Dr. Khosro Shahbazi is a tenured Associate Professor of Mechanical Engineering at South Dakota Mines. His expertise includes computational fluid dynamics, multiphase flows, and nanophysics applications in energy and medicine. He completed his Ph.D. at the University of Toronto and held postdoctoral positions at Brown University and University of Wyoming. Research interests focus on novel numerical methods for compressible flows, nanoparticle optoacoustics for cancer therapy, and turbulence modeling. His work bridges theory, computation, and experimentation in renewable energy, inertial confinement fusion, and biomedical systems. Recent publications emphasize high-order finite difference methods, compressible multiphase flows, mining ventilation systems, and photoacoustic diagnostics. Teaching includes Thermodynamics, Fluid Mechanics, Heat Transfer, and specialized graduate courses in computational transport phenomena. Education: B.S. (Sharif University), M.A.S. (Toronto), Ph.D. (Toronto) Laboratory: Computational Transport Phenomena Pedagogy: Emphasizes constructivism and rigorous challenge with support
Dr. Alexander A. Katz serves as Professor of Mathematics and Computer Science at St. John's College of Liberal Arts and Sciences, St. John's University, where he contributes to both teaching and advanced mathematical research in theoretical domains. Dr. Katz earned his Ph.D. from the University of South Africa, Pretoria, Republic of South Africa, and completed his B.S./M.S. at Tashkent State University, Republic of Uzbekistan, establishing a strong foundation for his specialized work in mathematical structures. His research focuses on Functional Analysis, Operator Theory, Ergodic Theory, Operator and Topological Algebras , and applications of Logic to Analysis . Dr. Katz investigates Topological Algebras, their classification, structure theory, representation theory and applications across Analysis, Operator theory, Ergodic theory, Probability theory, Non-commutative Geometry and Topology. His scholarly approach connects abstract mathematical concepts with practical applications in multiple scientific disciplines, demonstrating the versatility of theoretical mathematics. Analysis of Dr. Katz's publication history reveals consistent specialization in operator algebras, with particular emphasis on locally C*-algebras and JB-algebras. His work shows progression from foundational theoretical investigations to increasingly sophisticated examinations of algebraic structures, with growing attention to real algebras and their distinctive properties. Many publications explore representation theorems, structural characteristics, and applications to ergodic theory and boundary value problems. Dr. Katz has published in respected mathematical journals including: Indian Journal of Mathematics Journal of Mathematical Analysis and Applications Malaya Journal of Matematik Mathematical Studies (Tartu) Though specific advising details aren't documented in available materials, Dr. Katz's extensive publication record spanning over three decades suggests significant mentorship of graduate students. His sustained research activity indicates consistent funding support for theoretical investigations in functional analysis and operator theory. Dr. Katz collaborates with researchers including G.Ya. Grabarnik, O. Friedman, R. Kushnir, and L. Shwartz, participating in mathematical research teams focused on operator algebras and ergodic theory. His work is primarily theoretical, conducted through rigorous mathematical analysis rather than experimental laboratory settings.
Neil Dewar is an Associate Professor at the University of Cambridge's Faculty of Philosophy and a graduate of the University of Oxford (DPhil, 2016). He previously held an Assistant Professor position at the Munich Center for Mathematical Philosophy and studied as a visiting graduate student at Princeton. His research spans philosophy of physics, logic, mathematics, and metaphysics. Key research themes include: The philosophical implications of symmetry and gauge invariance in physical theories The role of category theory in understanding theoretical equivalence Structural realism and its relationship to model theory Humean supervenience in the context of quantum mechanics His publications analyze theoretical equivalence (via group theory and Ramsey sentences), interpretative frameworks in physics, and the metaphysical consequences of mathematical structures. Collaborative works with scholars like James Weatherall and Niels Linnemann explore gravitational energy and spacetime epistemology.
Johannes Lischner is a **Professor of Theory and Simulation of Materials** at the **Department of Materials, Imperial College London**, and director of the MSc in Advanced Materials Science and Engineering. He leads the Thomas Young Centre for Theory and Simulation of Materials and Molecules. With a PhD from Cornell University (2010), he specializes in electronic excitations in complex materials, focusing on twisted 2D materials, plasmonic hot carriers, and core-electron spectroscopy. His research combines advanced electronic structure methods (GW/BSE) to study nanomaterials and energy materials. Key areas include twisted bilayers of graphene and transition metal dichalcogenides, nanoplasmonic hot-carrier generation, and core-electron spectroscopy modeling. Notable contributions include predicting hot-carrier dynamics in gold nanoparticles for CO₂ reduction and explaining superconductivity in twisted bilayer graphene. Recent publications highlight atomistic modeling of plasmon-induced hot carriers, excitonic Mott insulators in moiré heterostructures, and core-electron binding energy calculations. His work bridges theory and experiment, collaborating with groups at Barcelona, Carnegie Mellon, and Heriot-Watt Universities. **Affiliations**: Imperial College London, Thomas Young Centre **Education**: PhD Physics (Cornell University, 2010) **Grants**: Royal Society University Research Fellowship (2014–present) Lischner’s lab develops novel methods for simulating excited-state phenomena in materials, with applications in renewable energy, nanotechnology, and quantum devices.
Professor Patricia M. Greenfield is a Distinguished Professor at the University of California, Los Angeles, specializing in interdisciplinary cultural psychology and human development. With a Ph.D. from Harvard University in Social Psychology/Personality Research, her research explores how social change, cultural evolution, and digital media reshape human cognition, values, and behavior. Her work spans three generations of Maya communities transitioning from subsistence to commercial economies, cross-cultural value conflicts among Latinx immigrants, and pandemic-induced cultural shifts during the COVID-19 stay-at-home orders. She leads the Children's Digital Media Center @ Los Angeles and Weaving Generations Together projects, which examine technology's developmental impact and traditional skill preservation. Key research areas: Social change effects on creativity, mortality salience, intergenerational learning, digital media adaptation, and cultural value mismatches Methodological expertise: Mixed-method and qualitative approaches in cross-cultural research Global collaborations: Studies in Mexico, Israel, China, Japan, Turkey, Indonesia, and the U.S. Scientific Honors: Elected member of American Academy of Arts and Sciences (2014) 133rd Faculty Research Lecturer at UCLA (2023) Jean Piaget Society Award for Distinguished Contributions (2022) Multiple prestigious awards from APA, SRCD, and cultural psychology associations Her teaching includes undergraduate courses on Culture and Human Development and graduate seminars on qualitative methodologies. She has mentored numerous students, including co-founders of the Children's Digital Media Center.
Rayhaneh Akhavan is an Associate Professor in the Department of Mechanical Engineering at the University of Michigan, Ann Arbor. She holds a tenured position and is actively involved in research and teaching. Her educational background includes: Ph.D. in Mechanical Engineering from the Massachusetts Institute of Technology (1987) M.S. in Mechanical Engineering from the Massachusetts Institute of Technology (1982) B.S. in Engineering and Applied Science from the California Institute of Technology (1980) Professor Akhavan's research focuses on fluid mechanics, with particular emphasis on turbulence physics, modeling, and control. She employs high-fidelity computations and reduced-order modeling for complex turbulent flows involving multi-phase systems, fluid-structure interactions, and viscoelastic effects. Her work also explores biomimetic and bio-inspired concepts for flow control and drag reduction, utilizing lattice Boltzmann, pseudo-spectral, and stochastic methods within high-performance computing frameworks. Applications span energy, environmental, aerospace, and naval disciplines. Her notable recognition includes the Robert T. Knapp Award from the American Society of Mechanical Engineers in 1995 for the best paper on "Dynamics of a Turbulent Jet Interacting with a Free Surface". As an advisor, Professor Akhavan adopts a "hands-on" mentoring style, meeting with students daily to weekly depending on their needs. She expects students to be actively engaged in literature review, coding, simulations, data analysis, and paper writing. Authorship is typically granted with the student as first author when they provide a complete draft. Students are required to publish at least one (preferably up to three) papers in top journals for graduation. Funding for conference attendance (such as APS/DFD) is provided through Rackham Travel Grants and her research funds. She maintains a small research group and does not hold regular group meetings, focusing instead on individual mentoring.
Prof. Ralph Spolenak is a Full Professor at ETH Zurich, leading the Laboratory for Nanometallurgy since 2004. He serves as Coordinator of the FIRST cleanroom facility and Chairman of the MaP Center of Competence. His research focuses on nanoscale material properties, in situ testing methods, additive manufacturing, and synthesis of nanometer-scale structures. Key interests include mechanical and functional properties of materials, with emphasis on novel fabrication techniques and material design. His work spans nanometallurgy, metamaterials, and microfabrication, addressing challenges in structural and functional material innovation. Collaborations with industry and academic partners drive advancements in materials for energy, electronics, and sensing applications. No scientific awards are explicitly listed, but his extensive publications reflect significant contributions to materials science. Advising and grants details are not specified in available texts, though his roles imply active research leadership. The MaP Center and FIRST lab are central to his research infrastructure.
Elizabeth Wonnacott is a Professor of Language Science at the University of Oxford’s Department of Education. Previously, she held positions as an Associate Professor at UCL’s Division of Language Sciences and an Assistant Professor in Psychology at the University of Warwick. Her academic journey includes a Junior Research Fellowship at Linacre College, Oxford, and degrees in Linguistics and Artificial Intelligence (University of Edinburgh) and Brain and Cognitive Sciences (University of Rochester). Her research focuses on human language learning mechanisms, particularly statistical learning processes in first and second language acquisition. Key interests include naturalistic versus classroom-based learning, educational implications for language teaching, literacy development, and discriminative learning theories. She explores how variability in input influences learning outcomes, with recent work supported by a Leverhulme Trust grant examining discriminative linguistics. Dr. Wonnacott teaches on the MSc in Applied Linguistics and Second Language Acquisition and the MSc in Applied Linguistics for Language Teaching. She supervises doctoral students interested in statistical learning, age-related differences, implicit vs. explicit learning, and computational modeling of language acquisition. Her work is centered at the Language Learning Lab , where she investigates topics like phonetic training, multi-word unit learning, and the role of iconicity in language development. Her research bridges cognitive science, linguistics, and education, emphasizing experimental and computational approaches to understanding language acquisition.
Pekka Orponen is a Professor of Computer Science at Aalto University's School of Science, Department of Computer Science. He holds academic appointments in both the Computational Life Sciences (CSLife) and Algorithms and Theoretical Computer Science (TCS) research areas. His roles include former Head of the Aalto Department of Information and Computer Science (2008–2014) and Vice-Head for Research of the Aalto Department of Computer Science (2015–2016). Education: MSc and PhD in Computer Science from the University of Helsinki (1983 and 1986) Postdoctoral research at the University of Toronto Research Interests: Focuses on algorithms and complexity theory, with contributions to computational complexity, neural network computation, stochastic algorithms, and nucleic acid nanostructure design. His notable work includes algorithmic design of 3D DNA nanostructures and RNA origami, recognized as an ISI highly-cited paper in biology/biochemistry (2015). Publications: Over 100 publications, emphasizing nanostructure design, computational methods, and interdisciplinary applications in bioengineering and materials science. Recent work includes automated DNA wireframe design tools and RNA secondary structure prediction via neural networks. Awards: 2021: Knight First Class of the Order of the White Rose of Finland 2018: Best Paper Award at UCNC 2023: Grant for Algorithmic Design of DNA Nanotechnology Advising & Grants: Led the ALBION project (2017–2021) on biomolecular nanostructure design. Supervised 4 theses and held leadership roles in CODATA and Informatics Europe. Engaged in academic service, including roles at the Finnish Academy of Science and Letters. Research Groups: Head of the Natural Computation research group at Aalto, focusing on DNA/RNA nanotechnology and algorithmic self-assembly. Maintains active collaborations in Europe and internationally.
Pontus Giselsson is a Senior Lecturer and Director of Third Cycle Studies at Lund University's Department of Automatic Control (Faculty of Engineering). He also serves as a Senior Lecturer at ELLIIT (Linköping-Lund initiative on IT and mobile communication) and is a Profile Area Member in LTH's AI and Digitalization initiative. His research focuses on optimization algorithms for large-scale problems, particularly operator splitting methods and automated algorithm analysis. He leads projects funded by the Swedish Research Council and collaborates on initiatives like ICARUS for wireless communication systems. His research interests include convex/nonconvex optimization, algorithm convergence analysis, and applications in machine learning, control systems, and statistical estimation. He develops frameworks for unifying operator splitting methods and tools for performance estimation of optimization algorithms. Notable projects include Model Predictive Control Stability Analysis and Bregman Optimization Algorithms. Recent publications emphasize Lyapunov analysis, monotone inclusions, and frugal splitting operators. He has organized conferences such as the LCCC Focus Period on Large-Scale and Distributed Optimization. His work bridges theoretical advancements with practical applications in engineering and computational mathematics.
Sarah Finkeldei is an Assistant Professor of Chemistry at the University of California, Irvine (UCI), affiliated with the School of Physical Sciences. She leads the Finkeldei Research Group, focusing on nuclear materials chemistry to advance clean energy technologies. Her research spans nuclear fuel development, waste management, and atomistic modeling. Finkeldei earned her PhD from RWTH Aachen University, Germany, in 2014, followed by postdoctoral roles at Oak Ridge National Laboratory and Forschungszentrum Juelich. She is tenured and recognized for her contributions to nuclear science, including awards like the 2016 Excellence Award from Forschungszentrum Juelich. Education: PhD in Chemistry, RWTH Aachen University, Germany (2014) Research Interests: Nuclear materials chemistry (oxide ceramics, radionuclide uptake) Nuclear waste form stability and corrosion Advanced fuel development for fission, fusion, and space systems Machine learning-driven atomistic modeling of nuclear materials Awards: Excellence Award (Exzellenzpreis), Forschungszentrum Juelich (2016) Borchers Badge, RWTH Aachen University (2016) ANS Nuclear News '40 Under 40' List (2024) Advising & Grants: Mentors students in nuclear materials research, including Jessica Granger-Jones (first-author publications) and Audrey (2025 Nuclear Engineering Delegate). Collaborates with national labs (Oak Ridge, Lawrence Livermore) and industry on projects funded by agencies like the Nuclear Regulatory Commission. Labs/Teams: Leads the Finkeldei Lab at UCI, focusing on interdisciplinary nuclear materials research. Engages in initiatives like the Seaborg Institute Nuclear Science program.
Judith Yang is a Professor in the Department of Physics & Astronomy at the University of Pittsburgh, holding a secondary appointment. Her research focuses on surface reactions (oxidation, catalysis) and electron microscopy, particularly using in situ ultra-high vacuum transmission electron microscopy (UHV-TEM) to study atomic-scale oxidation dynamics. She explores how oxides form non-uniform films, challenging classical theories based on thermogravimetric analysis. Her work combines experimental techniques like in situ TEM with computational methods (e.g., machine learning) to model oxidation mechanisms. Key areas include Cu-Ni alloy oxidation, surface segregation in dilute alloys, and catalyst design for energy applications. Collaborations span materials science, nanotechnology, and medical physics (e.g., radiation therapy imaging). Publications highlight contributions to understanding metal oxidation kinetics, electrocatalyst characterization, and multimodal operando approaches. She actively contributes to interdisciplinary research, bridging physics, chemistry, and engineering. Her research has implications for corrosion prevention, clean energy catalysts, and advanced materials development. She advises on grants related to in situ microscopy and collaborates with institutions globally. No specific awards are listed, but her extensive publication record reflects impactful contributions to materials science and surface physics.
Prof. Matthieu Verstraete is a Professor in Condensed Matter Theory, Statistical and Computational Physics at Utrecht University's Faculty of Science. His research focuses on quantum materials, nanomaterials, and electronic structure theory, with applications to thermoelectric materials, magnetic systems, and quantum phenomena. He leads the Ab initio simulations of quantum materials research group, leveraging computational methods like density functional theory (DFT) and advanced first-principles modeling. Key research areas include: Quantum magnetic materials and spintronics Thermoelectric properties of 2D and bulk materials Phonon physics and thermal transport Electronic structure theory for complex systems Notable contributions include developing the TDEP software for temperature-dependent effective potentials and pioneering studies on spin-entropy effects in 2D magnets. His work bridges computational materials science with experimental validation, collaborating globally on topics like FeRh phase transitions and graphene thermal properties.
Marcin Zamojski is a Research Fellow at the Centre for Finance within the School of Business, Economics and Law at the University of Gothenburg, Sweden. His research spans financial econometrics, asset pricing, and market microstructure, with particular expertise in liquidity measurement, hedge fund strategies, and high-frequency trading analysis. His research interests focus on Financial Econometrics , Asset Pricing , Market Microstructure , Hedge Funds , Liquidity Analysis , and Time-Series Modeling . Zamojski's methodological approach combines sophisticated statistical techniques with practical financial applications, often involving large-scale collaborative research efforts. Zamojski's publication record shows consistent output in high-impact finance journals, with his 2024 Journal of Finance paper 'Nonstandard Errors' being particularly influential (over 17,500 downloads). His research demonstrates a progression from methodological econometric work to applied finance topics, particularly in hedge fund strategy analysis. Nonstandard Errors (Journal of Finance, 2024) - Methodological contribution to financial research reliability Hedge Fund Strategy Experimentation and Clustering (2024) - Analysis of innovation diffusion in hedge funds Dynamic Trade Informativeness (2022) - High-frequency trading analysis Generalized autoregressive Method of Moments (2018) - Econometric methodology development Hedge Fund Innovation (2022) - Study of strategic innovation in alternative investments Zamojski maintains active research collaborations across multiple institutions including Vrije Universiteit Amsterdam, Universite du Luxembourg, and Singapore Management University. His work often involves coordinating large international research teams, as demonstrated by his Journal of Finance publication with over 200 co-authors from institutions worldwide.
Alexander Edström is a Researcher at KTH Royal Institute of Technology within the Department of Light and Materials Physics. His research focuses on materials theory, first principles calculations, and phenomena such as flexomagnetism. He holds a PhD in Materials Theory from Uppsala University (2016), followed by postdoctoral work at ETH Zürich (2016–2019) studying multiferroic materials. Currently, his research explores curvature-induced magnetic effects in 2D materials and magnetoelectric cross-caloric effects. Education: PhD in Materials Theory, Uppsala University (2016) Postdoctoral Researcher, ETH Zürich (2016–2019) KTH International Postdoc (2019–present) Research interests span computational modeling of magnetic materials, data-driven science, and applications in energy-efficient technologies. His work on flexomagnetism in CrI3 demonstrates how curvature alters magnetic states, while his studies on SrMnO3 revealed giant magnetoelectric caloric effects. He collaborates with experimentalists to design novel permanent magnets for green energy applications. He participates in the SeRC’s Method Development for Materials Design (MD2) project and the WISE/WASP joint program. His research bridges theoretical physics and advanced imaging techniques, including TEM simulations and quantum mechanical scattering analysis. Labs/Teams: Active in KTH’s Light and Materials Physics group and contributes to interdisciplinary projects at SeRC and ICMAB-CSIC (Barcelona).
