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John MuellbauerView profile

Professor

• Household economics
• Housing markets
• Finance-real economy interactions
• +3 more

Professor John Muellbauer is a Senior Research Fellow at Nuffield College and Professor of Economics at the University of Oxford. He co-directs the Macroeconomics and Finance Programme at the Institute for New Economic Thinking (INET) Oxford Martin School. His work bridges household economics, housing markets, and finance-real economy interactions, with a focus on credit channels and monetary transmission mechanisms. Education: B.A. from Cambridge University, Ph.D. from UC Berkeley Former roles: Professor at Birkbeck College (London), Lecturer at Warwick University His research emphasizes systems approaches to consumption, house prices, and debt, critiquing conventional central bank models. Recent work includes international house price cycles (Journal of Economic Literature, 2021) and ECB policy model critiques (2022). He collaborates with central banks globally and contributes to macroprudential policy frameworks. Scientific awards include the 2014 Kendrick Prize for his work on credit and housing markets. He is a Fellow of the British Academy, Econometric Society, and European Economic Association, and a CEPR Research Fellow. Policy engagement spans HM Treasury, OECD, Swedish Prudential Authority, and the Resolution Foundation. He advocates for a Green Land Value Tax to address UK housing challenges and frequently contributes to VoxEU debates.

Sadaf SobhaniView profile

Assistant Professor

• Thermal Management
• Energy Conversion
• Heat Transfer
• +12 more

Sadaf Sobhani is an Assistant Professor in the Sibley School of Mechanical and Aerospace Engineering at Cornell University. Her research focuses on thermal management and energy conversion with applications in high-efficiency, low-emission energy systems, spacecraft thermal control, and electrochemical reactors for carbon dioxide conversion. Dr. Sobhani's educational background includes a B.S. (2014), M.S. (2015), and Ph.D. (2019) in Mechanical Engineering from Stanford University. During her doctoral studies, she worked as a research associate at the NASA Ames Research Center and later joined the Lawrence Livermore National Laboratory as a postdoctoral researcher. Her research program integrates computational modeling, experimental techniques, and advanced manufacturing to investigate flow, heat transfer, and chemical reactions in porous media. She leverages the connection between micro-scale features and macro-scale transport properties to develop innovative solutions for energy systems. Her work spans multiple disciplines including combustion engineering, electrochemical systems, and thermal management for spacecraft. Dr. Sobhani's publications demonstrate a consistent focus on porous media combustion, heat transfer optimization, and advanced diagnostic techniques. Her recent work has increasingly incorporated additive manufacturing and machine learning approaches to solve complex thermal management challenges, particularly for space applications and carbon dioxide conversion systems. Gallery of Fluid Motion Award, American Physical Society (2018) Accel Innovation Scholarship, Stanford Technology Ventures Program (2017) Graduate Public Service Fellowship, Haas Center for Public Service (2016) Schneider/MAP Sustainable Energy Fellowship, Haas Center for Public Service (2016) Graduate Research Fellowship, National Science Foundation (2015) AIAA Niagara Frontier Section 2025 Young Professional of the Year Award NASA Early Career Faculty Award (2023) NASA Early Stage Innovations Award (2023) Dr. Sobhani leads an active research group and has secured significant funding including a NASA Early Career Faculty Award and a FuzeHub grant with industry partners Lithoz America and Dimensional Energy. She has developed a new spacecraft thermal management course at Cornell and is actively mentoring students in her laboratory research. The Sobhani Lab, located at 182 Grumman Hall, spans approximately 850 sq. ft. and focuses on spacecraft thermal control, combustion research, non-intrusive diagnostic methods, and ceramic additive manufacturing. The lab utilizes advanced facilities including the Cornell NanoScale Science and Technology Facility and the Cornell High Energy Synchrotron Source.

Ryan CaverlyView profile

Associate Professor

• Control Theory
• Dynamic Modeling
• Aerospace Systems
• +8 more

Ryan Caverly serves as an Associate Professor in the Department of Aerospace Engineering and Mechanics at the University of Minnesota, Twin Cities, holding the prestigious McKnight Land-Grant Professorship. His research bridges theoretical control frameworks with practical aerospace and robotics applications, focusing on dynamic modeling and system control. Education: BS in Honours Mechanical Engineering from McGill University MS in Aerospace Engineering from the University of Michigan PhD in Aerospace Engineering from the University of Michigan Professor Caverly's research centers on input-output stability, robust control of nonlinear systems, and computationally efficient modeling of flexible structures. His work spans aerospace vehicles, spacecraft, and robotic manipulators, emphasizing theoretical rigor alongside real-world implementation challenges in structural flexibility and control precision. Recent publications reveal strong emphasis on predictive control for orbital mechanics, hypersonic vehicle dynamics, and cable-driven systems. His work consistently integrates convex optimization, state estimation, and structural dynamics to solve complex problems in solar sail technology, UAV navigation, and hypersonic flow measurement. Scientific Awards: McKnight Land-Grant Professor Caverly leads multiple externally funded projects including NASA-sponsored research on solar sail momentum management, UAV state estimation with Honeywell, hypersonic bow shock measurements with the Air Force, and deployable space structure control. His grants portfolio demonstrates significant industry and government collaboration in aerospace innovation. He directs the Aerospace, Robotics, Dynamics, and Control (ARDC) Lab, which specializes in the intersection of dynamic modeling and control theory for flexible multi-body systems, with particular focus on cable-driven mechanisms and lightweight aerospace structures.

Ehsan NabaviView profile

Senior Lecturer

• Responsible Innovation
• Artificial Intelligence (AI) Ethics
• Human-AI Interaction
• +7 more

Dr Ehsan Nabavi is a Senior Lecturer in Technology and Society at the College of Asia and the Pacific (CPAS), Australian National University (ANU). He leads ANU’s Responsible Innovation Lab, focusing on responsible computing, modeling, and AI ethics. His interdisciplinary work bridges technical and social sciences, particularly addressing wicked problems in sustainability and water governance. Current Affiliation: ANU (CPAS), since 2020 Former Roles: Research Fellow at ANU School of Cybernetics (2018–2020), Harvard Kennedy School (2016–2017) Visiting Positions: SOAS University of London, University of Bonn (ZEF) His research spans responsible AI , transdisciplinary modeling , and socio-technical systems , as reflected in his publications across journals like Nature Humanities and Social Sciences Communications , IEEE Transactions on Technology and Society , and Water Alternatives . His recent articles emphasize ethical AI deployment, human-water systems, and integrating social aspects into modeling. Labs: Responsible Innovation Lab (ANU) Future Work: Developing frameworks for responsible AI in sustainability and policy contexts

Gavin SchwarzView profile

Professor

• Organizational Change
• Organizational Failure and Inertia
• Virtual Teams
• +2 more

Gavin Schwarz is a Professor and Head of School at the School of Management and Governance within the UNSW Business School . He specializes in organizational change , organizational failure and inertia , and the dynamics of virtual teams , with a focus on how organizations fail during change processes and how to develop applied strategies for change management . His work spans diverse sectors including healthcare, technology, and education, with publications in leading journals such as Academy of Management Learning and Education and Administrative Science Quarterly . Education : PhD in Management (University of Queensland), MPhil (Hons) in Management (University of Auckland), BA in Management and English (University of Auckland). Grants : 2020 Brock University grant for "University communication in times of COVID-19" , 2020 UNSW Medicine grant for "Translation and change: Embedding effective change management in health" , and earlier Australian Research Council and Gordon J. Samuels Fellowship awards. His research explores the development of knowledge in organizational theories , with an emphasis on collective responses to change , HR management during crises , and technology strategy . He has contributed to understanding organizational communication , team innovation , and structural inertia . His 15 most recent publications cover topics from AI’s role in organizational change to pandemic-driven research adaptation, with keywords spanning management science , behavioral economics , and digital transformation . Scientific Awards 2021-2023 : Outstanding Reviewer Awards (Academy of Management Review) 2017-2020 : Best Reviewer Awards (Journal of Organizational Behavior) 2019, 2013, 2011 : Best Paper Finalist/Awardee (Academy of Management divisions) 2007, 2006 : Editorial Board Excellence (Academy of Management Journal) and Gordon J. Samuels Fellowship As an active supervisor in organizational change and HR development , his work supports healthcare innovation and digital transformation. He serves as Editor-in-Chief for the Journal of Applied Behavioral Science and is on the editorial boards of Academy of Management Review , Journal of Management , and Journal of Organizational Behavior . Contact: g.schwarz@unsw.edu.au

• Mathematical Optimization
• Algorithmic Intelligence
• Energy Systems Modeling
• +5 more

Thorsten Koch serves as Head of the Department of Applied Algorithmic Intelligence Methods within the Division of Mathematical Algorithmic Intelligence at Zuse Institute Berlin (ZIB). His research spans mathematical optimization, energy systems modeling, quantum computing applications, and scientometrics. Koch leads significant research projects including FAN (focusing on AI in scholarly communication), UNSEEN (energy scenarios), HPO-NAVI (research software visibility), and Multi-Energy Models for European Energy System Planning. Koch's research interests center on developing advanced optimization algorithms for complex systems, particularly in energy networks and scientific data analysis. His work bridges theoretical mathematics with practical applications in gas network optimization, wind farm design, portfolio management, and quantum computing. He has pioneered methods for large-scale mixed-integer programming, scenario generation, and the integration of machine learning with traditional optimization techniques. His recent publications demonstrate growing emphasis on quantum optimization, scientometrics, and the application of AI to scientific communication infrastructure. His publication trends reveal a strategic expansion from traditional mathematical optimization into quantum computing applications and scientific data infrastructure. Recent work shows increasing collaboration across disciplines - connecting energy systems analysis with financial modeling, integrating machine learning with optimization solvers, and applying computational methods to scientometrics. The 15 most recent articles highlight three major thrusts: quantum optimization (33%), energy systems modeling (27%), and scientific data infrastructure (40%), reflecting his leadership in both theoretical algorithm development and practical implementation for societal challenges. Koch actively contributes to research infrastructure through leadership roles in projects like KOBV (Berlin-Brandenburg Cooperative Library Network), HDC (Humanities Data Centre), and CIB (future library networks). His work on the DeepGreen initiative focuses on establishing legally secure workflows for implementing open-access components in scientific publication licensing agreements, demonstrating his commitment to open science principles and research data management.

• Emergent Software Systems
• Runtime Adaptation
• Component-Based Software
• +7 more

Professor Barry Porter is a faculty member at Lancaster University in the School of Computing and Communications . His research focuses on emergent software platforms that address software complexity through component models , meta-software platforms , and machine learning . Key areas include distributed systems, cloud integration with sensor nodes, green computing, and real-time visualization. Research Interests : Runtime adaptation in complex systems Self-assembling software architectures Machine learning for code optimization Distributed emergent systems at scale Green computing for multi-core environments Edge-cloud continuum integration Recent Publication Trends : His 2025 work explores genetic improvement for software using speciation algorithms , program geometry projection , and multi-agent decision frameworks . Earlier studies (2022-2024) investigate edge-cloud systems , neural transfer learning , and ecosystem curation in emergent software. Supervision & Projects : He supervises PhD student Ben Craine and leads projects like B-EGI (Bio-Enhanced Genetic Improvement) and BBC Prosperity Partnership for media delivery. Collaborations span environmental IoT, multi-agent learning, and fog computing. Labs & Groups : Affiliated with the Lancaster Intelligent, Robotic and Autonomous Systems Centre , Centre of Excellence in Environmental Data Science , and the Distributed Systems group.

Markus BabstView profile

Professor

• Protein Trafficking
• ESCRT Complexes
• Eisosomes
• +5 more

Markus Babst is a Professor of Biological Sciences at the University of Utah, where he leads research at the Center of Cell and Genome Science. His work focuses on protein trafficking mechanisms in eukaryotic cells using Saccharomyces cerevisiae as a primary model system. His educational background includes: Diploma from Federal Institute of Technology, Switzerland Ph.D. from Federal Institute of Technology, Switzerland Dr. Babst's research centers on post-translational regulation of plasma membrane proteins through endocytosis and endosomal sorting. Key investigations include ESCRT-mediated protein sorting into multivesicular bodies for lysosomal degradation and eisosome-regulated storage of nutrient transporters. His work demonstrates how calcium signaling and proton gradients control eisosome disassembly and transporter endocytosis, revealing fundamental mechanisms of cellular stress adaptation. This research bridges membrane biophysics, metabolic regulation, and organelle dynamics. Analysis of his 15 most recent publications shows persistent thematic focus on membrane tension regulation, ESCRT complex dynamics, and nutrient transporter control. His work increasingly integrates mitochondrial metabolism with plasma membrane organization while maintaining yeast genetics as the core methodology. Recurring subfields include membrane contact sites, lipid domain organization, and stress-induced protein trafficking changes. Dr. Babst's scientific contributions are evidenced by his extensive publication record in high-impact journals, though specific awards are not documented in available sources. While student mentorship details are unavailable in the provided materials, his laboratory employs biochemical, genetic, and cell biological approaches to investigate membrane protein regulation. Grant funding specifics are not mentioned in the source text. His laboratory operates within the University of Utah's biological research ecosystem, utilizing S. cerevisiae to dissect conserved eukaryotic mechanisms. Current work emphasizes plasma membrane tension dynamics, ER-plasma membrane contact sites, and metabolic regulation of membrane protein trafficking, with implications for understanding cellular adaptation in changing environments.

Sean AnderssonView profile

Professor

• Systems and Control Theory
• Robotics
• Nanotechnology
• +12 more

Sean Andersson is a Professor in Mechanical Engineering and Systems Engineering at the College of Engineering, Boston University, and serves as Director of the BU Robotics Lab. His research bridges systems and control theory with applications in nanotechnology , atomic force microscopy , and robotics . His work in nanobioscience focuses on single molecule tracking and high-speed imaging in atomic force and fluorescence microscopy, leveraging control theory to enhance imaging capabilities. In robotics, he develops stochastic control methods for autonomous systems operating in complex environments, emphasizing multi-agent systems , sparsely sampled data , and symbolic control frameworks . Recent publications highlight trends in receding horizon control , persistent monitoring , neural style transfer for imaging , and stochastic policy optimization . The Andersson Lab also explores compressive sensing and optimal control for sensor networks and nanoscale fluid dynamics.

Lev LevitinView profile

Professor

• Information Theory
• Computer Networks
• Quantum Information
• +3 more

Lev Levitin is a Distinguished Professor in the Department of Electrical & Computer Engineering and Division of Systems Engineering at Boston University, with office PHO 332 and contact email levitin@bu.edu. His primary academic focus spans information theory, quantum communication, and computer network architecture. Education: PhD, USSR Academy of Sciences, Gorky University, 1969 Levitin's research integrates fundamental physics with information systems, emphasizing quantum measurement theory, bioinformatics, and the physical limits of computation. His work explores critical phenomena in network dynamics, virtual cut-through routing algorithms, and thermodynamic constraints in information processing. This interdisciplinary approach bridges theoretical physics, computer engineering, and complex systems analysis to address foundational questions in reliable computing. Analysis of his 15 most recent publications reveals two dominant research threads: (1) rigorous modeling of interconnection networks with emphasis on latency, saturation, and phase transitions in multidimensional topologies, and (2) quantum information theory investigations into physical limits of communication, energy requirements, and measurement constraints. The network studies consistently employ virtual cut-through routing as a core methodology, while quantum works establish fundamental bounds on information retrieval and computational speed. Scientific Awards: Life Fellow, IEEE Member, International Academy of Informatization Levitin leads the Reliable Computing Laboratory at Boston University, teaching foundational and advanced courses including Introduction to Engineering, Logic Design, Probability Theory, and specialized graduate courses in Information Theory and Discrete Mathematics. His educational contributions span undergraduate instruction through doctoral supervision, though specific student names and current grant funding details are not documented in the provided materials.

• Theoretical Chemistry
• Quantum Chemistry
• Relativistic Effects
• +7 more

Professor Kenneth Ruud is a leading expert in theoretical and computational chemistry at UiT The Arctic University of Norway. He serves as Director General of the Norwegian Defence Research Establishment and leads the Hylleraas Centre for Quantum Molecular Sciences. His research focuses on relativistic quantum chemistry, developing advanced ab initio methods for molecular property calculations, and integrating QM/MM and continuum solvent models. Education: PhD from University of Oslo (1998, supervised by Trygve Helgaker) Postdoc: University of San Diego with Peter Taylor (1998-2000) His work spans relativistic effects in molecular properties, vibronic coupling, and X-ray spectroscopy. He contributes to software development through programs like Dalton, Dirac, ReSpect, and OpenRSP. Recent publications highlight applications in spin-vibronic dynamics, heavy metal L/M-edge XAS, and topological materials. Key scientific contributions include relativistic DFT for nuclear spin-rotation constants, polarizable embedding models for vibrational spectra, and quantum dynamics frameworks. Awards recognize his impact through the Dirac Medal (2008) and multiple academy memberships. Elected to Norwegian Academy of Science and Letters Fellow of American Association for the Advancement of Science (AAAS) Foreign member of Finnish Academy of Science and Letters He actively participates in open science initiatives and serves on boards including Norges Forskningsråd and CAROS center for subsea robotics. Current projects involve quantum molecular science in extreme environments and computational protocol development.

Silvia CavagneroView profile

Professor

• Protein Folding
• Biomolecular Spectroscopy
• Neurodegenerative Diseases
• +4 more

Silvia Cavagnero is a Professor in the Department of Chemistry at the University of Wisconsin–Madison, with a research focus on protein folding and misfolding in cellular contexts. Her work integrates biomolecular spectroscopy, chemical biology, and computational methods to address fundamental questions in structural biology. B.S., First University of Rome ‘La Sapienza’ (1988) M.S., University of Arizona (1990) Ph.D., California Institute of Technology (1996) Her research explores the role of molecular chaperones like Hsp70 in protein biogenesis, the development of laser-driven NMR techniques for enhanced sensitivity, and the implications of protein aggregation in neurodegenerative diseases. Key projects include cotranslational folding studies at ribosomal exit tunnels and hyperpolarization methods for low-concentration NMR analysis. The 15 most recent publications highlight interdisciplinary advances in NMR spectroscopy optimization Protein folding kinetics Cryo-EM structural analysis Chaperone-client interactions Hsp70 antimicrobial design Hydration dynamics in folding Scientific contributions include A Prize for Going in Vivo (2017) Recognition for Diversity and Inclusion Efforts Students from the Cavagnero Group have pursued careers in academia, pharmaceutical industries, and national laboratories. Her lab emphasizes interdisciplinary training, blending physical chemistry, biology, and computational analysis.

Ilaria PerugiaView profile

Professor

• Numerical methods for PDEs
• Finite element methods
• Discontinuous Galerkin methods
• +6 more

Ilaria Perugia is a University Professor (Univ.-Prof.) and Chair of Numerics of PDEs at the Department of Mathematics, Faculty of Mathematics, University of Vienna. She also serves as Deputy Head of the Research Platform Erwin Schrödinger International Institute for Mathematics and Physics. Her research focuses on numerical methods for partial differential equations with applications in computational physics and engineering. Professor Perugia's primary research interests include: Numerical methods for PDEs Finite element methods Discontinuous Galerkin methods Trefftz methods Virtual element methods Space-time methods Computational electromagnetics Wave propagation problems Nonlinear reaction-diffusion problems Her work spans theoretical analysis, algorithm development, and practical implementation of numerical methods for solving complex physical phenomena. Her recent publications demonstrate a strong focus on space-time methods, virtual element methods, and structure-preserving discretizations for wave equations, heat equations, and other PDEs. She has made significant contributions to the development of stable and efficient numerical schemes that preserve important physical properties of the underlying continuous problems, particularly in the context of wave propagation and computational electromagnetics. Professor Perugia leads a research group comprising several researchers and students including Mattia Corti, Matteo Ferrari, Monica Nonino, Andrea Scaglioni, Paul Stocker, Enrico Zampa, and Marco Zank. Her group actively collaborates on projects related to numerical analysis and scientific computing, with particular emphasis on developing novel discretization techniques for challenging PDE problems.

Jamie MorgensternView profile

Associate Professor

• Machine Learning
• Ethics & Fairness
• Human-Centered AI
• +3 more

Jamie Morgenstern is an Associate Professor at the Paul G. Allen School of Computer Science & Engineering , University of Washington . She was previously an assistant professor at Georgia Tech and a Warren Center Fellow at University of Pennsylvania . Expertise: Ethics & Fairness, Human-Centered AI, Machine Learning Education: PhD in Computer Science from Carnegie Mellon University (2015) Her research examines the social impact of machine learning and ensuring ML models do not exacerbate societal inequalities. She investigates robustness to human-generated training data, fairness in clustering and active learning, and algorithmic equity in recommendation systems. Recent publications focus on interactive ML systems , fairness constraints , and privacy-preserving methods across conferences like NeurIPS, ICML, and AIES. Key subfields include multimodal learning , membership inference attacks , and data equity . Scientific Awards: NSF Career award for "Strategic and Equity Considerations in ML" Simons collaboration project Simons Award for Graduate Students in Theoretical Computer Science (2014-2016) NSF GFRP fellowship Microsoft Research Graduate Women's Scholarship Spotlight presentation at NeurIPS 2015 Mentoring: She advises current PhD students Rachel Hong , Jie (Claire) Zhang , and Yuanyuan (Chloe) Yang . Former advisees include Daniel Jiang (MS), Bhuvesh Kumar (PhD), and Angel (Alex) Cabrera (BS). Grants: Funded by NSF Career award and Simons collaboration projects. Previously supported by Simons, NSF, and Microsoft Research fellowships. Labs & Collaborations: Collaborates with researchers like Michael Kearns , Aaron Roth , and Avrim Blum . Affiliated with the Allen School's Artificial Intelligence research group.

Seth FreyView profile

Associate Professor

• Computational Social Science
• Governance of Commons
• Complex Systems
• +5 more

Seth Frey is an Associate Professor in the Department of Communication at the University of California, Davis, with affiliate status at Indiana University's Ostrom Workshop and as Research Director at Metagov. His research focuses on computational social science approaches to understanding self-governance in complex social systems, particularly through the lens of online communities as model institutions. Education: Ph.D. in Cognitive Science and Informatics (complex systems), Indiana University, 2013 B.A. in Cognitive Science, UC Berkeley, 2004 Research Interests: Frey specializes in computational approaches to institutional analysis and the cognitive science of strategic behavior . His work examines how communities design governance systems to overcome collective action problems, with emphasis on: Emergent institutional structures in digital commons Policy-as-data through NLP and institutional grammar frameworks Cognitive mechanisms underlying cooperative behavior Design principles for participatory change in online platforms His methodology integrates large-scale data analysis, web-based experiments, and computational modeling across diverse contexts including Minecraft, Reddit, and professional sports ecosystems. Publication Trends: Recent publications (2023-2025) demonstrate a cohesive trajectory toward computational institutional analysis, with increasing focus on NLP-driven policy analysis (e.g., NLP4Gov), decentralized governance architectures (DAOs, multi-level platform governance), and the cognitive foundations of collective action. His work consistently bridges theoretical institutional analysis with practical applications in digital community design, showing particular growth in translating Ostrom's design principles into computational frameworks. Awards: Honorable Mention Award for Best Paper at ACM CSCW 2019 Advising and Grants: Frey mentors students interested in data science applications at the intersection of communication, cognition, and complex systems, emphasizing resourcefulness and intellectual curiosity. His research has secured substantial funding from: National Science Foundation (NSF) NASA Ford Foundation Google Open Source Foundation He actively encourages aspiring graduate students with strong self-directed research skills to explore computational approaches to social phenomena. Labs and Teams: He leads the Computational Communication Lab at UC Davis and co-directs the Institutional Grammar Research Initiative. Through Metagov, he develops the 'Governance API' framework for modular community governance. His past affiliations include Disney Research (Walt Disney Imagineering) where he applied complexity science to theme park systems, and the New England Complex Systems Institute (NECSI). Current collaborations span Ethereum governance, Minecraft server ecosystems, and Colorado's cannabis monitoring infrastructure.