking. As Associate Editor for IEEE Transactions on Information Forensics and Security, he maintains active involvement in the academic community. Professor Ker has supervised numerous graduate students in computer security and steganography research, including doctoral candidates and master's students. He leads research within Cyber Security Oxford and has developed four major lecture series: Lambda Calculus and Types, Discrete Mathematics, Computer Security, and Advanced Security: Information Hiding. He remains active in teaching despite administrative responsibilities as Tutorial Fellow.

Jonathan M. Winterمشاهده پروفایل

دانشیار

• Climate Prediction
• Climate Impacts on Human Health
• Water Resources
• +۹ مورد دیگر

Jonathan M. Winter is an Associate Professor in the Department of Geography and an Adjunct Associate Professor in the Department of Earth Sciences at Dartmouth College . His research explores climate prediction and the impacts of climate variability and change on human health , water resources , and agriculture . He focuses on drivers of tickborne disease expansion, future water availability for irrigation, high-resolution climate projections, uncertainty in regional climate models, and the predictability of climate extremes in the Northeastern United States. Education : PhD and MS from Massachusetts Institute of Technology , BS from SUNY College of Environmental Science and Forestry . Research Interests : Climate impacts on agriculture and human health, bias correction of climate data, crop modeling, extreme precipitation analysis, and Lyme disease expansion. Teaching : Courses on Global Climate Change , Water Resources , and Climate Change Effects on Agriculture . Labs & Teams : Leads the Dartmouth Applied Hydroclimatology Group (AHCG) , mentoring graduate and undergraduate researchers. Publications : Recent work includes high-resolution climate projections , water scarcity implications , and climate-agriculture interactions , with a focus on the Northeastern U.S. and global croplands. Contact : Jonathan.M.Winter@dartmouth.edu | Phone : 603-646-6456

• Reinforcement Learning
• Machine Learning Theory
• Sequential Decision Making
• +۴ مورد دیگر

Csaba Szepesvári is a Professor and Canada CIFAR AI Chair in the Department of Computing Science at the University of Alberta, affiliated with Amii. Since 2017, he has been on partial leave, leading the Foundations team at DeepMind in Edmonton. His work bridges theoretical machine learning and practical reinforcement learning systems. His research interests lie at the intersection of reinforcement learning, sequential decision making, bandit algorithms, and theoretical machine learning . He develops and analyzes algorithms for efficient learning in complex environments, focusing on sample efficiency, function approximation, and optimization. His recent work explores policy gradient methods, offline and online RL, uncertainty estimation, and foundational limits of learning algorithms. The trends in his recent publications (2023–2024) emphasize theoretical advances in RL and bandits , particularly in q π -realizability, natural policy gradient, ensemble sampling, and lower bounds. The work spans NeurIPS, ICML, COLT, and AISTATS, reflecting deep theoretical engagement with practical implications. He is recognized as a Canada CIFAR AI Chair , a prestigious award supporting leading researchers in artificial intelligence. Csaba Szepesvári mentors students and collaborates extensively, often with researchers like András György, Tor Lattimore, Gellért Weisz, and Dale Schuurmans. He has co-organized RL theory seminars and workshops and co-authored the influential book Bandit Algorithms (2020). His leadership in both academic and industrial research (DeepMind) underscores his impact on the field. He is actively involved in the machine learning community through research, mentorship, and event organization, contributing to both foundational theory and real-world applications.

Daniel Klermanمشاهده پروفایل

استاد

• Civil Procedure
• English Legal History
• Law and Economics
• +۴ مورد دیگر

Daniel Klerman is the Edward G. Lewis Chair in Law and History at USC Gould School of Law, where he serves as Director of the Center of Law & Social Science (CLASS). He teaches Civil Procedure, Property, Concepts in American Law, and English Legal History, and was honored with the Student Bar Association Faculty Appreciation Award in 2014 for his outstanding teaching. Klerman's scholarly work bridges legal history and law and economics, focusing on civil procedure, English legal history, and the economic analysis of legal institutions. His research examines how legal systems evolve, how litigation and settlement function across different jurisdictions, and the historical development of common law institutions. He has made significant contributions to understanding jurisdictional competition, forum shopping, and the selection effects in litigation. His work demonstrates a consistent pattern of integrating historical analysis with rigorous economic modeling, particularly in examining how reputation mechanisms function in legal markets and how historical legal developments can inform contemporary legal problems. Klerman's scholarship often employs empirical methods to test theoretical propositions about legal institutions and their evolution. Sutherland Prize from the American Society for Legal History (2004) David Yale Prize from the Selden Society (2001) Student Bar Association Faculty Appreciation Award (2014) Klerman has held significant leadership roles in the academic legal community, serving as co-president of the Society for Empirical Legal Studies (2008-09) and on the Board of Directors of the American Law & Economics Association (2009-2012). He is a founding Editor-in-Chief of the Journal of Law & Empirical Analysis and serves on the editorial boards of Law & History Review and the Journal of Legal Analysis. As a member of the American Law Institute, he contributes to the development of legal principles. His extensive visiting professorships at institutions including Harvard, Stanford, Chicago, and international universities demonstrate his scholarly prominence. As Director of the Center of Law & Social Science, Klerman leads interdisciplinary research initiatives that connect legal scholarship with social science methodologies, fostering empirical approaches to legal questions across multiple domains of law.

Amber Huppمشاهده پروفایل

استاد

• Analytical Chemistry
• Environmental Chemistry
• Chemometrics
• +۲ مورد دیگر

Amber Hupp is a Professor in the Department of Chemistry at the College of the Holy Cross, where she serves as both a faculty member and Gifted High School Advisor. Her expertise spans Analytical Chemistry and Environmental Chemistry, with significant contributions to biodiesel analysis and chemistry education. She earned her Ph.D. from Michigan State University and teaches courses including Environmental Chemistry, Atoms & Molecules, Equilibrium & Reactivity, and Instrumental Chemistry/Analytical Methods. Professor Hupp's research focuses on applying Gas Chromatography-Mass Spectrometry (GC-MS) and chemometric methods like Principal Component Analysis (PCA) to characterize biodiesel feedstocks and blends. Her work develops analytical frameworks for identifying biodiesel sources, optimizing chromatographic separations, and extending ASTM standards to renewable fuels. She also pioneers creative pedagogical approaches for non-science majors, emphasizing societal relevance in chemistry education. Her publication record from 2006-2022 reveals three interconnected research streams: 1) Advanced chromatographic techniques for biodiesel analysis, 2) Chemometric modeling of complex fuel systems, and 3) Educational innovations in chemistry curriculum design. This work consistently bridges analytical method development with practical environmental applications. No scientific awards were mentioned in the source material. While specific grant details aren't provided, Professor Hupp actively mentors undergraduate researchers as evidenced by student co-authorships across her publications. Her educational work demonstrates commitment to advising non-science majors through curriculum development. She leads the Hupp Lab at Holy Cross, which specializes in analytical environmental chemistry using GC-MS instrumentation. The lab focuses on biodiesel characterization, chemometric data analysis, and forensic applications of fuel analysis, providing hands-on research experience for undergraduate students.

Nicholas V. Sarlisمشاهده پروفایل

استاد

• Solid State Physics
• Seismology
• Geophysics
• +۳ مورد دیگر

Nicholas V. Sarlis is a Professor of Experimental Solid State Physics at the Department of Physics , National and Kapodistrian University of Athens since 2017. With an h-index ≥36, he has authored 2 monographs, >155 peer-reviewed publications, and 80 conference communications. B.Sc. in Physics (1991), National and Kapodistrian University of Athens Ph.D. in Physics (1997), National and Kapodistrian University of Athens His research applies Natural Time Analysis to: Earthquake precursor identification Space weather and cosmic ray studies Climate phenomenon prediction (El Niño) Fracture mechanics and complex systems Seismicity and geoelectric field correlations Non-extensive statistical mechanics applications Recent work includes analyzing seismic entropy changes under time reversal and developing earthquake nowcasting systems. Publications span 2025's Statistical mechanics in geophysical contexts to 2024's cross-disciplinary disaster prediction tools.

Bozidar Stojadinovicمشاهده پروفایل

استاد

• Community Disaster Resilience
• Earthquake Engineering
• Structural Dynamics
• +۴ مورد دیگر

Bozidar Stojadinovic is a Full Professor and Chair of Structural Dynamics and Earthquake Engineering at ETH Zürich's Department of Civil, Environmental and Geomatic Engineering. He leads the Institute of Structural Engineering and previously held professorships at UC Berkeley and the University of Michigan. His research focuses on community disaster resilience, seismic design, and experimental methods like hybrid simulation. Education: PhD in Civil Engineering, UC Berkeley (1995) MS in Civil Engineering, Carnegie-Mellon University (1990) BS in Civil Engineering, University of Belgrade (1988) Research Interests: Performance-based probabilistic resilience evaluation of civil infrastructure. Earthquake engineering, including seismic isolation and response modification techniques. Development of experimental testing methods, such as hybrid simulations for dynamic structural analysis. Awards: ICE Journal John Henry Garrood King Medal (2023) ACI Chester Paul Siess Award (2017) NSF CAREER Award (1999) Teaching & Advising: Teaches courses on seismic design and structural dynamics at ETH. Advised 49 doctoral students to date. His work integrates advanced methodologies to enhance structural resilience against natural hazards. Labs/Teams: Leads ETH's Institute of Structural Engineering, advancing research in seismic protection and infrastructure resilience through experimental and computational innovations.

Elizabeth Curtisمشاهده پروفایل

مدرس ارشد

• Mentoring
• Values Education
• Children's Literature
• +۳ مورد دیگر

Elizabeth Curtis is a Senior Lecturer in the School of Education at the University of Southern Queensland. Her research focuses on mentoring, values education, and children's literature, with particular emphasis on early career teacher development and educational methodologies. She holds a PhD from Queensland University of Technology (2013), a GDipEd from ACU (1992), and a BA from the University of Queensland (1991). Her key research areas include future-focused mentoring strategies, the role of children's literature in education, and values education integration. She has supervised numerous doctoral and master's students, focusing on topics such as teacher wellbeing, online education for adult learners, and cultural hegemony in Catholic education. Dr. Curtis has received the Outstanding Doctoral Thesis Award (QUT, 2012) and actively contributes to peer-reviewed journals including Asia-Pacific Journal of Teacher Education and Professional Development in Education . Her work emphasizes collaborative learning communities, embodied arts-based practices in primary education, and the intersection of philosophy with pedagogy. Professional Memberships: Australian Teacher Education Association, Primary English Teaching Association Australia, Queensland College of Teachers

Hasan Jashariمشاهده پروفایل

استاد

• Sociology of Education
• Political Pluralism
• Interethnic Relations
• +۱ مورد دیگر

Hasan Jashari is a Full Professor (part-time) at the Faculty of Contemporary Social Sciences, South East European University. His academic journey includes a PhD in Sociology of Education and Family from Saint Cirillius and Methodius University (1999-2001), building on earlier degrees in Sociological Sciences and Sociology. His research focuses on: Political pluralism in post-communist societies Sociology of education systems Interethnic relations in the Balkans Methodologies in doctoral research Publications over the past decade demonstrate consistent focus on social transformation in Macedonia, with recurring themes of educational reform, political transitions, and research methodology. His most recent work examines data collection techniques in doctoral studies. No awards, students, or grants information was available in the source material.

• Computational Biology
• Genomics
• Cancer Research
• +۵ مورد دیگر

Adam Olshen, PhD, MA is a Professor in the Department of Epidemiology & Biostatistics at the University of California, San Francisco School of Medicine. His research program bridges advanced statistical methodology with cancer genomics and clinical applications, focusing on developing computational approaches to analyze complex genomic data and understand cancer biology and patient outcomes. Dr. Olshen's research spans multiple critical areas in computational biology and cancer genomics. He is best known for developing the circular binary segmentation algorithm, a foundational method for analyzing DNA copy number data that has become standard in cancer genomics research. His work consistently applies sophisticated statistical models to address biological questions, particularly in cancer research where he examines tumor biology, treatment response mechanisms, and patient symptomatology. Recent work demonstrates expertise in multi-omic analyses, single-cell sequencing methodologies, and machine learning applications for understanding cancer stem cells, treatment resistance mechanisms, and symptom biology. His research has particular strength in translating complex genomic data into clinically relevant insights. Analysis of Dr. Olshen's recent publications reveals a strong focus on applying cutting-edge computational methods to understand cancer biology at increasingly granular levels. His work spans multiple cancer types including breast cancer, leukemia, prostate cancer, and others, with particular attention to genomic alterations, epigenetic changes, and their relationship to clinical outcomes. A notable trend is the application of advanced computational techniques to analyze single-cell and multi-omic data to uncover novel biological insights about cancer heterogeneity, progression, and symptom biology. His publications frequently appear in high-impact journals across computational biology, oncology, and clinical medicine. Dr. Olshen maintains an active collaborative research program across UCSF, working extensively with clinicians and basic scientists to apply sophisticated statistical methods to complex biological questions. His work has contributed significantly to the development of novel biomarkers, improved understanding of cancer biology, and better methods for analyzing genomic data in clinical contexts. He has been instrumental in numerous studies examining the relationship between genomic alterations and clinical outcomes, as well as the development of novel computational methods for genomic data analysis that have been widely adopted by the research community.

• Credit Scoring
• Consumer Credit Risk Modelling
• Predictive Analytics
• +۳ مورد دیگر

Christophe Mues is a Professor of Data Science and Information Systems at the University of Southampton's School of Management, within the Department of Decision Analytics and Risk. His research focuses on credit scoring, consumer credit risk modeling, and applications of predictive analytics, including machine learning techniques for credit risk assessment. He leads the Information Systems & Business Analytics section and supervises multiple PhD students in Business Studies and Management. His work spans advanced statistical methods for predicting Probability of Default (PD), Loss Given Default (LGD), and loan profitability. He is actively involved in the academic community, serving on the organizing committee for the Credit Scoring and Credit Control conference. His teaching includes topics in information systems and business analytics. Contact: C.Mues@soton.ac.uk. Research Interests: Credit Scoring and Consumer Credit Risk Modelling Predictive Analytics in Finance Machine Learning Applications (Deep Learning, Graph Neural Networks) Non-Traditional Data Integration Credit Model Transparency and Fairness Debt Collection Optimization PhD Supervision: Currently guiding four students in Business Studies & Mngt: Kameswara Rao Korangi, Sarthak Gurnani, Pablo Casas, and Nora Agyei-Ababio. Professional Activities: Leads research groups and contributes to international conferences. His work bridges academic research with practical financial risk solutions, emphasizing ethical AI and regulatory compliance in credit modeling. Biography: Holds a PhD in Applied Economics from KU Leuven (Belgium). Joined the University of Southampton in 2004, advancing from researcher to his current leadership role in Decision Analytics and Risk.