جست‌وجوی استاد و پژوهشگر

• Statistical Learning
• Machine Learning
• Probability
• +۶ مورد دیگر

Stephan Clémençon is a Professor at Télécom Paris, working within the Information Processing and Communication Laboratory (LTCI) where he leads the Signal, Statistics and Learning (S2A) research team. His academic career spans multiple prestigious institutions including University of Paris X (2000-2005) and INRA Met@risk research unit (2005-07), with membership in the LPMA (Stochastic Modeling and Probability) laboratory of Paris 6 and Paris 7. Clémençon earned his PhD in Applied Mathematics from University of Paris 7 Denis Diderot with a visiting period at Stanford University's Department of Statistics (1997-1998). He currently serves as the responsible for the Specialized Master's in 'Big Data' at Télécom Paris and previously held the 'Machine Learning for Big Data' industrial chair (2013-2018), now actively involved in the 'Data Science and AI for Digitalized Industry and Services' chair. His research interests focus on statistical learning, probability, statistics, machine learning, stochastic processes, and nonparametric statistics, with applications spanning quantitative finance, biosciences, and signal/image processing. Clémençon has developed an 'immoderate taste for stochastic modeling and statistics applied in various application areas.' His extensive publication record (270 documents in HAL) demonstrates consistent research productivity with 15 recent publications (2020-2024) primarily focused on anomaly detection, ranking algorithms, statistical learning theory, and bias correction in machine learning. His work bridges theoretical statistics with practical applications in computer vision, telecommunications, and ethical AI considerations. Clémençon teaches advanced courses including Martingale Theory, Machine Learning, Advanced Nonparametric Statistics, and Big Data Projects at Télécom Paris, while also contributing to programs at Ensae Paris, Paris Diderot University, ENS Paris Saclay, and other institutions. His research team (S2A) operates within the Image, Data, Signal (IDS) department at Télécom Paris, focusing on the intersection of signal processing, data science, and statistical learning methodologies.

ZIDANI Hasnaaمشاهده پروفایل

استاد

• Optimal Control
• Mathematical Modeling
• Numerical Simulation
• +۳ مورد دیگر

Hasnaa ZIDANI is a Professor in applied mathematics at INSA Rouen Normandie , holding the COPTI Chair . Her research focuses on optimal control , mathematical modeling , and numerical simulation , with applications to transportation systems (e.g., autonomous vehicles, traffic management) and environmental modeling (e.g., resource management, marine ecology). She leads the COPTI project, funded by the ANR , Région Normandie , and EU’s ERDF , aiming to advance international-level research in optimal control methods. Research priorities include Optimal control of large-scale and stratified systems, Trajectory planning and crowd movement optimization, Image segmentation and optimal transport, Applications in aerospace (e.g., launcher trajectory optimization) and energy (e.g., demand response systems). ZIDANI actively contributes to academic events, such as the 2024 conference on Control Theory (co-chair) and the 2023 workshop on Optimal Control in Italy . She collaborates with institutions like Thales Alenia Space and develops tools like the ROC-HJ solver for reachability analysis and optimal control. Affiliations : Laboratoire de Mathématiques de l’INSA Rouen (LMI), CNRS EA-3226. Contact via Hasnaa.Zidani@insa-rouen.fr .

• Higher Education
• Bibliometrics
• Science Studies
• +۴ مورد دیگر

Jonas Lindahl is an Associate Professor in Library and Information Science at the Department of Sociology, Umeå University. His work focuses on academic socialization, doctoral student performance, and the use of bibliometric indicators for evaluating research. He is affiliated with the Sociology of Science, Knowledge and Information research group and has led projects on gender disparities in doctoral careers, the impact of parenthood on academic productivity, and the role of collaboration in early career development. Research Interests: Factors influencing doctoral student performance and career trajectories Bibliometric analysis for predicting research excellence Academic supervision dynamics and institutional support Gender and parenthood effects on academic outcomes Quantitative methods in social science research Current Projects: Explaining gender differences in doctoral student performance (2022-2023) Doctoral students' reliance on supervisors and research networks (2022-2024) Parenthood's impact on academic socialization (2021-2022) Teaching: Focuses on information retrieval, digital libraries, and information science methodologies. Advises on quantitative research design and bibliometric applications. Labs/Teams: Active member of the Sociology of Science, Knowledge and Information research group, collaborating with institutions on multidisciplinary projects.

Jeffrey Blumeمشاهده پروفایل

استاد

• Data Science
• Biostatistics
• Statistical Inference
• +۱۷ مورد دیگر

Jeffrey Blume is a tenured Professor of Data Science and Associate Dean for Academic and Faculty Affairs at the University of Virginia's School of Data Science. He previously held leadership roles at Vanderbilt University, including Director of Graduate Education at the Data Science Institute and Vice-Chair of Education in the Department of Biostatistics, with secondary appointments in Biomedical Informatics and Biochemistry. Ph.D. in Biostatistics, Johns Hopkins University B.A. in Statistics, State University of New York at Buffalo His research centers on the philosophical and methodological foundations of statistical inference, with a focus on likelihood-based approaches and evidential reasoning. He has made significant contributions to the development of second-generation p-values and empirical Bayes methods, particularly in biomedical contexts such as cancer diagnostics, nephrology, and neuroimaging. His work integrates rigorous statistical theory with practical applications in radiology, women’s health, and translational medicine. Though specific publications are not listed in the provided text, his research portfolio indicates a strong emphasis on methodological innovation in data science and biostatistics, particularly in areas involving prediction modeling, mediation analysis, ROC analysis, and sequential trial design. These works span both theoretical advancements and real-world applications in clinical and biomedical research. While no scientific awards are explicitly mentioned, his leadership in founding graduate programs in data science and public health underscores his national impact in academic education and program development. Blume has advised numerous graduate students and led major educational initiatives, though specific advisees are not listed. He has secured significant academic grants through his leadership roles and research programs, particularly in data science education and biomedical methodology development. As Associate Dean, he oversees academic and faculty affairs, shaping the strategic direction of the School of Data Science. He is affiliated with the PRISM Lab at the University of Virginia, where his team conducts research on statistical evidence and inference. His website, statisticalevidence.com, serves as a hub for his scholarly work on evidential approaches to data analysis.

• Statistics
• Biostatistics
• Missing Data Analysis
• +۷ مورد دیگر

Dr. Biao Zhang is a Professor in the Department of Mathematics and Statistics at the University of Toledo, College of Natural Sciences and Mathematics. His research focuses on advanced statistical methodologies, particularly in missing data analysis, empirical likelihood, and semiparametric modeling, with applications to clinical trials and biostatistics. University: University of Toledo School: College of Natural Sciences and Mathematics Email: biao.zhang@utoledo.edu Research Interests Dr. Zhang specializes in Statistics , Biostatistics , and Missing Data Analysis . His work develops innovative methods for: Empirical likelihood in clinical trials Density ratio models for ROC curve analysis Causal inference with missing responses Semiparametric estimation techniques Robust methods for treatment effect estimation Publications Overview His extensive publication record (59 papers, 1994-2024) reveals expertise in: Empirical likelihood applications ROC surface methodologies Multivariate analysis Case-control study frameworks Nonparametric and semiparametric approaches Statistical software development Co-author network includes Steven T. Haller , Christopher Cooper , and Pamela S. Brewster .

Maximilian Rabeمشاهده پروفایل

پژوهشگر ارشد

• Computational Modeling
• Eye-Movement Control
• Psycholinguistics
• +۴ مورد دیگر

Maximilian Rabe is a Postdoctoral Researcher in the Department of Experimental and Biological Psychology at the Faculty of Human Sciences, University of Potsdam, with a secondary affiliation at the University of Copenhagen. Currently on parental leave until September 22, 2025, he maintains active research involvement in computational cognitive science, with particular focus on eye-movement dynamics during reading processes and psycholinguistic modeling. His dual institutional appointments reflect his interdisciplinary research bridging German and Danish academic communities. Dr. Rabe completed his academic training with a B.Sc. in Psychology from the University of Potsdam (2016), followed by an M.Sc. in Psychology - Cognition and Brain Science from the University of Victoria, Canada (2018), and earned his Ph.D. in Cognitive Science from the University of Potsdam in 2024 under the supervision of Ralf Engbert and Shravan Vasishth. His research program centers on computational and statistical modeling of cognitive processes, with specific expertise in eye-movement control, psycholinguistics, and memory systems. Dr. Rabe develops integrated cognitive architectures that simulate how humans process language and allocate visual attention during reading. His methodological approach combines experimental psychology with advanced Bayesian statistics and dynamical systems theory to create predictive models of cognitive behavior. Analysis of his publication trajectory reveals a consistent focus on developing sophisticated computational frameworks for understanding reading processes, with increasing emphasis on integrated models that couple syntactic processing with eye-movement control. A distinctive feature of his work is the development of specialized methodological tools, particularly R packages that advance research practices in cognitive science. His publications span high-impact journals in psychology, cognitive science, and methodology, demonstrating strong interdisciplinary reach. Dr. Rabe is actively involved in multiple research projects funded by the German Research Foundation (DFG), including Project B03 of Collaborative Research Center 1287 on eye-movement control and parsing processes, and Project B03 of CRC 1294 on parameter inference in dynamical cognitive models. His work at the University of Copenhagen investigating visual attention in virtual reality environments receives support from Villum Fonden. As a methodological innovator, Dr. Rabe has developed and maintains several important R packages including hypr for hypothesis-driven contrast coding, designr for experimental design, appRiori for Bayesian analysis, and RStanTVA for visual attention modeling. His commitment to open science practices is evident in his software development and preprint sharing. Working within Ralf Engbert's research group at the University of Potsdam, Dr. Rabe contributes to a vibrant interdisciplinary environment that combines experimental psychology, computational modeling, and advanced statistical methods. His research has implications for understanding fundamental cognitive processes and developing more accurate models of human information processing during language comprehension.

• Landslide Risk Assessment
• Applied Geology
• Geohazard Mitigation
• +۵ مورد دیگر

Marco Mulas is an Associate Professor at the Department of Chemical and Geological Sciences, University of Modena and Reggio Emilia. He teaches courses including Landslide Risk Assessment and Mitigation , Geothematic Surveying and Cartography , and Applied Geology for Civil and Environmental Engineering, focusing on landslide dynamics, geomorphological mapping, and hazard modeling. His research spans landslide monitoring using UAV-RTK/LiDAR, GNSS arrays, InSAR, and Digital Image Correlation. Key projects include the SoLoMon framework for regional landslide monitoring and development of unconventional Micropiles Tripods Shields for earthflow control. He specializes in rainfall-landslide thresholds, groundwater contamination modeling, and multi-temporal displacement analysis. Recent publications (2025-2016) cover topics like high-frequency UAV surveys (Baldiola landslide), effective rainfall impact on flysch landslides, debris flow hazard zonation, and structural analysis of rockfall precursors via sinusoidal wave fitting. His work emphasizes integrating field surveys with advanced geospatial datasets for hazard assessment. Contact: marco.mulas@unimore.it | Office: via G. Campi, 103 - Modena

Eric Attiasمشاهده پروفایل

استاد پژوهشی

• Geophysics
• Marine Geology
• Environmental Science
• +۵ مورد دیگر

Eric Attias is a Research Professor at the Institute for Geophysics, Jackson School of Geosciences, The University of Texas at Austin. He also serves as a Guest Investigator at the Woods Hole Oceanographic Institution's Geology & Geophysics Department and an Affiliate Faculty at the University of Hawaii's School of Ocean and Earth Science and Technology. His research focuses on marine geohazards, lithosphere-biosphere interactions, and subsurface fluid dynamics using integrated geophysical techniques like marine electromagnetic (CSEM/MT) methods, seismic reflection, and oceanographic data. Key research areas include oceanic transform faults, methane hydrates, CO2 sequestration monitoring, and submarine freshwater reservoirs. His OCEEMlab group addresses environmental and societal challenges through high-resolution imaging of Earth’s subsurface structures. Eric's work bridges geophysics, marine geology, and environmental science to explore resource potential and geohazard mitigation strategies. His academic roles include advising postdoctoral researchers and students (details available in his CV) and collaborating with international institutions. He is affiliated with multiple geophysical organizations and has contributed to advanced marine instrumentation development for shallow-water and deep-sea surveys. Contact: attias@ig.utexas.edu | Office: ROC 3.258 | Mailing Address: Jackson School of Geosciences, 2305 Speedway, Austin, TX 78712

Lu Tianمشاهده پروفایل

استاد

• Survival Analysis
• Semiparametric Modeling
• Restricted Mean Survival Time
• +۴ مورد دیگر

Lu Tian is a Professor of Biomedical Data Science in the School of Medicine at Stanford University, with a courtesy appointment as Professor of Statistics. He has been at Stanford since 2008, progressing from Assistant Professor to his current full Professor position in the Department of Biomedical Data Science. Sc.D. in Biostatistics, Harvard University (1998-2002) M.S. in Mathematics, Nankai University, Tianjin, P.R. China (1995-1998) B.S. in Mathematics, Nankai University, Tianjin, P.R. China (1991-1995) Professor Tian's research spans several critical areas in biostatistics and data science. His work focuses on developing innovative statistical methodologies with direct clinical applications. He has made significant contributions to survival analysis, particularly in restricted mean survival time methods, which offer clinically interpretable alternatives to traditional hazard ratios in clinical trials. His research in meta-analysis addresses limitations of existing methods, especially for studies with sparse data or few studies. In personalized medicine, he develops statistical frameworks for identifying patient subgroups that benefit most from specific treatments. His recent publications demonstrate a clear trend toward developing statistically rigorous methods with direct clinical interpretability. Rather than focusing solely on theoretical advances, his work emphasizes practical applications in oncology and cardiology clinical trials, where he develops methods that provide clear, actionable insights for clinicians. His research bridges the gap between complex statistical theory and real-world clinical decision-making, with particular emphasis on survival analysis techniques that clinicians can readily interpret and apply. Wangkechang Scholarship, Nankai University (1991-1998) Howard Hughes Fellowship (1999-2002) Robert B. Reed Award for Excellence in Biostatistics, Harvard (2000) Distinction in Teaching Award, Harvard School of Public Health (2000-2002) Professor Tian has served as Associate Editor for several prominent journals including Biometrics, Statistics in Medicine, and Biostatistics and Epidemiology, and as Book Editor for Advanced Medical Statistics. He was a Board Director of the International Chinese Statistical Association (2015-2018). His methodological contributions have been widely adopted in clinical research, particularly his work on restricted mean survival time which has changed how treatment effects are reported in oncology trials. His development of exact inference methods for meta-analysis has provided more reliable approaches for combining evidence from small studies.

Karina Mochettiمشاهده پروفایل

استادیار

• Computer Science Education
• Gender Diversity in STEM
• Cybersecurity
• +۳ مورد دیگر

Karina Mochetti is an Assistant Professor of Teaching in the Department of Computer Science at the University of British Columbia (UBC), part of the Faculty of Science. She previously held an Adjunct Professor position at Fluminense Federal University (UFF), Brazil. Her research focuses on computing education, gender diversity in STEM, and mathematics foundations in computer science. Education: PhD and MSc in Computer Science (UNICAMP, Brazil) BSc in Computer Engineering (UNICAMP, Brazil) Postdoctoral Research in Formal Security Analysis (UNICAMP, Brazil) Academic Specialization in Higher Education Teaching (SENAC, Brazil) Research Interests: Active learning methods in computing education Addressing gender disparities in STEM through pedagogical innovation Mathematical foundations of computer science Historical contributions of women in computing Awards: UBC Open Education Resource Champions (2024) Google Brazil Women in Technology Award (2009) Best Paper Award at SBSeg (2007) Teaching & Service: Main instructor for CPSC 121 (Models of Computation) and CPSC 103 (Systematic Program Design) Coordinator of Include Meninas UFF (promoting women in computing) Reviewer for SIGCSE, ITiCSE, and SBSeg Member of UBC’s Women in Science Program Labs & Projects: ECOS Project (computing and education with KDE) Development of educational tools like Rocs (graph theory app)

Robert Meraمشاهده پروفایل

مدرس

• Climate Science
• Atmospheric Science
• Environmental Science
• +۳ مورد دیگر

Robert Mera is a Lecturer and Program Coordinator in the Department of Marine, Earth, and Atmospheric Sciences at North Carolina State University. His primary affiliation is with the College of Sciences. His research focuses on climate science, atmospheric processes, and environmental modeling, with notable work on climate change attribution, regional climate modeling, and climate-justice intersections. He has contributed to studies on CO2 emissions, heat extremes in California, and climate impacts on agriculture. His academic work bridges meteorological data analysis, hydrological modeling, and public health applications, such as using weather forecasts to manage meningitis in West Africa. His regional climate modeling efforts for the western U.S. and West African Sahel highlight his expertise in superensemble methodologies and climate variability. Mera's publications span climate change effects on crops, factor separation methods in climate models, and moisture dynamics in West Africa. His research emphasizes interdisciplinary approaches to address environmental challenges, including sustainability and policy implications of climate science findings.

Stephen J. Huttمشاهده پروفایل

استادیار

• Machine Learning
• Educational Data Mining
• Artificial Intelligence
• +۳ مورد دیگر

Stephen J. Hutt is an Assistant Professor of Computer Science at the Daniel Felix Ritchie School of Engineering & Computer Science, University of Denver. His research focuses on educational data mining and machine learning applications in self-regulated learning (SRL) frameworks. He holds a PhD in Computer Science from the University of Colorado Boulder and an MS in Computer Science and Artificial Intelligence from the University of York. His work operationalizes cognitive operations in mathematical problem-solving through automated detection systems. Recent contributions include developing machine learning models to identify SMART model operations (searching, monitoring, assembling, rehearsing, translating) using student textual responses and interaction data. These detectors demonstrate robust performance across diverse populations with AUC ROC scores between 0.76–0.89. Professional affiliations include the Association for Computing Machinery (ACM) and its Special Interest Group on Computer-Human Interaction (SIGCHI). His research bridges computational methods with educational theory, emphasizing scalable SRL assessment tools.

• Health Economics
• Stroke Treatment
• Emergency Medical Services
• +۳ مورد دیگر

Dr Peter McMeekin is a senior academic researcher at Newcastle University, specializing in health economics, stroke care, and vascular disease management. His work focuses on optimizing healthcare systems, particularly in stroke treatment eligibility, emergency stroke services, and the economic implications of medical interventions. Key research areas include: Modeling stroke thrombectomy accessibility and cost-effectiveness Comparative analysis of endovascular vs open surgical treatments for thoracic aortic aneurysms Evaluation of emergency care centralization impacts on mortality and efficiency Development of decision-support tools for thrombolytic treatment Notable contributions include: Systematic reviews on stroke pathway methodologies and aneurysm management Cost analyses of mechanical thrombectomy and air ambulance transfers in rural stroke care Development of the COMPASS decision aid for stroke thrombolysis Collaborations with multiple disciplines (e.g., neurology, vascular surgery, geriatrics) reflect his interdisciplinary approach to healthcare challenges.

Min Wuمشاهده پروفایل

دانشیار

• Biomedical Informatics
• Health Informatics
• Dental Informatics
• +۶ مورد دیگر

Min Wu serves as Associate Professor in the Department of Health Care Informatics and Administration at the University of Wisconsin-Milwaukee, where she directs the Health Care Informatics Certificate and MS programs. Her work bridges clinical practice and technological innovation to address critical gaps in healthcare communication and delivery. Her academic credentials include: Ph.D. in Biomedical Engineering, University of North Carolina (2003) M.S. in Physics, West Virginia University (1999) M.S. in Physics, Shandong University (1996) B.S. in Physics, Dalian University of Technology (1993) Dr. Wu's research centers on identifying unmet medical needs through technological solutions, with specializations in biomedical database applications, dental informatics, and public health informatics. Her current work emphasizes human-computer interaction in clinical settings and ROC analysis for medical education. Key methodologies include social media analysis for patient needs assessment and patient-centered design of medical reporting systems. Analysis of her recent publications (2019-2022) reveals a clear trajectory toward patient-centered health informatics, particularly in radiology reporting and obstetric ultrasound communication. Her work increasingly incorporates social media mining and qualitative methods to understand communication gaps, while maintaining strong foundations in dental informatics and big data applications for clinical decision support. Her professional recognition includes: Best Article Award (2010) from Journal of Digital Imaging Though specific advising details aren't provided in source materials, her mentorship role in the Health Care Informatics program indicates active student engagement. Her research demonstrates consistent funding through institutional support at UWM, with publications spanning clinical informatics, medical education tools, and health data analytics.

Mioara Joldesمشاهده پروفایل

استاد

• Computer Arithmetic
• Validated Computing
• Computer Algebra
• +۵ مورد دیگر

Mioara Joldes is a Directeur de Recherche (DR) at CNRS working in the ROC team at LAAS-CNRS (Laboratory for Analysis and Architecture of Systems) in Toulouse, France. She has been a permanent CNRS researcher at LAAS since 2013. Her work bridges rigorous mathematical computing with practical aerospace applications, particularly in space surveillance and collision avoidance. She collaborates extensively with French space agencies like CNES and has developed algorithms that have been tested on satellites including the ESOC OPS-SAT CubeSat and embedded in the CNES ASTERIA software. Dr. Joldes received her Ph.D. from École Normale Supérieure de Lyon in 2011. In 2019, she earned her Habilitation from Toulouse III Paul Sabatier University with a dissertation focused on building efficient symbolic-numeric objects, algorithms, and software tools with space-based applications. Her academic journey reflects a strong foundation in mathematical computing translated into practical aerospace solutions. Her research spans three interconnected domains: Computer Arithmetic, where she improves the accuracy and reliability of numerical computations; Symbolic-Numeric Algorithms, developing methods that combine symbolic and numerical approaches for validated computing; and Aerospace Applications, particularly in Space Surveillance and Tracking. Her work enables mathematically rigorous error bounds for computations used in critical aerospace systems, ensuring safety in space operations through validated numerics that provide "sure, yet reasonably tight, error bounds." Her recent publications demonstrate a strong focus on collision probability computation in space, validated numerical methods for differential equations, and efficient implementations of mathematical functions. She frequently collaborates with Denis Arzelier, Florent Bréhard, and other researchers in developing algorithms that have transitioned from theoretical foundations to practical space applications, with several papers receiving significant recognition in their fields. Distinguished Paper Award at ISSAC 2019 for work on algebraic reconstruction of measures with holonomic density Best Paper Award at ASAP 2020 for efficient FPGA implementation of the Probit function CNRS Bronze medal in 2021 recognizing her research contributions Dr. Joldes leads the PRESTO International Emerging Actions Project on Probabilistic Reliable and Efficient numerics for Stochastically-constrained Trajectory Optimization. Her research has received funding from CNES and other space agencies, supporting technology transfer from theoretical mathematics to operational space systems. She has supervised multiple students working at the intersection of validated computing and aerospace applications, with her methods being implemented in operational systems used by space agencies worldwide. As a key member of the ROC (Robotique et Calcul) team at LAAS-CNRS, she develops critical software tools like CAMPARY (CudA Multiple Precision ARithmetic librarY) and contributes to Sollya, an environment for developing safe floating-point code. Her work on orbital collision probability evaluation has been featured in major publications including CNRS-Le Journal, La Tribune, Les Echos, and L'usine Nouvelle, demonstrating the real-world impact of her research in ensuring safe space operations.