جست‌وجوی استاد و پژوهشگر

• High Dimensional Statistics
• Variable Selection
• Robust Statistics
• +۶ مورد دیگر

Mohamed Ndaoud is an Associate Professor of Statistics at ESSEC Business School and a member of the Statistics Department at CREST. Previously, he held a tenure-track position as Assistant Professor in the Department of Mathematics at the University of Southern California (USC) from August 2019. He earned his PhD in theoretical statistics under the supervision of A.B. Tsybakov. His educational background includes: PhD in Theoretical Statistics, supervised by A.B. Tsybakov. Dr. Ndaoud's research centers on high dimensional statistics, with core contributions in variable selection, estimation, and community detection. He also actively explores robust statistics, stochastic processes, harmonic analysis, random matrix theory, and spiked models, often bridging theoretical statistics with machine learning applications. Analysis of his publication record (2018-2024) reveals a consistent focus on developing robust and adaptive methods for high-dimensional data. Key themes include outlier-robust regression, clustering algorithms for mixture models, minimax optimal procedures, and harmonic analysis techniques for Gaussian processes. His work frequently introduces non-convex and tuning-free approaches to address challenges in sparse modeling and statistical learning. Scientific Awards: No awards were listed in the provided information. Research funding includes support from the CY Initiative of Excellence Paris-Seine. There is no information available regarding student advising or additional grants. Dr. Ndaoud is an integral member of CREST's Statistics Department and serves on the organizing committee for the Meeting in Mathematical Statistics (2023-2025) in Luminy, France. He actively participates in the international statistics community through workshops and tutorials, such as the upcoming Heidelberg-Paris workshop on mathematical statistics in January 2025.

Kamran Paynabarمشاهده پروفایل

استاد

• Statistical Learning Methods for Big Data Analytics
• Quality Engineering
• Manufacturing Systems
• +۳ مورد دیگر

Kamran Paynabar is the Fouts Family Chair and Professor at the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Institute of Technology . He specializes in Engineering-Driven Statistical Modeling , Statistical Learning Methods for Big Data Analytics , and Quality Engineering , with applications in Manufacturing Systems and Healthcare . B.S. and M.Sc. in Industrial Engineering from Iran University of Science and Technology and Azad University (2002, 2004) M.A. in Statistics and Ph.D. in Operations Engineering from the University of Michigan (2010, 2012) His research focuses on high-dimensional data analysis for system monitoring, diagnostics, and prognostics using semi-parametric and nonparametric approaches . Methodologies he develops address applications in automotive, aerospace, medical device manufacturing , and healthcare , including cardiac and orthopedic surgery analytics. Dr. Paynabar’s publications from 2016–2025 highlight trends in anomaly detection , tensor analysis , Gaussian process modeling , and active learning applied to photovoltaic systems , additive manufacturing , and smart grids . INFORMS Data Mining Best Student Paper Award Best Application Paper Award from IIE Transactions Wilson Prize for Manufacturing Systems Research POMS Best Paper Award Georgia Tech CETL/BP Teaching Excellence Award He has advised students like Xiaolei Fang (University of Florida), Hao Yan (Arizona State University), and Chitta Ranjan , with grants from the National Science Foundation (NSF) . His work emphasizes interdisciplinary collaboration , bridging industrial engineering , machine learning , and system optimization .

Wesley Klewerton Guez Assuncaoمشاهده پروفایل

دانشیار

• Software Modernization
• Variability Management
• Software Quality
• +۷ مورد دیگر

Wesley Klewerton Guez Assuncao serves as an Associate Professor in the Department of Computer Science within the College of Engineering at North Carolina State University. Previously, he held positions as a University Assistant/Senior Researcher at Johannes Kepler University Linz in Austria, Postdoctoral Researcher at Pontifical Catholic University of Rio de Janeiro in Brazil, and Assistant/Associate Professor at Federal University of Technology - Paraná in Brazil. His academic journey includes a Ph.D. in Computer Science from the Federal University of Paraná with a visiting period at Johannes Kepler University. Dr. Assuncao's research spans several critical areas in modern software engineering. His primary interests include Software Modernization (reverse engineering, re-engineering, and migration), Variability Management (variability mechanisms, software customization, and software reuse), and Software Quality (technical debt, code smells, and software refactoring). He also investigates Model-Driven Engineering (model inconsistency detection, repair generation, and change propagation), Collaboration in Systems Engineering (change synchronization, tool flexibility, and conflict awareness), Software Testing (regression testing, integration testing, and test case selection/prioritization), and AI4SE (Generative AI, Machine Learning, and Evolutionary Algorithms for Software Engineering). His recent publications reveal a strong focus on software modernization challenges, particularly regarding legacy systems transformation, microservices architecture, and the application of AI techniques in software engineering. The research shows increasing integration of Large Language Models in addressing software evolution problems, with emphasis on empirical validation through industrial collaborations. His work consistently bridges theoretical foundations with practical applications, as evidenced by multiple industry partnerships and real-world case studies. Dr. Assuncao has received numerous prestigious awards including Distinguished Reviewer Awards from FSE and SANER conferences, a Young Researcher Award from Johannes Kepler University, and multiple Best Paper Awards from top software engineering conferences. His research has been recognized with ACM SIGSOFT Distinguished Paper Awards and IEEE Computer Society TCSE Distinguished Paper Awards. In terms of academic service, he serves as Co-editor of the In Practice track at the Journal of Systems and Software and has held various leadership roles in major conferences including ICSE, SANER, MSR, and SPLC. He has successfully secured substantial research funding totaling approximately USD 1.91 million from sources including the Austrian Science Fund, Brazilian National Council for Scientific and Technological Development, and state-level Brazilian research foundations. Dr. Assuncao leads the Wolfpack Innovations in Software Engineering Research (WISER) Lab at NC State University, where he supervises graduate students working on cutting-edge software engineering research problems. His lab maintains strong collaborations with international institutions and industry partners including Dynatrace and ITPRO Consulting & Software GmbH.

• Microeconometrics
• Science and Technology Studies
• Fuzzy Numbers
• +۳ مورد دیگر

Bayram Veli Doyar is a Lecturer in the Department of Economics and Department of Economic Policy at Suleyman Demirel University's Faculty of Economics and Administrative Sciences. He holds a PhD in Economics (2023), an MA in Economics (2015), and a Licence in Economics (2012) from Turkish institutions. His research focuses on Microeconometrics Science and technology studies Fuzzy numbers in economic modeling with publications analyzing ICT development, oil price impacts on GDP/exchange rates, R&D-productivity linkages, and technology-life satisfaction dynamics. Recent work includes studies on Internet use in the Southern Caucasus Innovation in Ukraine during conflict Causality in Turkey-Azerbaijan-Kazakhstan trade using econometric models like Probit, ARDL bounds test, and bootstrap causality.

• Formal Methods
• Software Dependability
• Software Security
• +۷ مورد دیگر

Panagiotis Katsaros is a Professor at the Department of Informatics, Aristotle University of Thessaloniki. His research focuses on Formal Methods, Software Dependability & Security, and Internet of Things (IoT) systems. 1992: Dipl. in Mathematics, Aristotle University 1993: M.Sc in Software Engineering, Aston University 2002: PhD in Informatics, Aristotle University His work integrates model-based design with probabilistic verification to address challenges in embedded systems , IoT , and cloud environments . He has pioneered methodologies for correctness-by-construction and security-aware elasticity in distributed systems. Recent publications (2015-2019) highlight trends in multi-core embedded systems , probabilistic model checking , and logical error detection . His research bridges formal verification with practical system design . Scientific Recognition Best Paper Award at 17th Panhellenic Conference on Informatics (2013) Best Paper Award at 15th Panhellenic Conference on Informatics (2011) He has supervised PhD students Stylianos Basagiannis and Anakreon Mentis , and contributes to the Dependability & Security Group (DSG A.U.Th) . His work impacts spacecraft design , cloud security , and transaction processing systems .

• Materials Science
• Metallic Glasses
• High-Entropy Alloys
• +۷ مورد دیگر

Tero Mäkinen is a postdoctoral researcher at Aalto University's Department of Applied Physics, specializing in materials science and computational modeling. His work bridges disciplines like physics, rheology, and sustainable materials engineering. Key Affiliations: Aalto University, Complex Systems and Materials research group. His research focuses on: Mechanical properties of metallic glasses and high-entropy alloys Rheology of colloidal hydrogels and thermogelation processes Computational methods for material design and failure prediction Development of sustainable biobased foams and coatings Recent publications highlight his contributions to: Creep failure mechanisms in heterogeneous and amorphous materials Data-driven approaches for alloy phase prediction Open-source software tools for rheological analysis Green materials engineering using cellulose and lignin

• Statistics
• Quantile-Based Statistical Methodologies
• Robust Statistical Methods
• +۴ مورد دیگر

Dr. Shenal Dedduwakumara serves as a Lecturer in the School of Public Health within the Faculty of Health and Medical Sciences at the University of Adelaide, a position he assumed in 2024. Prior to this appointment, he held a Lecturer position in Statistics at the School of Computer and Mathematical Sciences for over three years following completion of his PhD. His academic trajectory demonstrates consistent specialization in statistical methodology with applications spanning health sciences and economics. Dr. Dedduwakumara's research centers on quantile-based statistical methodologies, with particular emphasis on robust estimation techniques applicable to health sciences and economic analysis. His scholarly contributions include the development of simulation frameworks, novel statistical measures, interactive web applications, and computational packages validated through real-world data applications. This work addresses critical challenges in poverty measurement, inequality analysis, and distribution modeling, providing methodological advancements for researchers across multiple disciplines. Analysis of his publication record reveals a focused research trajectory centered on quantile estimation and distribution modeling. His most recent work (2024-2025) concentrates on developing the 'rquest' R package for quantile hypothesis testing, while earlier publications (2019-2021) systematically address confidence interval construction for poverty metrics, inequality measures using grouped data, and parameter estimation for generalized lambda distributions. This progression demonstrates increasing methodological sophistication with practical applications in socioeconomic analysis and health statistics. As a co-supervisor eligible for Masters and PhD candidates, Dr. Dedduwakumara contributes to graduate education within the School of Public Health. His research program integrates theoretical statistical development with practical applications, particularly in poverty measurement and health data analysis, though specific grant funding details are not publicly documented in the available materials.

Henri Gavinمشاهده پروفایل

استاد

• Earthquake Engineering
• Structural Control
• Vibration Isolation
• +۵ مورد دیگر

Henri Gavin is a Professor in the Department of Civil and Environmental Engineering. His research focuses on developing innovative solutions for structural resilience, control systems, and earthquake engineering. He leads interdisciplinary projects integrating sensor technology, IoT systems, and machine learning for real-world applications such as earthquake early warning systems. Research Interests: Professor Gavin's work spans earthquake engineering, structural dynamics, vibration isolation, robotics, optimization, and machine learning applications in civil infrastructure. His signature project—Pilot Earthquake Early Warning in Kathmandu (Data+, 2022)—demonstrates his approach to combining AI models with geophone sensors and IoT networks for on-site seismic alerts. Publication Trends: His recent articles (2020-2024) emphasize structural reliability, decentralized control of robotic systems (UAVs/swarms), optimization algorithms, and seismic protection strategies. This reflects a consistent focus on theoretical rigor applied to practical engineering challenges, particularly in nonlinear dynamics and multi-agent coordination. Projects and Advising: He mentors students through Duke's Data+ program, guiding projects in energy data analytics and earthquake early warning systems. Notable funded initiatives include instrumentation and modeling of seismic isolation systems (EAGER, 2012) and performance analysis of base-isolated structures during seismic events.

• Fluid Dynamics
• Computational Fluid Dynamics
• Screw Compressors
• +۵ مورد دیگر

Aleksandar Kovacevic is a Professor at City, University of London, specializing in mechanical engineering with a focus on fluid dynamics and positive displacement machinery. With over two decades of research experience since completing his doctoral studies at City University London in 2002, he has established himself as a leading expert in screw compressor technology and computational fluid dynamics. Dr. Kovacevic's research spans multiple areas of mechanical engineering with particular emphasis on: Computational Fluid Dynamics (CFD) modeling of positive displacement machines Screw compressor design, optimization, and performance analysis Grid generation methodologies for complex rotary machinery Thermodynamic analysis of fluid systems Experimental validation of computational models His extensive publication record demonstrates consistent research productivity, with recent work focusing on advanced optimization techniques including Gaussian process regression and Bayesian methods applied to multi-stage screw compressors. His research shows a clear evolution from fundamental CFD modeling to increasingly sophisticated performance optimization and lifecycle cost analysis, with applications across multiple industries including HVAC, refrigeration, and industrial compression systems. Dr. Kovacevic has received recognition for his contributions to the field of positive displacement machinery, with numerous collaborations spanning academic institutions and industrial partners. His work has practical applications in energy-efficient compressor design and optimization, contributing to advancements in sustainable engineering solutions. Throughout his career, Dr. Kovacevic has mentored numerous researchers and students, many of whom have gone on to contribute to the field of mechanical engineering and fluid dynamics. His research has been supported by various grants enabling both fundamental and applied investigations into compressor technology. His laboratory work focuses on the development and validation of advanced CFD models for positive displacement machinery, combining computational approaches with experimental techniques such as Particle Image Velocimetry and infrared thermography for comprehensive system analysis.

• Applied Statistics
• Statistical Analysis
• Fuzzy Logic
• +۱ مورد دیگر

Zeynep GÖKKUŞ is a Lecturer at Kastamonu University , specifically within the Computer Programming Program of the Kastamonu Vocational School . She has held this full-time position since 2023, previously working at the same institution's Computer Technology and Programming Department in the İnebolu Vocational School from 2009 to 2021. Her academic career has been consistently focused on Applied Statistics and Statistical Analysis , with additional expertise in Fuzzy Logic and Biostatistics . PhD in Statistics from Ondokuz Mayıs University (2018) MSc in Mathematics from Kafkas University (2008) BSc in Mathematics from Atatürk University (2005) Research Interests : Her work spans Statistical Modeling for agricultural and aquacultural applications, including Fuzzy ANOVA , Cox Regression , and Group Sequential Testing . She has developed innovative approaches in Multi-Criteria Decision Making for environmental assessments and contributed to Nanofiber Synthesis and heavy metal removal studies. Article Trends : Her publications (2012–2025) show increasing focus on Fuzzy Logic integration with traditional statistical methods (ANOVA, AAS), alongside applications in Aquaculture Survival Analysis and Environmental Remediation . Recent works emphasize Circular Intuitionistic Fuzzy Control Charts and Geospatial Agricultural Quality Rankings . Scientific Impact : According to Google Scholar metrics, she has 10 citations and an h-index of 2 . Her research has been published in journals like Aquaculture (SCI) and Turk Tarım ve Doğa Bilimleri Dergisi (TR Dizin), with conference presentations at international events including INFUS 2023–2024 and the International Sport Sciences Congress.

• Earthquake Engineering
• Seismic Design
• Steel Structures
• +۲ مورد دیگر

Lydell Andree Wiebe is a Professor in the Department of Civil Engineering at McMaster University's Faculty of Engineering. He specializes in earthquake engineering, seismic design of steel structures, and resilient infrastructure systems. His academic credentials include a BASc (2005) and PhD (2013) in Civil Engineering from the University of Toronto, and an MSc in Earthquake Engineering (2008) from the ROSE School in Pavia, Italy. His research focuses on developing innovative structural systems such as controlled rocking braced frames and masonry walls to enhance seismic resilience. Key areas include performance-based design, structural dynamics, and risk assessment of infrastructure under seismic loads. He actively contributes to professional committees, including the CSA S16 Technical Committee (Steel Structures) and the Canadian Association for Earthquake Engineering. Dr. Wiebe's recent publications (2020–2025) emphasize seismic loss mitigation, optimization of controlled rocking systems, and machine learning applications in structural engineering. His work integrates experimental testing with advanced computational models to improve design methodologies for earthquake-resistant structures. Awards & Honors: GJ Jackson Fellowship Award (2005) HA Krentz Research Award (2015) McMaster Faculty of Engineering Teaching Excellence Award (2017) McMaster Students Union Teaching Award (2014, 2017) He teaches courses including Steel Structures , Seismic Design and Analysis , and Introduction to Civil Engineering , reflecting his commitment to education and mentorship.

• Statistics
• Mathematical Analysis
• Optimization
• +۲ مورد دیگر

Luis Alberto Ramil Novo is a full professor at the Faculty of Sciences , University of Santiago de Compostela , affiliated with the Department of Statistics, Mathematical Analysis and Optimization and the Galician Mathematical Research and Technology Center (CITMAga) . His research focuses on statistical modeling , goodness-of-fit tests , and optimization , particularly in polynomial regression and smoothing spline methods. PhD (1999): Thesis on goodness-of-fit tests for polynomial regression under Dr. Wenceslao González Manteiga His zbMATH profile lists two peer-reviewed publications (1998, 2000) in Statistica Sinica and Communications in Statistics. Simulation and Computation , co-authored with leading experts in mathematical statistics. These works address hypothesis testing and nonparametric regression techniques .

Alexandriah Nicole Alasمشاهده پروفایل

دانشیار بالینی

• Urogynecology
• Pelvic Floor Disorders
• Reconstructive Pelvic Surgery
• +۹ مورد دیگر

Dr. Alexandriah Nicole Alas is a board-certified, fellowship-trained urogynecologist and Clinical Associate Professor in the Department of Obstetrics & Gynecology at Stanford University School of Medicine. She maintains clinical practice through Stanford Health Care and specializes in minimally invasive urogynecology and reconstructive pelvic surgery using vaginal, laparoscopic, robotic and VNOTES approaches. Her educational background includes medical training at the University of Texas Medical Branch Hospital (2009), residency at Cedars Sinai Medical Center Obstetrics and Gynecology Residency (2013), and fellowship at Cleveland Clinic Florida Gynecology Fellowship (2016). She holds dual board certifications from the American Board of Obstetrics and Gynecology in both Obstetrics and Gynecology (2014) and Urogynecology and Reconstructive Pelvic Surgery (2019). Dr. Alas' research focuses on pelvic floor disorders, with particular expertise in pelvic organ prolapse, urinary and fecal incontinence, bladder pain, and vaginal fistulas. Her work frequently addresses mesh and non-mesh procedures, uterine preservation surgeries, and non-surgical management options. She is nationally recognized for her expertise and serves as a national oral board examiner for the American College of Obstetrics and Gynecology. Analysis of her recent publications reveals a strong focus on systematic reviews examining comparative outcomes in pelvic floor surgery, with particular attention to sexual function outcomes after prolapse repair, non-estrogen therapies for genitourinary syndrome of menopause, and optimal management of mesh complications. Her work consistently appears in high-impact journals including Obstetrics & Gynecology, American Journal of Obstetrics and Gynecology, and International Urogynecology Journal. Best San Antonio Doctor, San Antonio Magazine (2020-2024) Outstanding Women in Medicine, Scene in SA (2021-22) Top Doctor in Texas, Rising Star, Scene in SA (2020-2024) Excellence in Female Pelvic Medicine & Reconstructive Surgery (Resident), American Urogynecologic Society (2013) Cleveland Clinic Florida Grant Recipient, Women's Professional Staff Association (2013) Dr. Alas is passionate about teaching and previously served as associate fellowship director in Urogynecology. She currently supervises, teaches, and mentors medical students, residents, and fellows in urogynecology. She serves on the Society of Gynecological Surgeons Systematic Review Group and is active in multiple professional organizations including the American Congress of Obstetricians and Gynecologists, American Urogynecologic Society, Association of Professors of Gynecology and Obstetrics, and International Urogynecology Association. Her clinical practice includes a wide range of in-office procedures such as urodynamics, office cystoscopy, bladder Botox injections, urethral injections, PTNS, and sacral neuromodulation tests.

Jason Andrewsمشاهده پروفایل

استاد

• Infectious Diseases
• Tuberculosis
• Tropical Diseases
• +۴ مورد دیگر

Jason Andrews is a Professor of Medicine (Infectious Diseases) and, by courtesy, Epidemiology and Population Health at Stanford University School of Medicine. As a practicing infectious diseases physician and researcher, he focuses on tuberculosis and tropical diseases in resource-limited settings across Brazil, South Africa, Nepal, and India. Education: MD and BA from Yale University; SM from Harvard School of Public Health; DTM&H from Gorgas Memorial Institute. Clinical Expertise: Infectious disease diagnosis, treatment, and prevention. His research integrates mathematical modeling, microbial genetics, field epidemiology, and statistical inference to address challenges in infectious disease control. Key projects include: TB control strategies in Brazilian prisons Pharmacogenomic testing for personalized isoniazid dosing Genomic epidemiology of Mycobacterium tuberculosis and Salmonella Typhi BCG vaccine efficacy studies Serosurveillance for enteric fever His work has been recognized through: Election to the American Society of Clinical Investigation (2025) and American Epidemiological Society (2021) NIH Director's New Innovator Award (2016) Rosenkranz Prize for Healthcare Research (2015) Young Investigator Prizes from IDSA and The Union Clinical trials he leads include: SARS-CoV-2 monoclonal antibody efficacy trials Point-of-care pharmacogenomic testing for TB prevention He mentors postdoctoral and graduate researchers while collaborating with institutions like the Stanford Center for Innovation in Global Health and the Maternal & Child Health Research Institute.

Sihao Sunمشاهده پروفایل

پژوهشگر

• Aerial Robotics
• Robotics Perception
• Incremental Nonlinear Control
• +۳ مورد دیگر

Dr. Sihao Sun is a Researcher in Robotics at the Cognitive Robotics Department , Delft University of Technology. He focuses on planning, estimation, and control of aerial robotic systems, with notable contributions to fault-tolerant control algorithms and perception systems for quadrotors under extreme conditions. PhD in Aerospace Engineering (2020), Delft University of Technology Postdoctoral Researcher at University of Twente (2022-2023) and University of Zurich (2020-2021) His research spans Aerial Robotics , Robotics Perception , and Incremental Nonlinear Control , with applications in Multi-robot Systems , Agile Flight Control , and Aerodynamic Modeling . His recent work explores uncertainty modeling for meta-adaptive control and collaborative aerial manipulation. His 15 most recent articles emphasize robustness in fault-tolerant quadrotor systems, sensor-driven control, and high-speed flight dynamics. He has received the Veni grant (Dutch Research Council, 2024) and a Best Paper Award (IEEE Robotics and Automation Letters, 2020). Current projects include Accurate Aerial Manipulation under Uncertainties and High Efficiency Air Cargo Design . Key students include Jack Zeng (MSc, Cum-Laude distinction) and Fang Nan (ETH Medal winner).