جست‌وجوی استاد و پژوهشگر

Zhenke Wuمشاهده پروفایل

دانشیار

• Bayesian Statistics
• Precision Medicine
• Latent Variable Models
• +۵ مورد دیگر

Zhenke Wu is an Associate Professor (with tenure) in the Department of Biostatistics at the University of Michigan School of Public Health. He holds affiliate appointments at the Michigan Institute for Data and AI in Society (MIDAS) and leads the Michigan Statistics for Individualized-healthcare Lab (MiSIL). His research bridges statistical methodology and public health applications, with particular focus on precision medicine. Educational background includes: PhD in Biostatistics from Johns Hopkins University (2014) BS in Mathematics from Fudan University (2009) Dr. Wu's methodological research focuses on: Structured Bayesian latent variable models for disease subtyping and clustering Causal inference methods for sequential interventions in mobile health studies Reinforcement learning frameworks for personalized health interventions Scalable computation for high-dimensional biomedical data His applied work spans infectious diseases, mental health, autoimmune disorders, and cancer through collaborations with multiple research consortia including the Intern Health Study and UZIMA-DS project in Africa. Recent publications demonstrate strong focus on Bayesian methods, causal inference, and reinforcement learning applications in digital health. Article themes include mobile health interventions, synthetic EHR development, fair machine learning algorithms, and novel approaches for longitudinal and survival data analysis. Methodological innovations consistently address challenges in precision medicine and individualized health decision-making. Dr. Wu leads several collaborative initiatives including the Precision Health Use Case for mental health treatment (PROMPT) and partners with the Rogel Cancer Center. He advises multiple PhD students and postdoctoral researchers in statistical methodology development and health applications.

B. B. Caelمشاهده پروفایل

پژوهشگر

• climate mitigation
• climate sensitivity
• climatic extremes
• +۶ مورد دیگر

B. B. Cael is an interdisciplinary climate and ocean scientist affiliated with the Department of the Geophysical Sciences at the University of Chicago and the Climate Systems Engineering Initiative . His work integrates data analysis and theoretical models to investigate global-scale questions about Earth's carbon cycle and climate system. Cael's research interests span climate mitigation , climate sensitivity , climatic extremes , ocean biogeochemistry , plankton ecology , remote sensing , and paleoclimate . His publications focus on ocean carbon fluxes, climate feedbacks, and marine ecosystem dynamics, with recent work applying machine learning to oceanographic data. His academic journey includes a PhD from the MIT-WHOI Joint Program , a Simons Foundation Postdoctoral Fellowship at the University of Hawai’i at Manoa, and a Principal Scientist role at the UK's National Oceanography Centre. Current research at UChicago addresses both carbon dioxide removal and solar geoengineering strategies. Scientific Awards Simons Foundation Postdoctoral Research Fellow

• Software Technology
• Artificial Intelligence
• Educational Software
• +۵ مورد دیگر

Maria Virvou serves as Professor and Chair of the Department of Informatics at the University of Piraeus, where she also directs the Graduate Program in Informatics and leads the Research Laboratory 'Software Technology'. She holds significant institutional leadership roles including membership in the University Senate and has chaired the Department of Informatics for multiple terms. As Editor-in-Chief of Springer book series 'Learning and Analytics in Intelligent Systems' and 'Artificial Intelligence-Enhanced Software and Systems Engineering', she maintains substantial academic influence across international scholarly platforms. Dr. Virvou earned her PhD in Computer Science and Artificial Intelligence from the University of Sussex with a scholarship from the State Scholarships Foundation, a Master of Science in Computer Science from University College London, and her undergraduate degree from the Department of Mathematics at the National and Kapodistrian University of Athens. Her educational background in both mathematics and computer science has provided a strong foundation for her interdisciplinary research approach. Professor Virvou's research spans Software Technology, Artificial Intelligence, Educational Software and Games, User Modeling, and Human-Computer Interaction. She has pioneered work in personalized interactive software systems, applying fuzzy logic and machine learning techniques to create adaptive educational environments. Her recent work demonstrates a strategic expansion into AI applications for healthcare, with significant contributions to medical diagnostics using large language models and multimodal AI systems. She has also made notable advances in smart tourism applications through personalization techniques. With over 400 publications to her name, Professor Virvou's scholarly output shows a clear progression from foundational work in user modeling toward increasingly sophisticated AI applications across multiple domains. Her publication trends reveal a strategic focus on explainable AI, multimodal systems, and practical implementations that bridge theoretical advances with real-world applications, particularly in healthcare and education sectors. Ranked #1 worldwide in 'User Modelling' publications (147,450 total publications) according to Scopus Ranked #1 worldwide in 'Educational Software' publications according to both Scopus and Microsoft Academic Search Recognized among the top 2% of most influential Artificial Intelligence scientists worldwide by Stanford University General Co-Chair at the 14th IISA Conference 2023 Invited Keynote Speaker at the 35th IEEE International Conference on Software Engineering Education and Training (CSEE&T 2023) As Director of the Research Laboratory 'Software Technology', Professor Virvou has built a robust research team focused on AI applications across multiple domains. She co-founded and co-chairs the IEEE Intelligent Information Systems and Applications international conference series, creating a significant platform for scholarly exchange. Her leadership extends to editorial roles with major academic publishers and active participation in international research collaborations that have secured substantial funding for innovative projects in AI and software engineering.

Emma Spiroمشاهده پروفایل

دانشیار

• Social Networks
• Sociology
• Information Integrity
• +۳ مورد دیگر

Emma Spiro is an Associate Professor at the University of Washington Information School, with adjunct appointments in the Department of Sociology and Human Centered Design & Engineering. She co-founded the Center for an Informed Public (CIP) and directs the Social Media Lab (SoMeLab) and Data Science and Analytics Lab (DataLab). Her research focuses on online communication, misinformation dynamics, and network structures in both digital and physical contexts. Dr. Spiro’s work explores social networks and computational social science, analyzing how misinformation spreads during crises and elections. Her research has been funded by the National Science Foundation and Army Research Office, and published in top journals like PNAS, Social Networks, and Information, Communication & Society. She holds a Ph.D. in Sociology from UC Irvine and dual B.A.s in Applied Mathematics and Science, Technology & Society from Pomona College. As a Data Science Fellow at UW’s eScience Institute, she bridges technical and social science methodologies to study information integrity. Her affiliations include the UW Center for Statistics & the Social Sciences (CSSS) and the Center for Studies in Demography & Ecology (CSDE). She actively collaborates across disciplines to address strategic misinformation through labs, institutes, and multi-institutional initiatives like the Disinformation Summer Institute.

Kevin Panمشاهده پروفایل

دانشیار

• Business Analytics
• Data Analytics in Finance
• Data Analytics in Accounting
• +۵ مورد دیگر

Kevin Pan is an Associate Professor of Quantitative Analysis at Samford University’s Brock School of Business, where he teaches and researches at the intersection of business analytics, finance, accounting, management, healthcare, education, and Christian faith integration. Education Ph.D. in Biomedical Informatics, Stanford University M.S. in Electrical Engineering, Stanford University B.S. in Electrical Engineering (minor Mathematics), National Taiwan University Research Interests Dr. Pan’s work centers on leveraging data analytics to solve real-world problems in business and society. He explores financial fraud detection, corporate social responsibility, healthcare quality, and the ethical implications of data-driven decision making. A distinctive thread in his scholarship is the integration of Christian worldview with business analytics education and practice. Publication Trends His recent articles (2017-2023) reveal a dual focus: methodological contributions in data visualization and analytics education, and applied studies addressing opioid prescribing, hospital fraud, congregational fraud, and pandemic ethics. The work spans accounting, finance, healthcare, pharmacy, and business ethics journals. Service & Outreach He actively volunteers with Hope Heals , a Christian disability ministry founded by Samford alumni, and participates in local church activities in Birmingham, Alabama.

Nathan Juddمشاهده پروفایل

پژوهشگر ارشد

• Bayesian non-parametric methods
• Modern slavery data analysis
• Non-diffusive Wright-Fisher processes
• +۲ مورد دیگر

Nathan Judd is a Research Fellow in Statistics at the School of Mathematics, University of Birmingham. His research focuses on Bayesian non-parametric methods applied to modern slavery data and stochastic process modeling. He earned his PhD in Statistics from the University of Warwick (2024), MSc in Statistics from Lancaster University (2019), and BSc in Mathematics from the University of Kent (2018). Education: PhD in Statistics, University of Warwick (2024) MSc in Statistics, Lancaster University (2019) BSc in Mathematics, University of Kent (2018) Research themes include: Construction of non-diffusive Wright-Fisher processes Bayesian non-parametric models for crime-linkage analysis Testing methodologies for jumps in discretely observed stochastic processes Application of statistical models to socio-political challenges Recent publications demonstrate expertise in predictive modeling during disruptions, including the 2025 paper on zero-inflated mixed effects models for foodservice sales forecasting. Contact: n.a.judd@bham.ac.uk

• Renewable Energy
• Statistical Data Analysis
• Energy Management Systems
• +۳ مورد دیگر

Yu Fujimoto is a Professor at Waseda University's Advanced Collaborative Research Organization for SmartSociety. Previously, he served as an Associate Professor at Waseda University's Advanced Collaborative Research Organization for Smart Society (2015-2023) and Institute for Nanoscience & Nanotechnology (2012-2015), and as an Assistant Professor at Aoyama Gakuin University's Department of Integrated Information Technology (2009-2012). He holds a Doctorate from Waseda University's Graduate School, Division of Science and Engineering and is a member of IEEE and the Information Processing Society of Japan. His research focuses on statistical science applied to energy systems, with specific interests in renewable energy forecasting, statistical data analysis for energy management systems, and statistical machine learning theory. Fujimoto has published 109 papers with 1,191 citations and maintains an h-index of 16, demonstrating significant impact in his field. His recent publications show a strong trend toward integrating machine learning techniques with power system applications, particularly focusing on renewable energy integration, electric vehicle charging optimization, smart grid technologies, and carbon reduction strategies. His work frequently addresses the challenges of grid stability with high renewable penetration and develops innovative solutions using advanced data analytics. Best Paper Award for Big Earth Data (2025) for wind generation dataset research Best Paper Award in International Conference on Power, Energy and Electrical Engineering (2025) Second Best Paper Award in International Conference on Renewable Energy Research (2024) Top Downloaded Article in IET Smart Cities (2023) Top Downloaded Article in IEEJ Transactions (2022) Fujimoto's research bridges theoretical statistical methods with practical energy system applications, contributing significantly to Japan's efforts in smart grid development and renewable energy integration. His work often involves collaboration with industry partners and government research organizations to address real-world power system challenges.

• Supply Chain Management
• Risk Management
• Service Engineering
• +۵ مورد دیگر

Fabiana Pirola is an Associate Professor at the University of Bergamo, holding positions in both the Department of Engineering and Applied Sciences and the Department of Management, Information, and Production Engineering. Her academic career spans over 17 years since completing her PhD at the University of Bergamo, progressing through research fellow positions to her current associate professorship in the ING-IND/17 – MECHANICAL INDUSTRIAL SYSTEMS scientific sector. Her research interests focus on supply chain management , risk management , service engineering , and industrial maintenance , with recent emphasis on Industry 4.0 and 5.0 technologies. She investigates how digital innovations connect with the design and management of production and service delivery systems, bridging theoretical research with industrial applications through collaborations with organizations like AFIL (Lombardy smart factory technology cluster), Confindustria Bergamo, and Consorzio Intellimech. Analysis of her recent publications reveals a strong focus on Product-Service Systems (PSS), digital transformation in manufacturing, and workforce development for Industry 4.0/5.0. Her work spans sustainability assessment of PSS, AI education through learning factories, servitization business models, and human factors in technology adoption. A significant portion of her research addresses practical implementation challenges in industrial settings, particularly through data-driven approaches to maintenance, service delivery, and decision-making. Dr. Pirola leads multiple significant research projects including Co-Creative Decision-Makers for 5.0 Organizations (Erasmus+, 2023), Green and Resilient European Excellence Network for Smart MED SMEs (2024), Methods and Tools Supporting Digital Product Service System Passport (Horizon Europe, 2024), and TechFact - Design and adoption of Teaching Factories (PRIN Projects, 2023). She teaches various courses related to production management, operations modeling and simulation, and service engineering across multiple engineering programs at the University of Bergamo.

Meritxell Sáez Cornellanaمشاهده پروفایل

استاد

• Mathematical Biology
• Dynamical Systems
• Chemical Reaction Networks
• +۴ مورد دیگر

Meritxell Sáez Cornellana serves as a Contracted Professor of Ph.D in the Department of Mathematics and Data Analytics at IQS School of Engineering, part of Universitat Ramon Llull in Barcelona, Spain. She leads the ADAMIQS (Applied Data Analytics and Modelling IQS) research group funded by AGAUR, and participates in multiple interdisciplinary projects including CERTERA (advanced therapies development), NFT value drivers research, and international collaborations with China on sustainable development in population medicine. Her research focuses on applying mathematical modeling to biological systems, particularly in understanding cell fate decisions through dynamical landscapes and chemical reaction networks. She has developed geometrical frameworks for analyzing gene regulatory dynamics and cell differentiation processes, bridging mathematical theory with biological applications. Her work spans mathematical biology, dynamical systems theory, and data analytics, with particular expertise in bifurcation analysis, model reduction techniques, and statistical approaches to biological decision-making. Analysis of her publication record reveals a clear trajectory from foundational mathematical work in algebraic geometry toward increasingly biological applications, with a significant shift around 2016 toward systems biology. Her most impactful work involves creating geometrical landscapes that capture decision-making dynamics during cell fate transitions, which has been cited over 60 times. Recent publications show expansion into statistical approaches for university education and continued development of mathematical frameworks for understanding biological networks. Research leadership and funding: Principal Investigator for ADAMIQS project (Applied Data Analytics and Modelling IQS) funded by AGAUR (2022-2025) Researcher in CERTERA consortium for advanced therapies development (Carlos III Health Institute, 2024-2026) Researcher in NFT value drivers project (Fundación Ramón Areces, 2023-2026) Researcher in PoPMeD-SuSDeV project on sustainable development and global health (2023-2026) Researcher in 2IDLATRL project on learning analytics tools (2022-2023) Professor Sáez Cornellana directs the Applied Data Analytics and Modeling research line at IQS, supervising multiple research projects that integrate mathematical theory with practical applications in biology and education. Her team collaborates across disciplines, connecting mathematical modeling with biological experimentation and educational innovation, creating a unique interdisciplinary research environment focused on extracting meaningful insights from complex data systems.

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.

• Marketing
• Digital Transformation
• Sustainability
• +۳ مورد دیگر

Dr. Alessandro Inversini is an Associate Professor at the EHL Hospitality Business School, part of HES-SO University of Applied Sciences Western Switzerland. His primary role involves teaching and researching in the Department of Global Hospitality Business, focusing on marketing, digital transformation, and sustainability within the hospitality and tourism sectors. Educational Background: No explicit educational details provided in the text. Research Interests: Explores the transformative impact of emerging technologies (e.g., AI, ChatGPT) on hospitality operations and customer experience. Advocates for regenerative and net-positive approaches to sustainability in the hospitality industry. Investigates CSR communication strategies on social media and cross-cultural dynamics in tourism reviews. Analyzes digital media’s role in B2B relationships and customer-centric frameworks. Article Trends: Recent works emphasize AI-driven changes in tourism, ethical digital transformation, and CSR practices. Key themes include regenerative hospitality, cultural review differences, and leveraging social media for transformative learning. Awards: No scientific awards explicitly listed. Advising & Grants: Guided research on regenerative hospitality in Lebanon and transformative homestay experiences in Malaysia. Contributed to international conferences like ENTER2016 and EuroCHRIE 2024. Labs/Teams: Collaborates with interdisciplinary teams on projects involving digital tools for rural development and ethical technology integration in hospitality.

• Cybersecurity
• Data Privacy
• Machine Learning
• +۳ مورد دیگر

Dr. Dima Alhadidi is an Associate Professor in the School of Computer Science at the University of Windsor. His research focuses on Cybersecurity, Data Privacy, Machine Learning, and their applications in Health Informatics, Cloud Computing, and Smart Grids. He holds a PhD in Computer Science and Software Engineering from Concordia University (2010). His research interests include secure federated learning frameworks, privacy-preserving techniques for genomic and health data, and adversarial machine learning defenses. Notable contributions include Trustformer (2025), secure aggregation methods in federated learning, and hybrid malware classification using deep learning. Recent work emphasizes mitigating membership inference attacks and developing privacy-preserving analytics for distributed systems. Dr. Alhadidi actively advises graduate students on topics like social network clustering (NICASN 2022) and federated learning security. No scientific awards are explicitly listed. His research spans theoretical frameworks (e.g., λ_AOP calculus) to applied systems in smart grids and healthcare informatics.

Baris Coskunuzerمشاهده پروفایل

استاد

• Geometric Topology
• Topological Data Analysis
• Machine Learning
• +۳ مورد دیگر

Baris Coskunuzer is a Professor in the Department of Mathematical Sciences at the University of Texas at Dallas (UT Dallas), part of the School of Natural Sciences and Mathematics. He holds a PhD from Princeton University (2004) and has held academic positions at institutions including Yale University, MIT, Boston College, and Koç University. His research focuses on Geometric Topology, Topological Data Analysis (TDA), and Machine Learning, with applications in medical imaging, drug discovery, and blockchain analysis. He has led multiple NSF-funded research grants and collaborates internationally. Education: PhD in Mathematics, Princeton University, 2004 M.S. in Mathematics, Caltech, 2001 B.S. in Mathematics, Bogazici University, 1999 Research Interests: Geometric Topology Topological Data Analysis Machine Learning Medical Imaging Blockchain Analysis Data Science Grants & Awards: NSF-DMS ATD Research Grant (2023–2026) NSF-DMS AMPS Research Grant (2022–2025) Young Scientist Award, Turkish Science Academy (2016) Fulbright Scholar Award (2014) Over 60 peer-reviewed publications in journals such as Communications on Pure and Applied Mathematics and NeurIPS Advising & Labs: Supervises a research group focused on Topological Machine Learning (TML) Collaborates on projects like Topo-ML for medical diagnostics and GraphPulse for temporal graph analysis

• Networking and Communications
• Blockchain Technology
• Smart Grids and Energy Systems
• +۵ مورد دیگر

Roberto Rojas-Cessa is a Professor in the Department of Electrical and Computer Engineering at New Jersey Institute of Technology (NJIT), affiliated with the School of Applied Engineering and Technology. His research focuses on networking, blockchain applications in smart cities, energy systems, wireless communications, and high-performance switching. He has led multiple National Science Foundation (NSF)-funded projects, including initiatives on controlled delivery power grids and next-generation network quality of service. Notably, his work explores blockchain for energy metering, sustainable environmental measures, and smart grid optimization. He is also a Senior Member of the National Academy of Inventors (2024). His research interests span network protocols, distributed systems, and IoT applications. Recent projects include AMI-Chain (a blockchain-based power metering system) and studies on indirect free-space optical communications for vehicular networks. He has contributed to advancements in medium access control for crowded networks and energy packet switches for digital microgrids. Rojas-Cessa’s work integrates machine learning for network management and flood impact analysis. He has developed tools for time-lapse analysis of urban data and agent-based models to evaluate electric vehicle adoption. His publications emphasize scalability, security, and efficiency in both traditional and emerging technologies. Grants: Collaborative Research on Power Grids (NSF, 2016–2018), NeTS-NR: Quality of Service Networks (NSF, 2004–2008) Awards: Senior Member of the National Academy of Inventors (2024) His lab activities include experimental evaluations of digital microgrids and blockchain implementations for carbon footprint tracking. He actively collaborates on projects addressing emergency communications and resilient energy distribution systems.

• 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.