جست‌وجوی استاد و پژوهشگر

Nicholas Evansمشاهده پروفایل

استاد

• Endangered Languages
• Linguistic Typology
• Historical and Contact Linguistics
• +۵ مورد دیگر

Nicholas Evans is Distinguished Professor of Linguistics and Director of the ARC Centre of Excellence for the Dynamics of Language (CoEDL) at the Australian National University’s School of Culture, History & Language. His work bridges fieldwork-based language documentation with theoretical questions in typology, cultural evolution, and social cognition. Focus on endangered Australian and Papuan languages Director of ARC Laureate Project on 'The Wellsprings of Linguistic Diversity' Co-leader of SCOPIC (Social Cognition Parallax Corpus) study Collaborator in global linguistic diversity initiatives His research explores how micro-level community multilingualism shapes macro-level linguistic diversity, with fieldwork spanning seven years in remote Indigenous communities. Recent projects include PARABANK (paradigm syncretism analysis) and Southern New Guinea language studies, particularly Nen and Yam family languages. Scientific recognition includes the Ken Hale Award (Linguistic Society of America), Anneliese Maier Forschungspreis, and fellowships in the Australian Academy of Humanities, Australian Social Sciences Academy, and the British Academy.

Mark Schmidtمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Mathematical Optimization
• +۶ مورد دیگر

Mark Schmidt is a Professor in the Department of Computer Science at the University of British Columbia, Faculty of Science. He holds the Canada Research Chair in Large-Scale Machine Learning and is a Canada CIFAR AI Chair at the Alberta Machine Intelligence Institute. His research spans multiple centers including CAIDA (Centre for Artificial Intelligence Decision-making and Action), the Data Science Institute, and the Machine Intelligence Learning Discovery (MILD) group. Dr. Schmidt's educational background includes a Ph.D. from UBC (2005-2010), an M.Sc. from the University of Alberta (2003-2005), and a B.Sc. from the University of Alberta (2000-2003). His academic career progressed from Postdoc positions at Simon Fraser University, Ecole Normale Superieure, and UBC to Assistant Professor (2014-2019), Associate Professor (2019-2024), and current Professor (2024-present) at UBC. His research focuses on machine learning optimization, with particular emphasis on improving numerical algorithms for large-scale machine learning applications. His work bridges theoretical optimization and practical applications across computer vision, natural language processing, and reinforcement learning. He has developed numerous optimization techniques including variants of stochastic gradient methods, coordinate descent algorithms, and natural gradient approaches. Analysis of his recent publications reveals a strong focus on optimization for over-parameterized models, particularly transformers and large language models. His work addresses critical challenges in step-size selection, convergence guarantees, and efficient implementation of optimization algorithms. His research has significant implications for training deep neural networks more effectively and understanding why certain optimization methods outperform others in practice. Among his notable awards are the Dorothy Killam Fellowship (2025), Arthur B. McDonald Fellowship (2024), Sloan Research Fellowship (2017), and multiple UBC teaching awards. He has also received the Lagrange Prize in Continuous Optimization and Best Paper Award at AISTATS 2021. Dr. Schmidt actively supervises numerous graduate students, with over 40 PhD and Master's students listed as current or alumni members of his research group. His laboratory maintains strong connections with industry partners, with many alumni securing positions at leading AI companies including Google, Amazon, Meta, and Microsoft.

Moises A. Bernalمشاهده پروفایل

استادیار

• Fish Ecology
• Evolutionary Biology
• Marine Biology
• +۷ مورد دیگر

Moises A. Bernal is an Assistant Professor in the Department of Biological Sciences at Auburn University, where he leads the Fish Ecology and Evolution Lab. His research integrates ecological, evolutionary, and molecular approaches to study how marine fishes respond to environmental stressors such as climate change and ocean warming. Education: Postdoctoral Fellow – State University of New York at Buffalo (2018–2019) Postdoctoral Fellow – King Abdullah University of Science and Technology (2016–2018) Ph.D. in Marine Science – University of Texas, Austin (2009–2015) B.Sc. in Biology and Environmental Sciences – University of Panama (2003–2008) His research focuses on the metabolic, physiological, and epigenetic mechanisms underlying fish acclimation to climate change. He investigates transgenerational responses, thermal tolerance, and the role of environmental history in shaping population resilience. His work combines field studies with molecular tools such as transcriptomics and genomics. The recent publications highlight a strong trend in studying coral reef fishes under climate stress, particularly through molecular and genomic lenses. Key themes include thermal acclimation, gene expression changes during heatwaves, and evolutionary divergence in sympatric species. His work bridges organismal physiology with evolutionary genomics. He teaches courses such as BIOL 3040 (Biology of Marine Systems) and BIOL 5440/6440 (Climate Change Physiology and Evolution) at both undergraduate and graduate levels. He advises students in his lab and mentors research on fish responses to environmental change. The Fish Ecology and Evolution Lab conducts integrative research on marine fishes, focusing on stress response mechanisms and evolutionary adaptations. Lab activities include molecular analyses, experimental physiology, and fieldwork related to coral reef ecosystems.

• Reinforcement Learning
• Multi-Agent Systems
• Deep Learning
• +۲ مورد دیگر

Shimon Whiteson is Professor of Computer Science at the University of Oxford, leading the Whiteson Research Lab focused on reinforcement learning, multi-agent systems, and deep learning. His research develops algorithms for efficient learning in complex environments. Current work explores meta-reinforcement learning frameworks that enable agents to rapidly adapt to new tasks, with applications in autonomous driving simulation and robotics. Recent innovations include novel methods for offline reinforcement learning, multi-agent coordination, and morphology-aware control. Publications demonstrate advances in: Meta-RL algorithm design for few-shot adaptation Multi-agent reinforcement learning environments and benchmarks Imitation learning in autonomous driving Bayesian methods for sample-efficient learning Research outputs include widely used benchmarks and tools including JaxMARL for accelerated multi-agent RL research. Current doctoral supervision focuses on temporal abstraction in RL, multi-agent coordination, and reinforcement learning theory.

Trevor Campbellمشاهده پروفایل

دانشیار

• Bayesian Inference
• Bayesian Nonparametrics
• Machine Learning
• +۵ مورد دیگر

Trevor Campbell is an Associate Professor in the Department of Statistics at the University of British Columbia (UBC), Vancouver. He holds a Ph.D. in Machine Learning and Statistics from MIT and a B.A.Sc. in Aerospace Engineering from the University of Toronto. His research focuses on automated, scalable Bayesian inference algorithms, Bayesian nonparametrics, and streaming data analysis. Campbell is a core contributor to probabilistic programming tools like Pigeons.jl and has developed influential methods for coreset-based Bayesian inference. Education : Ph.D. in Machine Learning and Statistics (2016), MIT M.S. in Aeronautics and Astronautics (2013), MIT B.A.Sc. in Aerospace Engineering (2011), University of Toronto Research Interests : His work emphasizes scalable Bayesian computation, including variational inference, MCMC optimization, and coresets. He develops algorithms that balance statistical accuracy with computational efficiency, particularly for large-scale datasets. His recent work explores adaptive samplers (e.g., AutoStep, autoMALA) and theoretical guarantees for coreset methods. Applications span astrophysics, materials science, and network analysis. Awards & Grants : NSERC Discovery Grant (2025) Blackwell-Rosenbluth Award (2021) PIMS Early Career Award (2023) Google Perception Academic Funding (2020) Advising & Labs : He supervises a team of PhD and M.Sc. students at UBC, focusing on Bayesian methodology and computational tools. His lab collaborates with institutions like SFU and MIT on projects involving distributed sampling and probabilistic modeling. He co-organizes workshops on Bayesian computation and serves on editorial boards for Bayesian Analysis and TMLR.

Takuya Oouraمشاهده پروفایل

استادیار

• Numerical Analysis
• Numerical Integration
• Algorithms
• +۳ مورد دیگر

Takuya Ooura is an Assistant Professor at the Research Institute for Mathematical Sciences (RIMS) at Kyoto University, specializing in numerical analysis and mathematical software development. His work bridges theoretical mathematics with practical applications in scientific computing and software development. Dr. Ooura earned his educational credentials through a rigorous academic path: he graduated from Hokuriku High School in 1987; completed his undergraduate studies at Nagoya University's School of Science in 1992; earned his Master of Engineering from the Department of Applied Physics at the University of Tokyo in 1994; and completed his PhD (Engineering) from the same department in 1997. His academic journey continued with a Research Fellowship from the Japan Society for the Promotion of Science (1997) followed by a position as Research Associate at RIMS, Kyoto University (2000). Dr. Ooura's research focuses on numerical integration algorithms, particularly his groundbreaking double exponential formula for Fourier-type integrals, which has been incorporated into Mathematica's NIntegrate function. He has also developed a high-speed FFT library that's utilized in Google Chrome browser (visible in chrome://credits). His work on continuous Euler transformation for accelerating convergence of slowly decaying integrals represents significant innovation in numerical analysis. His research spans both theoretical development and practical implementation of mathematical algorithms with real-world applications. His publication record demonstrates consistent contributions to numerical analysis, with particular emphasis on quadrature methods, integral transforms, and high-precision computation. His work shows a clear progression from theoretical foundations to practical implementations, with several algorithms achieving widespread adoption in commercial and open-source software. Paper prize awarded by JSIAM (2000) for 'A continuous Euler transformation and its application to Fourier transforms of slowly decaying functions' Paper prize awarded by JSIAM (2001) for 'Improvement of the PI Calculation Algorithm and Implementation of Fast Multiple-Precision Computation' Paper prize awarded by JSIAM (2005) for 'An Improved Convergence Test for the Double Exponential Formula' Japan Society for Industrial and Applied Mathematics 4th Achievement Award (2014) for 'Pioneering and practical development of the double exponential numerical integration method' Dr. Ooura has developed several widely used mathematical software packages including the double exponential integral formula, Clenshaw-Curtis numerical integration rule, and a general-purpose FFT library. His FFT package is particularly notable for its speed and accuracy, with benchmark tests showing superior performance compared to other implementations. His software has been incorporated into major projects including Google Chrome and SETI@home, demonstrating the practical impact of his theoretical work. His future research directions include further development of numerical computation libraries and applying his methods to various computational problems.

Roger Boltshauserمشاهده پروفایل

استاد

• Tectonic Building Culture
• Grids and Order in Architecture
• Proportional Design
• +۶ مورد دیگر

Roger Boltshauser is a Full Professor and Deputy Head at the Department of Architecture, ETH Zurich. His practice and academic work emphasize tectonic building culture, proportion, and material experimentation, diverging from mainstream German-Swiss architectural trends. He combines teaching with active architectural practice, influencing younger generations at ETH and TU Wien. Key themes: Tectonic structures, grid-based order, social responsibility in architecture, critique of modernist canons Notable projects: Hochhaus H1 Zwhatt-Areal (2025), Masterplan Zoo Basel (2025), Centre for Dental Medicine Zurich (2025) His buildings prioritize mass and permanence , evident in projects like the Kopfholz School and Rauch House . Recent works at the EPFL Solar Biennale (2025) and Architecture of Memory exhibition (2025) explore sustainable design and cultural narratives. Boltshauser’s studio on Dubsstrasse exemplifies research into load-bearing structures and urban spatial relationships.

• Causal Inference
• Representation Learning
• Generative Models
• +۵ مورد دیگر

Arthur Gretton is a Professor at University College London (UCL), leading the Gatsby Computational Neuroscience Unit and serving as director of the Centre for Computational Statistics and Machine Learning. He also works as a Research Scientist at Google DeepMind. His research focuses on causal inference, representation learning, and nonparametric hypothesis testing with applications in machine learning and computational neuroscience. Academic Affiliations Gatsby Computational Neuroscience Unit, UCL Centre for Computational Statistics and Machine Learning, UCL Google DeepMind Arthur's research spans several key areas in modern machine learning, including: Kernel methods for causal effect estimation and two-sample testing Deep learning architectures for proxy causal learning with complex confounding Adaptive gradient flows for generative modeling Nonparametric statistical tests with theoretical guarantees Applications in distributional reinforcement learning and Bayesian inference His work addresses both methodological advancements and practical implementations, particularly for high-dimensional data settings. Recent publications demonstrate his focus on causal inference with hidden confounders (AISTATS 2025), deep learning adaptivity in instrumental variable regression (ICLR 2025), and novel approaches to two-sample testing with adaptive kernel selection (NeurIPS 2024). He has also contributed extensively to distributional reinforcement learning and hypothesis testing frameworks. As an advisor, he supervises multiple PhD students including Jakub Wornbard, Zikai (Steve) Shen, and Zonghao (Hudson) Chen, while co-supervising others. His methodological contributions are implemented in various software packages available at the Gatsby Unit, including tools for kernel-based covariate shift correction, independence testing, and maximum mean discrepancy calculations.

• RNA Biology
• Bioinformatics
• Plant Molecular Biology
• +۴ مورد دیگر

Peter Brodersen is a Professor at the Department of Biology, University of Copenhagen , specializing in Bioinformatics and RNA Biology . His research focuses on RNA modification (m6A), YTHDF proteins, and small RNA pathways in plants. Recent research trends from his group include: (1) molecular mechanisms of ARGONAUTE-small RNA interactions, (2) m6A-YTHDF regulatory systems in plant development, and (3) RNAi-independent roles of DICER-LIKE proteins in antiviral defense. Collaborations span Denmark and international institutions. Publications highlight cross-disciplinary work bridging computational biology and experimental plant genetics. Key subfields include RNA structure, epigenetic regulation, and antiviral immunity.

Gerald L. Neumanمشاهده پروفایل

استاد

• Human Rights Law
• Immigration Law
• Constitutional Law
• +۵ مورد دیگر

Gerald L. Neuman is the J. Sinclair Armstrong Professor of International, Foreign, and Comparative Law at Harvard Law School (HLS), where he serves as Director of the Human Rights Program. His expertise spans human rights, constitutional law, immigration law, and international legal frameworks. Previously, he taught at the University of Pennsylvania Law School (1984-1992) and Columbia Law School (1992-2006). Research Interests: Neuman's work focuses on: Human rights in populist and pandemic contexts Transnational constitutionalism Immigration law and extraterritorial constitutional rights Age discrimination as a human rights violation Impeachment's role in human rights protection Indirect discrimination analysis Selected Trends: His recent articles examine pandemic-era immigration policies ( Title 42 ), impeachment mechanisms for human rights violations, and age discrimination frameworks. Publications show consistent engagement with: Constitutional law intersections Comparative legal analysis Administrative law critiques Human rights treaty body applications Scientific Contributions: Neuman's work appears in leading journals like Harvard International Law Journal and Harvard Human Rights Journal . He has organized workshops on indirect discrimination related to: Sexual orientation/gender identity Chronological age Religious practice Disability rights Pandemic policy impacts He has served as a UN expert workshop organizer and contributed to international human rights literature through edited volumes and treaty analysis.

Bai Gaoمشاهده پروفایل

استاد

• Political Economy
• International Political Economy
• Industrial Policy
• +۳ مورد دیگر

Bai Gao is a Professor of Sociology at Duke University. His research focuses on political economy, globalization trends, and industrial policy, particularly in the context of China and East Asia. He holds a Ph.D. from Princeton University (1994). Research Interests : Impact of state industrial policy on emerging technologies (e.g., electric vehicles in China) Globalization dynamics and post-neoliberal policy paradigms Comparative analysis of developmental models (China, Japan, Germany) Geopolitical implications of China's Belt and Road Initiative Key Contributions : Author of Japan’s Economic Dilemma (2001) and High-Speed Rail: An Analysis of the Chinese Innovation System (2021) Edited volumes on China-Pakistan Economic Corridor and global political economy Grants & Funding : "FDI and the Chinese Model of Economic Development" (2005-2007) "Institutional Analysis of Global Production Networks" (2003-2004) Courses Taught : SOCIOL 541: The United States and the Asian Pacific Region SOCIOL 344: Technology and Organizational Environments

Eric V. Mazumdarمشاهده پروفایل

استادیار

• Machine Learning
• Economics
• Optimization
• +۴ مورد دیگر

Eric V. Mazumdar is an Assistant Professor at the California Institute of Technology (Caltech), jointly appointed in the departments of Computing and Mathematical Sciences and Economics. He holds a B.S. from MIT (2015) and a Ph.D. from UC Berkeley (2021), co-advised by Michael Jordan and Shankar Sastry. His research bridges machine learning and economics, focusing on deploying algorithms into societal systems through theoretical and practical lenses. Key areas include strategic classification, multi-agent reinforcement learning, and distributionally robust optimization, with applications in healthcare, online markets, and intelligent infrastructure. Education: B.S., Electrical Engineering and Computer Science, Massachusetts Institute of Technology, 2015 Ph.D., Electrical Engineering and Computer Science, University of California, Berkeley, 2021 Research Interests: Mazumdar’s work emphasizes understanding learning algorithms in strategic environments, including min-max optimization, game theory, and multi-agent systems. He explores how algorithms interact with human and algorithmic agents in dynamic systems, with practical applications in healthcare delivery, e-commerce, and autonomous systems testing. Awards: NSF CAREER Award (2023) Simons Institute Research Fellowship in Learning in Games Grants & Funding: Supported by NSF, DARPA, Amazon, and other organizations. His NSF CAREER grant focuses on strategic interactions in societal-scale systems. Teaching: Courses include Networks: Structure & Economics (CMS/CS/EE/IDS 144) and Topics in Learning and Games (CMS/Ec 248). At UC Berkeley, he contributed to courses like Data, Inference, and Decisions (DS 102). Students & Postdocs: Current students: Lauren Conger, Tinashe Handina, Yizhou Zhang Postdocs: Zaiwei Chen, Laixi Shi, Kishan Panaganti (co-advised with Adam Wierman)

John Evansمشاهده پروفایل

دانشیار

• Computational Mechanics
• Fluid Dynamics
• Fluid-Structure Interaction
• +۳ مورد دیگر

John Evans is an Associate Professor and Jack Rominger Faculty Fellow in the Department of Aerospace Engineering Sciences at the University of Colorado Boulder, affiliated with the Applied Mathematics program. He serves as Associate Chair for Undergraduate Curriculum and is part of the Aerospace Mechanics Research Center (AMREC). His research focuses on computational mechanics, particularly fluid dynamics, fluid-structure interaction, and turbulence modeling using high-order and structure-preserving methods. Evans holds a PhD (2011) and MS (2008) in Computational and Applied Mathematics from the University of Texas at Austin, and dual BS/MS degrees in Mathematics and Applied Mathematics from Rensselaer Polytechnic Institute (2006). Before joining CU Boulder, he was a postdoctoral fellow at the Institute for Computational Engineering and Sciences (ICES). His research interests include isogeometric analysis, immersed methods, and data-driven turbulence modeling. Notable contributions include development of divergence-conforming discretizations for incompressible flows, stabilized collocation methods, and invariant subgrid stress models. He leads the AMREC lab and collaborates on plasma-fueled propulsion systems and geometrically sensitive simulations. Key Awards: 2021: Rocky Mountain AIAA Educator of the Year 2021: Gallagher Young Investigator Medal 2019-2021: Clarivate Highly Cited Researcher Professional Activities: Editor of Engineering Computations, Senior AIAA Member, Simons Visiting Professor (2019) Evans' work bridges advanced numerical methods with real-world engineering challenges. His lab develops open-source tools like XIGA for multi-material problems and focuses on immersive simulation environments. Current projects explore turbulence closure models, plasma propulsion, and topology optimization with B-spline-based approaches.

Matthew Kayمشاهده پروفایل

دانشیار

• Human-Computer Interaction
• Information Visualization
• Uncertainty Communication
• +۲ مورد دیگر

Matthew Kay is an Associate Professor in the Department of Communication Studies at Northwestern University's School of Communication, with a secondary appointment in Computer Science. He serves as Co-Director of Graduate Studies for the PhD in Technology and Social Behavior program. His research focuses on human-computer interaction and information visualization, specializing in uncertainty communication, usable statistics, and personal informatics. He employs mixed-method approaches including behavioral analysis, interactive system development, and visualization technique evaluation to address real-world data interpretation challenges. Analysis of his recent publications reveals dominant themes in visualization literacy development, uncertainty representation for decision-making, and health informatics applications. His work consistently bridges theoretical frameworks with practical implementations, particularly in educational assessment tools and election forecast visualizations. Professor Kay co-directs the Midwest Uncertainty Collective (MU collective), a research group advancing uncertainty communication methodologies. Previously faculty at the University of Michigan School of Information, he maintains active contributions to visualization tool development including the ggdist R package for uncertainty visualization.

Sarah Hallمشاهده پروفایل

استاد

• Economic Geography
• Financial Services
• UK-EU Relations
• +۵ مورد دیگر

Professor Sarah Hall serves as the 1931 Professor of Geography and a Fellow at St John’s College, University of Cambridge. She is a Senior Fellow and Deputy Director at UK in a Changing Europe , where she examines UK-EU relations and the economic geography of post-Brexit financial services. Her research integrates cultural economy and political economy frameworks to analyze the spatial impacts of global financial shifts on cities, particularly focusing on London’s role as a financial hub and its evolving ties with China and the EU. Education: MA, PhD Her work, funded by the ESRC, British Academy, Leverhulme Trust, and Nuffield Foundation, explores how macroeconomic transitions—such as Brexit, China’s financial rise, and finance-led capitalism—reshape regional economies. She employs geographically sensitive approaches to bridge academic, public, and policy debates. Research Trends across her 2023–2015 publications emphasize: Post-Brexit financial services job displacement and regulatory divergence State capitalism’s role in Chinese banking expansion into London Interlocking corporate-policy networks between EU financial centres Offshore RMB market dynamics in the UK Marketization of higher education and elite recruitment Scientific Recognition : British Academy Mid-Career Fellowship (2016–17) Fellow of the Academy of Social Sciences (2020) She contributes to Geoforum as Co-Editor-in-Chief and mentors prospective PhD students in economic geography and financial geopolitics.