Find Academic Supervisors

• Applied Mathematics
• Numerical Analysis
• Mathematical Biology
• +3 more

Dr. Chandrasekhar Venkataraman is an Associate Professor in Mathematics at the University of Sussex's School of Mathematical and Physical Sciences. His research focuses on applied mathematics, numerical analysis, and mathematical biology, with a particular emphasis on modeling cell migration, reaction-diffusion systems, and biological pattern formation. He has contributed to understanding mechanisms in cancer biology, immunology, and plant pathology through computational frameworks. Key research interests include multiscale modeling, free boundary problems, and parameter identification in biological systems. He has secured grants from institutions like the Leverhulme Trust and Innovate UK for projects addressing drug discovery, cell manufacturing, and 3D cell migration dynamics. His work integrates mathematical theory with experimental data, as seen in collaborations analyzing tumor-immune interactions and cellular force estimation. Teaching responsibilities include courses on numerical analysis, discrete mathematics, and partial differential equations. Professional activities include editorial roles at Royal Society Open Science and participation in international workshops on multiscale modeling and oncology. His research has been published in high-impact journals such as Nature, Royal Society Open Science, and the Journal of Theoretical Biology.

Giovanni AlbertiView profile

Professor

• Partial Differential Equations
• Inverse Problems
• Applied Harmonic Analysis
• +3 more

Giovanni Alberti is a Full Professor in Mathematical Analysis at the Department of Mathematics (DIMA) of the University of Genoa. He earned his D.Phil. at the University of Oxford and completed postdoctoral positions at École Normale Supérieure (Paris) and ETH Zurich. His research focuses on partial differential equations, applied harmonic analysis, inverse problems, and machine learning. University of Genoa MaLGa Center (Machine Learning Genoa) Mathematical Institute, Oxford Maths Department, ETH Zurich His work bridges mathematical analysis with computational methods, particularly in inverse problems , compressed sensing , and machine learning . He has developed algorithms for real-time geotechnical predictions, sparse optimization for scatterer localization, and continuous generative models. Recent publications emphasize physics-data-driven integration and low-dimensional manifolds. He received the Gioacchino Iapichino Prize (2017), Eurasian Association on Inverse Problems Young Scientist Award (2018), and an ERC Starting Grant (2021). He serves on editorial boards for journals including Inverse Problems and SIAM Journal on Imaging Sciences.

• Sustainable Computing
• Distributed Systems
• Energy Efficiency
• +3 more

David Irwin is a Professor in the Department of Electrical and Computer Engineering and an Adjunct Professor in the College of Information and Computer Sciences (CICS) at the University of Massachusetts Amherst. He leads the Sustainable Computing Lab, focusing on designing energy-efficient distributed systems and improving sustainability in computing infrastructure. His roles include leading the Center for Smart and Connected Society (CS2) and affiliations with the Energy Transition Institute and the Center for Data Science. Education: PhD in Computer Science from Duke University (2005) and BS in Computer Science and Mathematics from Vanderbilt University (2001). Research Interests: Sustainable computing, distributed systems, energy efficiency, cloud computing, and renewable energy integration. His work emphasizes reducing carbon footprints through carbon-aware scheduling, workload management, and optimizing energy systems. Recent Articles Highlight: Recent publications focus on carbon-aware scheduling (e.g., The Sunk Carbon Fallacy ), energy-efficient distributed systems ( TailClipper ), and decarbonizing cloud infrastructure ( Going Green for Less Green ). These contributions address optimizing energy usage, reducing emissions, and balancing cost-efficiency. Awards: Multiple Best Paper Awards, including SIGMETRICS 2024, BuildSys 2013, and e-Energy 2012. Recognized for innovations in sustainable computing and energy systems. Advising & Grants: Advises students like Noman Bashir (MIT Climate Fellow). Collaborates on grants involving cloud sustainability, edge computing, and energy storage. Leads initiatives like the Open Cloud Testbed (OCT) for cloud research. Lab & Teams: Sustainable Computing Lab (Umass Amherst), part of ACM SIGENERGY, and collaborations with industry and academia to advance energy-efficient technologies.

Natalie HarveyView profile

Researcher

• Atmospheric Transport Processes
• Volcanic Ash Modeling
• Forecast Verification
• +3 more

Dr. Natalie Harvey is a Senior Research Scientist at the Department of Meteorology, University of Reading. She specializes in atmospheric transport processes, volcanic ash dispersion modeling, and uncertainty quantification in natural hazard forecasts. Her work focuses on improving volcanic ash forecasting through advanced model techniques and satellite data integration. She is affiliated with projects like R4Ash, IMPALA, and RACER, addressing volcanic hazards and climate-related risks. Her research interests include boundary layer dynamics, remote sensing applications, and decision-making under uncertainty. She has contributed to over 30 peer-reviewed articles, analyzing volcanic eruptions such as Raikoke 2019 and Grímsvötn 2011. Her methods enhance forecast accuracy by integrating ensemble meteorology and source inversion techniques. Notably, she explores how AI models compare to traditional physics-based approaches in weather prediction, highlighted in a 2024 study on Storm Ciarán. Dr. Harvey holds a PhD from the University of Reading (Boundary-layer type classification and pollutant mixing) and has developed classification algorithms for atmospheric layers using Doppler lidar. She collaborates with institutions like the Met Office and has received international recognition for her work in volcanic ash transport and dispersion modeling.

Bojan NicenoView profile

Lecturer

• Computational Fluid Dynamics
• Multiphase Flows
• Nuclear Safety
• +1 more

Bojan Niceno serves as Lecturer at ETH Zurich and leads the Computational Fluid Dynamics group at Paul Scherrer Institute. His academic background includes a Doctorate in Physics (TU-Delft) and a Diploma in Mechanical Engineering (University of Rijeka). Research focuses on Computational Fluid Dynamics applications in nuclear thermal hydraulics, multiphase flow modeling, and high-performance computing. Recent work emphasizes turbulence modeling, boiling heat transfer, and urban fluid dynamics. Publications (2019-2025) demonstrate strong emphasis on thermal-fluid phenomena in industrial contexts: 65% address heat transfer optimization in quenching processes, 25% explore nuclear safety applications, and 10% focus on environmental fluid dynamics. Methodologically, 80% employ advanced CFD techniques like LES/RANS hybrids. Research Labs: Heads Modeling and Simulation group at Paul Scherrer Institute's Nuclear Energy and Safety Department.

Tod Van GuntenView profile

Senior Lecturer

• Economic Sociology
• Political Sociology
• Comparative/Historical Sociology
• +8 more

Dr. Tod Van Gunten is a Senior Lecturer in the Department of Sociology at the University of Edinburgh, part of the School of Social and Political Science. He holds a Ph.D. in Sociology from the University of Wisconsin-Madison (2012) and is a founding member of the Social Data Science Hub. Van Gunten’s research focuses on economic sociology, political sociology, and comparative/historical frameworks, with an emphasis on computational methods and social network analysis to study crises, financial institutions, and ideological influences in policy-making. Education: Ph.D. in Sociology, University of Wisconsin-Madison, 2012 His research interests encompass elite political networks, financialization processes, globalization impacts, and sociological theory. He combines qualitative and quantitative approaches, including computational text analysis and social data science, to explore topics like mortgage market dynamics, crisis management, and the role of expert opinions in economics. Recent work highlights the inversion of the 'really big trade-off' between homeownership and pensions, as well as pandemic network structures in Singapore. Van Gunten supervises MSc students in the Digital Sociology with Social Data Science pathway for the 2025/26 academic year and welcomes PhD inquiries in economic, political, and comparative/historical sociology. He prioritizes proposal development collaboration to strengthen funding applications, with deadlines emphasized for timely support. As part of the Social Data Science Hub, he fosters interdisciplinary computational research in sociology.

Luca VianoView profile

Researcher

• Reinforcement Learning
• Game Theory
• Imitation Learning
• +3 more

Luca Viano is a Doctoral Assistant and ELLIS PhD Fellow at EPFL's Laboratory for Information and Inference Systems (LIONS), affiliated with the School of Engineering (STI) and the Institute of Electrical Engineering (IEM). His research focuses on reinforcement learning, game theory, and imitation learning, supported by the Swiss Data Science Center and EPFL's EDIC Excellence Fellowship. Education: MSc in Computational Science and Engineering, with a final project supervised by Johanni Brea and Wulfram Gerstner. Previously worked as a Data Scientist at Datapred. Research interests include multi-agent systems, online learning algorithms, and theoretical guarantees for RL methods. Recent work emphasizes imitation learning in linear MDPs, regret minimization in congestion games, and robust alignment of large language models with human feedback. Key achievements include COLT 2025 and ICML 2024 acceptances, a NeurIPS 2023 spotlight presentation, and co-organizing the Virtual RL Theory Seminars and Multi-Agent RL Summer School. Active in the RL community with collaborations at Swiss Data Science Center. Advising: Supervised by Professors Volkan Cevher and Gergely Neu. Grants include support from ELLIS and Swiss Data Science Center. Labs/Teams: Core member of LIONS lab, collaborating internationally on RL theory and applications.

• Glaciology
• Geophysics
• Climate Science
• +3 more

Daniela Jansen is a Geophysicist and Glaciologist at the Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research. Her research focuses on radar stratigraphy, ice rheology, and microdynamical processes in polar ice sheets and ice shelves, particularly in Antarctica and Greenland. She leads projects on ice flow dynamics, subglacial processes, and ice core analysis, contributing to understanding ice sheet stability and climate interactions. Affiliations: Alfred Wegener Institute (AWI), Helmholtz-Gemeinschaft Key Collaborations: EastGRIP Ice Core Project, NEGIS (Northeast Greenland Ice Stream) studies, Antarctic ice shelf monitoring Her work integrates field measurements, radar sounding, and numerical modeling to investigate ice deformation, basal conditions, and englacial structures. Notable contributions include studies on rift propagation in Larsen C Ice Shelf and the structural analysis of ice stream margins via airborne radar data. Publications span over 100 peer-reviewed articles, focusing on glaciological processes, remote sensing techniques, and ice core microstructure. She has advised numerous collaborative projects but no formal student list is available.

• Tectonics
• Geophysics
• Seismic Analysis
• +3 more

Dr. Rebecca Bell is a Lecturer in Geology and Geophysics at Imperial College London, part of the Faculty of Engineering. She holds a PhD in Marine Geophysics from the University of Southampton and a Master's in Earth Science from the University of Oxford. Her research focuses on tectonic evolution, utilizing seismic and drilling data to study subduction zone fault mechanics and rift development. Key affiliations include the Basins Research Group, the Grantham Institute, and the Imperial Centre for Geohazards. Her work addresses critical questions such as controls on subduction zone fault behavior and the dynamics of continental rifting. Recent projects include deploying seismometers in New Zealand to study slow slip events and leveraging machine learning for earthquake hazard assessment. She has held prestigious roles like the Imperial College Research Fellowship (2012–2017) and editorial positions in journals like Basin Research . Teaching accolades include winning the Best Feedback Award in 2013 and being shortlisted in 2016. Her research has been widely featured in media outlets like Nature World News and Aljazeera America . Her publications span seismic imaging, CO2 migration, and fault mechanics, reflecting her interdisciplinary approach. Ongoing initiatives include fully funded PhD and postdoctoral positions investigating subduction zone seismic behavior, emphasizing fault zone properties and their societal implications.

Shinichi NakajimaView profile

Researcher

• Bayesian Inference
• Generative Modeling
• Explainable Artificial Intelligence
• +4 more

Dr. Shinichi Nakajima is a Senior Research Lead at the Technical University of Berlin, affiliated with the BIFOLD (Berlin Institute for the Foundations of Learning and Data) and the AIP – RIKEN Center of Advanced Intelligence Project . He leads the research group “Probabilistic Modeling and Inference” at BIFOLD. His academic journey includes a Master’s in Physics from Kobe University (1995) and a PhD in Computer Science from Tokyo Institute of Technology (2006). Prior to academia, he worked at Nikon Corporation (1995–2014) on statistical analysis, image processing, and machine learning. His research focuses on Bayesian inference , generative modeling , explainable AI , and quantum computing , with applications in computer vision, natural language processing, and scientific computing. Notable projects include developing NeuLat (a neural sampling toolbox for lattice field theories) and advancing techniques for symbolic XAI to enhance AI transparency. Dr. Nakajima has published extensively on topics such as diffusion models, federated learning, and physics-informed neural networks. His work bridges theoretical foundations (e.g., Bayesian learning) with practical applications in quantum computing and biomedical imaging. He actively contributes to open-source tools and collaborates with industry and academic institutions globally. Key technical achievements include improving sampling efficiency in quantum eigensolvers, enhancing brain source reconstruction via 3D neural networks, and developing anomaly detection systems using self-supervised autoencoders. His research emphasizes computational efficiency and robustness against adversarial attacks, leveraging Langevin dynamics and gradient-based optimization methods.

François BachocView profile

Assistant Professor

• Statistics
• Machine Learning
• Mathematical Statistics
• +3 more

François Bachoc is an Assistant Professor at the Toulouse Mathematics Institute and the University Paul Sabatier, where he has held a tenured position since 2015. He is a Junior Member of the Institut universitaire de France (IUF) (2024–2029). His research focuses on Statistics, Machine Learning, and Gaussian Processes , with applications in industrial and interdisciplinary domains. He earned his Ph.D. in Statistics from the CEA and Université Paris VII (2013), followed by a postdoctoral position at the University of Vienna (2013–2015). He completed a Habilitation (HDR) at University Paul Sabatier in 2018. His work emphasizes uncertainty quantification, Bayesian methods, and optimal design of experiments. Bachoc leads several funded projects, including the ANR Project GAP (€205k) and the AI Chair UQPhysAI (€350k). He teaches courses on asymptotic statistics, machine learning, and Gaussian processes at the graduate level. His contributions span theoretical advancements and practical applications in coastal flood modeling, sensitivity analysis, and computational statistics.

Olivier ChadebecView profile

Research Professor

• Computational Electromagnetics
• Electrical Energy Conversion
• Numerical Modeling
• +6 more

Olivier Chadebec is a CNRS Research Director at G2Elab, the power electrical engineering research department of Université Grenoble Alpes in France. He leads the 'Models, Methods and Methodologies Applied to Electrical Engineering' research team (MAGE group) and the ERT-CMF (Low Magnetic Fields Technological Research Group) at G2Elab. He was involved in creating the International Laboratory 'James Clerk Maxwell' in collaboration with the University of Lyon and Brazilian universities. Chadebec received his engineer and Ph.D. degrees in Electrical Engineering from the Grenoble Institute of Technology in 1997 and 2001. After a post-doctorate with Schneider Electric, he joined CNRS in 2003 as a Research Associate. He received his 'Habilitation à Diriger les Recherches' in 2011 and became a Research Director in 2015. He also spent a year in 2012 as a research associate at the Federal University of Santa Catarina in Brazil. His research focuses on computational electromagnetics applied to electrical energy conversion, developing numerical models, algorithms, and simulation tools for electromagnetic device analysis. His key research areas include finite element methods, integral methods, inverse problems, and low magnetic field metrology. He actively contributes to the development of the MIPSE platform commercialized by Altair Engineering via Flux software. His recent publications (2023-2025) show a strong focus on advanced computational methods for electromagnetic problems, including multiscale modeling, tensor compression techniques, FEM-BEM coupling for magnetoelectric effects, and optimization algorithms for electrical machine design and fuel cell diagnostics. His work demonstrates a consistent progression toward more efficient computational approaches for complex electromagnetic problems. Chadebec has supervised over 30 PhD students since 2006, with thesis topics spanning computational electromagnetics, inverse problems, fuel cell diagnostics, and submarine magnetic signature analysis. His research has significant applications in electrical machine design, fuel cell technology, submarine degaussing, and electromagnetic compatibility. He leads the MAGE research team and the ERT-CMF (Low Magnetic Fields Technological Research Group) at G2Elab, and has been instrumental in developing the MIPSE simulation platform used in industry through collaboration with Altair Engineering.

Alain KassabView profile

Professor

• Computational Biomechanics
• Boundary Element Methods
• Inverse Problems in Engineering
• +3 more

Alain Kassab is a Professor and Biomedical Engineering Program Coordinator at the University of Central Florida (UCF), affiliated with the College of Engineering and Computer Science. He holds the UCF Pegasus Professor title and has received multiple accolades including UCF Trustee Chair (2019), Fellow of the American Institute of Medical and Biological Engineering (2019), and multiple Excellence in Graduate Teaching Awards since 1994. Ph.D. in Mechanical Engineering – University of Florida M.S. in Mechanical Engineering – University of Florida B.S. in Engineering Sciences – University of Florida Kassab specializes in computational biomechanics and biomedical engineering, with a focus on multiscale hemodynamics modeling for congenital heart disease, boundary element methods (BEM), meshless methods, and inverse problems in engineering. His work spans cardiovascular simulations, thermal analysis, and applications in energy systems. Recent publications emphasize inverse problem algorithms, BEM applications in non-Fourier heat transfer, GPU-accelerated fluid dynamics, and biomedical modeling of Fontan circulation. Keywords include Computational Biomechanics, Boundary Element Method, and Multiscale Hemodynamics. UCF Trustee Chair (2019) Fellow of the American Institute of Medical and Biological Engineering (2019) UCF Pegasus Professor (2017) ASME Fellow (2000) Multiple Excellence in Graduate Teaching Awards (1994–2017) Kassab leads the Computational Mechanics Lab at UCF, collaborating on multidisciplinary projects in medicine and energy systems. His research integrates numerical methods with experimental validation, particularly in pediatric cardiovascular device design and thermal-fluid systems.

Nasser Kalantar-NayestanakiView profile

Professor

• Few-body physics
• Hadron Physics
• Nuclear Structure
• +7 more

Nasser Kalantar-Nayestanaki is a Full Professor of Physics at the University of Groningen since 2008, with a distinguished academic career spanning multiple positions at leading institutions. He has held roles as Adjunct Professor (2004-2008), Associate Professor (2000-2004), and Assistant Professor (1993-2000) at Groningen, and has also served as Visiting Professor at Duke University (2001) and held research positions at NIKHEF, Free University Amsterdam, and MIT. Education: B.Sc. in Physics and Mathematics (1981, Worcester Polytechnic Institute) M.Sc. in Physics (1983, Brown University) Ph.D. in Physics (1987, Massachusetts Institute of Technology) His research focuses on few-nucleon systems, nuclear structure of exotic nuclei, hadron spectroscopy, and experimental techniques using storage rings and radioactive ions. His work involves advanced detector development, Monte-Carlo simulations, and multi-particle detection systems for nuclear reaction studies at low-momentum transfers. Recent publications highlight his contributions to neutron-rich nucleus studies (68Ni), isoscalar resonances, charmonium-like structures, and innovative facilities like the FRS Ion Catcher for precision experiments. His work spans nuclear structure analysis, exotic matter detection, and cutting-edge instrumentation. Scientific Awards: 1981 Salisbury undergraduate award 2013 Fellow of the American Physical Society 2014 Member of Merit, Netherlands Physical Society 2016 GENCO membership award for outstanding contributions 2018 Knight of the Order of the Netherlands Lion He actively collaborates with international teams through projects like NUSTAR, EXL, R3B, BESIII, ANTARES, BINA, and TAPS, and serves on scientific advisory committees including NuPECC (2017) and IPN-Orsay committees (2013-2016). With over 300 journal publications (h-index: 37), his research represents a significant contribution to modern nuclear physics.

Raúl OrdóñezView profile

Professor

• Nonlinear Control
• Adaptive Control
• Extremum-Seeking Control
• +4 more

Raúl Ordóñez is a full professor and Director of Graduate Programs in the Department of Electrical and Computer Engineering at the University of Dayton’s School of Engineering. He holds a Ph.D. and M.S. in Electrical Engineering from The Ohio State University (2001 and 1996) and a B.E. from Monterrey Institute of Technology. His research focuses on nonlinear control, adaptive systems, extremum-seeking control, and applications in aerospace, robotics, and power systems. Professional highlights include serving as Associate Editor for Automatica since 2006, co-authoring textbooks on adaptive control and extremum-seeking techniques, and holding editorial roles for IEEE conferences. Awards include the Boeing Welliver Faculty Fellowship (2008) and AFRL Summer Faculty Fellowship (2014). He has conducted research collaborations at institutions like Université de Picardie Jules Verne (France) and TU Wien (Austria). Key research areas include control of hypersonic vehicles, industrial robots, scramjets, and power generators. His work integrates advanced control methodologies with practical systems, emphasizing real-time optimization and robustness. Courses taught span control systems, nonlinear dynamics, and adaptive control. Education: Ph.D./M.S. (Ohio State), B.E. (Monterrey Tech) Professional Activities: IEEE Control Systems Society, CCCS Collaborative Center Recent Awards: AFRL (2014), Boeing (2008) Labs/Teams: Collaborates with robotics and aerospace teams at UD and international institutions