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Matthias SeissView profile

Researcher

• Differential Equations
• Differential Algebra
• Algebraic Geometry
• +6 more

Matthias Seiß is a Researcher at the University of Kassel's Faculty of Mathematics and Computer Science, Department of Algebra and Differential Equations. His research focuses on differential equations, algebraic geometry, and their applications in mathematical biology and systems theory. He has collaborated on topics such as singular initial value problems, chemical reaction network reduction, and differential Galois theory. Seiß has taught courses like Mathematical Biology, Linear Algebra, and Differential Algebra, leading exercise sessions and seminars. His work bridges algebraic methods with computational and applied mathematics, addressing challenges in modeling biological systems and analyzing complex dynamical behaviors. Key contributions include studies on Markov chain models, conservation laws in chemical networks, and numerical methods for singular equations. He is involved in projects like the Symbiont initiative, advancing symbolic computation in differential equations. His research has been published in journals such as SIAM Journal on Applied Dynamical Systems and Communications in Algebra.

Barbara PrinariView profile

Professor

• Nonlinear Waves
• Integrable Systems
• Solitons
• +3 more

Barbara Prinari is a Professor in the Department of Mathematics at the University at Buffalo. Her research focuses on nonlinear waves, integrable systems, solitons, and mathematical modeling in social and behavioral sciences. She has contributed to the development of the Inverse Scattering Transform (IST) for nonlinear Schrödinger equations and explored soliton dynamics in diverse physical systems, including Bose-Einstein condensates and optical media. Education: PhD in Physics (1999) from the University of Lecce, Italy. Research Interests: She investigates integrable systems, particularly nonlinear Schrödinger equations, soliton solutions, and their applications. Her work also extends to social cognitive theory modeling, applying dynamical systems to study stress response and behavioral interactions. Publications: Over 50 peer-reviewed articles and a book on discrete and continuous nonlinear Schrödinger systems. Notable contributions include review articles on inverse scattering techniques and collaborations on soliton interactions in multi-component systems. Awards: Selected as a featured article author in the Journal of Mathematical Physics (2015). Grants: Collaborative NSF grants on nonlinear wave equations and integrable systems (2016, 2013). Active in interdisciplinary research, combining mathematical physics with social science modeling. Labs/Teams: Leads research groups focused on inverse scattering theory and soliton dynamics, with applications to quantum and optical systems.

Krishnan SureshView profile

Professor

• Topology Optimization
• Additive Manufacturing
• Computational Mechanics
• +5 more

Prof. Krishnan Suresh is a Professor in Mechanical Engineering at the University of Wisconsin-Madison, affiliated with the College of Engineering. He directs the Engineering Representations and Simulation Laboratory (ERSL) and co-leads the Wisconsin Applied Computing Center. His research focuses on topology optimization, additive manufacturing, computational mechanics, and quantum computing applications. He holds prestigious awards including the Vilas Associate (2023), Mead Witter Professorship (2022), and Woodburn Teaching Award (2018). Education: PhD 1998, Cornell University MS 1995, Cornell University MS 1992, UCLA B.Tech 1990, IIT Madras Research Interests: Dr. Suresh pioneers methods for efficient finite element analysis over tangled meshes, integrates machine learning into topology optimization, and develops quantum computing solutions for engineering problems. His work bridges computational mechanics with advanced manufacturing, emphasizing eco-friendly design and multiscale material systems. Publications: Recent work explores surrogate neural networks for topology optimization, quantum annealing for linear systems, and AI-driven detector design for physics experiments. His methods address challenges in handling inverted elements and improving additive manufacturing support structures. Awards: 2023 Vilas Associate 2022 Mead Witter Professorship 2018 Woodburn Teaching Award 2017 ASME Fellow Advising & Grants: He advises on NSF/DOE-funded projects and collaborates with Sandia National Labs/Autodesk. Courses taught include design optimization, CAD engineering, and graduate research supervision. Labs & Teams: ERSL develops simulation tools for complex systems, integrating high-performance computing and AI for next-gen engineering solutions.

Talles Batista Rattis SantosView profile

Assistant Professor

• Electrical Impedance Tomography (EIT)
• Biomedical Engineering
• Medical Imaging
• +4 more

Talles Batista Rattis Santos is a Teaching Assistant Professor affiliated with the CU-CMU PARTNERSHIP PROGRAM, contributing to interdisciplinary research at the intersection of biomedical engineering and mathematical modeling. He previously held postdoctoral and research roles at Colorado State University, the Polytechnic School of the University of São Paulo, and the EIT Laboratory at Colorado State University. Education : B.Sc. in Electrical Engineering, Federal University of Minas Gerais (2012) M.Sc. and Ph.D. in Control Engineering and Mechanical Automation, University of São Paulo (2015, 2019) His research focuses on Electrical Impedance Tomography (EIT), particularly in developing advanced algorithms for medical imaging, such as D-bar methods with statistical and anatomical priors, hybrid imaging modalities, and machine learning-driven post-processing. He has contributed to applications in respiratory diagnostics, fluid volume estimation, and hydroponic systems. The 15 most recent publications highlight his expertise in computational imaging, with a trajectory from foundational algorithm development (e.g., D-bar methods, Schur complement integration) to applied innovations in clinical settings and agricultural engineering. Keywords span inverse problems, medical imaging, machine learning, and physiological modeling. His career includes collaborations with multidisciplinary teams across institutions, with a focus on improving EIT resolution and robustness through mathematical rigor and cross-domain integration.

Anuj KarpatneView profile

Associate Professor

• Knowledge-guided Machine Learning (KGML)
• Spatio-temporal Data Mining
• Environmental Science Data Analysis
• +5 more

Anuj Karpatne is an Associate Professor in the Department of Computer Science at Virginia Tech, leading the Knowledge-guided Machine Learning (KGML) Lab. He joined VT in 2018 and earned tenure in 2023. His research focuses on integrating scientific knowledge into machine learning, pioneering the KGML paradigm to create explainable and generalizable AI solutions for interdisciplinary scientific challenges. He holds a PhD from the University of Minnesota (2017) and an M.Tech. from IIT Delhi (2011). Research Interests: Knowledge-guided ML frameworks (KG-Loss, KG-Neural Architectures, Hybrid-Science-ML) Applications in physics (fluid dynamics, quantum mechanics), environmental sciences (aquatic systems, remote sensing), and biology (organismal biology, virology) Interdisciplinary collaborations across 15+ institutions for NSF-funded projects Recent Contributions: Developed Fish-Vista dataset for biological trait analysis Co-organized KDD 2024 workshop on Generative Models for Sustainable Development Secured $18M NSF PIPP II grant as co-PI for pandemic prediction systems Grants & Leadership: Lead PI on NSF CAREER Grant ($586K) for unifying ML with scientific knowledge Co-PI on NSF Imageomics Institute ($15M) integrating biology with AI Site PI for macrosystems biology grant ($1.1M) modeling lake water quality Labs & Teams: KGML Lab at VT collaborates with 30+ scientists across disciplines Leadership roles in NSF PIPP Institute and Imageomics Center

Robert HildebrandView profile

Assistant Professor

• Mixed Integer Nonlinear Programming
• Integer Programming
• Operations Research
• +6 more

Robert Hildebrand is an Assistant Professor in the Grado Department of Industrial and Systems Engineering at Virginia Tech. He holds a Ph.D. in Applied Mathematics from the University of California, Davis (2013). His research focuses on Mixed-Integer Nonlinear Programming, Integer Programming, Complexity Theory, and Redistricting Analytics, with applications in Operations Research and Discrete Geometry. He has received grants from the Air Force Office of Scientific Research and the Office of Naval Research, and the 2019 Sporn Teaching Award. Education: Ph.D., Applied Mathematics (UC Davis, 2013); B.Sc., Mathematics (University of Puget Sound, 2008). Professional history includes postdoctoral roles at ETH Zurich (2013–2015), IBM Watson Research (2015–2017), and a Simons Institute Fellowship (2017). Current roles include Associate Editor for Discrete Optimization and service on INFORMS committees. Research interests emphasize theoretical and computational aspects of optimization, with recent work on gerrymandering analysis, robotic assembly optimization, and algorithmic complexity bounds. Notable publications include advancements in integer programming formulations and scheduling algorithms for autonomous systems. Grants include Virginia Tech's Whole Health Consortium (2024) for veterans' healthcare optimization and an ICTAS Seed Grant for autonomous fleet algorithms (2023). He advises students on topics like rectangle packing and robotic trajectory planning, with former students advancing to doctoral and industry roles. Labs/Teams: Active in Virginia Tech's FASER Lab (robotics optimization) and collaborates with interdisciplinary groups on redistricting analytics and space exploration technologies.

• Machine Learning
• Operations Research
• Optimization
• +3 more

Silvia Villa is an Associate Professor in the Department of Mathematics (DIMA) at the University of Genoa (UniGe). Her teaching responsibilities include courses such as Game Theory, Machine Learning, Optimization and Operations Research, and Operations Research at both undergraduate and master's levels. Her research focuses on optimization theory and its applications in machine learning, inverse problems, and regularization methods. She has contributed to advancements in iterative regularization techniques, stochastic optimization algorithms, and convex optimization frameworks. Her work often bridges theoretical foundations with practical applications in signal processing and data science. Her research interests emphasize structured optimization problems, including low-complexity regularizers, bilevel optimization, and sparse recovery. Key methodologies in her studies include primal-dual dynamics, proximal algorithms, and variance reduction techniques. Recent trends in her publications highlight advancements in unrolled deep networks for sparse signal restoration, adaptive optimization strategies, and convergence analysis of stochastic methods. Villa's collaborative efforts span interdisciplinary projects involving applied mathematics, computer science, and engineering. While no specific awards are listed, her prolific publication record reflects her active role in the optimization and machine learning communities. She has advised multiple research projects but no formal student names are provided in the available data.

Vahid SeydiView profile

Research Fellow

• Machine Learning
• Data Science
• Deep Learning
• +6 more

Vahid Seydi is a Research Fellow in Data Science and Machine Learning at the School of Ocean Sciences, Bangor University, UK. He previously served as an Assistant Professor (2014–2020) and award-winning Lecturer (2010–2014) at Azad University South Tehran Branch, Iran. He holds a PhD in AI (2014) from Azad University, with prior M.Sc. (2007) and B.Sc. (2005) degrees in Computer Science and AI. Education : PhD in Artificial Intelligence (2014), Azad University M.Sc. in Artificial Intelligence (2007), Science and Research University B.Sc. in Software Engineering (2005), Azad University Research Interests : Deep Learning, Domain Adaptation, Generative Models, Explainable AI, Reinforcement Learning, Optimization, and applications in marine renewable energy and environmental sustainability. Key Contributions : His work focuses on ML-driven solutions for offshore energy systems, including predictive digital twins, passive acoustic monitoring, and domain adaptation for ecological impact analysis. He has contributed to projects like the SEEC Centre and EcoWind-ACCELERATE initiative. Awards : Global Talent Endorsement (UK Royal Society, 2023) Zillow Kaggle Competition Silver Medal (2017, top 2%) Excellence in Teaching Award (Azad University, 2014–2019) Grants & Projects : Smart Efficient Energy Centre (SEEC), 2019–2023 EcoWind-ACCELERATE, 2022–2026 Morlais Demonstration Zone, 2022–2024 Labs/Teams : Active in Bangor’s Ocean Sciences research groups, focusing on ML integration with marine energy and ecological data systems.

Qiuji YiView profile

Assistant Professor

• Artificial Intelligence
• Machine Learning
• Nondestructive Testing
• +4 more

Dr. Qiuji Yi is an **Assistant Professor** in the **Computer and Information Sciences Department** at **Northumbria University**, focusing on applying artificial intelligence (AI) and machine learning (ML) to nondestructive testing (NDT) and structural health monitoring (SHM). His research develops AI-driven tools for NDT instruments like eddy current testing, thermography, and ultrasound, addressing challenges in composite materials such as carbon fiber composites. Yi holds a **PhD in Electrical and Electronic Engineering** (Awarded: 6 Apr 2021). **Research Interests**: Physics-informed ML for material characterization Defect detection in composites using clustering, matrix factorization, and deep learning Integration of AI with engineering needs in manufacturing and maintenance **Collaborations & Funding**: Collaborates with industry partners (Rolls-Royce, GKN Aerospace, National Composite Centre) and academic institutions (TU Delft, Newcastle University). Secured notable grants including the EU ITN project NDTonAIR (€3.8m), EPSRC grants (e.g., Certest: £6.9m), and the UK-Italy Trustworthy AI Visiting Researcher Award (2023). **Key Contributions**: Published 19 papers, including high-impact journals like NDT&E International and IEEE Transactions on Industrial Informatics . Notable work includes an automatic delamination detection framework using Kernel Principal Component Analysis. **Advising & Grants**: Accepting PhD students and actively pursuing funding for interdisciplinary projects combining AI and sustainable engineering solutions.

Ahmad MousaviView profile

Part-Time Lecturer

• Machine Learning
• Sparse Optimization
• Portfolio Optimization
• +3 more

Ahmad Mousavi serves as an Industrial Associate at the University of Florida's Informatics Institute and a part-time lecturer in American University's Data Science Program within the College of Arts and Sciences, teaching graduate courses including Advanced Machine Learning and Statistical Machine Learning through Fall 2025. His academic foundation spans applied mathematics with specialized expertise in computational optimization. His educational background includes: Ph.D. in Applied Mathematics, University of Maryland, Baltimore County (2013-2019) M.S. in Applied Mathematics, Sharif University of Technology (2008-2011) B.S. in Applied Mathematics, University of Guilan (2004-2008) Dr. Mousavi's research centers on machine learning and optimization theory, with significant contributions to sparse recovery algorithms, portfolio optimization, and support vector machine development. His work bridges theoretical mathematics with practical financial and data science applications, particularly in developing constrained optimization frameworks for real-world problems requiring sparsity and volatility control. Analysis of his publication record reveals a consistent focus on optimization techniques applied to machine learning, with recurring themes in sparse modeling for financial portfolios, kernel methods for classification, and theoretical foundations of compressive sensing. His 2020 survey paper demonstrates expertise in synthesizing complex technical domains. His professional development includes postdoctoral research at the University of Florida's Informatics Institute and the University of Minnesota's Institute for Mathematics and its Applications, where he collaborated on interdisciplinary projects connecting mathematical theory with data-intensive applications.

• Deep Learning
• Finite Element Analysis
• Materials Processing
• +4 more

Seid Koric is an Adjunct Associate Professor in the Department of Mechanical Science and Engineering at the University of Illinois, affiliated with the National Center for Supercomputing Applications (NCSA) as Senior Technical Associate Director. He holds a PhD in Engineering from the University of Illinois. His research focuses on integrating artificial intelligence with high-performance computing (HPC), particularly in materials science, computational mechanics, and multiphysics modeling. Key research areas include deep learning for metamaterial design, finite element analysis, and real-time monitoring of complex systems using digital twins. He has pioneered applications of deep operator networks (DeepONets) for predicting solution fields in materials processing and additive manufacturing. His work bridges computational methods with industrial challenges, such as steel solidification and nuclear system monitoring. Notable achievements include the HPC Innovation Excellence Award (2011 and 2020), recognizing his contributions to HPC and AI integration. Collaborations span academia and industry, addressing challenges in aerospace, energy systems, and smart manufacturing. His lab develops scalable computational frameworks and open-source tools for exascale computing, emphasizing interdisciplinary problem-solving. Education: PhD in Engineering, University of Illinois Affiliations: NCSA (Senior Technical Associate Director), Mechanical Science and Engineering Department Labs/Teams: Leads computational research groups focused on AI-driven HPC applications at NCSA

• Thermodynamics
• Fluid Mechanics
• Renewable Energy
• +3 more

Joakim Wren is an Associate Professor and Docent at Linköping University's Department of Management and Engineering (IEI). His research spans thermodynamics, renewable energy, heat transfer, and biomedical engineering. He has contributed to studies on Organic Rankine Cycle systems for small-scale energy production, inverse heat conduction problems, and augmented reality applications in medical imaging. Wren also focuses on improving communication skills in engineering education. His work has been published in journals like Energy Reports, International Journal of Heat and Mass Transfer, and The International Journal of Cardiovascular Imaging. Awards: Associated with Againity, which won the 2016 Environmental Engineering Profile of the Year, 2015 Skapa Prize in Östergötland, and the 2016 Nordic Cleantech Open. His research interests include sustainable energy solutions, thermal systems optimization, and interdisciplinary applications of engineering principles in medicine. He collaborates with industry and academic partners to advance technologies with real-world impact.

Anna MaView profile

Assistant Professor

• Applied Mathematics
• Computational Mathematics
• Optimization
• +3 more

Anna Ma is an Assistant Professor in the Department of Mathematics at the University of California, Irvine (UCI). Her research focuses on optimization, computational mathematics, and algorithm design with applications to inverse problems, signal processing, and high-dimensional data analysis. Key research interests include iterative algorithms (e.g., randomized Kaczmarz methods), tensor recovery, stochastic gradient descent, and robust recovery techniques for noisy data. Her work emphasizes algorithmic efficiency, convergence guarantees, and applications in machine learning and data science. Recent publications highlight advancements in quantile-based iterative methods, tensor linear systems, and robust low-rank recovery. These studies address challenges in handling missing data, noisy observations, and scalable solutions for large-scale systems. No scientific awards are explicitly mentioned in the provided text. Her research has been supported by grants focused on algorithm development and data-driven methodologies. Anna collaborates on projects involving multidisciplinary teams and contributes to advancing numerical methods for modern data analysis challenges.

• Nonlinear PDEs
• Mathematical Physics
• Integrable Systems
• +2 more

Jonatan Lenells is Professor in the Department of Mathematics at KTH Royal Institute of Technology, currently on parental leave until August 2025. His research focuses on nonlinear partial differential equations and mathematical physics. Key research areas include integrable systems, soliton equations, and geometric methods in PDE analysis. He employs techniques from Riemann-Hilbert problems, asymptotic analysis, and differential geometry. Lenells has supervised multiple PhD students and postdocs working on analytical and numerical aspects of integrable PDEs.

Martijn C. van BeurdenView profile

Professor

• Computational Electromagnetics
• Inverse Scattering
• Antenna Design
• +2 more

Martijn van Beurden is a Full Professor in the Electromagnetics group at Eindhoven University of Technology's Department of Electrical Engineering. His research focuses on computational electromagnetics for high-tech systems, specializing in inverse scattering and antenna design. He holds a cum laude MSc (1997) and PhD (2003) from TU/e, where he received the C.I.V.I. Prize and ASML Prize for his thesis work. His research interests center on developing advanced numerical methods for electromagnetic modeling, with applications in wave behavior analysis, inverse scattering problems, and antenna optimization. Current projects include electromagnetic field modeling in stochastic environments and periodic structures. Recent publications demonstrate strong focus on computational efficiency in electromagnetic simulations, with innovations in Green's function discretization and FFT-accelerated integral equations applied to biomedical and scattering problems. Awards: C.I.V.I. Prize for Electrical Engineering (MSc thesis) ASML Prize for Best PhD Thesis in Applied Research He leads multiple projects including INNOSTAR and MAX META-XT, focusing on meta-lens design and electromagnetic scattering analysis. Manages the Electromagnetics research group and Center for Wireless Technology Eindhoven.