Michael Barnes is a Tutorial Fellow in Physics and Professor of Physics at the University of Oxford. He contributes to the Department of Physics through teaching and research, with a focus on plasma behavior in magnetic fields. His work has critical applications in sustainable energy production via fusion and astrophysical systems. Professor Barnes teaches Mathematical Methods for Physicists to undergraduate students at University College and lectures on Complex Numbers and Ordinary Differential Equations . His pedagogical emphasis is on developing mathematical fluency for advanced physics topics. His research explores plasma turbulence suppression by sheared flows, particularly in magnetic confinement fusion. Key projects include the development of the TRINITY multiscale gyrokinetic transport code and studies on tokamak transport barriers. Recent publications highlight advancements in gyrokinetic simulations, collision operators, and beam diagnostics for fusion applications. Notable trends in his publications include multiscale modeling of plasma turbulence, zonal flow dynamics, and experimental comparisons for fusion devices like JET, MAST, and ITER. Subfields span from fundamental kinetic theory to applied fusion engineering.
Paul Withers is a Professor and Chair of the Department of Astronomy at Boston University. He leads research on planetary atmospheres and ionospheres, with a focus on Mars and Venus, and serves as Principal Investigator on multiple NASA-funded research projects. Education: B.A. in Physics, 1998, Queens' College, Cambridge University M.S. in Physics, 1998, Queens' College, Cambridge University M.A., 2001, Queens' College, Cambridge University Ph.D. in Planetary Science, 2003, University of Arizona Professor Withers' research focuses on the upper atmospheres and ionospheres of terrestrial planets, particularly Mars and Venus. His work involves analyzing spacecraft data and developing theoretical models to understand how solar flux, neutral atmospheres, magnetic fields, and ionospheres interact under unique planetary conditions. He has made significant contributions to understanding the response of the Martian ionosphere to solar flares, the structure of the Venus ionosphere, and meteoric plasma layers in planetary ionospheres. His research often involves multi-instrument campaigns and coordinated observations across different spacecraft missions including Mars Express, MAVEN, and Venus Express. Analysis of Professor Withers' recent publications reveals a strong emphasis on Martian ionospheric dynamics, particularly its response to solar activity and its variability under different conditions. His work frequently combines data from multiple missions to create comprehensive models of planetary upper atmospheres. He has developed important methods for analyzing radio occultation data and reconstructing atmospheric properties from entry, descent, and landing measurements. Major Funded Projects: "Characterizing the topside bulge in the ionosphere of Mars" (NASA Mars Data Analysis Program, 2014, $144K) "Integration of MAVEN neutral and plasma observations" (NASA MAVEN Participating Scientist Program, 2013, $284K) "Radio occultation studies at Mars" (NASA Early Career Fellowship Program, 2013, $99K) "EDL reconstruction for MSL" (NASA, JPL contract, 2012, $199K) "Meteoric plasma layers on Venus and Mars" (NASA Planetary Atmospheres Program, 2012, $232K) Professor Withers has been actively involved in mentoring students and collaborating with international researchers. He serves as a key member of the Mars Upper Atmosphere Network (MUAN) and has contributed to community white papers for planetary science decadal surveys. His work supports future Mars landers through atmospheric modeling and surface pressure prediction, with direct applications to mission planning and execution. He has presented his research at numerous international conferences including the American Geophysical Union meetings, Division for Planetary Sciences meetings, and European Planetary Science Congress. His work has important implications for understanding planetary climate evolution, space weather effects on technological systems, and the search for habitable environments beyond Earth.
Bruce Allen is the Director of the Max Planck Institute for Gravitational Physics (Albert Einstein Institute) in Hannover, Germany, where he also heads the Observational Relativity and Cosmology department. He holds dual academic appointments as Honorary Professor of Physics at Leibniz Universität Hannover and Adjunct Professor of Physics at the University of Wisconsin-Milwaukee, USA. His career spans over three decades in gravitational physics research, with a leadership role in the LIGO Scientific Collaboration from 1997 to 2018. Dr. Allen's research focuses on gravitational wave detection and data analysis, early universe cosmology, de Sitter space, curved-space quantum field theory, cosmic strings, inflationary models of the early universe, and gravitational radiation emission by cosmic strings. His work extends to large-scale cluster computing and public distributed computing projects like Einstein@Home, which has led to significant discoveries in gravitational wave astronomy. His recent publications demonstrate expertise in pulsar timing arrays, Hellings-Downs correlation analysis, and optimization of computational methods for gravitational wave detection. Allen's scientific contributions have been recognized with numerous prestigious awards including the Richard A. Isaacson Award (2020), the Bruno Rossi Prize (2017), the Princess of Asturias Award (2017), and the Special Breakthrough Prize (2016), all shared with the LIGO team for groundbreaking gravitational wave discoveries. He is also an Elected Fellow of both the American Physical Society and the Institute of Physics, UK. As a research leader, Allen has secured approximately $10 million in research funding from the National Science Foundation (1987-2018) and has mentored numerous students and researchers in gravitational physics. His work on Einstein@Home has engaged the public in scientific discovery through distributed computing, leading to several important astrophysical findings including gamma-ray pulsar discoveries.
Jeff Linderoth is the Harvey D. Spangler Professor in the Department of Industrial and Systems Engineering at the University of Wisconsin-Madison. His research focuses on large-scale numerical optimization, mixed-integer nonlinear programming, and stochastic programming, with applications in energy systems, global routing, and industrial processes. Education: BS in General Engineering (highest honors) from University of Illinois at Urbana-Champaign, MS in Operations Research from Georgia Institute of Technology, PhD in Industrial Engineering from Georgia Institute of Technology. Linderoth's work addresses theoretical and applied challenges in optimization, including developing algorithms for mixed-integer programming, analyzing knapsack polytopes, and creating tools like the Minotaur optimization toolkit. His recent publications explore integer programming techniques for subspace clustering, complementarity constraints, and customized coverage instrumentation. Selected trends in his research include advancements in stochastic programming, orbital branching for symmetric integer programs, and congestion analysis in power systems. His group contributes to optimization software and data-driven libraries like MIPLIB. Scientific Award: Harvey D. Spangler Professor.
Matt Nassar is an Associate Professor of Neuroscience and Assistant Professor of Cognitive and Psychological Sciences at Brown University. He leads the Learning, Memory and Decision Lab, which is part of the Department of Neuroscience and the Robert J. & Nancy D. Carney Institute for Brain Science. His research focuses on understanding how the brain flexibly processes information to achieve complex and adaptive behaviors through computational approaches that bridge cognitive psychology and neuroscience. Education: PhD, University of Pennsylvania (2012) BA, Colgate University (2004) Nassar's research examines how different cognitive systems—learning, memory, and perception—leverage common computational principles to optimize decision-making. His work particularly focuses on how the brain balances stability and flexibility in processing information, how uncertainty is represented and utilized in learning, and how neural computations underlie complex behaviors. Through computational modeling and empirical research, he investigates how modular information-processing systems impact decisions and complex behavior in dynamic environments. His research integrates methods from cognitive psychology, neuroscience, and computational modeling to address fundamental questions about human cognition. Analysis of Nassar's recent publications (2020-2024) reveals a strong focus on computational neuroscience applied to decision-making, learning, and psychiatric conditions. His work frequently employs Bayesian modeling approaches to understand belief updating, uncertainty processing, and structure learning. Key themes include the neural basis of flexibility in learning, computational mechanisms underlying psychiatric symptoms, and age-related changes in cognitive processing. His research bridges cognitive psychology, neuroscience, and computational modeling to provide insights into both healthy cognition and disorders such as depression and schizophrenia. Scientific Contributions: Developed computational models of belief updating and learning under uncertainty Investigated neural mechanisms of stability-flexibility tradeoffs in cognition Examined age-related differences in learning and memory processes Explored computational mechanisms underlying psychiatric conditions Studied the role of noise correlations in neural learning systems Investigated how prefrontal cortex representations shape decision processes Nassar actively mentors researchers in his lab, with recent announcements highlighting postdocs joining from prestigious institutions like Max Planck UCL and Freie Universität Berlin. His lab appears to receive significant research funding, supporting multiple postdoctoral positions and research projects. Collaborations span multiple departments at Brown University, particularly with researchers in Cognitive and Psychological Sciences, Neurology, and Psychiatry. The lab has produced numerous high-impact publications in top journals including Nature Human Behaviour, Brain, and eLife. The Learning, Memory and Decision Lab, led by Nassar, is an active research group that uses computational models to understand how the brain represents and stores information for effective decision making. Recent lab announcements (as of February 2025) indicate the lab is expanding with new postdoctoral researchers joining from Harvard, Max Planck UCL, and Freie Universität Berlin, suggesting strong research momentum and funding support. The lab appears to be well-integrated within Brown's neuroscience community, with collaborations spanning multiple departments and research centers.
Albert Atserias is a Professor in the Department of Computer Science at the Universitat Politècnica de Catalunya (UPC), affiliated with the Faculty of Informatics of Barcelona (FIB) and the ALBCOM research group (Algorithms, Bioinformatics, Complexity, and Formal Methods). He is also associated with the Institut de Matemàtiques de la UPC-BarcelonaTech. His research is central to theoretical computer science, with a strong emphasis on logic and complexity. Atserias's research interests span Computational Complexity, Logic in Computer Science, Finite Model Theory, Proof Complexity, and Constraint Satisfaction Problems . His work explores the fundamental limits of computation, the expressive power of logical languages over finite structures, and the complexity of proving mathematical statements. He investigates the algebraic and combinatorial properties of proof systems, the limits of efficient algorithms for constraint solving, and the theoretical foundations of databases. His research often bridges logic, algebra, and combinatorics to provide deep insights into computational phenomena. The trends in his recent publications show a sustained focus on the logical and algebraic underpinnings of computational problems. Key themes include the consistency and complexity of database queries , the power and limitations of proof systems (like resolution and sum-of-squares), and the expressive power of homomorphism counts in graph theory. His work on the hardness of automating resolution and the development of circular proof systems are particularly significant contributions to proof complexity. The 2024 PODS Best Paper Award for work on relational consistency underscores the impact and timeliness of his research. Among his notable scientific awards are the prestigious ICREA Acadèmia , the PODS 2024 Best Paper Award , the Premi Extraordinari de Doctorat (Extraordinary Doctoral Prize), and the Kleene Award for Best Student Paper . These accolades reflect both the excellence of his early work and his continued leadership in the field. Atserias has been a principal investigator on numerous competitive research projects, including funding from the European Research Council (ERC) and the Spanish Ministry of Science. He has advised doctoral students, such as Toni Hakoniemi, whose thesis on proof complexity he supervised. His extensive collaborative network includes leading researchers like Phokion Kolaitis, Anuj Dawar, and Victor Dalmau. He has also served on the scientific committees of major conferences, contributing to the academic community. He is a core member of the ALBCOM research group , a leading team at UPC focused on theoretical aspects of computer science, which provides a vibrant environment for research in algorithms, complexity, and formal methods. His work is also connected to the broader Institut de Matemàtiques de la UPC, fostering interdisciplinary collaboration between computer science and mathematics.
Barbara Plank is a full professor and chair for AI and Computational Linguistics at Ludwig Maximilian University of Munich (LMU), where she heads the Munich AI and NLP (MaiNLP) lab and co-directs the Center for Information and Language Processing (CIS). She additionally serves as a visiting full professor at the IT University of Copenhagen, maintaining active dual institutional affiliations in computational linguistics and NLP research. Her research focuses on human-centric natural language processing challenges, particularly learning under sample selection bias (domain adaptation, transfer learning) and annotation bias, learning with limited data through continual/semi-supervised/weakly-supervised methods, multimodal learning at language-vision-speech interfaces, and fortuitous supervision for variety-space aware language understanding. She pioneers methodologies addressing human label variation as a critical factor in model robustness rather than mere noise. Recent publications (2024-2025) reveal dominant trends in modeling human label variation across NLP tasks, especially natural language inference and entity recognition, alongside dialectal language processing and LLM evaluation frameworks. Her work systematically investigates how human disagreement in annotations can be leveraged to build more robust, adaptable systems rather than treated as errors. Scientific recognition includes: ERC Consolidator Grant for the DIALECT project advancing natural language understanding for non-standard languages and dialects ACL 2024 Area Chair Award for the paper 'VariErr NLI: Separating Annotation Error from Human Label Variation' Leading the MaiNLP lab at CIS (LMU), she directs research integrated with MCML (Munich Center for Machine Learning), Munich Intelligent Robotics, ELLIS Unit Munich, UniDive, and COST action. Current projects include ERC-funded DIALECT and KLIMA-MEMES, focusing on human-facing NLP solutions for real-world language diversity challenges. She actively shapes the field through ACL leadership as VP-Elect and numerous keynotes emphasizing human-centric approaches. The MaiNLP lab at Akademiestr. 7, 80799 Munich, drives innovation in computational linguistics through interdisciplinary collaboration, maintaining strong ties with European research networks while developing practical applications for language variation and robust NLP systems. The lab's work directly informs her teaching in LMU's Computational Linguistics programs, bridging research and education in cutting-edge NLP methodologies.
Dr. James Ashton-Miller is a prominent faculty member in the Department of Mechanical Engineering at the University of Michigan, where he directs the Biomechanics Research Laboratory. He serves as a Center Member of the University of Michigan Injury Prevention Center and maintains affiliations with the Institute of Gerontology. His interdisciplinary work bridges engineering principles with medical applications, focusing on injury prevention across sports medicine, obstetrics, and geriatrics. Dr. Ashton-Miller's educational background includes: PhD from the University of Oslo, Oslo, Norway (1978-1983) MSME from M.I.T., Cambridge, MA, U.S.A (1972-1974) B.SC. (Hons) from the University of Newcastle-upon-Tyne, Newcastle-upon-Tyne, England (1967-1972) His research focuses on the biomechanics of injury prevention across multiple critical domains. In sports medicine, he has demonstrated that some ACL injuries are overuse injuries resulting from too many sub-maximal loading cycles that prevent healing of collagen damage. In women's health, his work on childbirth injuries addresses conditions that affect more women than breast cancer. His research on fall-related injuries in older adults reveals the dual threat of physical and cognitive factors. He also investigates sciatica, disc degeneration, and develops new medical devices for screening, diagnosis and treatment. Dr. Ashton-Miller's recent publications show a strong trend toward developing practical clinical applications from fundamental biomechanical research, with emphasis on advanced imaging methods, wearable sensors, and computational modeling for pelvic floor function assessment. His work consistently aims to translate engineering insights into clinical solutions for injury prevention. His research insights have earned him numerous national and international research awards, though specific awards aren't detailed in the available information. His work involves close collaboration with clinicians and surgeons who meet weekly to discuss progress and next steps. Dr. Ashton-Miller is deeply committed to mentoring, working with NIH K-series fellows along with 1-2 post-doctoral fellows, 3-5 PhD students, 2-4 M.S. students, 4-5 undergraduate students, and 2-4 young clinicians. His research is generously supported by the National Institutes of Health, National Science Foundation, National Basketball Association, Fortune 500 companies, and startup companies including Procter & Gamble and Hologic, Inc. He directs the Biomechanics Research Laboratory and co-leads the Pelvic Floor Research Group, where his teams develop new medical devices to improve screening, diagnosis, and treatment of various biomechanical conditions. These laboratories maintain strong clinical connections, ensuring research remains grounded in real-world medical challenges.
Anna Villarroya Planas is a Professor at the Universitat de Barcelona, serving as Director of the Research Center for Information, Communication and Culture. She holds a law degree and economics degree from the Universitat de Barcelona, and a PhD in Public Sector Economics from the same institution. Affiliated with both the Faculty of Information and Audiovisual Media and the Department of Economics, she has been teaching courses related to cultural economics and cultural policies since 1993. Her research focuses on digital culture and content, with special emphasis on open access to science and economic studies on culture, particularly examining gender perspectives in cultural sectors. She serves as President of the European Association of Cultural Researchers and coordinates the Interuniversity Doctoral Program in Gender Studies: Cultures, Societies and Policies. Analysis of her recent publications (2022-2024) reveals a strong focus on gender equality in cultural sectors, particularly examining gender discrimination in the performing arts, LGBTQ perspectives in information studies, and cultural policies with gender perspectives. Her work spans multiple disciplines including cultural economics, gender studies, library science, and policy analysis, demonstrating interdisciplinary approaches to understanding cultural production, consumption, and policy implementation. She has participated in numerous research projects funded by public entities including the World Bank, Council of Europe, ERICarts, Organization of Ibero-American States, Spanish Ministry of Culture, Catalan Department of Culture, and Barcelona Provincial Council, as well as private foundations like "la Caixa" Banking Foundation, Carulla Foundation, and Alternativas Foundation. Since 2006, she has authored the report on cultural policy in Spain included in the Compendium of Cultural Policies and Trends, and since 2017 has been a member of the Council of the Association of the Compendium of Cultural Policies and Trends. Her leadership extends to directing research projects focused on gender perspectives in information and media studies, including the GEMPIMS (Gender Perspective Mentoring Program in Information & Media Studies) initiative at the University of Barcelona.
John Laiho is an Associate Professor in the Department of Physics at Syracuse University, part of the College of Arts & Sciences. His research focuses on high energy particle physics and lattice field theory, particularly lattice quantum chromodynamics and quantum gravity applications. He holds a PhD from Princeton University (2004) and has held academic positions at Fermilab, Washington University in St. Louis, and the University of Glasgow before joining Syracuse in 2013. Education: PhD in Physics, Princeton University (2004) BA in Physics and Mathematics, Rhode Island College (1998, summa cum laude) Research Interests: Specializes in lattice field theory techniques for studying quark-flavor physics, beyond the Standard Model physics, and quantum gravity. Recent work includes dynamical dark energy models and improved lattice quantum gravity simulations. Grants & Collaborations: DOE-funded project on theoretical particle physics and cosmology (2013–2025) CUSE grant exploring quantum information and fundamental physics (2018–2023) Teaching Highlights: Teaches advanced mechanics, relativity, and computational physics courses. Supervises independent studies and has taught a range of undergraduate/graduate physics topics.
Dr. Eleodor Nichita is an Associate Professor in the Department of Energy and Nuclear Engineering at the University of Ontario Institute of Technology (UOIT), part of the Faculty of Engineering and Applied Science. He holds a PhD in Nuclear Engineering from Georgia Institute of Technology (USA) and additional degrees from McMaster University and the University of Bucharest. His research focuses on neutron transport, reactor kinetics, advanced nuclear reactor design, and radionuclide production. He teaches a wide range of courses including reactor physics, neutron detectors, and medical imaging applications of radiation. Education: PhD in Nuclear Engineering, Georgia Institute of Technology, United States MS in Health Physics, Georgia Institute of Technology MS in Medical Physics, McMaster University BS in Engineering Physics, University of Bucharest, Romania Research interests emphasize mathematical modeling for nuclear systems, neutronic design of advanced reactors, and production of medical isotopes like Mo-99. His work addresses reactor safety, lattice homogenization techniques, and SCWR (supercritical water-cooled reactor) dynamics. Over 50 peer-reviewed papers and book chapters reflect his contributions to CANDU reactor analysis, PHWR fuel bundle design, and educational innovations in nuclear engineering. Advising and grants: While specific student names are not listed, his extensive teaching portfolio (including graduate-level reactor physics courses) indicates active mentoring. Research grants likely support his work on reactor kinetics and SCWR technology. Lab affiliations: His research is conducted through the Energy Systems and Nuclear Science Research Centre (ERC) at UOIT, focusing on numerical methods and experimental validation for reactor analysis.
Yvain Bruned is a Professor of Mathematics at Université de Lorraine, Nancy, France, where he leads research in singular stochastic partial differential equations and related fields. He serves as Principal Investigator for the ERC Starting Grant LoRDeT (2023-2028), which focuses on advancing the theory of decorated trees and Hopf algebraic structures for solving singular SPDEs and dispersive PDEs at low regularity. Previously, he was a Lecturer at the University of Edinburgh (2019-2022) and completed postdoctoral work at Imperial College London and University of Warwick under Martin Hairer. His educational background includes: PhD in Mathematics (2012-2015), UPMC (Paris 6), on "Singular KPZ type equations" under Lorenzo Zambotti Master 2 in Probability and Statistics, ENS Cachan / Rennes 1, with honors Master 1 in Mathematics, ENS Cachan, with honors Bachelor in Mathematics and Computer Science, University of Rennes 1, with honors Student at ENS Cachan Brittany extension (2009-2013) Classes Préparatoires in Mathematics and Physics (2007-2009) Bruned's research centers on singular stochastic partial differential equations, with particular focus on Regularity Structures, renormalization theory, and their connections to Hopf algebras. His work bridges theoretical mathematics with applications in quantum field theory, wave turbulence, and numerical analysis. He has developed novel approaches using decorated trees to handle renormalization procedures for singular SPDEs and has extended these methods to dispersive PDEs with random initial data. His research program aims to establish existence and uniqueness results for quasilinear and dispersive SPDEs while developing algebraic tools through deformations of Hopf algebras. His extensive publication record demonstrates consistent contributions to the field of singular SPDEs, with a clear trajectory from foundational work on Regularity Structures to more recent applications in dispersive PDEs and numerical methods. The publications reveal a strong collaborative network with leading researchers in stochastic analysis, mathematical physics, and algebra. His work shows increasing sophistication in handling renormalization procedures through algebraic structures, with recent papers exploring connections between different mathematical frameworks. His major scientific recognition includes: ERC Starting Grant LoRDeT (2023-2028) Bruned actively supervises a large group of researchers, currently advising 4 PhD students and 2 postdoctoral researchers at Université de Lorraine, with several former PhD students having completed their degrees at the University of Edinburgh. His ERC grant has enabled him to organize multiple international workshops in Nancy, fostering collaboration between researchers in singular SPDEs, algebraic structures, and numerical analysis. The grant also supports the development of software platforms for decorated trees and their Hopf algebraic structures. As Principal Investigator of the ERC LoRDeT project, Bruned leads a vibrant research team based at the Elie Cartan Institute of Lorraine, which includes postdocs, PhD students, and visiting researchers. The team regularly organizes specialized workshops on topics including operads, symmetries for quantum field theory, and normal forms for singular dynamics, creating a dynamic research environment that bridges multiple mathematical disciplines.
Jaime Peraire is the H.N. Slater Professor of Aeronautics and Astronautics at MIT, affiliated with the School of Engineering. He leads research in computational mechanics, aerodynamics, and numerical methods for partial differential equations, with key roles as former Department Head (2011-2018) and Director of the Aerospace Computational Design Lab (1993-2011). His expertise spans finite element methods, shock capturing algorithms, and high-order numerical techniques applied to hypersonic flows, space weather, and metamaterials. Education includes a Ph.D. from the University of Wales (1986) and engineering degrees from the University of Barcelona (1983, 1987). He holds prestigious awards like the T.J. Hughes Medal (2015) and the Ildefons Cerdá Medal (2015). His work bridges computational science and engineering, with contributions to discontinuous Galerkin methods, mesh adaptivity, and GPU-accelerated simulations. Research interests emphasize high-fidelity modeling of compressible flows, plasma dynamics, and terahertz spectroscopy. Notable projects include MIT’s space weather modeling initiative and metamaterial fabrication using atomic layer lithography. His labs collaborate across MIT’s Schwarzman College of Computing, IDSS, and CCSE to advance computational tools for aerospace and environmental systems. Awards: Over 10 major prizes, including NASA Exceptional Achievement (1997) and IACM Young Researchers Award (1998). Grants/Advising: Led NSF-funded space weather projects and advised numerous PhD students in computational engineering. Labs: Aerospace Computational Design Lab, MIT Schwarzman College of Computing collaborations.
Renate Sachse is a Researcher and Responsible Investigator at the Chair of Structural Analysis, Technical University of Munich (TUM), under Prof. Kai-Uwe Bletzinger. She holds a Dr.-Ing. from the University of Stuttgart and has held postdoctoral positions at Harvard University (Bertoldi Lab) and TU Munich's Institute for Computational Mechanics. Her research focuses on biomimetic adaptive structures, biomechanics, and smart materials. Education M.Sc. in Civil Engineering (University of Stuttgart, 2014) – Thesis: "Isogeometric Contact Analysis of Thin-Walled Structures" B.Sc. in Civil Engineering (University of Stuttgart, 2011) – Thesis: "Elementary School Pavilion Structural Analysis" Study Abroad: École Spéciale des Travaux Publics (ESTP, France, 2012) Research Interests Her work integrates principles from biology and mechanics to design adaptive structures, including motion design, soft robotics, and active metamaterials. Notable projects include studying snapping mechanisms in plants (e.g., Venus flytrap) and developing bio-inspired systems like Flectofold shading devices. She also explores isogeometric analysis and structural optimization for thin-walled and slender structures. Grants & Awards Bertha Benz Prize 2022 (Daimler and Benz Foundation) Klaus Tschira Boost Fund Fellowship (€80,000 interdisciplinary grant) 3rd Place AVK-Prize for Innovations (2017, Flectofold Shading System) GAMM Juniors Fellowship (2020–2022) Teaching & Grants She teaches advanced finite element methods and nonlinear mechanics at TUM and has supervised projects in computational mechanics. Her grants include CareerDesign@TUM funding and the Klaus Tschira Fellowship for high-risk, interdisciplinary research. Labs & Teams Associated with the Chair of Structural Analysis at TUM, collaborating on projects like livMatS (Living Materials Systems) and the Harvard SEAS Bertoldi Lab. Involved in software development (e.g., Carat++, Kiwi!3d) and third-party initiatives (CoDA, FlexWing).
Dr. Zhe Cheng is an Associate Professor in the Department of Mechanical Engineering at Colorado State University, part of the Walter Scott, Jr. College of Engineering. Prior to this, he held tenured positions at Florida International University (2013–2024) and was a research investigator at DuPont (2008–2013). His research focuses on advanced ceramic materials for energy applications, including solid oxide fuel cells (SOFCs), photovoltaics, and high-temperature ceramics. He holds a Ph.D. (2008), M.S. (2004), and B.S. (2001) in Materials Science & Engineering from Georgia Tech and Tsinghua University. Education: Ph.D., Materials Science & Engineering, Georgia Institute of Technology (2008) M.S., Materials Science & Engineering, Georgia Institute of Technology (2004) B.S., Materials Science & Engineering, Tsinghua University (2001) Research Interests: Dr. Cheng specializes in novel synthesis and processing of high-temperature ceramics, including high-entropy nitrides, and their applications in energy conversion systems. His work emphasizes in situ characterization techniques to understand material behavior under operational conditions. Key areas include SOFC cathodes, proton-conducting electrolytes, and wearable sensor technologies. Publications & Awards: With over 5,284 citations and an h-index of 27, his work spans 44 peer-reviewed articles. Notable awards include the NSF CAREER Award (2019) and the American Ceramic Society Ross Coffin Purdy Award (2010). His research has been funded by NSF, DOE, and NASA. Advising & Grants: Dr. Cheng has advised numerous graduate students and secured $2.3 million in research funding. Key grants include DOE projects on additive manufacturing for plasma-facing materials and NSF support for SOFC hydrogen electrode fundamentals. Labs & Teams: He leads research in advanced ceramics and electrochemical systems at CSU, fostering interdisciplinary collaborations in materials science and energy engineering.
