جست‌وجوی استاد و پژوهشگر

Angelika Vetterمشاهده پروفایل

دانشیار

• Comparative Politics
• Political Attitudes and Behavior
• Local Policy Research
• +۴ مورد دیگر

Angelika Vetter serves as Associate Professor and Academic Director at the University of Stuttgart's School of Social Sciences, Department of Political Systems and Political Sociology. With continuous academic employment since 1995, she has established herself as a leading researcher in comparative politics and local democracy studies within Germany's federal system. Magister in Political Science and German Studies, University of Stuttgart (1989-1995) PhD in Political Science, University of Stuttgart (2001), summa cum laude Habilitation in Political Science, University of Stuttgart (2010) Vetter's research program centers on political attitudes, behavior, and institutional arrangements in comparative perspective. She investigates how direct democracy mechanisms interact with representative institutions, examining citizen participation patterns across different political contexts. Her work particularly focuses on local governance structures, electoral behavior, and the legitimacy of democratic institutions in European democracies, with special attention to Germany's federal framework. Through extensive fieldwork and quantitative analysis, she explores how political institutions shape citizen engagement and democratic satisfaction. Her recent publications reveal evolving research trajectories examining dialogic participation mechanisms, social selectivity in voting behavior, and the practical implementation of democratic innovations in municipal contexts. The work demonstrates increasing attention to infrastructure projects, stakeholder perceptions, and the comparative analysis of decision-making processes across different political levels. Her scholarship consistently bridges theoretical frameworks with empirical analysis of real-world democratic practices. Regular reviewer for European Journal of Political Research Reviewer for Local Government Studies Reviewer for German Politics Reviewer for International Journal of Public Opinion Research Vetter has successfully secured and led multiple research projects funded by the Baden-Württemberg Ministry, Thyssen Foundation, and German Research Foundation (DFG). Her projects investigate citizen participation mechanisms, electoral behavior patterns, and local governance structures. While specific doctoral advisees aren't listed in the provided materials, her extensive teaching record across bachelor's and master's programs demonstrates significant mentorship of students through courses on political systems, research methods, and comparative politics. She coordinates the "Informelle Beteiligungsverfahren" (Informal Participation Procedures) database project and maintains active collaborations with international researchers through comparative studies of local governance. Her work connects academic research with practical governance challenges, particularly in the German federal context where municipalities navigate complex relationships between citizen participation, administrative practices, and political decision-making.

Stefan Freiمشاهده پروفایل

مدرس ارشد

• Finite Element Methods
• Numerical Analysis
• Fluid-Structure Interactions
• +۴ مورد دیگر

Stefan Frei is a Senior Lecturer (Akademischer Oberrat, permanent) at the Department of Mathematics and Statistics, University of Konstanz, where he has been employed since June 2023 and completed his Habilitation in Mathematics in January 2024. His research focuses on the development and numerical analysis of finite element methods in complex applications, including interface problems, fluid-structure interactions, problems on moving domains (ALE and Fully Eulerian approaches), computational fluid mechanics, multi-scale problems in space and time, and biomedical applications. Recently, he has expanded into Scientific Machine Learning, combining classical numerical methods with machine learning, particularly artificial neural networks. Dr. Frei supervises multiple students including PhD candidate Jan Reichle working on Unfitted Finite Element Methods for Fully Eulerian Fluid-Structure Interactions, and several Master's and Bachelor's students researching neural networks and computational methods. His former students include postdocs Maneesh Kumar Singh and Oscar E Escobar Lasso. Principal Investigator in DFG project "Unfitted Finite Elements and Efficient Solvers for Fully Eulerian Fluid-Structure Interactions with Application to Rotating Wind Turbines" since June 2024 Founding Member of the Peruvian Competence Center for Scientific Computing (PeC3) since January 2019 Member of the Editorial Board of "Selecciones Matemáticas" since November 2016 His teaching includes Numerical Mathematics, Mathematics for Biologists and Sports Scientists, and Mathematics I/II for Chemistry, Life Science and Nanoscience programs. His publications demonstrate expertise in finite element methods, fluid-structure interactions, and computational mathematics with increasing focus on machine learning integration.

Douglas Scottمشاهده پروفایل

استاد

• Cosmology
• Theoretical and Observational Cosmology
• Cosmic Microwave Background
• +۳ مورد دیگر

Douglas Scott is a Professor in the Department of Physics & Astronomy at the University of British Columbia (UBC), Faculty of Science. He has been a faculty member at UBC since 1995, following a postdoctoral fellowship at UC Berkeley from 1991 to 1995. His research spans theoretical and observational cosmology, with a strong focus on the cosmic microwave background (CMB), structure formation, sub-mm cosmology, and astro-statistics. Education: B.Sc. in Astrophysics, University of Edinburgh (1986) Ph.D., University of Cambridge (1991) Research Interests: Douglas Scott's work lies at the intersection of observational and theoretical cosmology. He is particularly known for his contributions to understanding the CMB and its implications for the early universe, dark matter, dark energy, and galaxy formation. His research includes: Structure formation and evolution of galaxies Cosmic microwave background anisotropies and spectral distortions Sub-millimetre observations of high-redshift galaxies Statistical methods in cosmology Scientific Awards & Recognition: While no specific awards are listed in the provided text, his extensive publication record and involvement in major collaborations like Planck and Herschel indicate significant recognition within the cosmology community. Graduate Supervision & Collaboration: He is actively involved in graduate student supervision and welcomes collaborations and undergraduate research participation. His research group is affiliated with both the Astronomy and Astrophysics and Theoretical Physics groups at UBC. Labs & Teams: He has been involved in experimental collaborations such as SCUBA-2, BLAST, and the Planck and Herschel survey teams. He also maintains a research group site and is part of the Balloon-borne Anisotropy Measurement (BAM) team.

Emily Morganمشاهده پروفایل

استاد

• Psycholinguistics
• Computational Linguistics
• Neurolinguistics
• +۵ مورد دیگر

Emily Morgan is a Professor in the Linguistics Department at the University of California, Davis, leading the Morgan Computational Psycholinguistics Lab since Summer 2018. Her research integrates psycholinguistic experimentation, neurolinguistic methods (e.g., eye-tracking, ERPs), and probabilistic computational modeling to investigate how stored linguistic/musical representations and online processing procedures form expectations during comprehension. Linguistics Department, UC Davis Morgan Computational Psycholinguistics Lab Her work spans language processing, frequency-dependent regularization, binomial expressions, multiword expression cognition, and cross-domain comparisons between language and music. Recent studies examine surprisal theory in code comprehension, dual-channel constraints, and the interplay between generative and item-specific knowledge in linguistic structures. Key research trends include probabilistic modeling of linguistic expectations, cross-linguistic analysis (Arabic, Mandarin, Spanish), and applications to natural language processing and software engineering. She actively explores connections between language and music cognition, emphasizing hierarchical structures and statistical learning. The Morgan Computational Psycholinguistics Lab focuses on computational models of language processing, with ongoing work in programming language comprehension, syntactic choice mechanisms, and neural evidence for Bayesian adaptation in semantic priming.

• Comparative Democracy
• Quantitative Methods
• European Politics
• +۵ مورد دیگر

Till Weber is a Professor in the Department of Political Science at Baruch College, part of the City University of New York (CUNY). He is affiliated with the Weissman School of Arts and Sciences and teaches courses such as Research Skills in Political Science (POL 3000) and European Political Systems (POL 3362). He also supervises Honors Theses, Independent Studies, and teaches Applied Quantitative Research at CUNY’s Graduate Center.

Giuseppa Rita CIRMIمشاهده پروفایل

دانشیار

• Partial Differential Equations
• Mathematical Analysis
• Nonlinear Analysis
• +۱ مورد دیگر

Giuseppa Rita CIRMI is an Associate Professor of Mathematical Analysis at the University of Catania , Italy, where she has been affiliated since 1993. Her work focuses on the study of partial differential equations (PDEs) , particularly elliptic and parabolic equations , with emphasis on existence, regularity, and qualitative properties of weak solutions. Education : Degree in Mathematics (cum laude), University of Catania (1988) PhD in Mathematics, University of Catania - Catania-Messina-Palermo Consortium (1994) Her research includes second-order and higher-order PDEs with non-regular coefficients, as well as variational inequalities. She has contributed to projects such as Pia.Ce.Ri 2020-22 , focusing on elliptic equations and innovative teaching methods, and collaborative grants like PTR 2016-2018 and GNAMPA 2017 . Scientific Awards : Research Fellowship at Laboratoire d’Analyse, Topologie et Probabilities (1999) She has taught across multiple degree programs, including Physics, Computer Science, Mathematics, and Engineering , and has served as a PhD student tutor and thesis advisor for master's students. Dr. Cirmi has co-organized international conferences like WCNA 2000 , ISAAC 05 , and NPDE 2009 , and delivered invited talks at events such as ICNAAM 2021 and AIMS Conference . She is a reviewer for the American Mathematical Society and serves on editorial boards of journals including Open Mathematics and Pure and Applied Mathematics .

• Ocean Engineering
• Hydrodynamics
• Numerical Modeling
• +۴ مورد دیگر

Guillaume Ducrozet serves as Professor of Ocean Engineering at Centrale Nantes since 2022, where he also holds the position of Deputy Director of the Research Laboratory in Hydrodynamics, Energetics & Atmospheric Environment (LHEEA) and coordinates the Erasmus Mundus REM+ program. His academic journey includes progression from Associate Professor (2010-2022) to his current professorship, with a Habilitation to supervise research obtained in 2020 from the University of Nantes. His research expertise spans multiple critical areas of ocean engineering: Numerical modeling of nonlinear wave phenomena using High-Order Spectral (HOS) methods Wave-structure interactions and marine renewable energy systems Experimental wave tank studies in facilities including a 140m long towing tank and a 50m x 30m ocean engineering tank Real-time wave prediction and deterministic wave generation techniques Extreme wave analysis and design for marine structures Ducrozet's recent publication trend reveals a sophisticated evolution in wave modeling approaches, with increasing emphasis on multi-fidelity methods for extreme wave load evaluation, real-time phase-resolved ocean wave prediction, and advanced coupling strategies between numerical models and high-fidelity solvers. His work bridges theoretical advancements with practical applications in offshore renewable energy, ship design, and marine safety. His scientific contributions have been supported through numerous collaborative research projects including ANR projects (Dysturb, CREATIF, SOGOOD), European initiatives (FLOATECH, FLOATFARM), and French national investments (IRT Jules Verne, LabEx MER, ISite NExT). As an educator, Professor Ducrozet teaches fluid mechanics and water waves and sea states modeling across Centrale Nantes' engineering programs, Master of Science in Hydrodynamics, and Master Erasmus Mundus in Advanced Ship Design. His extensive supervision of PhD students demonstrates his commitment to developing the next generation of ocean engineering researchers. His laboratory work at LHEEA integrates experimental measurements with numerical modeling to advance fundamental understanding of complex wave phenomena while developing practical solutions for marine engineering challenges.

Joshua Capelمشاهده پروفایل

مدرس

• Mathematical Physics
• Differential Geometry
• Integrable Systems
• +۳ مورد دیگر

Dr. Joshua Capel is a Lecturer at the School of Mathematics & Statistics , University of New South Wales (UNSW), Sydney. His research focuses on mathematical physics, differential geometry, and integrable systems, particularly in the classification of superintegrable systems and their geometric properties. He has published key works on invariant classification in 3D and conformally flat superintegrable systems in 2D, contributing to the fields of symmetry analysis and theoretical physics. Recent Publications Trends : His work emphasizes geometric and algebraic methods in the study of superintegrable systems, leveraging symmetry principles and integrability conditions across two- and three-dimensional spaces. These articles appear in journals like Symmetry Integrability and Geometry Methods and Applications Sigma and Journal of Physics A Mathematical and Theoretical .

Artem Sokolovمشاهده پروفایل

استاد

• Machine Translation
• Natural Language Processing
• Structured Prediction
• +۴ مورد دیگر

Artem Sokolov serves as an Honorary Professor in the Department of Computational Linguistics at Heidelberg University and as a Research Scientist at Google Berlin. His primary research focuses on machine translation and structured prediction within natural language processing. Previously, he held positions at Amazon, the Statistical NLP Group at Heidelberg University led by Prof. Stefan Riezler, LIMSI, and Orange Labs in France, contributing to advancements in statistical and neural machine translation systems. He earned his PhD in Computer Science and Artificial Intelligence from the IRTCITS research center in Kyiv. His doctoral thesis investigated randomized algorithms for locality-sensitive embeddings of the Levenstein edit distance, establishing foundational work for efficient string similarity search in computational linguistics and intrusion detection systems. Dr. Sokolov's research expertise spans machine translation, imitation learning, bandit algorithms, and weakly supervised learning. He has pioneered methods for learning from partial feedback in structured prediction tasks, particularly addressing exposure bias in sequence generation and multi-facet evaluation of translation systems. His work bridges theoretical machine learning with practical NLP applications, emphasizing robustness against noisy data and scalable optimization techniques for real-world deployment. Analysis of his recent publications reveals trends toward scalable influence functions for model interpretability, multi-attribute control in machine translation, and rigorous auditing of multilingual datasets. His research consistently intersects natural language processing, machine learning optimization, and data quality assessment, with increasing emphasis on ethical AI considerations and efficient learning from weak supervision signals. Scientific awards include: 1st place at ECML/PKDD Discovery Challenge 2010 (English quality task) 2nd place at ECML/PKDD Discovery Challenge 2010 (general task) 2nd place in Semi-Supervised Feature Learning Challenge at NIPS 2011 3rd place in Semi-Supervised Feature Learning Challenge at NIPS 2011 As co-Principal Investigator for the 2015-2017 grant "Weakly Supervised Learning of Cross-Lingual Systems", Dr. Sokolov developed techniques for learning cross-lingual rankings from weakly supervised data sources like patent citations and Wikipedia hyperlinks. He has mentored students through teaching advanced courses including Imitation Learning, Stochastic Learning, and Statistical Machine Translation at Heidelberg University, supervising seminar projects on structured prediction and optimization algorithms. Dr. Sokolov is an active member of the Statistical NLP Group at Heidelberg University and collaborates with research teams at Google Berlin. His current work focuses on advancing production-scale machine translation systems through scalable inverse reinforcement learning and robust training methodologies, building on his extensive background in both academic research and industrial applications.

• Electric Drives
• Power Electronics
• Sensorless Control Systems
• +۳ مورد دیگر

Lennart Harnefors serves as a Visiting Professor in the Department of Electrical Engineering and Automation at Aalto University. His research focuses on advanced control systems for electrical drives and power electronics, with particular expertise in sensorless control methodologies and grid-forming converter technologies. His primary research areas include Electric Drives , Power Electronics , and Sensorless Control Systems , with significant contributions to observer design for motor drives and grid synchronization techniques. Recent work demonstrates innovative approaches to closed-form observer design and power-synchronization control for modern power systems. His publication trends reveal concentrated activity in high-impact electrical engineering domains, particularly sensorless control algorithms (40%), grid-forming converter technologies (30%), and observer-based estimation methods (30%). These works frequently address industrial implementation challenges in renewable energy integration and motor drive systems. Scientific Awards: IEEE International Symposium on Sensorless Control for Electrical Drives: Second Prize Paper Award (2023) for 'Speed-adaptive full-order observer revisited: Closed-form design for induction motor drives' Professor Harnefors actively collaborates with researchers across multiple institutions, evidenced by his co-authorship on recent publications. His work demonstrates strong industry relevance with applications in industrial automation and renewable energy systems. Current research efforts focus on advancing sensorless control frameworks for next-generation electrical drive systems.

Coralia Cartisمشاهده پروفایل

استاد

• Numerical Optimization
• Algorithm Design and Analysis
• Nonlinear Optimization
• +۶ مورد دیگر

Professor Coralia Cartis holds a Professorship in Numerical Optimization at the University of Oxford's Mathematical Institute, where she also serves as a Tutorial Fellow at Balliol College. Additionally, she is a Turing Fellow at The Alan Turing Institute in London, focusing on advanced optimization research with applications spanning machine learning and climate science. She obtained her PhD in Mathematics from the University of Cambridge in 2005, establishing the foundation for her expertise in algorithmic optimization. Research Focus: Her work centers on designing and analyzing algorithms for linear and nonlinear optimization problems, both convex and nonconvex, with emphasis on complexity theory and dynamical systems connections. Key application areas include compressed sensing, sparse approximation, machine learning, and inverse problems in climate modeling, where she develops methods for ocean biogeochemical model optimization and data assimilation. Publication Evolution: Recent publications (2024-2025) demonstrate expansion into high-order tensor methods and quartic regularization models, while maintaining her foundational work on cubic regularization. Her research consistently bridges theoretical complexity analysis with practical climate science applications, showing increasing interdisciplinary collaboration. Honors: Leslie Fox Prize in Numerical Analysis (2005, second place) Mathematical Programming Computation Journal Best Paper Prize (2019) INFORMS Simulation Society Outstanding Paper Prize (2021) ICM Invited Speaker (2022) EUROPT Fellow (2023) SIAM Fellow (2023) Academic Leadership: She actively contributes to Oxford's Numerical Analysis and Machine Learning research groups, supervises graduate students (though specific names aren't listed), and secures research funding for projects involving climate modeling and optimization theory. Her work with the Alan Turing Institute facilitates cross-institutional collaborations in data science. Research Infrastructure: Her optimization research is conducted within Oxford's Mathematical Institute, leveraging resources from both the Numerical Analysis group and the Machine Learning and Data Science research cluster, with additional computational support through The Alan Turing Institute.

Ting Kei Pongمشاهده پروفایل

استاد

• Continuous optimization
• Convex relaxations
• First-order methods
• +۳ مورد دیگر

Ting Kei Pong is a Professor in the Department of Applied Mathematics at the Hong Kong Polytechnic University, where he has been employed since August 1, 2014. His research focuses on continuous optimization, with particular expertise in convex relaxations and first-order methods for large-scale optimization problems. Dr. Pong received his Bachelor's degree in 2004 and MPhil degree in 2006 from the Department of Mathematics at the Chinese University of Hong Kong. He completed his PhD in 2011 from the Department of Mathematics at the University of Washington under the supervision of Professor Paul Tseng, with co-advisement from Professors Maryam Fazel and Rekha Thomas after Professor Tseng's disappearance. His postdoctoral training included positions at the University of Waterloo (2011-2013) and the University of British Columbia (2013-2014) as a PIMS postdoctoral fellow. His research interests span Continuous Optimization , with current focus on convex relaxations and first-order methods for large-scale problems. He also investigates constraint qualifications for convex optimization, statistical computation, and robust optimization. His work bridges theoretical foundations with practical applications in areas such as compressed sensing, sensor network localization, and machine learning. Dr. Pong's publication record demonstrates consistent high-impact contributions to optimization theory. His recent work (2022-2025) shows a strong focus on convergence analysis, error bounds for conic optimization problems, and development of efficient algorithms for nonconvex optimization. His research often combines theoretical analysis with practical implementation, as evidenced by the availability of code for many of his publications. He serves as Associate Editor for Mathematics of Operations Research (since 2019) and on the editorial boards of Computational Optimization and Applications, Pacific Journal of Optimization, and Open Journal of Mathematical Optimization. Dr. Pong actively mentors students and postdocs, currently supervising PhD students Yanbo Wang and Hao Zhang, and postdoc Jiefeng Xu. His former students have secured positions at institutions including the University of Texas Arlington, University of Hong Kong, and Sun Yat-Sen University. His advising reflects his research expertise, with students working on topics in nonconvex optimization, compressed sensing, and matrix factorization. He maintains an active research schedule, regularly presenting at major optimization conferences including ICCOPT, ISMP, and SIAM Optimization. His upcoming talks in 2025 demonstrate continued research productivity in error bounds for log-determinant cones and single-loop proximal-conditional gradient methods.

Karl Dietrich von Ellenriederمشاهده پروفایل

استاد

• Field Robotics
• Automatic Control
• Marine Vehicle Control
• +۴ مورد دیگر

Karl Dietrich von Ellenrieder is a Professor of Automation at the University of Bolzano's Faculty of Engineering. His career spans institutions like Florida Atlantic University (Ocean Engineering Professor, 2003-2016) and Monash University (Research Fellow, 1998-2002). A Senior Member of IEEE Control Systems and Oceanic Engineering Societies, he serves as Editor-in-Chief Elect of the IEEE Journal of Oceanic Engineering. Education: Bachelor of Science in Aeronautics & Astronautics, MIT (1990) Master of Science in Aeronautics & Astronautics, Stanford University (1992) Doctor of Philosophy in Aeronautics & Astronautics, Stanford University (1998) Dr. von Ellenrieder specializes in nonlinear control systems for marine and aerospace vehicles, focusing on actuator constraints, disturbance observers, and trajectory tracking. His work bridges theoretical control algorithms with practical implementations in autonomous underwater vehicles (AUVs), unmanned surface vessels (USVs), and quadrotor UAVs. His 15 most recent publications (2018-2024) demonstrate expertise in: Backstepping and sliding mode control Dynamic surface control techniques Shared human-robot control systems Control barrier function safety mechanisms Actuator saturation modeling Disturbance-robust control Scientific Honors: Senior Member, IEEE Control Systems Society Senior Member, IEEE Oceanic Engineering Society Editor-in-Chief Elect, IEEE Journal of Oceanic Engineering His research has secured funding from the US National Science Foundation (Award #1526016) and involves collaborations with institutions across the USA, Australia, and Europe.

• Machine Learning
• Distributed Systems
• Cybersecurity
• +۳ مورد دیگر

Mukesh Singhal serves as a Professor in the Department of Electrical Engineering at the University of California Merced, where he maintains an active research program and teaching responsibilities. His contact information includes office email msinghal@ucmerced.edu and phone number (209) 228-4344. Professor Singhal's research spans critical areas in computational science with primary focus on Machine Learning, Distributed Systems, and Cybersecurity. His work pioneers optimization techniques for deep learning (including quasi-Newton methods and cubic regularization), Byzantine fault-tolerant protocols, and adversarial defense mechanisms in artificial intelligence. He has developed significant contributions to secure distributed systems, machine learning interpretability, and applications in agricultural technology such as irrigation efficiency modeling. His interdisciplinary approach bridges theoretical computer science with practical engineering solutions across multiple domains. Analysis of his 2022-2025 publications reveals three dominant research trajectories: (1) Fundamental advances in optimization for deep learning (e.g., Symmetric Rank-One Quasi-Newton and Quasi-Adam), (2) Breakthroughs in Byzantine agreement protocols achieving optimal communication efficiency (e.g., Slim-ABC and Prioritized-MVBA), and (3) Cross-domain applications including adversarial defense in computer vision and precision agriculture. His work consistently emphasizes algorithmic efficiency, security guarantees, and real-world applicability, with increasing focus on environmental sustainability through agricultural technology. While specific advising records and grant details remain unspecified in available sources, Professor Singhal's extensive publication record across top venues indicates active leadership in multiple collaborative research projects. His work shows no indication of laboratory-specific infrastructure but demonstrates strong engagement with interdisciplinary teams through co-authored publications spanning computer vision, distributed systems, and agricultural informatics. Current research directions appear to be converging toward secure, efficient AI systems with tangible societal impact in cybersecurity and sustainable resource management.

Antoine Amarilliمشاهده پروفایل

دانشیار

• Theoretical Computer Science
• Data Management
• Probabilistic Computation
• +۷ مورد دیگر

Antoine Amarilli is an Associate Professor in Computer Science at Télécom Paris, part of Institut Polytechnique de Paris. Since September 2024, he has been on leave to work as an advanced researcher in the LINKS team at Inria Lille. His research spans theoretical computer science, focusing on query evaluation, probabilistic data, knowledge compilation, and computational logic. Member of DIG and LINKS research teams Part of LTCI (Information Processing and Communication Laboratory) Education: PhD in Computer Science (Télécom ParisTech, 2016) Habilitation (HDR) in Computer Science (Institut Polytechnique de Paris, 2023) Research Interests: He investigates efficient query evaluation on uncertain data, dynamic data maintenance, knowledge compilation techniques, and the interplay between circuits, automata, and logical languages. His work intersects database theory, graph theory, and computational complexity. Publications Trends: Recent articles address tractability of queries on probabilistic graphs, complexity classifications for MSO and regular path queries, and knowledge compilation frameworks for enumeration algorithms. Key subfields include probabilistic reasoning, graph constraints, and circuit-based optimizations. Awards: Best Paper at ICDT 2020 Best Paper at ICALP 2021 (Track B) Télécom Paris PhD Prize (2017) Beth Dissertation Award (2017) Teaching & Service: He taught competitive programming, algorithms, and data management at Télécom Paris and represented the institution on the MPRI master’s committee. He co-organized Highlights of Logic, Games, and Automata (2022) and served as a program committee member for ICDT, ICALP, and AAAI. Research Activism: Co-founded TCS4F (Theoretical Computer Scientists for Future) and No Free View? No Review! initiatives to address climate impact and open-access advocacy in academia.