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Estefanía PeñaView profile

Professor

• Computational Mechanics
• Biomechanics
• Numerical Modeling of Soft Biological Tissues
• +4 more

Estefanía Peña is a Full Professor (Catedrática de Universidad) at the University of Zaragoza's School of Engineering and Architecture, Department of Mechanical Engineering. She leads research in computational biomechanics with a focus on vascular tissues and medical device interactions. Key roles include coordination of the Mechanical, Naval and Aerospace Engineering Subarea for Spain's Ministry of Science (2022–present) and Deputy Director of the Aragón Institute of Engineering Research (I3A, 2015–2019). Educational Background: Ph.D. Mechanical Engineering, University of Zaragoza (2004) M.Sc. Mechanical Engineering, University of Zaragoza (2000) Research Interests: Computational mechanics of soft biological tissues, multiscale modeling of inelastic effects, atherosclerosis progression, drug delivery systems, and experimental biomechanics. Her work bridges computational models with clinical applications, particularly in cardiovascular diseases and medical devices. Notable Achievements: Recipient of major awards including the Spanish ECCOMAS Best PhD Thesis Award (2005) and Young Research Medal from the Royal Society of Engineering (2015). Supervised over 25 PhD and M.Eng. students, focusing on atherosclerosis mechanics, drug-eluting stents, and fascia biomechanics. Grants & Collaborations: Active in EU and national projects, collaborating with institutions like TU Dortmund, Stanford University, and KTH Royal Institute of Technology. Research emphasizes translational medicine and computational modeling. Labs/Teams: Leads the Biomedical Engineering Division at I3A, part of CIBER-BBN (Spanish Biomedical Engineering Network). Her lab integrates experimental and numerical methods to study vascular mechanics and tissue engineering.

Carla MolteniView profile

Professor

• Physics
• Materials Science
• Biology
• +3 more

Carla Molteni is a Professor of Physics at King's College London, affiliated with the Department of Physics within the Faculty of Natural, Mathematical & Engineering Sciences. She holds roles such as Co-Director of the Biological Physics Across Scales (BiPAS) Centre for Doctoral Training and Director at the Thomas Young Centre for the Theory and Simulation of Materials. Her research focuses on atomistic simulations of materials and biomolecules, bridging physics with chemistry, materials science, and biology. She has held prestigious fellowships, including an EPSRC Advanced Research Fellowship and College Research Fellowship at New Hall. Molteni's work spans computational modeling of nanomaterials, ion channel mechanisms, ice growth kinetics, and drug design. She contributes to interdisciplinary initiatives like the Materials and Molecular Modelling Hub and the Multiscale Biofilm Research Hub. Awards include Fellowship of the Institute of Physics and leadership in EU-UK scientific diplomacy. Her research outputs include studies on molecular dynamics, machine learning in biophysics, and structural phase transformations under pressure. Education: Laurea and PhD in Physics from the University of Milan (Italy). Professional roles include membership in the JCMaxwell node of CECAM and leadership in the Psik European network. Current projects involve ligand-gated ion channel mechanisms, polyphenol interactions, and pressure-induced structural transformations in nanomaterials. Awards and grants: EPSRC funding for the Materials and Molecular Modelling Hub, contributions to EU-UK scientific relations, and recognition in computational biophysics. Supervised 11 research students and authored/co-authored over 90 publications.

• High Pressure Chemistry
• Carbon Sequestration
• Materials Synthesis
• +5 more

George Wolf is an Associate Professor at the School of Molecular Sciences, Arizona State University (ASU), specializing in high-pressure chemistry, materials synthesis, and carbon sequestration. His work focuses on understanding chemical behavior under extreme conditions, leveraging high-pressure diamond anvil cells for experimental studies. He holds a B.S. in Chemistry from UC San Diego, an M.S. from UC Berkeley, and a Ph.D. in Geophysics from UC Berkeley. Wolf is a recipient of the NSF Presidential Investigator Award and ASU Centennial Teacher of the Year Award. Research Interests: Wolf’s lab explores high-pressure synthesis of novel materials, including graphitic derivatives and carbon sequestration mechanisms. His group uses advanced techniques like NMR, Raman spectroscopy, and X-ray diffraction to study phase transitions and material dynamics. Current projects include developing bionic robotic prosthetics and investigating CO2 mineralization processes. Grants & Collaborations: Wolf has led multiple grants from NSF, DOE, and industry partners, focusing on carbon sequestration, ceramic materials, and high-pressure research. Notable collaborations include work with Argonne National Laboratory and Qynergy Corp. He teaches courses in physical chemistry and research methods, mentoring students in experimental physics and environmental chemistry. Service & Recognition: Wolf actively serves on ASU Senate committees, chairs curriculum development initiatives, and advises undergraduate research programs. His contributions to education and research have been recognized through teaching awards and national conference presentations.

Lorenzo PareschiView profile

Professor

• Applied Mathematics
• Computational Mathematics
• Multiscale Modeling
• +9 more

Lorenzo Pareschi is a Professor and Chair of Applied and Computational Mathematics at the School of Mathematical & Computer Sciences, Heriot-Watt University, Edinburgh, UK. He holds a Ph.D. in Mathematics from the University of Bologna, Italy, and has held visiting professorships at institutions including Georgia Tech, University of Wisconsin-Madison, and Imperial College London. His research focuses on multiscale modeling, numerical methods for nonlinear PDEs, and applications in physics, engineering, and socio-economic systems. Recently, he has explored uncertainty quantification, optimal control, and machine learning. He is a Royal Society Wolfson Fellow (2023) and has served as Head of the Department of Mathematics and Computer Science at the University of Ferrara. He is an associate editor for journals like SIAM Journal on Scientific Computing and Multiscale Modeling & Simulation. Research interests include hyperbolic and kinetic equations, fluid dynamics, plasma physics, epidemiology, and computational hemodynamics. His work contributes to UN Sustainable Development Goals through applications in healthcare and environmental modeling. He has authored over 200 publications and five books, reflecting his leadership in applied mathematics and interdisciplinary collaboration. Education: Ph.D. in Mathematics, University of Bologna (Italy). Awards: Royal Society Wolfson Fellowship (2023), Nelder Fellowship (2015), John von Neumann Professorship (2019). Roles: Member of SIMAI steering committee, EMS CAIR committee, and editorial boards of key journals. Research Trends: Recent articles emphasize machine learning integration with kinetic models, plasma control strategies, and multiscale methods for epidemic spread. His work bridges theoretical developments with practical applications in fusion energy, traffic dynamics, and healthcare. Grants & Advising: Actively supervises PhD students and leads projects on computational methods for complex systems. His grants focus on advancing numerical techniques for multiscale phenomena and uncertainty quantification. Labs/Teams: Collaborates with global networks in computational mathematics and applied physics, contributing to high-impact journals and conferences.

Kamaljit SinghView profile

Associate Professor

• Multiphase flow
• Digital rock physics
• Porous media
• +9 more

Kamaljit Singh is an Associate Professor at the Institute for GeoEnergy Engineering (IGE) within the School of Energy, Geoscience, Infrastructure and Society at Heriot-Watt University in Edinburgh, UK. He has held this position since 2021, after serving as an Assistant Professor at the same institution from 2019 to 2021. Prior to joining Heriot-Watt, he held research positions at Imperial College London, the European Synchrotron Radiation Facility in France, and the Max-Planck Institute in Germany. His research employs advanced imaging techniques to study fluid dynamics in porous media with applications to energy storage and environmental challenges. Dr. Singh's educational background includes: PhD in Civil Engineering from the University of New South Wales at Australian Defence Force Academy, Australia (2004-2009) M.E. in Environmental Engineering from Punjab Engineering College/Panjab University, India (2000-2002) B.E. in Civil Engineering from GNE/Punjab Technical University, India (1996-2000) His primary research focuses on 3D imaging of fluid flow in permeable media using both in-house and synchrotron X-ray micro-CT. His current projects investigate H 2 and CO 2 storage in subsurface rocks, pore-to-core scale fluid displacement dynamics, wettability effects on multiphase flow, multi-scale rock characterization, and thermoregulation in termite nests for bio-inspired building design. Dr. Singh's work directly contributes to UN Sustainable Development Goals related to clean energy and climate action, with his research group ( https://digiporflow.site.hw.ac.uk ) advancing digital imaging techniques for porous flow systems. Analysis of Dr. Singh's recent publications (2024-2025) reveals a strong emphasis on hydrogen storage mechanics in geological formations, particularly examining permeability evolution and deformation in sandstones under cyclic loading. His work also maintains significant focus on CO 2 sequestration in carbonate reservoirs and has produced notable interdisciplinary research on termite nest ventilation as a model for sustainable building design. His methodological approach consistently integrates advanced imaging techniques with computational modeling to address energy transition challenges. Dr. Singh serves as Global Course Leader for Reservoir Engineering (MSc) and teaches Introduction to Petroleum Engineering (undergraduate). His research has received considerable media attention, including coverage in The New York Times, The Straits Times, and Süddeutsche Zeitung for his work on bio-inspired ventilation systems. With 41 research publications, 4 datasets, and 7 invited talks, he maintains an active research program focused on solving critical challenges in subsurface energy storage and sustainable engineering.

• Computer Vision
• Machine Learning
• Statistics
• +3 more

Marcus A. Brubaker is an Associate Professor at York University's Department of Electrical Engineering and Computer Science within the Lassonde School of Engineering. He also holds an Adjunct Professor status at the University of Toronto's Department of Computer Science and serves as a Research Scientist at Google DeepMind. His primary affiliations include co-directorship of the Computational Vision and Imaging Lab (CVIL) at York University and membership in the Vector Institute as a Faculty Affiliate. Brubaker's research spans Computer Vision, Machine Learning, Statistics, and their applications to problems like cryo-electron microscopy (cryo-EM) and 3D molecular reconstruction. Education: He earned a Ph.D. in Computer Science from the University of Toronto (2011), with a thesis on physical models of human motion. His M.Sc. (2006) and B.Sc. (2004) in Computer Science were also from the University of Toronto. Research focuses include cryo-EM structure determination (notably the cryoSPARC software), normalizing flows, and probabilistic modeling. His work bridges computational imaging, statistical inference, and deep learning. Key contributions include advancements in cryo-EM algorithms, generative models, and camera noise analysis. He co-founded Structura Biotechnology and has contributed to open-source tools like Stan's math library. Publications emphasize novel view synthesis, neural radiance fields, sensor noise modeling, and cryo-EM reconstruction techniques. His research trends highlight integration of physics-based models with machine learning for solving complex imaging problems. Awards and recognition include impactful contributions to cryo-EM through cryoSPARC, a Nature Methods paper in 2017, and leadership in academic-industrial collaborations. Brubaker's labs and teams focus on advancing imaging technologies and their applications in structural biology and computer vision.

Mark A IwenView profile

Associate Professor

• Computational Harmonic Analysis
• Mathematical Data Science
• Signal Processing
• +4 more

Mark A Iwen is an Associate Professor at Michigan State University, holding dual appointments in the Department of Mathematics and the Department of Computational Mathematics, Science and Engineering (CMSE) . His research focuses on computational harmonic analysis, mathematical data science, signal processing, and algorithms for analyzing high-dimensional datasets. He has contributed to advancements in sparse Fourier transforms, compressive sensing, and sublinear-time algorithms for large-scale data. Key research themes include: Efficient algorithms for high-dimensional PDEs and spectral methods Phase retrieval and inverse problems in imaging Optimization of tensor decompositions and dimensionality reduction techniques Development of sparse approximation methods with theoretical guarantees His recent work emphasizes applications in: Sublinear-time algorithms for function approximation Terminal embeddings for manifold data Fast JL embeddings with bi-Lipschitz properties Tensor completion and low-rank approximations Notable contributions include: Development of Sparse Harmonic Transforms for functions of many variables Advancements in distributed SVD algorithms for large networks Empirical and theoretical analysis of phase retrieval techniques Efficient sparse FFT implementations (e.g., DMSFT, GFFT) Iwen collaborates on open-source code projects, including sparse FFT libraries and phase retrieval tools. His work bridges mathematical theory with practical applications in engineering and computational science.

Guowei WeiView profile

Professor

• Machine Learning
• Topological Data Analysis
• Biochemistry
• +4 more

Guowei Wei is a MSU Research Foundation Professor in the Department of Mathematics and the Department of Biochemistry & Molecular Biology at Michigan State University. He holds a dual appointment in the BioMolecular Science Gateway and is affiliated with the Wei Lab. His research focuses on integrating topological data analysis, machine learning, and computational methods to address complex problems in biochemistry, biophysics, and materials science. Dr. Wei’s work spans multiple disciplines, including topological machine learning for protein-ligand interactions, persistent homology applications in molecular flexibility analysis, and AI-driven drug discovery. He develops novel algorithms combining algebraic topology with deep learning to model biological systems and material properties. His recent contributions include advancements in predicting protein binding affinities, analyzing viral mutations, and optimizing drug candidates for opioid use disorder and addiction treatments. He teaches advanced graduate courses (MTH 890/990) and maintains an active research lab. His methodologies bridge mathematical theory with practical applications in computational biology and healthcare, leveraging tools like the Poisson-Boltzmann equation and transformer networks for molecular analysis. Dr. Wei’s work emphasizes interdisciplinary approaches to solve challenges in drug design, pathogen evolution, and systems biology. Key contributions include topological deep learning frameworks for biomedical data, persistent Laplacian techniques for scarcely labeled data classification, and AI models for predicting mutation impacts on protein function. His research has significant implications for personalized medicine, pandemic preparedness, and understanding complex biological networks.

• Mechanical Metamaterials
• Origami-Inspired Structures
• Wave Propagation
• +3 more

Charles Dorn is an Assistant Professor in the Department of Aeronautics and Astronautics at the University of Washington. His research focuses on structural mechanics, architected materials, and reconfigurable systems. Prior to UW, he was a Postdoctoral Fellow at ETH Zurich (2021–2024), and completed his PhD in Space Engineering at Caltech (2021), with additional degrees from Ecole Polytechnique and UW-Madison. Education: PhD, Space Engineering, California Institute of Technology, 2021 M.S., Multiphysics and Multiscale Modeling, Ecole Polytechnique, 2018 M.S., Space Engineering, Caltech, 2017 B.S., Engineering Mechanics, UW-Madison, 2016 Research Interests: His work bridges mechanics, computation, and design to create structures with extreme properties like wave steering and shape reconfiguration. Key areas include mechanical metamaterials, origami-inspired systems, and spatially graded metamaterials for vibration suppression. Recent projects explore inverse design methods and multi-configuration rigidity. Key Achievements: Developed ray-tracing tools for elastic waves in graded metamaterials Winner of 2018 R&D 100 Award for video-based vibration measurement techniques Collaborated with industry leaders like Prof. Sergio Pellegrino (Caltech) and Prof. Dennis Kochmann (ETH Zurich) Labs/Teams: Leads a research group at UW focused on advanced structural architectures and their applications across aerospace, robotics, and electronics.

• Materials Science
• Nanotechnology
• Biomedical Engineering
• +5 more

Dr. Benjamin Noble is a Research Fellow in the School of Engineering at RMIT University. His research focuses on materials modeling, nanotechnology, biomaterials, and computational chemistry. He is open to supervising Masters and PhD students and can be contacted at benjamin.noble2@rmit.edu.au . His research interests include the design of functional materials, such as dynamic metal-phenolic networks and sequence-defined macromolecules, as well as the application of multiscale molecular simulations to study electromagnetic bioeffects and biomolecular interactions. He collaborates with industry partners like BlueScope to develop advanced materials for biomedical and industrial applications. Dr. Noble’s recent work emphasizes polymer synthesis methodologies (e.g., RAFT polymerization), drug delivery systems, and the development of contamination-resistant surfaces. His studies also explore photochemical reactivity prediction and mitochondrial uncouplers for pharmacological applications. He contributes to the Materials Modelling and Simulation Group led by Distinguished Professor Irene Yarovsky, which investigates nanomaterials for biomedicine and industry. The group’s research has led to innovations in drug delivery materials, biosensors, and sustainable surfaces.

Hadi T. NiaView profile

Assistant Professor

• Mechanobiology
• Tumor Microenvironment
• Physical Sciences of Cancer
• +3 more

Hadi T. Nia is an Assistant Professor in the Department of Biomedical Engineering and Division of Materials Science and Engineering at Boston University. He holds a PhD from MIT (2013) and leads the Nia Lab, focusing on mechanobiology, tumor microenvironment, and pulmonary pathogenesis. His work integrates physical sciences, molecular biology, and immunology to study cancer metastasis, aging, and lung diseases. Key innovations include the 'Crystal Ribcage' platform for ex vivo lung research, enabling real-time optical imaging and functional analysis. Research interests span mechanobiology, mechano-immunity, tumor microenvironment dynamics, and lung resilience mechanisms. Awards include the 2024 Sloan Research Fellowship, NSF CAREER Award (2023), and NIH Director’s New Innovator Award (2022). He is affiliated with the Center for Multiscale and Translational Mechanobiology (CMTM), Pulmonary Center, and Synthetic Biology programs at BU. Publications emphasize interdisciplinary approaches, with recent work addressing lung mechanics, cancer solid stress, and imaging technologies. His grants and awards reflect sustained support for mechanistic studies in disease and aging.

• Porous Media
• Computational Mechanics
• Bayesian Inference
• +3 more

Mina Karimi is a Postdoctoral Scholar Research Associate in the Department of Mechanical and Civil Engineering at California Institute of Technology (Caltech). She is part of the Bhattacharya group, advised by Professor Kaushik Bhattacharya. Her research focuses on computational mechanics, poromechanics, and Bayesian inference applied to porous media systems. Key areas include reactive flow modeling, multiscale simulations, and data-driven approaches for subsurface engineering challenges. Her work integrates advanced computational methods with geomechanical and materials science problems, emphasizing energy systems and environmental applications. Recent studies explore carbon sequestration mechanisms, chemo-poro-mechanical coupling, and high-dimensional parameter estimation using Bayesian frameworks. She also investigates crack-healing phenomena in shape memory alloy composites and develops accelerated micromechanical models for solute transport. Publications span topics from machine learning-enhanced groundwater modeling to Hessian-informed sampling techniques for high-dimensional inverse problems. Her research bridges theoretical developments with practical applications in subsurface energy storage, geological carbon sequestration, and material behavior under extreme conditions. Advising is conducted under the mentorship of Prof. Bhattacharya, with affiliations to the Resnick Sustainability Institute at Caltech. Current efforts emphasize computational tools for poromechanics and uncertainty quantification in complex multiphase systems.

• Multiresolution Methods
• Numerical Analysis
• Computational Mathematics
• +4 more

Michael Multerer is an Associate Professor at the Faculty of Informatics, Università della Svizzera italiana (USI). His research focuses on multiresolution methods, scattered data analysis, and numerical analysis with applications in computational mathematics and engineering. He leads projects such as the SNSF Starting Grant on multiresolution methods for unstructured data, emphasizing nonlinear approximation and kernel-based techniques. Research Interests: Development of fully discrete multiresolution methods for unstructured data Wavelet theory and kernel matrix algebra Uncertainty quantification in partial differential equations Scattered data compression and approximation Key Software Contributions: FMCA: Fast multiresolution covariance analysis for scattered data Bembel: Boundary element library for solving Laplace and Helmholtz equations SPQR: Anisotropic sparse grid quadrature in MATLAB Funding: Holder of the SNSF Starting Grant (2025) for advancing multiresolution techniques in unstructured data processing. Labs/Teams: Active in the research group at USI’s Faculty of Informatics, collaborating with institutions like TU Darmstadt and University of Basel on numerical methods and engineering applications.

• Geomorphology
• Hydrology
• Remote Sensing
• +3 more

Jaclyn Cockburn is an Assistant Professor in the Department of Geography at the University of Guelph. Her research focuses on fluvial geomorphology, hydrology, and environmental processes, with an emphasis on river restoration, sediment dynamics, and climate-landscape interactions. She employs cutting-edge technologies like LiDAR and remote sensing to study aquatic ecosystems and geomorphic processes in diverse environments, including Arctic regions and urban streams. Her work spans topics such as ice-covered stream hydraulics, varve-based climate proxies, and the impacts of urbanization on river morphology. She has contributed to methodological advancements in geomorphometric analysis and digital soil mapping. Notably, she has addressed systemic issues in academia through a 2019 paper examining gender dynamics in academic careers. Dr. Cockburn's research bridges field observations, computational modeling, and policy-relevant applications. Her articles consistently address interdisciplinary challenges in environmental science, emphasizing the interplay between physical processes and ecological outcomes.

Wenqing HuView profile

Associate Professor

• Probability
• Data Sciences/Machine Learning/Optimization
• Operations Research
• +2 more

Roles & Affiliations: Wenqing Hu is an Associate Professor of Mathematics at the Department of Mathematics and Statistics, Missouri University of Science and Technology (formerly University of Missouri, Rolla). He holds memberships in professional societies including The Bachelier Finance Society, INFORMS, SIAM, and the Society of Actuaries. Education: Ph.D. in Mathematics, University of Maryland at College Park (2013) B.S. in Mathematics, Peking University (2008) Research Interests: His work spans Probability (stochastic analysis, multiscale problems), Machine Learning (optimization algorithms, neural networks), Operations Research (smart grid systems), Math Biology (Ao's potential function), and Cryptology (zero-knowledge proofs in blockchain). Recent breakthroughs include contributions to zero-knowledge virtual machines and high-dimensional data analysis . Publications Overview: His 15 most recent works (2025-2012) span topics like cryptology (e.g., zkEVM), stochastic differential equations, and reinforcement learning applications in manufacturing. Key themes include stochastic processes, nonlinear optimization, and interdisciplinary applications in energy systems and biology. Awards & Grants: Miner Alumni Teaching Award (2018) Simons Foundation Collaboration Grant (2020-2025) NSF-funded travel support for the 2018 ICM Labs & Collaborations: Active in cross-disciplinary projects such as subspace indexing on Grassmann manifolds and nonlinear optimization in ML .