Find Academic Supervisors

• Econometrics
• Macroeconomics
• Time Series Analysis
• +3 more

Cem Çakmaklı serves as Associate Professor of Economics at Durham University Business School and Koç University's College of Administrative Sciences and Economics (currently on leave). Previously, he held Assistant Professor positions at Koç University (2015-2023) and the University of Amsterdam (2011-2013), following an AXA Postdoctoral Fellowship at Koç University (2013-2015). His academic credentials include: Ph.D. in Economics/Econometrics, Tinbergen Institute, Erasmus University Rotterdam (2012) M.Phil in Economics (Cum Laude), Tinbergen Institute (2007) MA in Economics (with distinction), Istanbul Technical University (2005) B.Sc. in Management Engineering, Istanbul Technical University (2002) His research integrates advanced econometric methodologies with macroeconomic policy analysis, specializing in time series modeling for economic forecasting, pandemic impact assessment, and financial market dynamics. Recent work demonstrates innovative applications of Bayesian semiparametric techniques to yield curve modeling, GDP nowcasting, and vaccination economics, with particular emphasis on emerging market contexts. His methodological contributions bridge theoretical econometrics with real-world policy challenges. Publication trends reveal a strategic pivot toward pandemic economics since 2020, producing high-impact studies on global vaccination strategies and lockdown effectiveness, while maintaining core expertise in time-varying parameter models and financial econometrics. His work consistently addresses policy-relevant questions through rigorous quantitative frameworks. Scientific recognition includes: AXA Research Fund Post-Doctoral Fellowship (2013-2015) Fellowships at Economic Research Forum (ERF), Centre for Macroeconomic Policy, and Rimini Center for Economic Analysis (RCEA) Research funding comprises multiple TUBITAK grants totaling over 400,000 TL for projects on central bank credibility (2019-2020), large time-varying parameter VARs (2019-2020), and emerging market business cycle indicators (2017-2019). As an educator, he has developed and taught advanced econometrics curricula at PhD and graduate levels across multiple institutions, while his Capital magazine columns translate complex economic concepts for public discourse. His professional ecosystem integrates academic research through Ethos Economics Consulting with policy engagement via media commentary.

• Veterinary epidemiology
• Canine health
• Feline health
• +3 more

Dan G. O'Neill is an Associate Professor in Companion Animal Epidemiology at The Royal Veterinary College (RVC), specializing in veterinary epidemiology and animal welfare research. He co-leads the VetCompass Programme, a major research initiative collecting primary-care veterinary clinical data to generate evidence on breed health in dogs and cats. His work spans both research and teaching, supervising numerous undergraduate and post-graduate projects. Dr. O'Neill's educational background includes: MVB MRCVS (Faculty of Veterinary Medicine) from University College Dublin (1982-1987) BSc(hons) Pharm (Faculty of Science) from University College Dublin (1991-1992) Multiple Graduate Certificates (SAP, FelP, Derm, B&PS) from International School of Veterinary Post-graduate Studies MSc(VetEpi) from The Royal Veterinary College (2009-2010) PhD in Pathobiology and Population Sciences from The Royal Veterinary College (2010-2013) PGCertVetEd FHEA from Royal Veterinary College (2018-2019) Dan O'Neill's research focuses on veterinary epidemiology, particularly breed-related health issues in companion animals. His work examines the health implications of extreme conformations in dogs, especially brachycephalic (flat-faced) breeds. Through the VetCompass Programme, he has generated substantial evidence on breed predispositions to disease, contributing significantly to animal welfare science. His research combines large-scale data analysis with clinical studies to address critical welfare issues in companion animals. His extensive publication record demonstrates a consistent focus on canine and feline epidemiology, with particular emphasis on brachycephalic conditions, breed-related disorders, and welfare assessment. The research shows strong methodological rigor using large-scale primary care data, with applications spanning clinical practice, breeding standards, and public policy. His work increasingly addresses the intersection of animal welfare science and public perception, particularly regarding extreme conformations in popular dog breeds. Dr. O'Neill has received numerous prestigious awards recognizing his contributions to veterinary science: Fellowship of the Royal College of Veterinary Surgeons (2018) BSAVA - Blaine Award for Advancement of Small Animal Science (2019) RVC - James Bee Student Nominated Award for Outstanding Project Supervisor (2019, 2023) RVC - First prize for PGCertVetEd (2020) Kennel Club Charitable Trust 'International Canine Health Award' (2021) All Party Parliamentary Group 'Phillippa Robinson Dog Welfare Award' (2024) As an educator, Dr. O'Neill has supervised numerous undergraduate and postgraduate students through the VetCompass Programme, which serves as a teaching resource for student projects. His research has been supported by significant grants from organizations including the RSPCA (for his PhD development of VetCompass), Dogs Trust (for post-doctoral research on welfare scoring methods), and the Kennel Club Charitable Trust (as their Companion Animal Epidemiologist from 2014-2017). Dr. O'Neill chairs the UK Brachycephalic Working Group (since 2016) and is a founding member of several important organizations including the International Collaborative on Extreme Conformations in Dogs (ICECDogs), the Dog Breeding Reform Group (DBRG), and the Legal Advisory Group on Extreme Conformation in Dogs (LAGECDogs). He also serves on the RCVS Knowledge Quality Improvement board and the RVC Clinical Research Ethical Review Board, demonstrating his leadership in shaping veterinary policy and research ethics.

• Myopia Development and Control
• Choroidal Thickness and Eye Growth
• Optical Coherence Tomography
• +5 more

Scott A. Read is a Professor in the School of Optometry at Queensland University of Technology's Faculty of Health. He is a leading researcher in the field of myopia development and control, with extensive expertise in ocular biometry, choroidal thickness measurements, and optical coherence tomography applications. His work bridges basic vision science and clinical optometry, focusing on understanding the mechanisms of eye growth and developing strategies for myopia management. Dr. Read's research program centers on myopia development and control, with particular emphasis on choroidal thickness dynamics, ocular biometry changes during visual tasks, and the effects of light exposure on eye growth. His work reveals that the choroid plays a critical role as an optical signal transducer in eye growth regulation, with significant diurnal variations and responses to visual stimuli. He has extensively documented how myopic defocus, accommodation, and light exposure patterns influence choroidal thickness and axial elongation in children and young adults. His research has established important links between outdoor light exposure and reduced myopia progression, contributing significantly to evidence-based myopia control strategies. Analysis of Dr. Read's recent publications shows a strong focus on advanced imaging techniques, particularly optical coherence tomography and its applications in measuring choroidal thickness, vascular changes, and biomechanical properties of the eye. His work increasingly incorporates artificial intelligence methods for image analysis while maintaining a strong clinical orientation toward understanding myopia mechanisms and developing effective control strategies. His research spans from fundamental investigations of visual processing pathways to clinical trials of myopia control interventions like atropine therapy. Dr. Read has been instrumental in several major collaborative efforts, including the International Myopia Institute reports that have shaped global understanding of myopia mechanisms and control strategies. His work has been foundational in establishing the choroid's role as a key tissue in eye growth regulation and myopia development. Through his laboratory at QUT, Dr. Read mentors numerous PhD students and early-career researchers, fostering the next generation of vision scientists. His research team employs a multidisciplinary approach combining optometry, ophthalmology, biomedical engineering, and data science to address critical questions in myopia research. Current projects focus on understanding the mechanisms of atropine's myopia control effects, developing advanced imaging biomarkers for myopia progression, and investigating the impact of modern visual environments on eye development.

Daniel JeffaresView profile

Senior Lecturer

• Leishmania Genomics
• Population Genetics
• Quantitative Genetics
• +3 more

Dr. Daniel Jeffares is a Senior Lecturer in the Department of Biology at the University of York. His research focuses on Leishmania genomics, population genetics, and quantitative genetics, with an emphasis on understanding evolutionary processes and molecular biology in parasitic organisms. His work contributes to global health initiatives, including collaborations through the UK-Brazil Joint Centre Partnership in Leishmaniasis (JCPiLeish) and the PREV_PKDL project analyzing visceral leishmaniasis in East Africa. He leads the Jeffares Lab, which utilizes advanced genomic techniques to study parasite evolution and pathogenesis. Research Interests: Leishmania population genomics and drug resistance Genomic analysis of co-infections and pathogen diversity Evolutionary adaptations in trypanosomatids and extremophiles Applications of genomic methods in disease research Awards : Recipient of the Visiting Professorship (2022), recognizing his contributions to collaborative research and capacity-building in genomic methodologies. Grants & Collaborations: Principal Investigator on projects funded by MRC, Wellcome Trust, and NERC Leadership in multidisciplinary initiatives addressing visceral leishmaniasis in Sudan, Ethiopia, Uganda, and Kenya Active partnerships with institutions in Brazil and global health networks Labs/Teams: The Jeffares Lab at the University of York, part of the Biology Department’s research cluster focusing on microbial genomics and evolutionary processes.

• Ophthalmic Optics
• Visual Optics
• Myopia Research
• +3 more

Professor David Atchison is a distinguished academic at Queensland University of Technology (QUT), affiliated with the School of Optometry and Vision Science within the Faculty of Health. He holds a Professorship and has been a leading figure in visual optics research for over 35 years. His academic qualifications include a Doctor of Science (QUT), PhD (University of Melbourne), and advanced optometry degrees. Research Focus: Ophthalmic optics, myopia progression mechanisms, retinal shape analysis, and color vision. Key Contributions: Over 230 journal publications, 2 books, and significant grants from ARC and NHMRC. Awards: HB Collin Medal (2014), Glenn F. Fry Award (2011), and Fellowships from major optical societies. Teaching: Leads courses in optometry, including OPB353/453 (Ophthalmic Optics) and OPN164 (Research Methods). Research Interests: Explores the interplay between eye optics and visual performance, design of corrective lenses, and color vision deficiencies. Current projects include longitudinal studies on myopia progression in children and advanced imaging techniques for retinal morphology analysis.

Rui ChenView profile

Researcher

• Genomics
• Machine Learning
• Cancer Biology
• +3 more

Rui Chen, PhD, is a Researcher in the Department of Molecular Physiology and Biophysics at Vanderbilt University School of Medicine. Their work focuses on integrating multi-omics data with advanced computational methods to uncover genetic mechanisms in complex diseases, including schizophrenia, cancer, and neurodevelopmental disorders. Dr. Chen’s research emphasizes leveraging machine learning for precision medicine applications, such as predicting cancer outcomes and identifying druggable genes. Key areas of expertise include functional genomics, Bayesian statistical frameworks, and computational tool development for genomic data quality control (e.g., the DRAMS tool). Their studies often bridge basic science and clinical translation, using zebrafish models and biobank resources for disease discovery. Dr. Chen has contributed to advancements in understanding gene regulatory networks in plants (e.g., rice stamen development) and has pioneered methods for analyzing non-coding variants using deep learning. Their work consistently addresses translational challenges in genetics, such as improving rare variant association studies and refining GWAS interpretations through multi-omics integration. Contact: rui.chen.1@vanderbilt.edu

• Machine Learning
• Climate Modeling
• Environmental Science
• +3 more

Dr. Shuolin (Shawn) Li is a Postdoctoral Research Scientist at Columbia University's Data Science Institute, collaborating with Professors Pierre Gentine, Upmanu Lall, and Tian Zheng. He holds a Ph.D. in Fluid Dynamics and Hydrology and an M.S. in Computer Science from Duke University. His research focuses on developing machine learning algorithms for Earth observations, particularly in climate model parameterization using Bayesian inference, neural networks, and physical parameterizations. He collaborates with the Learning the Earth with Artificial Intelligence and Physics (LEAP) initiative and scientists at the National Center for Atmospheric Research (NCAR). Research interests include data assimilation, environmental fluid mechanics, and interdisciplinary applications of machine learning in hydrology, turbulence, and climate science. His work bridges computational methods with physical processes, addressing challenges in sediment transport, vegetation dynamics, and turbulent flow modeling. Recent publications highlight advancements in generative data assimilation, sediment flux parameterization, and turbulence modeling. His contributions span environmental engineering, climate science, and mathematical modeling, emphasizing scalable solutions for complex Earth systems. Collaboration with LEAP and NCAR underscores his commitment to advancing AI-driven Earth science. No formal awards are listed, but his work reflects significant contributions to interdisciplinary environmental research.

• Water Treatment
• Resource Recovery
• Environmental Biotechnology
• +2 more

Professor Ana Soares is Professor of Biotechnology Engineering at Cranfield Water Science Institute, Cranfield University, and an IWA Fellow specializing in environmental water protection and resource recovery. Her research develops sustainable solutions for municipal and industrial wastewater management, focusing on bioreactor technologies and circular economy approaches. Education includes a PhD in Environmental Engineering from Lund University and MEng in Biotechnology Engineering from Algarve University. Research explores innovative bioreactor management for nutrient and energy recovery from wastewater, with emphasis on phosphorus recovery, anaerobic treatment optimization, and carbon capture. Work bridges scientific innovation and practical applications through collaborations with water utilities and industrial partners. Publications focus on advancing wastewater resource recovery technologies, with recent work on neural network corrosion modeling, phosphorus recovery methods, and SARS-CoV-2 wastewater surveillance. Honors include recognition as IWA Fellow for contributions to water research and leadership in international conferences. Directs research projects funded by EU Horizon 2020 (SMART-PLANT, nextGen, ENERWATER) and supervises PhD students in water biotechnology. Chaired IWA conferences on ecotechnologies and leading edge water technologies.

Gregory S. OkinView profile

Professor

• Dryland Geomorphology
• Aeolian Processes
• Mineral Aerosols
• +4 more

Gregory S. Okin is a Professor of Geography and Chair of the Department of Geography at UCLA, affiliated with the Institute of the Environment and Sustainability. He holds a PhD in Geochemistry from Caltech (2001) and has been at UCLA since 2006. His research focuses on dryland geomorphology, aeolian processes, and mineral aerosol dynamics, with emphasis on dust emission impacts on climate, ecosystems, and human health. Okin employs remote sensing, field studies, and modeling to analyze soil-vegetation-atmosphere interactions in arid regions, particularly wind erosion's role in grassland-to-shrubland transitions. He co-leads NASA's Earth Surface Mineral Dust Source Investigation (EMIT), creating global mineral maps using imaging spectroscopy. Recent work addresses dust-climate links, wildfire health impacts, and rangeland monitoring via satellite-cloud computing integration. Education Background: Ph.D., Geochemistry, California Institute of Technology (2001) M.S., Geology, California Institute of Technology (1997) B.A., Chemistry & Philosophy (Double Major), Middlebury College (1995) Postdoctoral Research, Geography, UC Santa Barbara (2001–2002) Research Interests: Dr. Okin's work integrates aeolian geomorphology , remote sensing , and Earth system modeling to explore: - Dust emission mechanisms and climate feedbacks - Dryland ecosystem resilience under climate change - Vegetation-soil connectivity in arid landscapes - Applications of imaging spectroscopy (e.g., EMIT mission) His projects often involve interdisciplinary collaborations, combining field measurements with computational modeling. Scientific Contributions: Key innovations include developing dust emission models (e.g., MAPTALE) and advancing spectral unmixing techniques for mineral mapping. His findings on pet food environmental impacts (e.g., carbon pawprints) have gained media attention.

Jilles VreekenView profile

Professor

• Causal Inference
• Machine Learning
• Data Mining
• +5 more

Prof. Dr. Jilles Vreeken is tenured faculty at the CISPA Helmholtz Center for Information Security , where he leads the Exploratory Data Analysis group. He also serves as an Honorary Professor at Saarland University . Research focuses on causal inference, machine learning, and data mining Develops unsupervised methods for robust, interpretable models PI on grants like HAICU's Neuro-Explicit Models and Crushing Antimicrobial Resistance His recent work spans causal discovery in non-stationary time series ( SPACETIME ), federated binary matrix factorization, interpretable neural search patterns, and data modification rule mining from event logs. He applies information-theoretic approaches to address hidden confounding, selection bias, and multi-environment causal modeling. Key trends in his publications include: Integrating causal inference with machine learning via algorithmic Markov conditions Advancing federated learning for privacy-preserving causal discovery Creating interpretable pattern mining frameworks for graphs, sequences, and high-dimensional data Developing MDL-based methods for reliable dependency and rule discovery Scientific Recognition: 2018 - IEEE ICDM Tao Li Award 2018 - IEEE ICDM Best Paper 2015 - UdS-CS Busy Beaver Teaching Award 2011 - ACM SIGKDD Best Student Paper 2010 - ACM SIGKDD Doctoral Dissertation Runner-Up 2009 - ECML PKDD Best Student Paper As an educator, he has supervised 15+ PhD/MSc students and taught courses like Topics in Algorithmic Data Analysis and Information-Theoretic Machine Learning . His research group pioneers methods for trustworthy information processing and causal anomaly detection , with applications in materials science, epidemiology, and cybersecurity.

Houman SotoudehView profile

Associate Professor

• Neuro-oncologic Imaging
• Stroke Imaging
• Quantitative Imaging/Radiomics
• +5 more

Houman Sotoudeh, M.D., is an Associate Professor at UT Southwestern Medical Center's Department of Radiology, specializing in Neuroradiology. He previously served as Associate Professor of Radiology and Neurology, and Director of Clinical Research at the UAB School of Medicine (Birmingham, AL). His clinical expertise focuses on Brain, Head/Neck, and Spinal Imaging, with research emphasizing Neuro-oncologic Imaging, Stroke Imaging, and Quantitative Imaging/Radiomics. Education: Medical Degree and Radiology Residency from Tehran University of Medical Sciences (Iran). Fellowship training at Massachusetts General Hospital and Washington University in St. Louis. Professional Affiliations: American Society of Neuroradiology, Radiological Society of North America. Dr. Sotoudeh has published over 80 peer-reviewed articles, with recent work addressing AI-driven diagnostic tools, stroke epidemiology, and radiomics applications in oncology. His research bridges clinical practice innovations in imaging with advancements in precision medicine. Key contributions include developing MRI-based radiomics models for tumor grading, analyzing global stroke burden trends, and improving CT perfusion interpretation for subacute ischemic stroke.

Nancy ReidView profile

Professor

• Statistical Theory
• Mathematical Statistics
• Asymptotic Methods
• +6 more

Nancy Reid is a University Professor in the Department of Statistical Sciences at the University of Toronto, Canada. Her academic leadership includes roles such as OC (Order of Canada), FRS (Fellow of the Royal Society), and FRSC (Fellow of the Royal Society of Canada). She specializes in foundational statistical theory, asymptotic methods, likelihood inference, and Bayesian-frequentist comparisons. Reid's research bridges theoretical advancements and practical applications in biostatistics, machine learning, and interdisciplinary data science. Her work emphasizes robust statistical methods for high-dimensional and complex data, including contributions to partial likelihood (Cox model), saddlepoint approximations, and accuracy in directional inference. Key recognitions include the COPSS Distinguished Achievement Award, Gold Medal from the Statistical Society of Canada, and numerous editorial and advisory roles. Reid collaborates globally, delivering invited lectures at institutions like Harvard, Stanford, and the Royal Society. Reid's academic output spans over 30 years, with influential books like Theory of the Design of Experiments (with D.R. Cox) and Applied Asymptotics . Her recent focus includes replicability in data science, foundations of statistical inference, and bridging gaps between theoretical and applied statistics. She actively promotes methodological rigor and interdisciplinary collaboration through initiatives like the Canadian Statistical Sciences Institute.

Jenni NikuView profile

Senior Lecturer

• Multivariate Statistical Modeling
• Latent Variable Models
• Statistical Method Development
• +3 more

Jenni Niku is a Senior Lecturer at the Department of Mathematics and Statistics, University of Jyväskylä, specializing in multivariate statistical modeling and computational methods for ecological data. She is actively involved in the Predictive Community Ecology Group and leads research projects on latent variable models for complex ecological structures. Research Focus: Latent variable models for community ecology Spatial-temporal pattern analysis Statistical methodology for multivariate data Joint species distribution modeling Computational tools for ecological datasets Recent Publications: Jenni has contributed to advancements in analyzing compositional count data, fungal dispersal dynamics, chronic disease modeling, and peatland restoration. Her work bridges statistical innovation with ecological applications, particularly in handling high-dimensional data. Collaborations: She works extensively with researchers from Biological and Environmental Science departments, focusing on interdisciplinary applications of her statistical models.

ANA DE CASTRO RIOSView profile

Professor

• Toxicology
• Forensic Toxicology
• Analytical Chemistry
• +12 more

Ana de Castro Ríos is a Professor at the University of Santiago de Compostela, affiliated with the Faculty of Pharmacy, the Department of Forensic Sciences, Pathological Anatomy, Gynecology and Obstetrics and Pediatrics, and the Institute of Forensic Sciences "Luis Concheiro" (INCIFOR). She is an active member of the FORTOX (Forensic Toxicology) research group, focusing on advanced analytical methods in toxicology. Her research interests lie primarily in Toxicology , Forensic Toxicology , and Analytical Chemistry , with a strong emphasis on developing and validating LC-MS/MS methods for detecting drugs in alternative biological matrices such as hair, nails, oral fluid, and meconium. Her work bridges clinical and forensic applications, including therapeutic drug monitoring, occupational exposure assessment, drug-impaired driving, and perinatal drug exposure. The recent trend in her publications (2019–2024) demonstrates a consistent focus on the application of liquid chromatography-tandem mass spectrometry for multi-analyte detection in challenging matrices. Her research spans forensic contexts like drug-facilitated crimes and DUID profiling, as well as clinical areas such as immunosuppressant monitoring and neonatal exposure to tobacco and synthetic drugs. PhD in Toxicology, University of Santiago de Compostela, 2007 Ana de Castro Ríos has contributed significantly to the validation and application of analytical methods in forensic and clinical toxicology. Her publications reflect active collaboration and leadership in method development, particularly for detecting drugs in keratinous tissues and meconium. There is no mention of grant funding or student supervision in the provided texts. She is involved in research that supports legal and medical investigations through robust analytical science. She maintains an active research profile with publications up to 2024, indicating ongoing scientific contributions in her field.

Leah B. ShawView profile

Associate Professor

• Mathematical Biology
• Epidemiology
• Ecological Modeling
• +7 more

Leah B. Shaw is an Associate Professor in the Department of Mathematics at the College of William and Mary, with an affiliate appointment in the Applied Science Department. Her research integrates applied mathematics, physics, and biology to model population dynamics in ecological and epidemiological systems. Education: BS in Physics and Mathematics, Virginia Tech MS in Mathematics, Virginia Tech PhD in Physics, Cornell University Her research focuses on the interplay between network structure, stochasticity, and population dynamics. She develops mathematical models to study epidemic spread, ecosystem resilience, and extinction risks. Key areas include adaptive networks, marine population dynamics (particularly oysters and blue crabs in the Chesapeake Bay), and stochastic extinction pathways. Her methodological toolkit spans statistical mechanics, nonlinear dynamics, and computational simulations. The analysis of her recent publications reveals a strong focus on epidemic modeling in complex networks, with recurring themes of adaptive behavior, multistrain interactions, and control strategies. Her work often bridges theoretical frameworks with real-world applications in public health and environmental conservation. She also explores fundamental questions in nonequilibrium systems and synchronization phenomena. Scientific Contributions: Developed models for epidemic control using behavioral adaptation and vaccination in networked populations. Studied bistability in oyster reef ecosystems, informing restoration strategies. Advanced theoretical understanding of stochastic extinction paths in finite populations. Mentored undergraduate researchers in mathematical biology projects. Published extensively in interdisciplinary journals such as Bulletin of Mathematical Biology , Physical Review E , and Journal of Theoretical Biology . Dr. Shaw mentors PhD students in Applied Science with interests in mathematical biology and welcomes undergraduate researchers, particularly those seeking summer fellowships. She emphasizes collaboration and interdisciplinary training. Her group investigates topics ranging from polio control using defective interfering particles to opinion dynamics and nonequilibrium traffic models, reflecting a broad yet cohesive research vision.