Find Academic Supervisors

• Remote Sensing
• Satellite Image Processing
• Wireless Sensor Networks
• +3 more

Maurizio Spadavecchia serves as an Associate Professor at the Polytechnic University of Bari, Italy, specializing in engineering disciplines under SSD ING-INF/07. His research bridges theoretical signal processing with practical environmental and energy applications through satellite imagery analysis, sensor networks, and medical instrumentation. His core research focuses on Remote Sensing (particularly water body monitoring via SNOWED dataset), Wireless Sensor Networks for distributed environmental data collection, and Battery Health Monitoring using data-driven approaches. Recent projects include Po River monitoring via Sentinel-2 imagery, deep learning-based coastline measurement, and smartphone-based vital sign tracking systems. His work consistently integrates convolutional neural networks for signal analysis in infrastructure and ecological contexts. Analysis of his 2021-2025 publications reveals a strong trend toward AI-enhanced environmental monitoring, with 60% of recent papers applying deep learning to satellite image segmentation and water edge detection. Parallel research streams address lithium-ion battery diagnostics (20% of publications) and medical signal processing (20%), demonstrating interdisciplinary expertise spanning environmental science, energy systems, and biomedical engineering.

Mitsuru IgamiView profile

Associate Professor

• Industrial Organization
• Economics of Innovation
• International Trade
• +6 more

Mitsuru Igami is an Associate Professor of Economics at the University of Toronto. His research focuses on industrial organization, economics of innovation, and international trade. He holds a Ph.D. in Global Economics and Management from UCLA Anderson School of Management, along with multiple advanced degrees in Economics from UCLA and the University of Tokyo. Education: Ph.D., Global Economics and Management, UCLA Anderson School of Management M.A., Economics, UCLA (June 2009) M.A., Economics, University of Tokyo B.A., Latin American Area Studies, University of Tokyo His research explores topics such as patent statistics as innovation indicators, creative destruction in technology industries, and the dynamics of offshoring. Notable projects include analyzing LCD panel innovation welfare effects and detecting Edgeworth cycles in commodity markets. Igami’s work frequently employs structural estimation methods, as seen in his analysis of AI-driven strategic games like AlphaGo. His articles consistently bridge theoretical frameworks with empirical analysis of real-world industries, including hard disk drives, pharmaceutical cartels, and coffee trade. This interdisciplinary approach highlights his expertise in linking microeconomic theory to practical policy implications. No scientific awards are explicitly listed. His advising and grant activities are not detailed in the provided text, though his extensive publication record suggests active research leadership. No specific lab affiliations are mentioned, though his work likely involves collaboration with computational economics and industrial analysis teams.

• Numerical Linear Algebra
• Partial Differential Equations
• Integral Equations
• +7 more

Difeng Cai is an Assistant Professor in the Department of Mathematics at Southern Methodist University. His research focuses on developing efficient algorithms for numerical linear algebra, partial differential equations, and integral equations, with applications spanning physics, statistics, machine learning, and deep learning. University: Southern Methodist University Department: Mathematics Academic Rank: Assistant Professor Professor Cai's work addresses computational challenges in scientific computing and data science by leveraging mathematical properties to design scalable algorithms. His recent interests involve integrating deep learning techniques, such as generative models, with traditional numerical methods to solve complex scientific problems. His research trends highlight interdisciplinary applications of numerical linear algebra and machine learning in scientific computing. Publications appear in venues like Numerical Linear Algebra and Its Applications , Numerische Mathematik , and SIAM Journal on Scientific Computing , emphasizing algorithm design, error estimation, and kernel matrix approximation.

Joseph ByrnesView profile

Research Professor

• Geophysics
• Seismology
• Mantle Dynamics
• +3 more

Joseph Byrnes is an Assistant Research Professor at Northern Arizona University, transitioning to UT Dallas. His research focuses on geophysics, seismology, and tectonics, particularly in seismic imaging of subduction zones, mantle dynamics, and lithospheric structure. He leads NSF-funded projects including studies on ocean-mantle dynamics using OBS data and collaborative SZ4D MultiArray initiatives. Key research interests include crustal anisotropy, seismic attenuation tomography, and joint inversion methods applied to surface-wave and scattering data. His work addresses continental margins, cratonic structures, and volcanic systems like the East African Rift and Iceland. Byrnes employs advanced techniques such as transdimensional Bayesian inversion and machine learning for seismic data analysis. Current grants include NSF awards for projects like 'Constraining ocean-mantle dynamics' and 'Data-driven optimization of SZ4D MultiArray.' He collaborates extensively as a Co-PI on multi-institutional initiatives. His fieldwork spans locations such as the Galápagos, Alaska, and the Indo-Burman margin, yielding insights into lithospheric architecture and mantle processes.

• High-Performance Computing
• Finite Element Methods (FEMs)
• Discontinuous Galerkin Methods
• +3 more

Dr. Stefano Giani is an Associate Professor in High-Performance Computing within the Department of Engineering at Durham University. His research focuses on Finite Element Methods (FEMs), particularly discontinuous Galerkin methods, and their applications in solid mechanics, fluid dynamics, photonics, and AI-driven systems. He also specializes in Deep Neural Networks for machine monitoring, natural language processing, and CAD analysis. His work includes advancements in adaptive finite element methods, fracture mechanics, and computational mechanics. He has collaborated extensively on projects involving phase field fracture modeling, topology optimization, and high-order numerical techniques. Notable contributions include hp-adaptive methods for radiative transfer and photonic crystal applications. Dr. Giani supervises research postgraduates in computational mechanics and leads interdisciplinary projects combining AI with traditional engineering methods. Recent publications highlight innovations in machine learning for fault detection, CAD file analysis, and lecture capture enhancement. His research bridges computational mathematics with real-world engineering challenges, emphasizing both theoretical rigor and practical implementation.

• Mathematical Biology
• Dynamical Systems
• Medical Signal Analysis
• +5 more

Philip James Aston is a Professor of Mathematics at the University of Surrey, affiliated with the School of Mathematics and Physics and the Department of Mathematics. His research focuses on dynamical systems, mathematical biology, and medical signal analysis. He holds a BSc (First Class Honours) and PhD from Brunel University, followed by a postdoc at the University of Bath. Key research interests include bifurcation theory, Lyapunov exponents, pharmacokinetics, and machine learning applications in healthcare. Notable contributions include the Symmetric Projection Attractor Reconstruction (SPAR) method for waveform analysis, used in ECG and PPG signal processing. He has secured grants, including £50,000 for wearable sensor development. His work bridges mathematics and medicine, addressing challenges in cardiovascular monitoring, genetic mutation detection, and drug efficacy modeling. He collaborates with clinicians and engineers, contributing to datasets like PTB-XL+ and advancing AI-driven diagnostics.

Lisa KreusserView profile

Researcher

• Partial Differential Equations
• Machine Learning
• Climate Modeling
• +3 more

Lisa Kreusser is a researcher affiliated with the University of Bath, holding roles in the EPSRC Centre for Doctoral Training in Statistical Applied Mathematics (SAMBa), the Centre for Climate Adaptation & Environment Research (CAER), and the Institute for Mathematical Innovation (IMI). Her research focuses on the intersection of partial differential equations, data analysis, and mathematical formulations for machine learning, with applications in climate modeling, biology, engineering, and industry. She actively leads and collaborates on projects addressing challenges such as cloud parameterization in atmospheric models, diabetic foot ulcer care through digital health technologies, and neural network-based solutions for complex systems. Her academic contributions span over 20 research outputs, emphasizing interdisciplinary approaches to nonlinear problems. Key areas include information propagation on graphs, stochastic differential equations in machine learning, and anisotropic diffusion models for pattern formation. Kreusser has secured funding for eight projects, demonstrating expertise in both theoretical and applied research. She is open to supervising doctoral students across topics in machine learning, climate science, and biological systems. Education: Advanced degrees in applied mathematics and computational science (specific details not provided). Grants: Includes projects on computational collective decision-making, weather prediction models using machine learning, and diabetic foot ulcer care technologies. Labs/Teams: Collaborates within multi-institutional teams, such as the Institute of Mathematical Innovation and CAER, driving interdisciplinary research initiatives.

Stefan WagerView profile

Associate Professor

• Causal Inference
• Statistical Learning
• Machine Learning
• +4 more

Stefan Wager is an Associate Professor at Stanford Graduate School of Business and Courtesy Associate Professor of Statistics. He holds a PhD and BS in Mathematics from Stanford. His research bridges causal inference, machine learning, and decision science, focusing on leveraging modern methods for statistical problems in economics and policy. Wager has advised numerous PhD students and postdocs, and his work has been recognized with awards like the Facebook Faculty Award and the Applied Statistics Dissertation Award. He contributes to open-source tools like the Generalized Random Forests (grf) package and serves on editorial boards for top journals. Education: PhD in Statistics (Stanford, 2016); BS in Mathematics (Stanford, 2011). Research Interests: Causal inference, policy learning, treatment effect estimation, machine learning applications in econometrics and healthcare. Awards: Philip F. Maritz Faculty Scholar (2024–2025), Spence Faculty Scholar, and multiple grants from NSF and industry partners. Grants: NSF Methodology, Stanford Human-Centered AI, and collaborations with Google, Uber, and Facebook. His work emphasizes scalable methods for policy evaluation, including synthetic difference-in-differences and optimized regression discontinuity designs. He has developed influential software for causal forests and treatment effect estimation, impacting both academia and industry.

• Artificial Intelligence in Healthcare
• Health Policy
• Opioid Use Disorder
• +5 more

Md Mahmudul Hasan is an Assistant Professor in the Department of Pharmaceutical Outcomes and Policy at the University of Florida (UF), with a joint appointment in the Department of Information Systems and Operations Management. He also serves as Assistant Director and Faculty Lead of the Medical Marijuana Research Repository under the Florida-funded Consortium for Medical Marijuana Clinical Outcomes Research. His affiliations include the UF Center for Drug Evaluation and Safety (CoDES) and Center for Addiction Research and Education (CARE). Appointed through UF’s AI initiative, his research focuses on AI-driven clinical decision support systems and prescriptive models to address substance use disorders, opioid-related adverse events, and chronic disease management. Dr. Hasan holds a PhD from Northeastern University’s Decision Analytics Lab and completed a postdoctoral fellowship at the FDA’s Center for Drug Evaluation and Research (CDER). His work integrates machine learning, statistical modeling, and management science to improve healthcare decision-making, with notable projects funded by the CDC and Massachusetts Department of Public Health. His research has been featured in media outlets including NPR, The Main Street Daily News, and Medical Press. His interdisciplinary studies emphasize opioid use disorder (OUD), predicting treatment discontinuation, and mitigating biases in AI models. He explores telehealth’s role in OUD treatment outcomes and develops frameworks to reduce opioid diversion through secure disposal programs. Dr. Hasan’s methodological contributions include causality-aware machine learning models and fairness-aware algorithms for healthcare applications. Teaching responsibilities include courses on pharmaceutical policy, managerial statistics, and decision analytics. His research has generated 15+ peer-reviewed publications since 2015, spanning journals like Computers in Biology and Medicine , Addiction , and Journal of the American College of Cardiology . Current projects aim to advance AI ethics in healthcare and optimize resource allocation for chronic disease management.

Romain FiévetView profile

Researcher

• Storm-resolving climate modeling
• Cloud microphysics
• Mesoscale tropical convection
• +5 more

Romain Fiévet is a Research Scientist in the Climate Physics Department at the Max Planck Institute for Meteorology (MPI-M) in Hamburg, Germany. His work focuses on high-resolution climate modeling with specialization in storm-resolving simulations and cloud microphysics. He leads computational infrastructure development within the SCLab and Climate Surface Interaction research group. Fiévet's research centers on understanding fine-scale atmospheric processes through advanced numerical modeling. His work examines how cloud microphysics and turbulence at kilometer-scale resolutions can feedback onto larger climate systems. Key research areas include convective cold pools, mesoscale tropical convection organization, and the development of high-resolution climate models like ICON. His methodology combines observational data from field campaigns with sophisticated numerical simulations to validate and improve climate models. Recent work includes participation in the 2024 ORCESTRA field campaign studying tropical convection organization, where he conducted high-resolution ICON model simulations to investigate convective-cold-pool dynamics. His research demonstrates how systematic grid refinement from 800m down to 25m resolution affects the representation of atmospheric phenomena. Fiévet maintains active research across both climate science and aerospace engineering domains, with recent publications spanning climate modeling and hypersonic flow dynamics. His technical expertise bridges atmospheric science and computational fluid dynamics, with strong emphasis on developing and applying high-resolution numerical methods to complex fluid systems.