Find Academic Supervisors

Mei-Hua LeeView profile

Associate Professor

• Motor Development
• Motor Learning
• Infant and Early Childhood Development
• +5 more

Mei-Hua Lee is an Associate Professor in the Department of Kinesiology at Michigan State University. She holds a Ph.D. from The Pennsylvania State University and leads the Motor Development and Learning Lab (SDLab). Her work focuses on motor development across the lifespan, particularly how infants and young children learn to interact with their environment through reaching and grasping behaviors. She integrates kinematic analysis, biofeedback, and qualitative methods to study motor skill acquisition and its implications for motor learning and rehabilitation theories. Her research explores the transition from spontaneous movements to goal-directed actions in infancy, leveraging advanced technologies like wearable sensors and machine learning for activity classification. She has pioneered body-machine interface systems to enable assistive device control for individuals with severe motor impairments. Lee’s studies also address methodological challenges in motor learning research, including missing data practices and the design of rigorous experimental protocols. Key collaborations involve interdisciplinary teams from engineering, neuroscience, and pediatrics. Her lab actively develops novel tools for early detection of developmental disorders through movement analysis and promotes translational research in neurorehabilitation technologies.

Erik SantisoView profile

Associate Professor

• Materials Science
• Computational Chemistry
• Molecular Modeling
• +7 more

Erik Santiso is an Associate Professor & University Faculty Scholar at NC State University's Department of Chemical and Biomolecular Engineering, part of the College of Engineering. His research group focuses on computational discovery of advanced materials and chemicals using molecular modeling, metaheuristic sampling, and machine learning. Key applications include catalyst design, biomimetic polymers, green surfactants for CO₂/water systems, and polymeric materials with exceptional mechanical properties. The group also explores oxygen conductors for chemical looping combustion and microprocessor manufacturing through simulations. His work bridges theoretical and applied chemistry, emphasizing predictive tools like the DESPASITO package for SAFT EOS parametrization. Santiso has received the Sigma Xi Young Faculty Research Award, recognizing his contributions to computational materials science. The Santiso Research Group collaborates across disciplines to tackle challenges in energy, environment, and biomedical engineering. Lab/Team: Santiso Research Group Key Projects: Crystal nucleation modeling, peptoid structure prediction, CO₂ sequestration, and drug delivery systems Publications emphasize interdisciplinary approaches, with over 50 peer-reviewed articles since 2016. His research often integrates quantum chemistry, atomistic simulations, and mesoscopic methods to accelerate material discovery.

Sylvia SullivanView profile

Assistant Professor

• Atmospheric Science
• Climate Modeling
• Cloud Physics
• +7 more

Sylvia Sullivan is a Joint Assistant Professor in the Department of Hydrology and Atmospheric Sciences and an Assistant Professor (home department) in the Department of Chemical and Environmental Engineering at the University of Arizona. Her research focuses on multiscale atmospheric interactions, including ice crystal nucleation, storm dynamics, and climate modeling. She leads the Lab for Multiscale Cloud Modeling and Experimentation , which integrates benchtop experiments with high-performance computing to study cloud processes and their impacts on precipitation and energy balance. Dr. Sullivan holds a PhD in Chemical Engineering from the Georgia Institute of Technology and a BS in Chemical Engineering and Environmental Science from the California Institute of Technology. Her expertise spans computational modeling, remote sensing, and atmospheric chemistry, with emphasis on aerosols, cloud physics, and radiative processes. Her research interests include mesoscale convective systems, tropical cloud dynamics, and the climatic impacts of precipitation patterns. She explores how microphysical processes influence large-scale atmospheric phenomena, particularly in the context of climate change. Recent work addresses extreme weather events, such as monsoon rainfall variability and dust storms in the Sonoran Desert. Dr. Sullivan has contributed to studies on cloud-radiative heating sensitivity, storm-resolving model validation, and the radiative impacts of convective systems. Her 2016 Waldemar T. Ziegler Award recognizes contributions to atmospheric science. She collaborates with NASA through projects like EXCITE, advancing Earth sciences research at Hispanic-serving institutions. Her work bridges laboratory experiments and global climate models, emphasizing interdisciplinary approaches to address pressing environmental challenges. Current projects include quantifying ice microphysics uncertainties and developing frameworks to assess future precipitation extremes driven by atmospheric dynamics.

Ed BrambleyView profile

Academic

• Aeroacoustics
• Computational Fluid Dynamics
• Wave Propagation
• +2 more

Dr. Ed Brambley is a Reader at the University of Warwick specializing in aeroacoustics, computational methods, and industrial mathematics. His research combines theoretical fluid dynamics with applied industrial challenges, particularly in acoustic liner optimization for aerospace and mathematical modeling of metal forming processes. Recent work advances boundary layer acoustics, wave propagation algorithms, and rolling process simulations. Collaborations span aerospace engineering and manufacturing sectors.

Joana FonsecaView profile

Senior Lecturer

• Geotechnical Engineering
• Multiscale Mechanics
• Image-Based Geotechnics
• +11 more

Dr. Joana Fonseca is a Senior Lecturer in Geotechnical Engineering at City St George's, University of London, within the Department of Civil Engineering in the School of Mathematics, Computer Science and Engineering. She holds a PhD from Imperial College London and has extensive research experience in multiscale geomechanics, image-based characterization, and granular media behavior. Her research interests include: Multiscale mechanics Image-based geotechnics using X-ray tomography and microscopy Experimental geomechanics of soils and rocks Micromechanics of granular media Centrifuge modelling Localized deformation and grain breakage Characterization of semi-solid alloys Her recent publications focus on advanced imaging techniques to understand soil fabric evolution, particle-scale interactions in sand-rubber mixtures, and microstructural modeling of carbonate sands for offshore foundations. These works span disciplines from geotechnical engineering to materials science, emphasizing computational and experimental integration. Scientific honors include: Chartered Member, Portuguese Institution of Engineers Principal Investigator on EPSRC-funded MuMShell project (2016) Associate Editor, Canadian Geotechnical Journal Member, Advisory Panel of Géotechnique She has advised several research students including Sadegh Nadimi, Deqiong Kong, and Ciaran Kennedy. She teaches core civil engineering courses such as Soil Mechanics (CV2401), Geotechnical Engineering (CV3401), and the Major Project (CVM412). She also serves as Guest Editor for special issues on image-based geotechnics and women in the built environment. Her research lab integrates advanced imaging, computational modeling, and experimental techniques to study geomaterials across scales, with applications in offshore engineering, sustainable construction, and materials science.

Marco GiomettoView profile

Assistant Professor

• Environmental Engineering
• Wind Engineering
• Smart Cities
• +4 more

Marco Giometto is an Assistant Professor of Civil Engineering and Engineering Mechanics at Columbia University, affiliated with the Columbia School of Engineering and Applied Science. His research focuses on fluid dynamics and turbulence, particularly in environmental flows, atmospheric boundary layers, and wind engineering. He employs supercomputing and physics-data-driven methods to advance understanding of natural systems and urban infrastructure resilience. Education: PhD in Civil Engineering (Braunschweig TU University and University of Florence, 2014); PhD in Mechanical Engineering (École Polytechnique Fédérale de Lausanne, 2016, EDME Award recipient). Experience: Postdoctoral roles at UBC and the Center for Turbulence Research (Stanford/NASA Ames); Amazon Visiting Academic (2020–present). Research Interests: Environmental Engineering, Wind Engineering, Smart Cities, Sustainable Infrastructure, and Computational Mechanics. His work bridges fundamental fluid dynamics with applied challenges in urban sustainability and climate resilience. Awards/Grants: NSF CAREER Award (2024), NSF Physical & Dynamic Meteorology Program grant (2024), Army Research Office DURIP Award (2023). Collaborations include Amazon Prime Air and field campaigns like CoURAGE. Lab & Outreach: Leads the Environmental Flow Physics Laboratory, advancing computational tools and field experiments. Teaches at Columbia and mentors students like Gurpreet Hora (PhD Teaching Fellow).

Xin XingView profile

Assistant Professor

• Medical Imaging
• Machine Learning
• Neuroscience
• +3 more

Xin Xing is an Assistant Professor at the University of Nebraska at Omaha, College of Information Science & Technology, Department of Computer Science. Their research bridges machine learning, neuroscience, and biomedical applications. Education: Ph.D. in Computer Science (2023), University of Kentucky M.S. in Information Technology (2016), University of Stuttgart B.S. in Communications Engineering (2011), Shandong University Research Interests: Medical Imaging AI: Developing models for Alzheimer's disease diagnosis using 3D PET/MRI and transformers (e.g., ADViT, CAT-XPLAIN) Gut-Brain Axis: Investigating microbiome impacts on neurodegeneration, particularly in APOE4 carriers Computer Vision: Innovating diffusion models, self-supervised learning, and attention mechanisms for biomedical and geospatial applications Publication Trends: Recent work focuses on neuroimaging biomarkers for Alzheimer's, microbiome interventions, and scalable vision-language architectures. They integrate cutting-edge ML techniques with clinical data analysis. Collaborations: Engaged in interdisciplinary projects spanning genetics, nutrition, and traffic safety. Their work appears in journals like Communications Biology and explores real-world applications such as roadway hazard detection via satellite imagery.

Miriam SchulteView profile

Professor

• Computational Fluid Dynamics
• High-Performance Computing
• Scientific Computing
• +3 more

Miriam Schulte is a Professor at the University of Stuttgart’s Institute for Parallel and Distributed Systems, leading the Institute for the Simulation of Large Systems. She holds a Carl von Linde Junior Fellowship and has held academic roles since 2002, including heading the CFD Group at TUM. Her expertise spans computational fluid dynamics (CFD), high-performance computing (HPC), and numerical methods for PDE solvers. She earned her diploma (1997) and PhD (2001) in mathematics from TUM, followed by habilitation in Computer Science (2010). Her research focuses on optimizing algorithms for efficient simulation software, integrating mathematics and computer science. Key areas include fluid-structure interactions, multi-physics coupling, and scalable parallel computing. She has contributed to frameworks like Peano for adaptive Cartesian grids and developed methodologies for partitioned fluid-structure interaction simulations. Publications highlight advancements in HPC, multi-physics coupling, and parallel algorithms. Awards include the Bayerische Begabtenfoerderung (1993–1997). Her work bridges computational methods with real-world applications, emphasizing scalability and efficiency in large-scale simulations.

• Neuroimaging
• MRI Segmentation
• Brain Structure Analysis
• +5 more

Dr. Pierre-Louis Bazin is a Researcher in the Department of Neurophysics at the Max Planck Institute for Human Cognitive and Brain Sciences. He holds a PhD from INRIA Rocquencourt (France) and conducted postdoctoral research at Brown University and Johns Hopkins University in the US. His work focuses on advanced neuroimaging techniques, including high-resolution MRI parcellation, quantitative MRI, and subcortical structure analysis. Key projects involve developing automated segmentation tools (e.g., Nighres), exploring cerebellar and subcortical anatomy, and investigating neurodegenerative markers using multi-modal approaches. Education 1995-1998: Electrical Engineering Diploma, SUPELEC (France) 1998-2001: PhD in Computer Vision & Graphics, INRIA Rocquencourt (France) 2001-2003: Postdoc in Computer Vision & Archaeology, Brown University (USA) 2003-2006: Postdoc in Neuroimaging, Johns Hopkins University (USA) Research Interests High-field MRI (7T+) applications in structural and functional brain mapping Development of neuroimaging software tools (e.g., Nighres) Subcortical and cerebellar morphometry in health and disease Quantitative MRI techniques for histology correlation Connectivity analysis in psychiatric disorders (e.g., schizophrenia) Awards Royal Netherlands Academy of Arts and Sciences (KNAW) membership (2020, 2024) Grants & Labs : Directs the Neurophysics lab at MPI-CBS. Active in large-scale projects like the Amsterdam Ultra-high field adult lifespan database (AHEAD). Collaborates on tools like MP2RAGEME for multi-parametric MRI. Labs/Teams : Leads neuroimaging method development and clinical translation efforts in Leipzig, Germany.

• Inverse Problems
• Image Processing
• Optimization
• +5 more

Markus Grasmair is a Professor at the Department of Mathematical Sciences at the Norwegian University of Science and Technology (NTNU). His research focuses on inverse problems, mathematical methods in image processing, and optimization. He has contributed to nonsmooth variational methods and Lavrentiev regularization for ill-posed problems. Education: Habilitation in Mathematics (University of Vienna, 2011), PhD in Mathematics (University of Innsbruck, 2006), MSc in Mathematics (University of Innsbruck, 2003). Markus's research spans mathematical imaging, biomedical applications, and PDE-based modeling. He has developed regularization techniques for inverse problems, including Lavrentiev and L1 methods, and applied these to medical imaging and cervical cancer risk stratification. His work integrates sparse optimization, shape analysis, and data-driven approaches. Recent publications highlight interdisciplinary trends, combining inverse problems with medical imaging (X-ray calibration, cervical cancer prediction) and extending variational methods to PDEs, multiscale modeling, and graph-based systems. Key subfields include monotone operators, matrix factorization, and elastic shape analysis. He teaches courses in optimization and has held academic positions at the University of Vienna, Catholic University of Eichstätt-Ingolstadt, and University of Innsbruck. Contact: markus.grasmair@ntnu.no. Current Role: Professor (NTNU) Past Roles: Substitute Professor (Catholic University of Eichstätt-Ingolstadt, 2012-2013), Assistant Professor (University of Vienna, 2009-2012)

Yi Lu MurpheyView profile

Professor

• Machine Learning
• Pattern Recognition
• Computer Vision
• +5 more

Dr. Yi Lu Murphey is a Professor in the Department of Electrical and Computer Engineering at the University of Michigan-Dearborn's College of Engineering and Computer Science. She directs the Intelligent Systems Lab and holds editorial responsibilities at the Journal of Pattern Recognition. Ph.D. in Computer/Information/Control Engineering (1989) from University of Michigan M.S. in Computer Science (1983) from Wayne State University Her research focuses on machine learning applications for: Automotive diagnostics and prognostics Vehicle power management optimization Robotic vision systems Driver behavior analysis Medical data processing Recent publications demonstrate expertise in: Deep learning architectures Autonomous vehicle systems Biomedical signal processing Power electronics optimization Multimodal data fusion Intelligent transportation Scientific honors include: IEEE Fellow Major funding sources: National Science Foundation National Institutes of Health Toyota Research Institute Ford Motor Company Nissan USA Her lab develops technologies for: Driver workload estimation Pedestrian detection Personalized route prediction Vehicle context detection Electric vehicle energy management

Rodrigo Ledesma AguilarView profile

Academic

• Multiphase Flows
• Phase Changes
• Capillarity and Wetting
• +7 more

Rodrigo Ledesma Aguilar is a Reader in Chemical Engineering at the University of Edinburgh, School of Engineering, affiliated with the Institute for Multiscale Thermofluids. His research focuses on interfacial phenomena, including multiphase flows, wetting, capillarity, and engineered liquid-infused surfaces. He teaches undergraduate and postgraduate courses in Process Dynamics and Control and Chemical Engineering Research Projects. Education: PhD in Physics, University of Barcelona DEA in Physics, University of Barcelona Diploma in Chemical Engineering, National Autonomous University of Mexico Research Interests: His work centers on fundamental and applied aspects of fluid-surface interactions, particularly in engineered surfaces with tailored wettability. He investigates phenomena such as contact line dynamics, capillary imbibition, droplet snapping, and biofilm resistance, with applications in microfluidics, antifouling coatings, and heat transfer. His research combines experimental, theoretical, and computational approaches. Publication Trends: His recent publications (2019–2025) show a consistent focus on liquid-infused and slippery surfaces, with emphasis on stability, wettability control, droplet dynamics, and antibiofilm performance. Key journals include Langmuir , ACS Applied Biomaterials , and Physical Review Fluids , reflecting interdisciplinary work at the intersection of fluid mechanics, materials science, and chemical engineering. Scientific Awards and Memberships: Fellow of the Higher Education Academy Member of the Institute of Physics Member of the EPSRC College of Reviewers Member of the UK Consortium of Mesoscale Engineering Sciences Advising and Grants: Dr. Ledesma Aguilar supervises PhD students and postdoctoral researchers. He has served as Principal Investigator and Co-investigator on multiple research projects funded by the Leverhulme Trust, EPSRC, and UK government bodies. These projects focus on wettability-patterned liquid surfaces, biofilm-resistant coatings, and antiviral surfaces, with total funding spanning from 2019 to 2026. Labs and Teams: He leads a research group within the Institute for Multiscale Thermofluids, collaborating closely with Professors Glen McHale and Gary Wells. His team works on experimental and theoretical aspects of fluid-surface interactions, utilizing facilities in microfluidics, surface characterization, and materials synthesis.

• Statistical Bioinformatics
• Computational Biology
• Spatial Transcriptomics
• +4 more

Dr. Shila Ghazanfar is an Australian Research Council DECRA Fellow at the University of Sydney, Faculty of Science. She is an expert in statistical and computational analysis of spatial transcriptomics and single-cell RNA-seq data, with a focus on developing bioinformatic approaches for integrating complex biological datasets across various omics modalities. Her educational background includes: Undergraduate studies in statistics and statistical bioinformatics at The University of Sydney PhD in statistics and statistical bioinformatics at The University of Sydney Royal Society Newton International Fellowship at The University of Cambridge under Dr. John Marioni in computational biology Dr. Ghazanfar's research interests center on developing statistical bioinformatic and biomedical data science approaches for meaningful integration of complex and high-dimensional biological datasets. Her multidisciplinary expertise spans statistics, statistical bioinformatics, and computational biology, enabling her to devise strategies to jointly model processes generating diverse data sources. Her work aligns with the University of Sydney Faculty of Science Research Strengths in Complex Systems, Precision and Digital Health, and Data and Decisions. Analysis of her recent publications (2021-2025) reveals strong focus areas including spatial transcriptomics infrastructure development, single-cell data integration methodologies, and applications in cancer research and cardiovascular biology. Key trends show progression from foundational method development to increasingly sophisticated multi-modal integration approaches, with significant emphasis on creating open-source computational tools for the research community. Her scientific recognition includes: Australian Research Council DECRA Fellowship Royal Society Newton International Fellowship Dr. Ghazanfar actively mentors research students, including Angel GUAN working on melanoma immunotherapy research. Her grant portfolio demonstrates substantial research support: 2023: Statistical Bioinformatics for Single Cell, Spatial and Multiomic Biotechnologies (Faculty of Science) 2022: Defining spatiotemporal mechanisms for peripheral nerve regeneration (Partnership Collaboration Award) 2022: Multiscale data integration for single cell spatial genomics (Chan Zuckerberg Initiative) Australian Research Council DECRA for statistical approaches in spatial genomics As a member of the Charles Perkins Centre, Dr. Ghazanfar participates in interdisciplinary research addressing complex health challenges through computational biology approaches, with collaborations spanning multiple domains from basic science to clinical applications.

Wesley TanseyView profile

Assistant Professor

• Statistical Machine Learning
• Computational Oncology
• Bayesian Statistics
• +3 more

Wesley Tansey serves as Assistant Professor in the Computational Oncology group within the Department of Epidemiology and Biostatistics at Memorial Sloan Kettering Cancer Center (MSKCC). His research bridges statistical machine learning with cancer biology, focusing on developing novel computational frameworks for oncology applications. Dr. Tansey's research program centers on Bayesian statistical methods for biological data analysis, with particular emphasis on spatial transcriptomics (evidenced by his BayesTME framework), drug response modeling , and multi-omics integration . His lab develops scalable algorithms for high-dimensional biological data, including UnitedMet for metabolite imputation and MultiTME for spatial profiling analysis. Current projects address combinatorial drug screening optimization, tumor microenvironment characterization, and predictive oncology platforms for rare cancers. His recent publications (2023-2025) demonstrate strong focus areas: Bayesian active learning for drug screening (6+ publications) Spatial biology methods (BayesTME, MultiTME) Metabolomics-transcriptomics integration (UnitedMet) Causal inference in biological systems Scientific recognition includes serving as Area Chair for AISTATS 2022 and frequent invited talks at major conferences including SIAM's Mathematics of Data Science meeting. Dr. Tansey actively mentors lab members including Sophie Jaro (Spotlight presenter at ICML Workshop), Haoran Zhang (contributed talk presenter), Christopher Tosh (Associate Research Scientist), and Jeff Quinn (Bioinformatics Software Engineer). His lab receives research funding supporting development of computational oncology platforms with clinical translation potential. The VIVO Lab maintains active GitHub repositories for core methodologies including BayesTME, reflecting strong software engineering practices in computational biology.

Nelson Yaw DzadeView profile

Assistant Professor

• Surface Science
• Catalysis
• Computational Materials Chemistry
• +7 more

Nelson Yaw Dzade serves as an Assistant Professor in the John and Willie Leone Department of Energy & Mineral Engineering within Penn State University's College of Earth and Mineral Sciences. His research integrates computational approaches with energy materials science to address critical challenges in sustainable energy systems. His office is located in 212 Hosler Building at University Park, PA. Dr. Dzade has extensive expertise in surface science, catalysis, and computational materials chemistry, with research focused on employing state-of-the-art computer simulations based on Density Functional Theory (DFT). His work centers on designing new materials with tailored functionalities, describing interface phenomena including adsorption and surface chemical reactions, and obtaining atomic-level information that may be difficult to access experimentally. His recent research specifically targets the computer-aided design of iron-sulfide nanocatalysts for solar-driven conversion of CO2 to fuels, aligning with his broader research themes of Climate and Natural Systems and Integrated Energy Systems. Analysis of Dr. Dzade's publication record reveals a strong focus on energy conversion technologies, particularly in solar energy harvesting and storage. His research spans heterojunction engineering for photovoltaic applications, catalytic materials for CO2 reduction, and electrocatalysts for water splitting. The publications demonstrate expertise in computational modeling combined with experimental validation across multiple energy-related domains, with particular emphasis on materials interfaces and surface phenomena. DOE Early Career Research Award (2024) Dr. Dzade leads research projects including 'Addressing the Energy Transition Puzzle Through Combined Experimental and Computational Efforts' and 'Rational Design of Bio-Inspired and Earth-Abundant Catalysts for Carbon Dioxide Reduction into Fuels and Fine Chemicals.' His work appears to involve significant collaboration with researchers across multiple institutions, as evidenced by his extensive co-authorship network. While specific grant details aren't provided in the source material, his research aligns with major funding priorities in sustainable energy and climate solutions. His research group appears to focus on computational materials design with strong connections to experimental validation, particularly in the areas of surface science and catalysis. The work leverages advanced computational techniques to understand and design materials for energy applications, with particular emphasis on interface phenomena that govern performance in energy conversion systems.