Find Academic Supervisors

Serguei Alex OushakineView profile

Professor

• Cultural Anthropology
• Slavic Studies
• Soviet and Post-Soviet Cultures
• +5 more

Serguei Alex Oushakine is a Professor of Anthropology and Slavic Languages and Literatures at Princeton University. His interdisciplinary research bridges cultural anthropology and Slavic studies, focusing on Soviet and post-Soviet cultural artifacts, affective politics, and visual/material culture. PhD in Anthropology (Columbia University, 2005) Kandidat in Political Sciences (St. Petersburg State University, 1995) Key research areas include: Visual and material culture of Soviet modernity Affective dimensions of sonic cultures (war chanson, estrada) Postsocialist nostalgia and trauma studies Formalism and Constructivism in Russian modernism His recent publications analyze Soviet children’s literature as ideological primers, including The Pedagogy of Images (2021) co-edited with Marina Balina and multi-volume The Formal Method: An Anthology of Russian Modernism (2016-2023). Current projects examine Soviet spatial reconfigurations and cultural engineering after Stalin. Scientific Contributions Curated special issues on Soviet jocularity, cinematic emotions, and transmedial books Editorial work on Boris Arvatov’s cultural theory and war letters Teaching includes graduate seminars on Russian formalism and undergraduate courses on Soviet daily life, socialist utopia, and trauma narratives.

Kevin HughesView profile

Senior Lecturer

• Fuel Combustion
• Fuel Cells
• Process Modelling in Carbon Capture and Storage (CCS)
• +3 more

Kevin Hughes is a Senior Lecturer in the Energy Engineering Group at the Department of Mechanical Engineering, School of Mechanical, Aerospace and Civil Engineering, University of Sheffield. He holds a PhD and first degree in Chemistry from the University of Leicester (1987) and focuses on fuel combustion, fuel cells, and process modelling in carbon capture and storage (CCS) systems. His research combines experimental and theoretical approaches, including planar laser diagnostics, quantum chemistry, and CFD simulations. Education: PhD and BSc in Chemistry from University of Leicester. Research Interests: Fuel combustion, pollutant chemistry, PEM fuel cells, CCS process modelling, catalyst development, and combustion in supercritical CO2. Grant Projects: FP7-ENERGY-2010-2 (RELCOM), Gas-FACTS (EPSRC), EP/J020788/1, EP/M001482/1 (Selective EGR), TEABPP (Energy Technology Institute). Scientific Contributions Publications: Over 50 papers on fuel combustion mechanisms, fuel cell optimization, CCS systems, and alternative fuels. Collaborations: Regular work with M. Pourkashanian, D.B. Ingham, S. Michailos, and M.S. Ismail. Technical Expertise Chemical Kinetics Validation Quantum Chemistry Applications Gas Diffusion Layer Analysis Surrogate Fuel Development Supercritical Combustion

Rory CooperView profile

Research Fellow

• Evolutionary Developmental Biology
• Biological Pattern Formation
• Developmental Biology
• +7 more

Dr. Rory Cooper is an Independent Research Fellow in the School of Biosciences at the University of Sheffield. His work bridges evolutionary developmental biology and biophysics , focusing on skin appendage morphogenesis across diverse vertebrates including sharks, chickens, and crocodiles. Formerly a 5-year postdoc in the LANE lab at University of Geneva, he earned his PhD at Sheffield under Dr. Gareth Fraser and Dr. Nicola Nadeau. Current position: Research Fellow (Sheffield) Alumni: Geneva postdoc (2020-2025), Sheffield PhD Expertise: EvoDevo, mechanochemical patterning, volumetric imaging His research investigates how molecular signals and mechanical forces interact to shape complex patterns in embryonic development. Key findings include: Discovery of compressive forces in crocodile scale sculpting Experimental induction of mechanical skin folding in chickens Identification of dual chemical/mechanical patterning in tortoise headscales Recent publications highlight mechanical forces as fundamental to pattern robustness across species. His 2024 Nature paper demonstrated physical sculpting of crocodile cranial scales, followed by 2025 Open Biology and Cell Reports iScience studies on avian/tortoise systems. Scientific recognitions include: Independent Research Fellowship Three major journal cover features (Nature, Open Biology, PLOS Biology) As a PhD advisor for Alex Liu at UC San Diego, he continues fostering next-generation EvoDevo researchers. Current work explores cross-species mechanosensitive development using advanced imaging and multiscale modeling.

• Wind Energy
• Wind Turbine Engineering
• Condition Monitoring
• +3 more

Simon J. Watson is a Professor in the Faculty of Aerospace Engineering at Delft University of Technology, specializing in Wind Energy through the TU Delft Wind Energy Institute (DUWIND). His work focuses on advancing wind turbine technology, wind farm optimization, and renewable energy integration within the university's aerospace framework. His research spans wind turbine engineering, condition monitoring systems, atmospheric effects on energy production, and wind farm design. Key investigations include damage detection in turbine components (blades, drivetrains), simulation of atmospheric gravity waves for improved energy forecasting, and hybrid wind-storage systems for grid stability. Recent work emphasizes machine learning applications for predictive maintenance and high-fidelity modeling of boundary layer conditions. Professor Watson's 2025 publications reveal a strong trend toward AI-driven condition monitoring and refined atmospheric simulations, with consistent focus on operational reliability and damage detection across wind energy systems. His work bridges computational fluid dynamics, structural health monitoring, and energy storage integration. He actively supervises students and leads the €4.2M MERIDIONAL project (2022-2026) on multiscale wind farm modeling, developing advanced toolchains for performance assessment and load prediction. This EU-funded initiative involves collaboration with Siemens Gamesa, Vestas, and ENEL. As a core member of DUWIND, he co-develops industry partnerships and experimental facilities including wind tunnel testing and field monitoring systems for offshore wind farms. His team maintains close ties with the Netherlands Wind Energy Association and European Wind Energy Technology Platform.

• Remote Sensing
• Hyperspectral Image Processing
• Spatial Data Analysis
• +6 more

Associate Professor Xiuping Jia is a faculty member at the School of Engineering and Information Technology, The University of New South Wales at Canberra, Australia. She has been with the institution since 1988, holding the position of Associate Professor with expertise in remote sensing and image processing. Xiuping Jia earned her B.Eng. degree from the Beijing University of Posts and Telecommunications in January 1982. She later completed her Ph.D. in electrical engineering and a Graduate Certificate in Higher Education from The University of New South Wales, Australia, in 1996 and 2005, respectively, through part-time study. Her primary research interests focus on remote sensing , hyperspectral image processing , and spatial data analysis for remote sensing applications. Her work spans various aspects of image processing including image registration, image correction, feature mining and learning, and spectral-spatial based classification. She has made significant contributions to subpixel mapping through spectral unmixing techniques and super resolution reconstruction approaches. Professor Jia is the coauthor of the widely recognized textbook Remote Sensing Digital Image Analysis (Springer-Verlag, 3rd and 4th editions) and the author of Field Guide to Hyperspectral/Multispectral Image Processing (SPIE, 2022). Her recent publication record shows a strong focus on deep learning applications for hyperspectral image analysis, with numerous papers in top journals including IEEE Transactions on Geoscience and Remote Sensing, IEEE Transactions on Neural Networks and Learning Systems, and IEEE Transactions on Pattern Analysis and Machine Intelligence. Her work demonstrates a progression from traditional image processing techniques to cutting-edge deep learning approaches for remote sensing applications. Professor Jia has made substantial contributions to the field of remote sensing with highly cited publications (as evidenced by her Google Scholar profile). Her research bridges theoretical developments with practical applications in remote sensing data analysis. She has supervised numerous research projects and students in the areas of remote sensing, hyperspectral image processing, and spatial data analysis. Her work has been supported by various research grants focusing on advanced image processing techniques for remote sensing applications. Professor Jia's research activities involve collaboration with multiple research teams working on remote sensing applications, hyperspectral data analysis, and geospatial information systems. Her work contributes significantly to the advancement of remote sensing technologies and their practical implementation in various environmental and urban monitoring applications.

Stefano CurtaroloView profile

Professor

• Artificial Intelligence Materials Science
• Autonomous Materials Design
• Computational Materials Science
• +4 more

Stefano Curtarolo is the Edmund T. Pratt Jr. School Distinguished Professor of Mechanical Engineering and Materials Science at Duke University’s Pratt School of Engineering. He also holds professorships in the Department of Physics and Electrical and Computer Engineering, and serves as Director of the Center for Extreme Materials (2025–present). Education: M.S. in Physics, University of Padua (1995, 1998) M.S. in Physics, Pennsylvania State University (1999) Sc.D. in Physics, Massachusetts Institute of Technology (2003) Research Interests: Curtarolo’s work bridges Materials Science , Physics , and Artificial Intelligence , focusing on Autonomous Materials Design , High-Entropy Disordered Systems , and Materials for Extreme Environments . His methodologies integrate Quantum Mechanics , Machine Learning , and High-Throughput Computing . Recent Research Trends: His 2024–2025 publications highlight advancements in High-Entropy Ceramics , including Soliquidy (a geometric descriptor for disorder), Dual-Phase Ceramic Synthesis , and Machine Learning Interatomic Potentials . These works emphasize disorder engineering , plasticity enhancement , and AI-driven material discovery for Aerospace and Deep Space Exploration applications. Scientific Awards: European Academy of Sciences (2024) Clarivate Highly Cited Researcher (2021) Friedrich Wilhelm Bessel Research Award (2016) DOD MURI Awards (2013, 2015) NSF CAREER (2008) ONR Young Investigator (2008) PECASE (2007) Grants & Leadership: Current projects include DETER (2024–2027, Missouri S&T) and Transition States Algorithms (2024–2027, ONR). He co-leads NRT-HDR (2020–2026, NSF). Labs & Teams: Curtarolo directs the Center for Extreme Materials and leads the AFLOW consortium , a global materials design ecosystem. His lab combines machine learning , quantum simulations , and experimental validation for multiscale materials discovery .

• Materials Science
• Mechanical Engineering
• Nanotechnology
• +3 more

Dr. Xuliang Li is a Postdoctoral Research Fellow at the School of Mechanical and Mining Engineering, The University of Queensland, within the Faculty of Engineering, Architecture and Information Technology. His work focuses on advanced materials processing, particularly in precision machining of brittle solids and nanoscale surface engineering. Research Themes: Brittle material machining, graphene oxide applications, machine learning integration in manufacturing, and multiscale modeling of deformation mechanisms Recent Publications: 14 studies (2019-2024) covering nanoscratch mechanics, laser 3D-printed ceramics, and innovative polishing techniques Funding: Currently developing ductile grinding techniques for dental prostheses applications (2025-2026) Expertise: Available for supervision in advanced manufacturing and materials science domains Key areas include computational modeling of grinding processes, hybrid nanoparticle suspensions, and ductile-to-brittle transition analysis. His collaborations span multiple disciplines in mechanical engineering and materials science.

• computational multiscale modeling
• digital twin development
• applied artificial intelligence
• +7 more

Davood Pourkargar is an Assistant Professor in the Tim Taylor Department of Chemical Engineering at Kansas State University. He is also a Graduate Faculty Member at the Food Science Institute and a Faculty Researcher at the Johnson Cancer Research Center. His work focuses on integrating data with first-principle models to understand complex systems across multiple scales. Ph.D. in Chemical Engineering from Pennsylvania State University (2015) M.S. in Process Simulation and Control from Sharif University of Technology (2010) B.S. in Chemical Engineering from Sharif University of Technology (2008) His research interests span computational multiscale modeling, digital twin development, applied artificial intelligence, and optimization-based control of complex process networks. Dr. Pourkargar's work integrates process systems engineering with artificial intelligence to address challenging problems in chemical, biological, energy, and food systems. He develops intelligent frameworks for controlling complex process networks, designing cyber-physical architectures for smart manufacturing, and advancing system identification using machine learning and process data analytics. A significant aspect of his research involves physics-informed machine learning applied to cancer dynamics modeling and drug distribution in the human body. Dr. Pourkargar's publication record shows a strong focus on predictive modeling and control of chemical processes, particularly ammonia synthesis systems, polysilicon reactor systems, and food extrusion processes. His recent work increasingly incorporates machine learning techniques, especially transformer architectures and physics-informed approaches, applied to both traditional chemical processes and emerging areas like organ-on-a-chip systems for drug discovery. 2024 Carl R. Ice College of Engineering Outstanding Assistant Professor Award NSF EPSCoR Research Fellowship 2023 Kansas EPSCoR First Award AFOSR Faculty Fellowship Big XII Faculty Fellowship Robert F. Smith School Distinguished Junior Researcher Award from Cornell University (2017) O. Hugo Schuck Best Paper Award (2014) Dr. Pourkargar has successfully mentored numerous graduate students through their master's and doctoral research, with several receiving departmental and college-level awards. His research has been supported by significant grants from the National Science Foundation, Kansas EPSCoR, and K-State's Global Food Systems initiative. His lab has presented extensively at major conferences including AIChE Annual Meetings and American Control Conferences. The Intelligent Systems and Process Systems Laboratory (ISPSL) led by Dr. Pourkargar operates computational and experimental facilities in Durland Hall. The lab is expanding into robotic additive manufacturing and autonomous biomanufacturing, supported by research infrastructure grants. The group maintains active collaborations with the Johnson Cancer Research Center and the Terasaki Institute for Biomedical Innovation.

Daniel BarretoView profile

Professor

• Discrete Element Method (DEM)
• Soil Mechanics
• Geotechnical Engineering
• +5 more

Daniel Barreto is a Professor at Edinburgh Napier University , specializing in Discrete Element Method (DEM) simulations and soil mechanics . His research emphasizes particle shape and size distribution effects on granular material behavior, with applications in permeability estimation, liquefaction mitigation, and nature-inspired ground engineering. Education : PhD in Soil Mechanics (Imperial College London, 2010), MSc in Soil Mechanics and Engineering Seismology (Imperial, 2005), Civil Engineering (Universidad de los Andes, 2003). Research : Focuses on DEM, grading entropy theory, and particle-scale interactions in sand-rubber mixtures, with implications for seismic isolation and sustainable soil stabilization. Awards : Royal Academy of Engineering Leverhulme Trust Fellowship COST Action Chair for Open Network on DEM Simulations (ON-DEM) Emeritus Member of Royal Society of Edinburgh Young Academy of Scotland Publications : Pioneering studies on DEM-based critical state behavior, particle breakage quantification, and permeability models for granular soils, with a 2023 Transportation Geotechnics paper on hydraulic radius applications. Collaborations : Leads ON-DEM, a global network advancing open-source DEM tools, and contributes to interdisciplinary projects involving synthetic root mimics and microbial soil interactions.

Mehdi PouraghaView profile

Associate Professor

• Geomechanics
• Constitutive Modeling
• Granular Materials
• +7 more

Mehdi Pouragha is an Associate Professor in the Department of Civil and Environmental Engineering at Carleton University's Faculty of Engineering and Design in Ottawa, Canada. He holds a BSc and MSc from Sharif University of Technology in Iran and a PhD from the University of Calgary. His academic career focuses on advanced geomechanics research with applications in both theoretical and practical engineering contexts. Dr. Pouragha's research spans multiple interconnected areas within geomechanics, with particular emphasis on constitutive modeling of geomaterials, micromechanics of granular materials, thermo-hydro-mechanical behaviors, and computational geomechanics. His work integrates theoretical approaches with advanced numerical methods to address complex problems in soil mechanics, permafrost engineering, and unsaturated soil behavior. His research program bridges fundamental micromechanical understanding with practical engineering applications, particularly in cold regions and climate change adaptation contexts. Analysis of Dr. Pouragha's recent publications reveals a strong trend toward multiscale modeling approaches that integrate discrete element methods with continuum mechanics. His work increasingly incorporates machine learning techniques to enhance computational efficiency while maintaining physical accuracy. The research spans both fundamental theoretical developments in constitutive modeling and practical applications in permafrost engineering, tailings management, and soil-structure interaction problems. His publications consistently demonstrate a focus on connecting microscale mechanisms to macroscale material behavior. Dr. Pouragha teaches several core civil engineering courses including Geotechnical Engineering, Fundamentals of Geomechanics, Numerical Methods in Geotechnical Engineering, and Professional Practice. His teaching portfolio reflects his research expertise while providing students with both theoretical foundations and practical engineering skills.

• Catalysis
• CO₂ Valorization
• Kinetic Modeling
• +4 more

Dr. Sofia Angeli is a Group Leader at the Institute of Catalysis Research and Technology (IKFT), Karlsruhe Institute of Technology (KIT), Germany, since March 2024. Previously, she held roles as Senior Scientist/Group Leader (2021–2024) and Postdoctoral Researcher (2017–2019) at KIT. Her research focuses on catalysis for energy transition, CO₂ valorization, kinetic modeling, and digitalization of catalytic processes. She leads interdisciplinary projects integrating experimental and computational methods to advance sustainable chemical processes. PhD in Chemical Engineering (2016): Aristotle University of Thessaloniki, Greece. Thesis: Hydrogen production via intensified methane steam reforming processes. M.Sc. in Advanced Materials (2012): Aristotle University of Thessaloniki. Diploma in Chemical Engineering (2009): Aristotle University of Thessaloniki. Research Interests : Sofia’s work spans kinetic modeling, CO₂ conversion, catalytic pollutant removal, and digital tools for catalysis research. She develops novel catalysts for methane reforming and designs data management systems like Adacta and CaRMeN to streamline reaction mechanism analysis. Her projects often address industrial challenges in emissions reduction and renewable energy. Her recent publications emphasize catalytic processes for energy sustainability (e.g., methane oxidative coupling) and automated modeling frameworks. She collaborates widely, contributing to journals like Chemical Engineering Journal and ACS Catalysis . Advising & Teams : As a Group Leader, she oversees researchers in catalysis and data-driven processes. Her team works on cutting-edge projects supported by KIT’s infrastructure and interdisciplinary networks. Labs/Teams : Active in the IKFT and the Deutschmann Group, focusing on catalyst design and process intensification.

Nicole AbaidView profile

Associate Professor

• Dynamical Systems
• Control Theory
• Multi-Agent Systems
• +5 more

Nicole Abaid is an Associate Professor in the Department of Mathematics at Virginia Tech, affiliated with the College of Science. Her research integrates dynamical systems, control theory, and robotics to study collective behavior in biological and engineered systems. She explores biologically-inspired models, such as bat swarms and brain networks, to develop applications in robotics and environmental monitoring. Her work combines agent-based modeling, network analysis, and data-driven approaches to understand interactions in animal groups and robotic systems. Notable projects include analyzing bat echolocation for acoustic sensing in robotics and studying stress effects in human crowd dynamics during emergencies. She has also contributed to piezoelectric energy harvesting and ex vivo muscle stimulation via Functional Electrical Stimulation (FES). Abaid’s research has been supported by grants like the NSF CAREER Award for collective behavior studies. She collaborates on interdisciplinary projects, including outreach programs in biomimetics and robotics for K-12 education. Her publications span journals and conferences, emphasizing applications in ecology, robotics, and control systems.

Riccardo BonazzaView profile

Professor

• Experimental Fluid Mechanics
• Impulsive Fluid Flows
• Shock-Interface Interactions
• +7 more

Riccardo Bonazza is a Professor in the Department of Mechanical Engineering at the University of Wisconsin-Madison, affiliated with the College of Engineering and the Nuclear Engineering & Engineering Physics program. His research focuses on experimental investigations of impulsive fluid flows, shock-interface interactions, and shock-driven mixing phenomena with applications in inertial confinement fusion, combustion systems, and aerospace engineering. Bonazza holds a PhD (1992) and MS (1985) from Caltech, and a Laurea in Mechanical Engineering (1983 cum laude) from Università di Ancona. His experimental work uses advanced techniques like planar Mie scattering, laser-induced fluorescence (PLIF), and particle image velocimetry (PIV) in the Wisconsin Shock Tube Laboratory. Key research areas include Richtmyer-Meshkov instability dynamics, shock-accelerated vortex rings, and reactive shock flows. His studies explore both detrimental mixing effects in fusion applications and beneficial mixing enhancement in supersonic combustion systems. Recent experiments involve shock-bubble interactions, reshock phenomena, and turbulent mixing quantification. Notable awards include the 2016 Leaders in Engineering & Diversity Scholar Award and 2011 Outstanding Instructor Award. His 2023 work includes novel bovine thermodynamic models and advanced shock tube diagnostics. Bonazza teaches courses in aerodynamics, gas dynamics, rocket propulsion, and independent research supervision. Key facilities: Wisconsin Shock Tube Laboratory. Active collaborations include CFD validation, laser diagnostics development, and multi-phase flow studies.

Daan CrommelinView profile

Professor

• Uncertainty Quantification
• Stochastic Modeling
• Data-Driven Modeling
• +6 more

Daan Crommelin holds a part-time professorship in Numerical Analysis and Dynamical Systems at the KdV Institute for Mathematics, University of Amsterdam, and is a senior researcher at CWI Amsterdam's Scientific Computing group. He serves on CWI's management team and previously led its Scientific Computing group (2013–2021). His research focuses on stochastic modeling of multiscale systems, uncertainty quantification, and rare event analysis, with applications in climate science, renewable energy, and fluid dynamics. Crommelin combines methods from scientific computing, applied probability, and dynamical systems to address challenges in atmosphere-ocean-climate modeling. He has contributed to projects like the EU-funded VECMA initiative for exascale computing and collaborated on superparameterization techniques for climate models. His work also extends to epidemic modeling and computational chemistry. Crommelin earned his PhD in 2003 from Utrecht University, with a thesis co-supervised by KNMI, and holds an MSc in theoretical physics and an MA in philosophy from the University of Amsterdam.

• Applied Mathematics
• Numerical and Computational Mathematics
• Partial Differential Equations
• +4 more

Prof. Vanessa Styles is a Professor and Head of the School of Mathematical and Physical Sciences at the University of Sussex. Her research focuses on mathematical and computational analysis of nonlinear partial differential equations, with applications in physical sciences, including materials science, fluid dynamics, and biological modeling. She specializes in numerical methods for evolving surfaces, phase field models, and free boundary problems. Her work often involves interdisciplinary collaborations, addressing challenges in lithium batteries, tumor growth, and cell migration. Prof. Styles has led major grants, including ModCompShock (EU) and Leverhulme-funded projects on cell migration mechanics. She teaches calculus courses and actively contributes to academic leadership. Her research interests span computational fluid dynamics, numerical analysis of PDEs, and mathematical biology. Recent work emphasizes tumor growth modeling via phase field approaches, crystal growth in batteries, and cell migration force estimation. She has published extensively in journals like Interfaces and Free Boundaries and Journal of Computational Physics , with a focus on rigorous error analysis and algorithm development. Grants include funding for modeling grain boundary motion (EPSRC), 3D cell migration (Leverhulme), and shock interface modeling (EU). Her research bridges theoretical analysis and practical applications, with contributions to both fundamental mathematics and engineering/medical fields.