Find Academic Supervisors

• Statistics
• Quantile-Based Statistical Methodologies
• Robust Statistical Methods
• +4 more

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 GavinView profile

Professor

• Earthquake Engineering
• Structural Control
• Vibration Isolation
• +5 more

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
• +5 more

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
• +1 more

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
• +2 more

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
• +2 more

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 AlasView profile

Associate Clinical Professor

• Urogynecology
• Pelvic Floor Disorders
• Reconstructive Pelvic Surgery
• +9 more

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 AndrewsView profile

Professor

• Infectious Diseases
• Tuberculosis
• Tropical Diseases
• +4 more

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 SunView profile

Researcher

• Aerial Robotics
• Robotics Perception
• Incremental Nonlinear Control
• +3 more

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).

Petros SiderisView profile

Associate Professor

• Resilient Infrastructure
• Sustainable Structural Systems
• Computational Structural Mechanics
• +7 more

Dr. Petros Sideris is an Associate Professor in the Zachry Department of Civil and Environmental Engineering at Texas A&M University . He holds the E.B. Snead '25 Career Development Professorship and leads the Structural Engineering and Mechanics (SEM) Research Group, focusing on seismically resilient and sustainable infrastructure through novel materials and computational methods. Ph.D., University at Buffalo-SUNY (2012) Research Hub: Gulf Resilience Coastlines and People GAANN Program: 3D Printed Construction Research Interests center on resilient infrastructure systems , integrating computational mechanics , experimental testing , and advanced materials like polyurethane and hempcrete. His work addresses aging infrastructure , performance-based engineering , and accelerated construction for seismic regions. 3D Printed Concrete and Hempcrete Hybrid Sliding-Rocking (HSR) Bridge Columns Gradient Inelastic Beam Theory Thermo-Chemo-Hygro-Mechanical Concrete Modeling Scientific Awards include: E.B. Snead '25 Career Development Professorship NSF Grant CMMI 1538585/1748031 NIST Disaster Resilience Research Grant Advising and Grants highlight collaborations with students on seismic retrofit , 3D construction , and hempcrete sustainability . His group has secured funding from NSF, NIST, HUD, and BASF. Notable projects include polyurethane-enhanced bridge columns and large-scale structural testing of 3D-printed buildings.

Babak JamhiriView profile

Researcher

• Structural Health Monitoring
• Geotechnical Engineering
• Machine Learning Applications in Civil Engineering
• +3 more

Babak Jamhiri holds a Doctoral Researcher position with expertise in structural health monitoring and reliability analysis. He is affiliated with the 3DCP and Construction Automation Research Group, focusing on AI-driven simulations, infrastructural risk assessment, and probabilistic modeling. His research spans geoenvironmental reliability, machine learning algorithms for signal processing, and risk assessment in construction materials. Education: BSc, MSc, and PhD in relevant disciplines (institutions unspecified). Current projects include structural health monitoring of 3DCP components, machine learning for signal processing, and probabilistic crack propagation in problematic soils. Recent work involves performance assessment of post-tensioned concrete beams and developing compressed earth bricks with marl sediment. Research interests emphasize geotechnical challenges such as expansive soils, desiccation cracking, and soil stabilization using industrial byproducts. He explores Bayesian inference, Markov decision chains, and fractal approaches in clay-based materials for nuclear waste disposal applications. Contributions include YouTube tutorials on information value theory, signal processing for anomaly detection, and probabilistic modeling with R. His work bridges theoretical advancements with practical engineering solutions, emphasizing uncertainty quantification and decision-making under uncertainty.

Frederick W. FaltinView profile

Academic

• Process Monitoring and Control
• Manufacturing and Service Process Improvement
• Risk Management
• +3 more

Frederick W. Faltin is a Professor of Practice in the Department of Statistics at Virginia Tech's College of Science , serving as Director of Corporate Partnerships for the Academy of Data Science . His roles include bridging academic research with industry applications through collaborative projects and educational initiatives. Education: S.B. in Mathematics, Massachusetts Institute of Technology M.S. in Operations Research and Mathematical Statistics, Cornell University Research Interests: Focuses on process monitoring and control , manufacturing/service process improvement , and risk management . Specializes in statistical methodologies for quality assurance, including control charts, algorithmic process optimization, and failure mode analysis. Extends his expertise into healthcare analytics and software testing frameworks. Publications & Contributions: Authored/co-edited influential works like Statistical Methods in Healthcare (2012) and Encyclopedia of Statistics in Quality & Productivity (2007). Recent research explores statistical engineering paradigms and defect rate paradoxes in supplier-customer dynamics. Awards & Recognition: Fellow, American Statistical Association (ASA) Shewell Prize, American Society for Quality (ASQ) Teaching & Outreach: Instructs CMDA 4864 (Senior Capstone in Data Analytics) and STAT 4214 (Advanced Regression Methods) . Leads corporate partnerships to integrate industry needs into academic curricula. Office located at 312 Data and Decision Sciences, Blacksburg, VA. Labs & Teams: Engaged in Virginia Tech's Academy of Data Science, fostering cross-disciplinary collaborations between academia and enterprise sectors.

Meimei LiuView profile

Assistant Professor

• Deep learning
• Bayesian data analysis
• Learning theory
• +3 more

Dr. Meimei Liu is an Assistant Professor in the Department of Statistics at Virginia Tech. She holds a Ph.D. in Statistics from Purdue University (2018), an M.S. from the University of Science and Technology of China (2013), and a B.S. in Mathematics from Anhui University (2010). Prior to Virginia Tech, she conducted postdoctoral research at Duke University under Prof. David Dunson, focusing on Bayesian methods and neural imaging analysis. Her research interests span deep learning (graph embedding, variational autoencoders), Bayesian data analysis, learning theory (stochastic gradient descent, variational inference), big data methodologies (domain adaptation, random projection), and semi/non-parametric inference. She has been recognized with awards including the Virginia Tech ICTAS Diversity Inclusion Seed Fund (2023-2024) and COLT Travel Award (2019). Dr. Liu teaches courses such as CMDA 2005: Integrated Quantitative Science and STAT 3615: Biological Statistics. Her work emphasizes interdisciplinary applications, particularly in neuroscience and medical imaging. She actively contributes to the academic community through memberships in the American Statistical Association, International Chinese Statistical Association, and Women in Machine Learning.

Salvador Garcia MunozView profile

Visiting Professor

• Chemical Engineering
• Pharmaceutical Engineering
• Process Analytics
• +5 more

Salvador Garcia Munoz serves as a Visiting Professor in the Department of Chemical Engineering at Imperial College London's Faculty of Engineering. His expertise spans pharmaceutical engineering, process analytics, and chemometrics, with a strong focus on applying multivariate data analysis methods to chemical and pharmaceutical processes. Education: PhD in Chemical Engineering from McMaster University, Canada MS in Chemical Engineering from Monterrey Tech, Mexico BS in Chemical and Computer Systems Engineering from Monterrey Tech, Mexico Professor Garcia Munoz specializes in multivariate data analytics for process understanding and control, particularly in pharmaceutical manufacturing. His work bridges the gap between theoretical chemical engineering principles and practical industrial applications. He has developed expertise in Principal Components Analysis (PCA), Partial Least Squares (PLS), process monitoring, and optimization techniques. His research focuses on creating data-driven models for complex systems where data is abundant, correlated, and noisy, with direct applications in continuous manufacturing, quality by design, and real-time release testing in the pharmaceutical industry. His publication record demonstrates a strong trend toward integrating advanced statistical methods with chemical engineering principles to solve pharmaceutical manufacturing challenges. The majority of his recent work focuses on process analytics, model-based design, and optimization for pharmaceutical processes, with particular emphasis on continuous manufacturing, design space definition, and quality control. His articles reveal expertise in applying chemometrics to pharmaceutical unit operations including granulation, dissolution, and powder flow systems. Professor Garcia Munoz actively contributes to the academic community through teaching specialized courses on process analytics and multivariate methods. He is also a key contributor to open-source scientific software, notably as one of the authors of KIPET (Kinetic Parameter Estimation Toolkit), which is used for parameter estimation in chemical reaction systems. His work with PYOMO (Python-based Optimization Modeling Objects) demonstrates his commitment to developing accessible computational tools for the engineering community. His research group collaborates extensively with industry partners, particularly in the pharmaceutical sector, to develop practical solutions for manufacturing challenges. The focus on real-world applications is evident in his publications, which often include case studies using data from actual industrial processes. His work bridges the gap between academic research and industrial implementation, making significant contributions to the advancement of pharmaceutical manufacturing technologies.

• Applied Econometrics
• Time Series Analysis
• Forecasting
• +3 more

Filip Van den Bossche serves as Professor and Vice Dean of Education and Students at the Faculty of Economics and Business (FEB) of KU Leuven, where he is affiliated with the Department of Decision Sciences and Information Management and the Operations Research and Statistics Research Group (ORSTAT). He also holds membership in the KU Leuven Institute for Mobility and has been a visiting professor at IÉSEG School of Management in Lille, France since 2016. His administrative leadership extends to the FEB Council, Board, International Mobility and Admissions Committee, and multiple university education committees. His research expertise spans applied econometrics, time series analysis, and composite indicator methodology, with primary applications in road safety and transportation policy. He has developed macroscopic safety models, uncertainty assessment frameworks for safety indices, and evaluation tools for road safety campaigns. His work also addresses market efficiency in finance through adaptive markets hypothesis testing and robust CSR investment screening, alongside healthcare logistics optimization for organ transplant systems. Teaching responsibilities include Statistics for Data Science, Predictive Business Analytics, and time series forecasting courses. Analysis of his 15 most recent publications reveals consistent methodological focus on econometric modeling and time series analysis applied across transportation safety, financial markets, and healthcare logistics. The research demonstrates evolution from foundational road safety modeling toward interdisciplinary applications in sustainable development goals and pandemic response evaluation, with growing emphasis on composite indicators and uncertainty quantification. Cross-cutting themes include policy evaluation, optimization under constraints, and translation of quantitative methods to real-world decision support. As an educational leader, Van den Bossche has directed multiple academic programs since 2012 and currently oversees FEB's educational strategy as Vice Dean. His current sabbatical project (2025-2026) focuses on time series analysis for Sustainable Development Goals. He actively contributes to ORSTAT's research on operations research applications and the KU Leuven Institute for Mobility's interdisciplinary transportation studies, fostering collaborations between business, economics, and public policy domains.