George diCenzo is an Assistant Professor in the Department of Biology at Queen's University, Canada. His research focuses on systems biology approaches to understand microbial metabolism and symbiosis, with applications in synthetic biology and agricultural biotechnology. He holds a Ph.D. (2017) and B.Sc. (2012) in Molecular Biology and Genetics from McMaster University. Research interests include microbial symbiosis with legumes, metabolic modeling of Sinorhizobium meliloti, and biodegradation of plastics. He employs experimental techniques (molecular genetics, omics) and computational methods (metabolic modeling, phylogenetics). Recent work explores engineered symbioses and bio-inoculants to improve crop yields and reduce fertilizer reliance. diCenzo has published extensively on rhizobial genomics, plasmid evolution, and metabolic pathways. His lab also investigates cold-tolerant crops and microbial communities in agricultural systems. Key awards include the Armand Frappier Outstanding Student Award (2017). Cross-Appointment: Assistant Professor at Beaty Water Research Centre Teaching: BIOL 538 Research Mentorship Biology I Labs: Focus on microbial systems biology and sustainable agriculture
Lorna Stabler is a Research Fellow at the School of Social Sciences, Cardiff University, specializing in children’s social care and family dynamics. She holds academic roles within CASCADE and the University of Exeter Medical School, focusing on interventions to reduce children’s entry into care systems. Her work integrates realist evaluation methodologies and emphasizes family-centered approaches, including kinship care and shared decision-making processes. Education: BA, MA, MSc, PhD Key Roles: Principal Investigator (NIHR study on Family Group Conferences), Nuffield Foundation-funded study on Special Guardianship Orders in Wales Her research explores how social work practices and policy interventions impact family outcomes, with a focus on sibling kinship care, privacy in digital services, and socioeconomic factors affecting child welfare. She has been awarded the Winston Churchill Memorial Fellowship (2019) to study foster care approaches in Asia. Teaching: Contributes to MA Social Work programs, supervising undergraduate and master’s dissertations. Research Interests: Family group conferences, mental health interventions, participatory decision-making, and policy implementation. Over 50 peer-reviewed publications and reports on topics like child protection systems, advocacy services, and online engagement for care-experienced youth. Labs/Teams: CASCADE (Collaboration for Applied Social Care and Evidence Dissemination), DECIPHER (Digital Evaluation of Care Interventions for Health Equity Research).
Raymond Speth is a Senior Research Scientist in the Department of Aeronautics & Astronautics at MIT and the Associate Director of the Laboratory for Aviation and the Environment (LAE). He holds a PhD in Mechanical Engineering from MIT, with prior roles including Postdoctoral Associate in the Green Research Group. His research focuses on sustainable energy technologies, combustion simulation tools (e.g., Cantera and Ember), and environmental impacts of aviation. Key projects include evaluating hydrogen-rich flames, lifecycle analyses of fuels, contrail formation, and emissions regulations. Education: B.S., M.S., and Ph.D. in Mechanical Engineering from MIT. His work spans numerical methods, combustion instabilities, and environmental policy applications. He leads development of Cantera, a widely used chemical kinetics tool, and Ember, a flame simulation solver. Notable contributions include studies on aviation’s role in air quality, supersonic aircraft impacts, and alternative jet fuels. Scientific contributions include over 50 peer-reviewed articles on topics like aviation emissions, contrail radiative forcing, and hydrogen aircraft design. His interdisciplinary approach bridges engineering and environmental science to address climate challenges in transportation sectors.
Themistoklis P. Sapsis is the William I. Koch Professor of Mechanical and Ocean Engineering at MIT, affiliated with the Schwarzman College of Computing. He serves as Director of the Center for Ocean Engineering and Associate Director of MIT Sea Grant. His research focuses on nonlinear dynamical systems, probabilistic modeling, and data-driven methods applied to fluid flows, ocean engineering, and extreme events. Notable roles include editorial positions at journals such as Journal of Nonlinear Science and SIAM/ASA Journal of Uncertainty Quantification . Education: Ph.D. in Mechanical Engineering, MIT (2006–2011) Diploma in Naval Architecture and Marine Engineering, National Technical University of Athens (2001–2005) Research Interests: Sapsis’ work bridges nonlinear dynamics, probability, and machine learning to predict extreme events in complex systems such as turbulent flows, nonlinear waves, and ship motions. His methods emphasize statistical quantification and optimization of systems with transient features. Awards and Honors: Bodossaki Award on Basic Sciences: Mathematics (2021) Vannevar Bush Faculty Fellowship (DoD, Applied Mathematics) Alfred P. Sloan Research Fellowship (2015) Three Department of Defense Young Investigator Awards (Navy, Army, Air Force) Multiple career development chairs at MIT Grants and Advising: Sapsis has secured funding for projects on climate modeling, ocean engineering, and machine learning applications. His advising includes collaborations on reduced-order models and extreme event prediction. He leads the Stochastic Analysis and Nonlinear Dynamics (SAND) Lab at MIT. Labs and Teams: The SAND Lab focuses on integrating data-driven methods with physical models to address challenges in fluid mechanics, climate science, and ocean engineering. Collaborations span academia and industry, emphasizing real-world applications of predictive modeling.
Alba Muixí is a Serra Húnter Assistant Professor at the Universitat Politècnica de Catalunya (UPC). Her research focuses on computational mechanics, fracture mechanics, and reduced-order modeling, with applications in biomechanics, materials science, and multi-physics systems. She specializes in developing numerical methods such as phase-field modeling, kernel-based dimensionality reduction, and hybridizable discontinuous Galerkin techniques. Her work emphasizes adaptive algorithms for crack propagation analysis, physics-informed manifold learning, and parametric studies in complex systems. Key research areas include: Phase-field modeling of fracture in composites and layered materials Reduced-order methods for parametric PDEs and biomechanical systems Nonlinear dimensionality reduction techniques Computational methods for tissue engineering and osteoinduction Her publications demonstrate contributions to coupled multi-physics analysis, structure-preserving formulations, and adaptive mesh refinement strategies. While no specific awards are listed, her active publication record reflects ongoing research excellence. She has advised no listed students in the provided data but contributes to UPC's academic and research initiatives through her role as an assistant professor.
Professor Rubén Sevilla is a Computational Engineering academic at the Faculty of Science and Engineering , Swansea University . He holds a Chair in Civil Engineering and has held leadership roles including President of the UK Association for Computational Mechanics and Chief Editor of the European Journal of Computational Mechanics . PhD in Civil Engineering (2009), UPC-BarcelonaTech Postdoctoral Researcher (2009-2012), Zienkiewicz Centre for Computational Engineering Lecturer (2012), Senior Lecturer (2015), Associate Professor (2016), Full Professor (2021) Research Interests focus on high-order numerical methods for engineering problems, including: Face-Centred Finite Volume Methods (FCFV) Hybridizable Discontinuous Galerkin (HDG) NURBS-Enhanced Finite Element Methods (NEFEM) Reduced Order Modeling Machine Learning for Mesh Optimization Computational Fluid and Electromagnetic Dynamics Geometrically Parametrized Problems Article Trends show a focus on hybrid numerical methods for fluid-structure interaction, geometrically accurate mesh generation using NURBS, machine learning integration for flow simulations, and parametric modeling of complex systems. His work bridges CAD and FEM through NEFEM while advancing reduced-order models for real-time engineering applications. Scientific Awards include: European Association for Computational Methods in Applied Sciences award Spanish Association for Computational Methods in Engineering award Birkhauser-Verlag Best Thesis award (Spain/Europe) EMERALD award SIAM award Welsh Government recognition Teaching & Supervision spans modules like Finite Element Computational Analysis and Problem Solving with MATLAB . He supervises PhD students in computational mechanics and co-led the International MSc in Computational Mechanics since 2012. Grants & Projects include: EPSRC-funded "Feature-Independent Mesh Generation" (2020-2023, £427,929) ELEMENT - Exascale Mesh Network (2020-2021, £245,611) EPSRC Solar Absorber Project (2017-2020, £315,556) H2020 Advanced Model Reduction (2015-2019, €2,080,164.96)
Alessandro Lucantonio is an Associate Professor at the Department of Mechanical and Production Engineering in Aarhus University , specializing in computational mechanics and machine learning applications in soft matter systems. His work focuses on predictive modeling of active materials, transient morphing structures, and biomedical device optimization. Research Areas: Soft robotics, machine learning for mechanics, poroelastic materials, and bioinspired design. Contact: a.lucantonio@mpe.au.dk , +45 93 51 77 76 His recent publications highlight interdisciplinary approaches combining symbolic regression, computational modeling, and experimental validation in soft robotics and responsive materials. Key trends include adaptive shape morphing, fluid-structure interactions, and predictive simulations for biomedical applications.
Chengyu Li is an Associate Professor in the Department of Mechanical and Aerospace Engineering at Case School of Engineering, Case Western Reserve University. His research program focuses on developing computational models to investigate the underlying flow physics and transport phenomena associated with biological and biomedical flows. Dr. Li received his PhD in Mechanical & Aerospace Engineering from the University of Virginia in 2016, following an M.S. from the same institution in 2014. He completed his undergraduate education with a B.S. in Mechanical Engineering from Dalian Jiaotong University, China, in 2010. Prior to joining Case Western, he served as an Assistant Professor at Villanova University from 2018 to 2024 and completed a postdoctoral position at The Ohio State University (2016-2018). Dr. Li's research spans multiple areas of fluid dynamics with applications in both engineering and healthcare. His work integrates computational fluid dynamics, immersed boundary methods, and high-performance computing to address complex problems in biological locomotion and biomedical flows. Specific research foci include: Bio-inspired propulsion mechanisms, studying how biological systems like insects and ctenophores achieve efficient locomotion Human nasal airflow dynamics, with applications to understanding and treating conditions like empty nose syndrome Fluid-structure interaction in flapping flight, examining how wing flexibility affects aerodynamic performance Odor-guided navigation, investigating how insects balance aerodynamic performance with olfactory sensitivity Analysis of Dr. Li's publication record reveals a strong interdisciplinary approach that bridges fundamental fluid dynamics with practical applications. His recent work shows increasing focus on the intersection of fluid dynamics and sensory biology, particularly how insects use airflow information for navigation. He has maintained a consistent research program in biomedical flows, with particular attention to nasal airflow and its clinical implications across multiple conditions including empty nose syndrome, nasal septal perforation, and olfactory sensitivity. Dr. Li has received several prestigious awards recognizing his research excellence: Young Investigator Program (YIP) Award 2024 from AFOSR Lewis F. Moody Award 2022 from ASME Faculty Early Career Development Program (CAREER) Award 2021 from NSF Ralph E. Powe Junior Faculty Enhancement Award 2019 from ORAU Polak Young Investigator Award 2017 from AChemS Dr. Li leads the Flow Simulation & Flow Physics Lab, where he mentors students in computational fluid dynamics research. His work has been supported by multiple grants including AFOSR FA9550-11-1-0058, AFOSR FA9550-12-1-007 monitored by Dr. Douglas Smith, and NSF CEBT-1313217. He collaborates extensively with researchers across disciplines, including Tyson Hedrick (UNC Chapel Hill), Kai Zhao (OSU), and Haibo Dong (UVa). Dr. Li actively contributes to the academic community through service roles including CFDTC Vice Chair (2024-2026) and Secretary (2022-2024) for ASME, and as a member of the Division of Fluid Dynamics at APS since 2011.
Dr. Ankit Dilip Kumar is a Henslow Research Fellow at Robinson College, University of Cambridge , affiliated with the Department of Engineering . His work focuses on mathematical modeling of thermoacoustic and hydrodynamic instabilities in turbulent reacting flows, with applications in energy systems and aerospace engineering. Research Interests: Decarbonization of energy systems through hydrogen fuel retrofitting Combustion noise prediction and mitigation Nonlinear dynamical systems in combustion Swirl-stabilized flame instabilities Drag reduction in supersonic flows Thermoacoustic-hydrodynamic coupling Publications highlight research on hydrogen combustion dynamics, instability characterization using spectral analysis, and computational modeling of complex flame structures. His work addresses both fundamental and applied challenges in zero-carbon energy systems and aerospace noise reduction. Scientific Awards: Henslow Research Fellow (2024)
Einar Malvin Rønquist is a Professor and has served as Head of the Department of Mathematical Sciences at the Norwegian University of Science and Technology (NTNU) since August 2013. He has been a faculty member at the same department since 1999 and is affiliated with the differential equations and numerical analysis (DNA) research group, which he led from 2010-2011. His extensive publication record demonstrates a sustained research career spanning over three decades in computational mathematics. Professor Rønquist's research focuses primarily on numerical methods for partial differential equations, with special emphasis on spectral methods, reduced basis methods, and computational fluid dynamics. His work addresses challenges in high-order approximation, model reduction, and efficient computation for complex physical systems. He has made significant contributions to the development of tensor-product solvers for deformed domains, interface tracking algorithms, and the theoretical foundations of reduced basis methods. His research bridges theoretical mathematics with practical computational applications across engineering and physical sciences, with particular relevance to fluid dynamics, geometric problems, and multi-physics systems. Analysis of Rønquist's publication trajectory reveals a consistent evolution from foundational spectral element methods to advanced model reduction techniques. His work shows increasing sophistication in handling parametric problems and uncertainty quantification while maintaining rigorous mathematical foundations. The publications demonstrate strong contributions to high-order numerical methods, with applications spanning fluid dynamics, geometric problems, and multi-physics systems. Recent work shows particular emphasis on practical computational implementation, including parallel algorithms and efficient solvers for real-world applications that require solving problems on complex or deformed domains. Rønquist has maintained active international collaborations throughout his career, particularly with researchers at MIT (notably Anthony Patera), Université Pierre et Marie Curie (Yvon Maday), and other leading institutions across Europe and North America. His work has appeared consistently in top journals in computational mathematics and scientific computing, including Journal of Computational Physics, SIAM Journal on Scientific Computing, and Computer Methods in Applied Mechanics and Engineering. As department head since 2013, Rønquist has played a significant administrative role in shaping mathematics education and research at NTNU. His leadership extends beyond pure administration, as evidenced by his 2016 commentary in Universitetsavisa regarding the organization of technical cybernetics and electrical power engineering at NTNU, and his 2021 opinion piece in Aftenposten about mathematics examinations. He continues to actively contribute to both the academic and public discourse on mathematics education and research.
Michael Pürrer serves as an Adjunct Assistant Professor in the Department of Physics and Computational Scientist at the Center for Research Computing, University of Rhode Island. A member of the NSF-funded LIGO Scientific Collaboration since 2013, he contributes to gravitational-wave astronomy through advanced data analysis and source modeling. Education: Ph.D. in Theoretical Physics, University of Vienna, Austria, 2007 Diploma in Theoretical Physics, University of Vienna, Austria, 2003 Dr. Pürrer specializes in gravitational-wave signal modeling for binary black hole and neutron star mergers, employing Bayesian inference and deep learning techniques for simulation-based conditional density estimation. His work integrates high-performance computing with statistical learning to enhance gravitational-wave detection and parameter estimation accuracy, directly supporting LIGO-Virgo-KAGRA observational campaigns. Current research emphasizes neural network applications for rapid inference in next-generation detector networks. His publication record reveals a decisive shift toward machine learning integration in gravitational-wave astronomy since 2020, with deep learning methods now central to waveform modeling and inference pipelines. Key focus areas include noise adaptation, surrogate modeling for precessing binaries, and accuracy requirements for future detectors like Cosmic Explorer and Einstein Telescope. Scientific Awards: 2016 Special Breakthrough Prize in Fundamental Physics (LSC) 2016 Gruber Cosmology Prize (LSC) Premio Princesa de Asturias de Investigación 2017 (LSC) 2017 RAS Group Achievement Award ‘A’ (LSC) As a lead contributor to the GWTC-1 catalog paper and developer of critical waveform models, Dr. Pürrer’s work underpins major gravitational-wave discoveries. His research is sustained through LIGO Scientific Collaboration funding, with significant publications in Physical Review Letters and Astrophysical Journal. Though student advising details are unspecified, his leadership in LVK working groups demonstrates mentorship within the collaboration framework. Dr. Pürrer actively participates in the LIGO Scientific Collaboration’s Compact Binary Coalescence group and contributes to the Science Book for future gravitational-wave observatories, positioning him at the forefront of next-generation detector development and multi-messenger astronomy initiatives.
Clarence Rowley is the Sin-I Cheng Professor in Engineering Science at Princeton University , affiliated with the Department of Mechanical and Aerospace Engineering and the Program in Applied and Computational Mathematics. His research bridges dynamical systems, control theory, and fluid mechanics, focusing on reduced-order modeling and feedback control of complex fluid systems. Develops data-driven models for fluid flows using simulation/experimental data Applications include cavity flows, tokamak plasmas, and aerospace dynamics Research Interests center on: Model reduction for nonlinear systems Geometric methods in fluid mechanics Balanced truncation and Koopman operator theory Feedback control of unstable flows Optimal actuator/sensor placement Teaching includes courses in control theory, dynamical systems, and computational methods, notably a cross-departmental Software Engineering for Scientific Computing course. His Publications (15 most recent) span control theory, fluid mechanics, and computational methods, with a focus on time-periodic flows (2022), nonlinear model reduction (2022), and DMD-based analysis (2014). Scientific Awards include: NSF CAREER Award AFOSR Young Investigator Award Multiple teaching awards at Princeton University Advising has produced 16 PhD graduates (2003-2022) including Steve Brunton (2012) and Sam Otto (2022), with current advisees in MAE and PACM. Labs & Teams include the Rowley Group at Princeton's Engineering Quadrangle, collaborating with AIAA Journal editorial board and Springer book series initiatives.
Xiang Zhang serves as Associate Professor in the Department of Mechanical Engineering at the University of Wyoming, where he has held a faculty position since 2019. He directs the Computations for Advanced Materials and Manufacturing Laboratory (CAMML), focusing on establishing microstructure-processing-performance relationships through advanced computational models. His work bridges material microscale phenomena with structural-scale applications in high-performance materials and manufacturing processes. His educational foundation includes: Ph.D. in Civil Engineering from Vanderbilt University (2017) M.S. in Solid Mechanics from Beihang University (2012) B.S. in Engineering Mechanics from Northeastern University (2009) Dr. Zhang's research centers on multiscale and multiphysics computational modeling, with emphasis on deformation and damage mechanisms in metals and composites. His group develops crystal plasticity finite element models, interface-enriched generalized finite element methods (IGFEM), and reduced-order homogenization techniques. Current projects target frontal polymerization for composite 3D printing, metal additive manufacturing, and microstructure-informed material design. This work integrates computational modeling with experimental validation to solve challenges in structural integrity and manufacturing efficiency. Recent publications (2019-2023) reveal strong thematic continuity in multiscale modeling of composite manufacturing processes, particularly frontal polymerization applications in 3D printing. His work consistently connects microscale material behavior (e.g., crystal plasticity, interface damage) with structural performance through reduced-order modeling frameworks. Key journals include Computer Methods in Applied Mechanics and Engineering , Composite Science and Technology , and Additive Manufacturing , demonstrating cross-disciplinary impact in computational mechanics and materials engineering. His honors include: NSF CAREER Award (2023) for multiscale modeling of hybrid composites Dolling & Scott Faculty Research Award (2022) Multiple national conference awards including Melosh Medal Finalist (2017) Student paper competitions at Engineering Mechanics Institute (2016) Dr. Zhang actively mentors graduate researchers through CAMML, currently advising three PhD students and one MS student, with eight alumni completing degrees under his supervision. His NSF CAREER grant enables integrated research, education, and workforce development partnerships with Idaho National Laboratory, industry collaborators, and university centers including the School of Computing and Advanced Research Computing Center. The lab maintains strong industry connections for technology transfer in advanced manufacturing. The CAMML laboratory operates within the University of Wyoming's R1 research infrastructure, maintaining collaborations with Vanderbilt University, University of Illinois, and national laboratories. Current projects involve metal 3D printing, frontal polymerization composites, and reduced-order modeling frameworks, supported by state-of-the-art computational resources. The team actively recruits graduate students for positions requiring expertise in computational mechanics, materials science, and programming.
Maria Strazzullo is a Fixed-term Assistant Professor at the Department of Mathematical Sciences (DISMA) within Politecnico di Torino, Italy. Her research focuses on reduced order methods, optimal control theory, and numerical analysis for parametrized partial differential equations. Numerical Analysis and Scientific Computing Model Order Reduction Neural Networks for Parametrized PDEs Uncertainty Quantification Her recent publications address convection-dominated flows, bifurcating nonlinear PDEs, and optimal control problems with random inputs. She collaborates on interdisciplinary projects integrating machine learning with computational physics.
Venkat Athmanathan is a Senior Research Scientist at Purdue University's Zucrow Labs and the Department of Mechanical Engineering within the College of Engineering. He is affiliated with the Meyer Research Group and maintains an active Google Scholar profile. His research focuses on advanced combustion systems, optical diagnostics, and alternative fuels for next-generation propulsion technologies. Education: B.E. in Aeronautical Engineering from Anna University, Chennai, India (2015) M.S. in Mechanical Engineering from Purdue University (2018) Ph.D. in Aeronautics and Astronautics from Purdue University (2021) Research Interests: Combustion Physics, Spectroscopic Analysis of Reacting Flows, Optical Diagnostics Development for Thermal Environments (e.g., gas-turbines, rockets), and Alternative Fuels for Gas Turbines (Hydrogen and Ammonia). His work emphasizes experimental and computational studies of Rotating Detonation Engines (RDEs) and their integration with turbine systems. His research employs cutting-edge diagnostics like high-speed laser imaging (PLIF, CARS) and MHz-rate thermal measurements to study detonation wave dynamics, fuel injection, and heat transfer in extreme environments. Recent work focuses on hydrogen and ammonia as sustainable fuels in rotating detonation combustors. Labs/Teams: Active member of Zucrow Labs and the Meyer Research Group, collaborating on projects related to high-pressure optical RDEs (e.g., THOR engine) and turbine integration challenges.
