Find Academic Supervisors

• Structural identifiability
• Observability analysis
• Nonlinear systems
• +3 more

Alejandro F. Villaverde is a Ramón y Cajal research fellow at the Department of Systems & Control Engineering, Universidade de Vigo. His research focuses on structural properties of nonlinear systems, particularly structural identifiability and observability analysis. His primary research interests include: Structural identifiability of dynamic models Observability analysis of nonlinear systems Control theory and systems engineering Development of computational tools for model analysis Applications in systems biology and pharmacokinetics Villaverde's work centers on developing mathematical methods and software tools to analyze structural properties of nonlinear models. His most notable contributions include the STRIKE-GOLDD MATLAB toolbox and its Python counterpart StrikePy, which implement the FISPO algorithm for simultaneous assessment of state observability and parameter identifiability. His research has practical applications in systems biology, biochemical network analysis, and pharmacokinetic modeling where model validation and parameter estimation are critical. Among his scientific recognitions, he holds the prestigious Ramón y Cajal research fellowship, a competitive program in Spain supporting experienced researchers. Villaverde actively supervises students, with David Rey Rostro having developed the StrikePy toolbox under his supervision. His work demonstrates strong connections between theoretical control engineering and practical computational implementations, making structural analysis methods more accessible to researchers across various disciplines.

Ole MadsenView profile

Professor

• Robotics
• Automation
• Industry 4.0
• +6 more

Ole Madsen is a Professor at the Department of Materials and Production within The Faculty of Engineering and Science at Aalborg University . His research focuses on Robotics and Automation , particularly in 5G Smart Production , AI for Manufacturing , and Industry 4.0 applications. He also holds a part-time position at Adding Robotics , applying his expertise in robot integration for industrial and healthcare settings. Research Interests : Robotics, Automation, AI, Sensor Systems, Modular Manufacturing, Welding Technology, Digital Twins, Human-Centered Robotics, Industry 4.0 His work spans 35+ years with over 205 publications , emphasizing smart production systems and robot-assisted processes . Key contributions include Swarm Production Architectures , 5G-Enabled Robotics , and Human-Robot Collaboration frameworks. He has supervised 10 PhD students and led major projects like RAU (Robot-Assisted Ultrasound) and GINP: Robotics & AI Innovation Network . Scientific Awards : SCAP2020 Best Presentation Award (2020) Ole's 31 projects include AP2030: Aseptic Factory 2030 (pharma production), AddSmart (robotics R&D), and 5G-Enabled Autonomous Systems . His 127 press/media mentions highlight advancements in robotic welding , industrial metaverse , and swarm production . He maintains an ORCID: 0000-0003-2133-2541 with extensive publication records across Swarm Robotics , Modular Manufacturing , and AI-Driven Production .

Linda ForssmanView profile

Associate Professor

• Developmental Psychology
• Child Development
• Early Language Acquisition
• +5 more

Linda Forssman is an Associate Professor and researcher in developmental psychology at Uppsala University's Department of Psychology, Division of Developmental Psychology. She works at the Uppsala Child & BabyLab, where she conducts research on how young children perceive and interact with their world, and how different experiences shape the development of cognitive, social and linguistic skills during early childhood. Dr. Forssman's research focuses on developmental psychology with particular emphasis on early language acquisition, cognitive development, social development, and infant cognition. Her work explores how various experiences shape children's development across multiple domains, with special attention to vulnerable populations and intervention strategies. She employs methodologies including eye-tracking, longitudinal studies, and randomized controlled trials to investigate fundamental developmental processes. Her recent publications show strong trends in cross-cultural developmental research, early language intervention, social cognition in infants, and the impact of socioeconomic factors on child development. Her work spans multiple methodologies from laboratory experiments to field studies in diverse cultural contexts, including research conducted in rural Malawi. Dr. Forssman has secured significant research funding as Principal Investigator and Co-Investigator on numerous projects. Her major grants include: Promoting Children's Early Language Development in Socioeconomic Disadvantaged Areas (PI, Marcus and Amalia Wallenberg Foundation, €312,200, 2025-2029) How does preschool quality relate to children's learning outcomes? (PI, Swedish Institute for Educational Research, €454,000, 2022-2024) The Impact of Book-Sharing on Infant Language and Cognition (PI, Riksbankens Jubileumsfond, €362,000, 2019-2022) Nordic evidence based high quality early childhood education and care research network (Co-I, Swedish Research Council, €102,500, 2023-2026) She is actively involved with the Uppsala Child & BabyLab, a research environment focused on infant and child development using advanced methodologies including eye-tracking. Her work bridges basic developmental research with practical applications for early childhood education and intervention programs, particularly those targeting children in socioeconomically disadvantaged areas.

Hyunjae ParkView profile

Research Professor

• Energy Conversion Systems
• Computational Fluid Dynamics (CFD)
• Thermal Analysis
• +3 more

Hyunjae Park is a Research Professor in the Department of Mechanical Engineering at Marquette University College of Engineering . His work bridges energy conversion systems with educational innovation in engineering pedagogy. Ph.D., 1992, Mechanical Engineering, University of California at Los Angeles M.S., 1983, Mechanical Engineering, Yonsei University, Korea B.S., 1981, Mechanical Engineering, Han Yang University, Korea Dr. Park specializes in thermal analysis of energy systems , including computational fluid dynamics (CFD) modeling of coiled tube systems, heat exchangers, and printed circuit board cooling. His educational research focuses on integrating entrepreneurial mindset development in freshman engineering courses through problem-solving and design challenges. Recent publications highlight his dual expertise: Engineering Computing with MATLAB editions (2020, 2018, 2017) and Freshman Discovery Course papers spanning 2014-2020. These demonstrate his commitment to engineering education reform and technical software tools while maintaining research in thermal systems optimization .

• Biomass Gasification
• Computational Fluid Dynamics
• Combustion Engineering
• +5 more

Tian Li is an Adjunct Associate Professor at the Department of Energy and Process Engineering, Faculty of Engineering, Norwegian University of Science and Technology (NTNU). Based at the Varmeteknisk building on the Gløshaugen campus, Dr. Li is affiliated with the ComKin Group and has been actively involved in numerous research projects focused on biomass conversion and combustion technologies since 2011. Dr. Li's research primarily focuses on: Biomass gasification and combustion technologies Computational Fluid Dynamics (CFD) modeling of energy conversion processes Multiphase flow and reaction kinetics in thermochemical processes Turbulence modeling in combustion systems Development of simulation tools for bioenergy applications Over the past decade, Dr. Li has led or contributed to multiple significant research projects funded by the Norwegian Research Council and industrial partners, including BioCarbUp, GASPRO, GrateCFD, GAFT, BioCarb+, CenBio, and GasBio. These projects have focused on optimizing biomass conversion processes for sustainable energy production. Dr. Li's publication record shows consistent contributions to high-impact journals in the energy and combustion fields, with a strong emphasis on computational modeling approaches. The research demonstrates expertise in developing and validating models for biomass conversion processes, with applications ranging from industrial-scale biomass furnaces to fundamental particle-level phenomena. Dr. Li has developed significant expertise in various computational tools and programming languages: Software: OpenFOAM, ANSYS Fluent, ANSYS ICEM CFD, Star-CD, MFiX, CHEMKIN, LOGEsoft, LabVIEW Programming: C/C++, Python, Fortran, Matlab Through participation in major research centers like CenBio (Bioenergy Innovation Centre), Dr. Li has contributed to advancing Norway's bioenergy research capabilities and fostering collaboration between academia and industry in the sustainable energy sector.

• Deep Learning
• Federated Learning
• Computer Vision
• +7 more

Alexandre BENOIT is a Professor at Polytech Annecy-Chambéry, Université Savoie Mont-Blanc, and a permanent member of the LISTIC laboratory. His research focuses on deep learning, federated learning, computer vision, remote sensing, and explainable AI, with applications in astrophysics, environmental monitoring, and healthcare. He leads projects on glacier modeling, federated learning bias mitigation, and satellite image analysis. His teaching activities include courses on deep learning (TensorFlow/PyTorch), image processing (Matlab/OpenCV), and programming (C/C++/Python) at undergraduate and graduate levels. He has supervised over 10 PhD students and collaborates with industries like Total, Renault, and startups on AI integration. Research highlights include developing the GammaLearn framework for Cherenkov Telescope Array data analysis and bio-inspired retina models integrated into OpenCV. He co-organized major conferences such as CBMI 2012 and EUSFLAT 2011, and serves on editorial boards for IEEE Transactions on Image Processing and other journals. Current projects address federated learning fairness, glacier thickness estimation via deep learning, and oil slick detection using SAR imagery. His work emphasizes frugal models, physically informed AI, and ethical AI practices in collaborative environments.

Delf EggeView profile

Professor

• Satellite Positioning and Navigation
• Hydrography
• Geodesy
• +3 more

Prof. Dr.-Ing. Delf Egge was a renowned academic in geodesy, hydrography, and software engineering. He served as a Professor at HafenCity University Hamburg (HCU) since 2006, previously at the Hamburg University of Applied Sciences (HAW Hamburg) from 1987–2005. His research focused on satellite positioning, multibeam echo sounder data processing, and geospatial software development using Java and MATLAB. He was a key figure in establishing international standards for hydrographic education through his membership on the FIG/IHO/ICA Board. Notable contributions include pioneering work in GNSS precise point positioning and the development of certified hydrography courses. Egge passed away unexpectedly on August 7, 2015, leaving a lasting impact on geomatics education and research. Career Highlights: Professor at HCU (2006–2015) and HAW Hamburg (1987–2005) Assistant Professor of Civil Engineering, University of Washington (1985–1987) Member of the FIG/IHO/ICA International Board (since 2003) Contributed to the International Maritime Academy, Trieste (1991–2004) Research Interests: Egge’s work bridged theoretical geodesy and practical applications. His studies on satellite positioning advanced real-time geoscience applications, while his focus on multibeam echo sounder data processing revolutionized marine survey methodologies. He also led software development initiatives, creating tools like GEOTRANS for coordinate transformation and exploring Java-based solutions for ECDIS systems. Publications & Recognition: Over 15 key publications span hydrographic standards, GNSS techniques, and educational frameworks. His work emphasized interdisciplinary collaboration and practical field applications. Egge’s legacy includes foundational contributions to hydrographic education and geomatics innovation. Affiliations: Deutsche Hydrographische Gesellschaft (DHyG) Deutscher Verein für Vermessungswesen German Hydrographic Consultancy Pool (GHyCoP) Advisory Board

• Applied mathematics
• Numerical and computational mathematics
• Advection-diffusion-reaction models
• +7 more

Dr. Elliot Carr is a Senior Lecturer in the School of Mathematical Sciences at Queensland University of Technology (QUT), Faculty of Science. He holds a PhD in Mathematics from QUT and has been a faculty member since 2015, progressing from Lecturer to his current rank. His research and teaching focus on applied and computational mathematics, with strong interdisciplinary applications. Education: PhD in Mathematics, Queensland University of Technology, 2009–2012 Bachelor of Applied Science (Honours) in Mathematics, QUT, 2008 Bachelor of Mathematics, QUT, 2005–2007 Elliot Carr's research lies at the intersection of applied mathematics and real-world physical systems. His work centers on developing and analyzing mathematical models of advection, diffusion, and reaction processes, particularly in heterogeneous media. He employs both deterministic (PDE-based) and stochastic (random walk) frameworks, contributing to analytical solutions, multiscale modeling, surrogate models, and numerical methods such as finite volume and Newton-Krylov techniques. His research has been applied to diverse fields including groundwater contamination, drug delivery, heat transfer, and tumor spheroid modeling. The latest publications reflect a consistent focus on transport phenomena in complex geometries and heterogeneous environments. Key themes include dual-grid mapping for contaminant transport, analytical modeling of drug release from spherical capsules, thermal diffusivity in shell geometries, and stochastic models of biological systems. His methodological contributions span analytical, numerical, and statistical approaches, demonstrating versatility across applied mathematics. Scientific Awards and Recognitions: JH Michell Medal, ANZIAM (2022) ARC DECRA Fellowship (2015) QUT Outstanding Doctoral Thesis Award (2012) University Medal, QUT (2008) Dean’s Award for top graduate in both Honours and Bachelor programs Keynote and plenary speaker at major conferences including ANZIAM and Forum “Math-for-Industry” Dr. Carr actively supervises PhD and Masters students, with completed and ongoing projects on diffusive transport, tumor modeling, and sports analytics. He has secured competitive research funding, including an ARC Discovery Project on multiscale modeling. His teaching includes computational mathematics, linear algebra, and differential equations, with a focus on MATLAB-based implementation. He is a member of the Australian Mathematical Society (AustMS) and ANZIAM. Research Labs and Teams: While not explicitly tied to a named lab, Carr is part of the broader Applied Modelling and Computation research environment at QUT. He collaborates extensively with researchers such as Ian Turner, Matthew Simpson, and Chris Drovandi, contributing to interdisciplinary teams in mathematical biology, environmental modeling, and statistical computation.

• Renewable Energy Systems
• Photovoltaic Power Plants
• Energy Optimization
• +3 more

Ali DURUSU is a researcher at Yıldız Technical University, College of Engineering , with a focus on Renewable Energy Systems and Photovoltaic Power Plants . His work spans techno-economic modeling, energy optimization, and smart grid integration. Research Interests : Solar power plant design, grid integration, energy economics, MPPT algorithms, hybrid systems, and smart grid technologies Advisory : Has advised 25 theses on renewable energy topics Projects : Involved in 15 research initiatives Recent publications emphasize peer-to-peer energy trading, distributed storage, and optimization techniques. He has contributed to 80 WoS-indexed publications with significant citations (H-Index: 8 in WoS, 11 in Scopus) and participated in 43 peer-reviewed conferences.

Jacob MotaView profile

Assistant Professor

• Muscle Function Assessment
• Neuromuscular Adaptations
• Occupational Health (Firefighters/Law Enforcement)
• +4 more

Jacob Mota, Ph.D. , is an Assistant Professor in the Department of Kinesiology & Sport Management at Texas Tech University. His research focuses on neuromuscular adaptations and muscle function assessment across diverse populations including firefighters, law enforcement officers, and athletes. Ph.D. in Human Movement Science (2020), University of North Carolina Chapel Hill School of Medicine M.S. in Kinesiology (2016), Texas Tech University B.S. in Exercise and Sport Sciences (2014), Texas Tech University Dr. Mota's work examines the relationship between chronic health conditions and musculoskeletal injury risk in occupational athletes, with emphasis on resistance training adaptations and muscle quality analysis. His Neuromuscular and Occupational Performance Laboratory develops evidence-based strategies for injury prevention and performance optimization. Recent publications analyze firefighter health standards, muscle asymmetry impacts on occupational performance, and technological advancements in muscle assessment. His research integrates exercise physiology, biomechanics, and public safety health metrics. Outstanding Mentorship of Undergraduate Students in Research – University of Alabama (2022) Apple Polishing Award – TTU Mortar Board (2023) Dr. Mota teaches courses in applied exercise physiology and advanced strength conditioning, contributing to Texas Tech's Kinesiology undergraduate and graduate programs.

• Robotics
• Neuroscience
• Artificial Intelligence
• +3 more

Tom Knowles is a Lecturer in Robotics at the University of the West of England , affiliated with the School of Engineering . He teaches technical subjects such as C Programming , Matlab Programming , Python Programming , Robotics , and Neuroscience . Education: MSc BSc Mr. Knowles bridges conventional and spiking neural networks to develop artificial systems that mimic biological capabilities, particularly in navigation. His work explores how mammalian brains represent space and models robust, flexible behaviors through robotics, intersecting Robotics , Neuroscience , and Artificial Intelligence . Publications trends highlight biomimetic robotics , spiking neural networks , and computational neuroscience . His research integrates machine learning , bio-inspired models , and sensory reconstruction to solve navigation challenges, with collaborations at the University of Sussex and Technische Universität München (TUM) . Collaborations: University of Sussex TUM (Technische Universität München) Contact: Email: Tom.Knowles@uwe.ac.uk Phone: +44117 965 6261

• Applied Analysis
• Numerical Analysis
• Data Science
• +6 more

Leon Bungert is a Professor of Mathematics of Machine Learning at the University of Würzburg, working in applied analysis and numerics with a particular focus on data science and machine learning. His research investigates PDEs and variational models on graphs, adversarial robustness of machine learning, variational regularization, and nonlinear optimization. Dr. Bungert serves as a guest editor for the European Journal of Applied Mathematics, an associate editor for Advances in Continuous and Discrete Models: Theory and Applications, and is a member of the program committee at SSVM 2025. He is also an ELLIS member and actively organizes conferences and workshops, including "MIA'25" at IHP in Paris (January 13-15, 2025), "Synergies of Machine Learning and Numerics" in Osaka (March 11-13, 2025), and "Mathematical Analysis of Adversarial Machine Learning" in Oaxaca (August 17-22, 2025). Research Interests Dr. Bungert's primary research areas include: PDEs on graphs Adversarial robustness in machine learning Inverse problems Optimization Variational problems in L-infinity Nonlinear eigenvalue problems Image reconstruction with structural priors His work bridges theoretical mathematics with practical applications in machine learning, particularly focusing on the mathematical foundations of deep learning and developing robust algorithms that can withstand adversarial attacks. He has made significant contributions to understanding the connections between partial differential equations and machine learning algorithms. Research Trends Analysis of Dr. Bungert's recent publications reveals a strong focus on the intersection of machine learning and mathematical analysis. A key theme is the application of variational methods and partial differential equations to machine learning problems, particularly in understanding and improving the robustness of neural networks against adversarial examples. His work on Lipschitz learning on graphs has established important theoretical foundations for graph-based semi-supervised learning. Additionally, his research on the infinity Laplacian and p-Laplacian equations provides deep insights into the mathematical structure of machine learning algorithms. The development of Bregman learning frameworks for sparse neural networks represents a significant contribution to efficient deep learning model training. Professional Activities Dr. Bungert is actively involved in the academic community through editorial roles and conference organization. His current professional activities include: Guest editor for the European Journal of Applied Mathematics Associate editor for Advances in Continuous and Discrete Models: Theory and Applications Member of the program committee at SSVM 2025 ELLIS member Co-organizer of multiple international conferences and workshops Technical Contributions Dr. Bungert has developed several open-source software packages that implement his theoretical contributions, including: Code for convergence rates of Lipschitz learning on graphs A Bregman training framework for sparse neural networks CLIP: Cheap Lipschitz Training of Neural Networks Nonlinear Power Method for Proximal Operators and Neural Networks Robust Image Reconstruction with Misaligned Structural Information These implementations are primarily in Python and MATLAB, demonstrating his commitment to making theoretical advances accessible for practical applications.

Akos LedecziView profile

Professor

• Computer Science Education
• Wireless Sensor Networks
• Model Integrated Computing
• +3 more

Akos Ledeczi is a Professor of Computer Science and Electrical and Computer Engineering at Vanderbilt University's School of Engineering. His research focuses on computer science education and wireless sensor networks (WSN) , with notable contributions to visual programming tools like NetsBlox and anti-poaching systems like WIPER. He holds a Ph.D. from Vanderbilt University and a Diploma from the Technical University of Budapest. His education initiatives include the NetsBlox platform for K-12 STEM education and a popular Coursera MOOC on introductory programming. In WSN, his team developed a countersniper system with real-time shooter localization and a wearable system for military applications. He is also a leader in Model Integrated Computing , creating tools like the Web-based Generic Modeling Environment. Recent work includes NSF-funded projects on cybersecurity education in middle schools and animal-borne acoustic monitoring. His awards include the ACM SenSys Test of Time Award (2014) and the Best Showpiece Award at IEEE VL/HCC (2021). Key grants and collaborations span NSF projects, Vodafone innovation programs, and the Cyber Makerspace initiative. His lab develops tools like DeepForge and RoboScape Online , emphasizing open-source and collaborative platforms. Students supervised include Devin Jean (K-12 tools), Gordon Stein (robotics simulations), and Brian Broll (NetsBlox extensions).

Afrooz JalilzadehView profile

Assistant Professor

• Stochastic Optimization
• Variational Inequalities
• Nash Games
• +7 more

Afrooz Jalilzadeh is an Assistant Professor in the Department of Systems and Industrial Engineering at the University of Arizona, part of the College of Engineering. She is also a member of the Applied Mathematics and Statistics Graduate Interdisciplinary Programs (GIDP), highlighting her strong cross-disciplinary research profile. She leads the Optimization and Mathematical Analysis (OPTIMA) Lab, which focuses on algorithmic innovation for stochastic optimization and variational problems. PhD in Industrial Engineering and Operations Research, The Pennsylvania State University BS in Mathematics, University of Tehran, Iran Her research lies at the intersection of stochastic optimization , variational inequalities , and machine learning , with applications in game theory, healthcare, and power systems. She develops and analyzes algorithms such as stochastic approximation, primal-dual methods, and variance-reduced schemes to solve complex minimax and equilibrium problems. Her work emphasizes theoretical convergence guarantees and computational efficiency. The recent publications show a strong trend in nonconvex-concave saddle-point problems , stochastic Nash games , and projection-free optimization . These are central to modern machine learning and adversarial training. Keywords across her work include stochastic approximation, accelerated methods, risk aversion, and distributed computing, indicating a deep engagement with both theoretical and applied aspects of optimization. Her scientific recognition includes: Teacher of the Year, College of Engineering, University of Arizona (Spring 2022) Gerald J. Swanson Prize for Teaching Excellence NSF Grant: Generalized Stochastic Nash Equilibrium Framework James E. Marley Graduate Fellowship Max and Joan Schlienger Graduate Scholarship Third Place in INFORMS Poster Competition (2018) University Graduate Fellowship, Penn State (2015) H. Marcus Dean’s Chair Scholarship, Penn State (2015) She actively advises students and researchers in her OPTIMA Lab, with multiple publications co-authored with graduate students. She has secured competitive grants, including an NSF award, supporting her research group. Her lab seeks students with strong mathematical and coding skills (MATLAB/Python) for PhD-level research in optimization and mathematical analysis. The OPTIMA Lab conducts cutting-edge research in algorithm design for stochastic variational inequalities , Nash equilibrium computation , and minimax optimization . The lab emphasizes theoretical rigor and practical implementation, with applications spanning machine learning, healthcare, and energy systems. It has published in top venues such as NeurIPS, ACM TOMACS, and Mathematical Programming.

Giles HookerView profile

Professor

• Machine Learning
• Functional Data Analysis
• Differential Equations
• +9 more

Giles Hooker is a Professor of Statistics and Data Science at the Wharton School, University of Pennsylvania, with his office located at 409 Academic Research Building, 265 South 37th Street, Philadelphia, PA 19104. He teaches courses including STAT4700 (Data Analy & Stat Comp), STAT5030, STAT9916 (Sem in Adv Appl of Stat), STAT9950 (Dissertation), and STAT9999 (Independent Study). Dr. Hooker's research focuses on the interfaces of statistical inference with models from machine learning and applied mathematics. His primary interests include Machine Learning , Functional Data Analysis , Differential Equations , Computational Statistics , and Statistical Ecology . He specializes in uncertainty quantification in random forests and ordinary differential equations models, as well as robust statistics. Much of his work is application-driven, particularly in ecology, epidemiology, environmental sciences, and medicine. His recent publications (2021-2024) reveal a strong emphasis on interpretable machine learning, statistical stability in model distillation, and ecological modeling. He has developed significant software packages including fda for functional data analysis in R and Matlab, and CollocInfer for fitting differential equations to data, complete with extensive documentation. Dr. Hooker has presented at conferences including the Conference on Applied Statistics in Ireland (2023) where he delivered a short course on Inference and Interpretation in Machine Learning. His methodological contributions span from profiled estimation of differential equations to functional data analysis techniques.