Find Academic Supervisors

• Fluid Dynamics
• Thermal Convection
• Computational Fluid Dynamics
• +3 more

Roberto Verzicco is a Full Professor at the University of Twente's MESA+ Institute, specializing in the Physics of Fluids. His research focuses on fluid dynamics, thermal convection, and computational fluid dynamics (CFD), with particular emphasis on non-Newtonian fluids and turbulence. He has contributed to advancing numerical methods for simulating complex fluid systems, including porous media flows and phoretic particle dynamics. Key research areas include direct numerical simulation (DNS), heat and momentum transport, and rheological modeling. His work integrates computational approaches with experimental insights, addressing challenges in melting phenomena, multiphase flows, and parameter identifiability in fluid models. Collaborations with researchers like Detlef Lohse and Roberto Stevens highlight his engagement in international networks. His datasets and presentations underscore contributions to understanding rotating Rayleigh-Bénard convection and confined thermal systems. Supervision of 15 works reflects his academic mentorship.

• Computational Geosciences
• Numerical Methods
• Thermodynamics
• +3 more

Duc Thach Son Vu is a Research Fellow in the Department of Earth & Planetary Sciences at Yale University. His work focuses on computational geosciences, numerical methods, and thermodynamics, with a particular emphasis on multiphase systems and deep learning applications. He is affiliated with the Eisaman Lab, contributing to interdisciplinary research at the intersection of mathematics, earth sciences, and computational tools. His research interests include developing novel algorithms for solving complex equilibrium problems in multiphase mixtures and creating computational frameworks like the SpinDoctor MATLAB toolbox for diffusion MRI simulation. His publications highlight advancements in numerical techniques, graph neural networks, and predictive modeling for geoscientific challenges. No formal awards or grants are listed in the provided materials, but his contributions to the Eisaman Lab and Yale’s research community are notable. He holds a personal email (thachsonnt94@gmail.com) and can be reached via ducthachson.vu@yale.edu for professional inquiries.

Luca MagriView profile

Professor

• Scientific Machine Learning
• Data Assimilation
• Fluid Mechanics
• +8 more

Luca Magri is a Professor of Scientific Machine Learning at Imperial College London's Department of Aeronautics (Faculty of Engineering), leading the MagriLab . He holds dual roles as Director of Research in Aeronautics and Director of the Research Centre in Data-Driven Engineering. He is also a Professor in Fluid Mechanics at Politecnico di Torino under the PNRR project on Twin Real-time digital twins. His research bridges physics-aware machine learning, data assimilation, and fluid mechanics, with applications in quantum computing, turbulence modeling, and thermoacoustics. Magri completed his PhD in Engineering at the University of Cambridge (2012-2015) and held postdoctoral roles at Stanford University (2015-2016). He has been a Royal Academy of Engineering Research Fellow (2016-2021), Simons Fellow (2022-2023), and Hans Fischer Fellow at TUM (2018-2021). He leads the Alan Turing Institute's Scientific Machine Learning group and collaborates with institutions like Pembroke College and the Isaac Newton Institute. His research interests span quantum reservoir computing, data-driven stability analysis, and real-time digital twins for combustion systems. Key projects include optimizing wind farm layouts and suppressing extreme events in chaotic flows. He has pioneered methods like Proper Latent Decomposition (PLD) and physics-constrained neural networks for turbulence reconstruction. Magri has secured funding from EPSRC, Royal Academy of Engineering, and EU initiatives. His group includes engineers, physicists, and computer scientists addressing net-zero challenges in aerospace propulsion and energy systems. Recent work focuses on quantum computing for nonlinear PDEs and AI-driven process design in manufacturing.

Marta D'EliaView profile

Adjunct Professor

• Scientific Machine Learning
• Nonlocal Modeling
• Fractional Calculus
• +4 more

Marta D'Elia is an Adjunct Professor at Stanford's Institute for Computational and Mathematical Engineering (ICME), specializing in Scientific Machine Learning and nonlocal modeling. Her research develops data-driven algorithms for multiscale/multiphysics simulations, integrating numerical analysis, uncertainty quantification, and fractional calculus. Core applications include subsurface transport, turbulence modeling, image processing, and materials science. She leads innovations in nonlocal operator regression, physics-informed neural networks, and fractional Laplacian formulations. Current work focuses on embedded machine learning for constitutive modeling, Bayesian uncertainty frameworks, and computational homogenization. D'Elia pioneered approaches for nonlocal-to-local model coupling and fractional Helmholtz decompositions, advancing simulation capabilities for anomalous transport phenomena. Her Ph.D. in Applied Mathematics (Emory University) underpins rigorous mathematical foundations, while collaborations with national labs address high-performance computing implementations. Research contributes to open-source scientific software and computational mathematics education through ICME courses on numerical methods and machine learning.

Michel GoemansView profile

Professor

• Combinatorial Optimization
• Algorithms
• Approximation Algorithms
• +3 more

Mitchell Goemans is the RSA Professor of Mathematics at the Massachusetts Institute of Technology (MIT), where he also serves as the Head of the Department of Mathematics. He is a member of the Theory of Computation group at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL). His research focuses on combinatorial optimization, approximation algorithms, and algorithmic mathematics. He is an ACM Fellow, recognizing his contributions to theoretical computer science and discrete mathematics. Goemans has taught numerous advanced courses at MIT, including Advanced Combinatorial Optimization , Algorithms , and Topics in Theoretical Computer Science . His work bridges foundational mathematics with computational challenges, emphasizing techniques like semidefinite programming and matroid theory. Key research areas include non-bipartite matchings, matroid intersection, submodular function optimization, and approximation algorithms for NP-hard problems. His contributions to the Traveling Salesman Problem and semidefinite programming relaxations are particularly influential. Awards and recognitions include the ACM Fellowship (2009) and leadership roles in academic administration, reflecting his dual impact on research and education.

• Supply Chain Management
• Healthcare Improvement
• Healthcare Logistics
• +3 more

Dr. Ali Hajiaghabozorgi is a Research Fellow in Supply Chain Management at the Institute of Transport and Logistics Studies (ITLS) within the University of Sydney's Business School. He joined in April 2019 to work on an IMCRC-funded project focusing on vaccine supply chain optimization in Australia. Previously, he held postdoctoral positions at Clemson University (2014-2015) and Sharif University of Technology (2016-2018), researching healthcare logistics and supply chain design. His academic background includes a PhD in Industrial Engineering from the University of Central Florida (2014), an MSc, and a BSc. His research interests span Supply Chain Management , Healthcare Improvement , and the intersection of Supply Chain in Healthcare . Key projects include vaccine supply chain design, blood bank inventory optimization, and airline network congestion management. He has also consulted for organizations like Universal Studios Orlando and automotive supply companies. Dr. Hajiaghabozorgi’s work addresses sustainability challenges in supply chains, such as carbon emissions and cold chain logistics. His publications explore network design, inventory modeling, and operational efficiency in healthcare and energy sectors. He contributes to teaching ITLS5000 Foundations of Supply Chain Management and MMGT6012 Business Tools for Management . His research aligns with the Sydney Institute of Agriculture, emphasizing practical solutions for complex logistics problems. Current projects focus on enhancing supply chain resilience in healthcare and optimizing resource allocation under uncertainty.

• Maritime Engineering
• Mechanical Engineering
• Oceanography
• +5 more

Dr. Stephan Kramer is an Advanced Research Fellow in the Department of Earth Science & Engineering at Imperial College London's Faculty of Engineering. He holds affiliations with the Applied Modelling and Computation Group and focuses on interdisciplinary research at the intersection of computational geophysics, renewable energy systems, and numerical methods. His work integrates advanced computational techniques with geodynamic and oceanographic processes. Education: MSc Theoretical Physics and Mathematics (University of Utrecht, 1994–2001), PhD (TU Delft, 2002–2007). Research Interests: Maritime Engineering, Mechanical Engineering, Oceanography, Geophysics, and Computational Fluid Dynamics. His research emphasizes numerical modeling of geophysical flows, tidal energy systems, and mantle dynamics. Recent work explores mesh adaptation techniques for ice flow and mantle convection, optimization of tidal turbine arrays, and inversion methods for reconstructing Earth's mantle evolution. Kramer has contributed to advancing open-source frameworks like Firedrake for geodynamic simulations. Publications from 2023–2024 highlight advancements in mesh movement networks, tidal energy optimization, and geodynamic inversion. His studies address challenges in energy extraction efficiency, environmental impact mitigation, and computational efficiency in large-scale geophysical models. No scientific awards or grants are explicitly listed, but his extensive publication record reflects sustained academic engagement. He is affiliated with the Applied Modelling and Computation Group, contributing to interdisciplinary research teams at Imperial College.

John HenryView profile

Senior Lecturer

• Serious Games
• Gamification in Education
• Healthcare Technology
• +4 more

Dr. John Henry is a Senior Lecturer and Programme Leader for BSc Games Design at Manchester Metropolitan University's Department of Computing and Mathematics. His roles include teaching undergraduate and postgraduate courses on games design, development, and gamification. He leads the Games Science Lab and the Serious Games for Health theme at the Manchester Games Centre, focusing on applying games and technology to healthcare, education, and human-centered computing. His research emphasizes the effectiveness of games in mental/physical rehabilitation, gamification in higher education, and IoT integration in games-based solutions. He supervises projects across Final Year, apprenticeship, and master’s programs, including a PhD on avatars in gamification for micro-teaching. Key research themes include wearable technology in exergames, stress/anxiety recognition via machine learning, and IoT-driven student engagement. He has collaborated on projects like the Affective Audio Dataset and frameworks merging serious games with IoT systems. Henry’s work bridges technology and societal challenges, with outputs spanning clinical psychology, AI ethics, and healthcare technology. His interdisciplinary approach addresses real-world problems through innovative game design and sensor-based solutions.

Annika RiegerView profile

Assistant Professor

• Environmental Sociology
• Global Political Economy
• Quantitative and Computational Methods
• +4 more

Annika Rieger is an Assistant Professor of Sociology at Singapore Management University's School of Social Sciences. Her research focuses on environmental sociology, global political economy, and the corporate drivers of climate change. She employs quantitative and computational methods to investigate transnational corporations' roles in climate change and mitigation strategies. Rieger holds a Ph.D. from Boston College (2023) and has been recognized with awards including the Donald J. White Teaching Award (2022) and fellowships from Morrissey Graduate School and Boston College. Education: Ph.D., Sociology, Boston College (2023) M.A., Sociology, Boston College (2018) B.S., Sociology, Southern Methodist University (2016) Research Interests: Corporate emissions, environmental policy, computational text analysis, and the intersection of technology and sustainability. Her work explores how macro-institutional factors like pro-business regulations and foreign investment influence corporate and national emissions. Notable projects include analyzing corporate earnings calls to assess shareholder engagement on climate issues and studying ICT's impact on material consumption. Rieger's interdisciplinary approach bridges sociology with theology and environmental economics. Publications span journals such as Social Problems , Environmental Sociology , and Routledge Handbook of Climate Change and Society . She teaches courses on research methods, environmental sociology, and introductory sociology. Grants & Awards: Morrissey Dissertation Fellowship (2022–23), Clough Fellowship (2021–22), Boston College Presidential Fellowship (2016–21). Rieger collaborates with institutions globally and contributes to sustainable development initiatives through her research and teaching.

Jens HegerView profile

Professor

• Manufacturing Engineering
• Production Technology
• Simulation
• +5 more

Jens Heger is a Professor of Engineering specializing in Modeling and Simulation of Technical Systems and Processes at Leuphana University Lüneburg, Germany. He is affiliated with the Institute for Production Technology and Systems (IPTS) and the Research Center for Digital Transformation. His academic career spans over a decade of research and teaching in production engineering, manufacturing systems, and simulation technologies. Dr. Heger completed his Master of Science in Computer Science with a minor in Business Studies at the University of Paderborn (1999-2006). He then earned his doctorate degree at the University of Bremen in the Department of Production Engineering. His dissertation focused on "Dynamic selection of rules for sequence planning in workshop and flexible flow production" under the supervision of Prof. Scholz-Reiter and Prof. Jürgen Branke from the University of Warwick, UK. Professor Heger's research interests center around the intersection of manufacturing engineering, simulation, and artificial intelligence. His work demonstrates a strong focus on applying advanced computational methods to solve complex production challenges. Key research areas include: Manufacturing process optimization through simulation and modeling Application of machine learning and reinforcement learning in production scheduling Development of intelligent control systems for manufacturing processes Energy efficiency and sustainability in production systems Quality control and prediction in sheet metal forming processes Integration of AI technologies in traditional manufacturing environments Analysis of Professor Heger's recent publications reveals a clear trajectory toward increasingly sophisticated applications of artificial intelligence in manufacturing. His work has evolved from traditional simulation and optimization techniques toward integrating deep learning, reinforcement learning, and computer vision technologies into production systems. A significant portion of his research focuses on dynamic scheduling and optimization problems, particularly using reinforcement learning approaches to address complex manufacturing challenges. His publications also demonstrate a growing interest in sustainable manufacturing practices, energy efficiency, and quality control applications through AI technologies. Professor Heger has contributed significantly to the field through numerous publications in manufacturing engineering, production systems, and AI applications. His work spans both theoretical advancements in scheduling algorithms and practical implementations in industrial settings, particularly focusing on small and medium enterprises.

Yann DisserView profile

Professor

• Combinatorial Optimization
• Online Algorithms
• Graph Exploration
• +6 more

Yann Disser is a Professor (W2) at the Department of Mathematics, Technische Universität Darmstadt. His research focuses on combinatorial optimization, online algorithms, and graph theory. He holds a PhD in Theoretical Computer Science from ETH Zurich (2011) and has held academic positions at TU Berlin and ETH Zurich. Disser has led numerous DFG-funded research projects, including work on incremental maximization and potential-based flow networks. His teaching includes courses on discrete optimization, algorithmic discrete mathematics, and online optimization. He supervises a research group with members such as Júlia Baligács and Annette Lutz, and has advised multiple PhD students including David Weckbecker. His recent work explores competitive analysis, algorithmic lower bounds, and graph exploration. Education: PhD in Theoretical Computer Science (ETH Zurich, 2011), MSc in Physics (TU Darmstadt), Diplom in Computer Science (TU Darmstadt). Research Interests: Combinatorial Optimization, Online Algorithms, Graph Exploration, Computational Complexity, Incremental Algorithms, Approximation Algorithms. Grants: DFG TRR 154 (2022–2026), DFG Individual Grants (2019–2023), EU EFRE Project (2016–2019). Labs: Deep Learning Lab (TU Darmstadt), Algorithms Lab (ETH Zurich).

Tobias PreußerView profile

Professor

• Mathematical Modelling
• Partial Differential Equations
• Bio-Medical Process Simulation
• +7 more

Prof. Dr. Tobias Preußer is a Professor of Mathematical Modelling of Medical Processes at the School of Computer Science and Engineering, Constructor University Bremen gGmbH. His research focuses on mathematical modeling in biomedical processes, numerical analysis, image processing, and scientific visualization. He holds a PhD in Mathematics from the University of Duisburg-Essen (2001-2003), a Diploma in Mathematics from the University of Bonn (1994-1999), and completed an exchange semester in applied mathematics at New York University. His academic roles include Deputy Institute Director and Head of Modeling and Simulation at Fraunhofer MEVIS, General Manager at TechsoMed GmbH, and Visiting Assistant Professor at the University of Bremen. His work emphasizes interdisciplinary collaboration, particularly in systems biology and medical imaging applications. Key research interests include partial differential equations, bio-medical process simulation, anisotropic diffusion techniques, and multiscale methods. He has contributed to advancements in radiofrequency ablation modeling, liver pharmacokinetics simulations, and uncertainty quantification in medical visualization. His publications span computational biology, medical physics, and visualization techniques, with notable contributions to virtual liver modeling and stochastic collocation methods for optimal control problems. He has led collaborative projects involving academic and industrial partners, advancing both theoretical and applied aspects of mathematical modeling in healthcare.

Serigne GueyeView profile

Lecturer

• Operational Research
• Combinatorial Optimization
• Urban Planning
• +3 more

Serigne Gueye is a Lecturer in Computer Science at Avignon Université, affiliated with the Research and Innovation Support Department (DARI). His research focuses on operational research, combinatorial optimization, and their applications in urban planning and transportation systems. Key interests include graph partitioning, mathematical optimization, and multi-agent simulation for solving real-world problems with societal impact. Notable contributions include winning the 2015 Data Crossing Prize for work on mobile phone data exploitation and co-authoring impactful studies on urban planning optimization and air passenger flow estimation. His work bridges theoretical research and practical applications, often collaborating with international teams (e.g., Senegal, Brazil). Projects include OPTIDENS (urban accessibility modeling) and mobility analysis in smart cities. Technical expertise spans hybrid algorithms, quadratic programming, and network flow analysis. His research narrative emphasizes interdisciplinary approaches, combining optimization techniques with computational methods to address complex societal challenges. Awards: Data Crossing Prize (Data for Development Challenge, 2015). Key Collaborations: Multi-national teams in Senegal, Brazil, and France. Labs/Platforms: Contributed to Avignon Université’s Metaboscope Platform and Science Ouverte initiatives.

• Geometric Measure Theory
• Calculus of Variations
• Optimal Transportation
• +3 more

Andrea Marchese is an Associate Professor in Mathematical Analysis at the University of Trento, Department of Mathematics. His research focuses on geometric measure theory, calculus of variations, and optimal transportation networks. He actively participates in organizing academic events such as the Analysis seminar and international workshops on geometric measure theory and partial differential equations. His work addresses topics like minimal surfaces, modulo p problems, and singular set analysis in geometric flows. Marchese collaborates with leading institutions and frequently presents at conferences worldwide, including recent talks in Munster and upcoming events in Pisa and Trento. Education: Completed his Ph.D. in 2014 with a thesis on optimal irrigation networks. Professional activities include seminar organization across multiple academic years and participation in high-impact international conferences. His research interests bridge pure mathematics with applied problems in network optimization and geometric analysis. Scientific contributions include over 38 publications in top journals like Journal of Functional Analysis and Communications on Pure and Applied Mathematics . His work emphasizes rigorous analysis of geometric variational problems and their applications to real-world transportation models.

Jayati DeshmukhView profile

Research Fellow

• Multi-Agent Systems
• Responsible AI
• AI Ethics
• +6 more

Jayati Deshmukh is a Research Fellow and Postdoctoral Researcher at the University of Southampton's School of Electronics and Computer Science, part of the Department of Electronics and Computer Science. She is affiliated with the Agents, Interaction and Complexity (AIC) research group, the UKRI Trustworthy Autonomous Systems (TAS Hub), and the Responsible AI UK (RAI UK) and Citizen-Centric AI Systems (CCAIS) initiatives. Her work focuses on designing responsible multi-agent systems and addressing ethical challenges in AI. Education: PhD in Computer Science from IIIT-Bangalore (advisor: Prof. Srinath Srinivasa), MTech in Data Science from IIIT-Bangalore, and BE in Computer Engineering from Gujarat Technological University. She is a gold medalist in both her undergraduate and graduate programs. Research interests include Multi-Agent Systems, Responsible AI, AI Ethics, Network Science, Game Theory, and applications of NLP, Machine Learning, and Knowledge Graphs. She has 4+ years of industry experience at Accenture Technology Labs, where she developed NLP, deep learning, and knowledge graph solutions. Awards: Best Demonstration Award at AAMAS 2025. Active in collaborative projects on ethical AI governance and citizen-centric AI systems. Publications span topics like ethical preference elicitation, fairness in allocation games, and consent management architectures. Advising and Grants: Collaborates with Prof. Sarvapali Ramchurn and Prof. Sebastian Stein on major research projects. Involved in interdisciplinary teams addressing global challenges in trustworthy AI and autonomous systems. Labs/Teams: Core member of the AIC research group and UKRI TAS Hub. Engages with interdisciplinary initiatives linking computer science, ethics, and social science.