جستجوی استاد راهنمای دانشگاهی

Tianyi Linمشاهده پروفایل

استادیار

• Optimization
• Game Theory
• Machine Learning
• +۳ مورد دیگر

Tianyi Lin serves as an Assistant Professor in the Department of Industrial Engineering and Operations Research (IEOR) at Columbia Engineering, Columbia University, a position he assumed in 2024. He holds dual affiliations as a verified Data Science Institute (DSI) Member and an Affiliated Member of both the Financial and Business Analytics Center and the Foundations of Data Science Center. His academic credentials include: Ph.D. in Electrical Engineering and Computer Science, UC Berkeley Postdoctoral Researcher, Laboratory for Information & Decision Systems (LIDS), MIT (2023-2024) M.S. in Operations Research, UC Berkeley M.S. in Pure Mathematics and Statistics, University of Cambridge B.S. in Mathematics, Nanjing University Dr. Lin's research spans optimization theory , game-theoretic models , and machine learning algorithms , with emphasis on nonconvex minimax problems , variational inequalities , and data science applications . His work bridges theoretical guarantees with practical implementations in high-dimensional settings, particularly focusing on convergence properties and computational efficiency in complex systems. Analysis of his 15 most recent publications (2022-2025) reveals dominant themes in high-order optimization methods , no-regret learning in games , and optimal transport algorithms . His contributions demonstrate consistent innovation in developing doubly optimal algorithms for monotone games, spectral regularization techniques for policy optimization, and structure-driven approaches for nonconvex problems, reflecting strong interdisciplinary connections between operations research, computer science, and applied mathematics. No scientific awards or honors were documented in the provided source material. Information regarding student advising and research grants remains unspecified in the current documentation, though his center affiliations suggest active participation in collaborative research initiatives. Dr. Lin maintains significant interdisciplinary engagement through his affiliations with Columbia's Data Science Institute and specialized research centers, positioning his work at the intersection of theoretical optimization and real-world data science applications.

Benjamin Rechtمشاهده پروفایل

استاد

• Machine Learning
• Optimization
• Statistics
• +۵ مورد دیگر

Benjamin Recht is a Professor in the Department of Electrical Engineering and Computer Sciences and Department of Statistics at the University of California, Berkeley. Previously, he was an Assistant Professor in the Department of Computer Sciences at the University of Wisconsin-Madison. Recht received his BS in mathematics from the University of Chicago and his MS and PhD from the MIT Media Laboratory, followed by a postdoctoral fellowship at Caltech's Center for the Mathematics of Information. His research interests span Machine Learning, Optimization, Control Theory, and Statistics , with a focus on both theoretical foundations and practical applications. Recht's work addresses fundamental questions in reproducibility, generalization, and robustness of machine learning systems, while also developing novel methods for control, computer vision, and data analysis. Recht's recent publications reveal a strong focus on reproducibility in machine learning , with papers like "The Mechanics of Frictionless Reproducibility" (2024), alongside continued contributions to statistical learning theory ("Interpolating Classifiers Make Few Mistakes", 2023) and computer vision ("Plenoxels", 2022; "K-planes", 2023). His work increasingly addresses societal implications of AI , including papers on systemic harm detection and post-deployment evaluation. NSF Career Award Alfred P. Sloan Research Fellowship 2012 SIAM/MOS Lagrange Prize in Continuous Optimization Presidential Early Career Award for Scientists and Engineers 2014 Jamon Prize 2015 William O. Baker Award for Initiatives in Research 2017 and 2020 NeurIPS Test of Time Awards Recht has advised numerous PhD students who have gone on to faculty positions at top universities and research roles at leading technology companies. His work on optimization algorithms has been widely influential, including the development of methods like HOGWILD! for parallel stochastic gradient descent. He co-founded the Conference on Learning for Decision and Control and has served on editorial boards for the Journal of Machine Learning Research and Mathematical Programming. His research group spans both theoretical and applied work, with connections to healthcare (adaptive medication tapering), computer vision (radiance fields), and social impact (systemic harm detection in deployed systems).

Arno Onkenمشاهده پروفایل

استادیار

• Data Science
• Machine Learning
• Computational Neuroscience
• +۷ مورد دیگر

Dr. Arno Onken is a Lecturer (Assistant Professor) in Data Science for Life Sciences at the School of Informatics, University of Edinburgh, where he is also affiliated with the Institute for Adaptive and Neural Computation. He leads a research group focused on developing machine learning and statistical methods for modeling neural activity and analyzing large-scale neuroscience data. His work bridges artificial intelligence and computational neuroscience. His research interests lie at the intersection of machine learning, statistics, and neuroscience. He develops flexible probabilistic models such as copulas and Gaussian processes, deep learning architectures like Vision Transformers for brain activity prediction, and matrix/tensor factorization techniques for dimensionality reduction in neural datasets. His group aims to uncover interpretable structure in complex neural recordings and understand how behavior and cognition are encoded in population activity. The recent publications reflect a strong trend in combining modern deep learning with classical statistical modeling to analyze large-scale neural recordings. His work spans from foundational methods in copula modeling and information theory to applications in predicting visual cortex responses and modeling brainstem-hippocampus interactions across sleep states. The research has been published in top venues including NeurIPS, CVPR, eLife, and PLoS Computational Biology. Dr. Onken actively supervises PhD students and has developed several open-source scientific software packages, including the Mixed Vine Toolbox and Population Spike Train Factorization Toolbox. He teaches core courses in Machine Learning and Pattern Recognition and Data Mining and Exploration at the University of Edinburgh.

• Sociology of Religion
• Secularization
• Religious Change
• +۴ مورد دیگر

Detlef Pollack is a Senior Professor and Chair of Sociology of Religion at the Institute for Sociology, University of Münster. He has held this position since August 2022, following a distinguished academic career that includes serving as Professor of Sociology of Religion at the Westphalian Wilhelms University Münster from 2008 to 2022, and as Professor of Comparative Cultural Sociology at the Viadrina European University Frankfurt (Oder) from 1995 to 2008. Pollack has been a member of the board of the Excellence Cluster "Religion and Politics" at the University of Münster since 2023 and previously served as its deputy speaker (2019-2021) and speaker (2015-2018). Pollack's academic background includes a habilitation from Bielefeld University (1994) in Sociology, a doctorate in Sociology of Religion (summa cum laude) from Leipzig University (1984), and theological studies at Leipzig University (1976-1981). His academic journey reflects a deep engagement with both theological and sociological perspectives on religion. Professor Pollack's research focuses on religious change in Western and Eastern Europe and the USA, political culture in Eastern and Central Europe, GDR research, systems theory, and differentiation theory. His work bridges historical analysis with contemporary sociological theory, particularly examining how religious institutions and practices adapt to modern social structures. He has made significant contributions to understanding secularization processes, religious pluralism, and the relationship between religion and modernity. Pollack's extensive publication record demonstrates consistent engagement with major debates in the sociology of religion. His recent work explores religious responses to global crises, religious fundamentalism, and the complex relationship between religion, politics, and national identity in contemporary Europe. His research often employs comparative methodologies, examining religious phenomena across different cultural and national contexts. Among his notable achievements are the Karl-Polányi-Preis from the Hungarian Sociological Society (2018), his fellowship at the Lichtenberg-Kolleg in Göttingen (2011), and his Max Weber Chair at New York University (2003). He has also been recognized with fellowships at Princeton University (1993) and the Wissenschaftskolleg Berlin (1996). Pollack actively contributes to academic discourse through his leadership roles in professional organizations, including the International Society for the Sociology of Religion (since 1995) and the Sociology of Religion section of the German Sociological Association (since 1994). His teaching portfolio includes seminars on postcolonial theory, Western modernity in times of global crisis, religious fundamentalism, secularization, and the relationship between religion and politics in Russia, Germany, and the USA.

Jean-François Godboutمشاهده پروفایل

استاد

• Data Science
• AI Safety
• Generative AI
• +۶ مورد دیگر

Jean-François Godbout is a Professor in the Department of Political Science at the Université de Montréal and an Associate Academic Member of Mila - the Quebec AI Institute. He directs the undergraduate program in Big Data Analytics in Social Sciences and Humanities at UdeM and conducts interdisciplinary research through the Complex Data Lab. Affiliated with IVADO (AI Consortium) Member of CÉRIUM (International Research Centre) and CECD (Democratic Citizenship Centre) His research focuses on: Data Science applications in political institutions AI Safety and generative AI's impact on political attitudes Misinformation Mitigation through large language models Comparative Political Development in Canadian and Lower Canada contexts Legislative Institutions and voting records analysis Political Polarization in online societies Recent publications analyze social media disinformation, AI persuasion on harmful topics, and education-focused text simplification. His articles frequently combine graph mining , machine learning , and political science methodologies. Scientific collaborations include: Mila researchers (Andreea Musulan, Maximilian Puelma Touzel) IVADO data science initiatives McGill University interdisciplinary projects He supervises students in: Political science (Julien Robin, Matthew Taylor) Artificial Intelligence (Kellin Pelrine, Camille Thibault) Computational social science applications

Udo Fiedlerمشاهده پروفایل

استاد

• Manufacturing Engineering
• High-Speed Machining
• Production Engineering
• +۷ مورد دیگر

Prof. Dr.-Ing. Udo Fiedler is a faculty member at the Technical University of Central Hesse (THM), Department of Business Administration and Economics, where he serves as Head of the Production Engineering Laboratory and Member of the Senate. His academic work focuses on manufacturing engineering with specialization in high-speed machining, production processes, and machine tools. His research interests include: High-Speed Machining (HSC) and precision manufacturing Green machining of sintered parts in the green state Process optimization using statistical experimental design Machine tool technology and NC programming Industry 4.0 applications in manufacturing education Process monitoring and control for increased manufacturing safety Prof. Fiedler's publication record demonstrates an evolution from fundamental machining processes toward integrating AI with traditional manufacturing. His recent work shows strong emphasis on applying artificial intelligence to quality prediction, optimizing green machining processes, and implementing Industry 4.0 concepts through learning factory approaches, bridging traditional manufacturing engineering with modern digital technologies. His significant scientific contributions include: Development of methods for NC programming of complex workpieces Research on stability lobe diagrams for milling processes Studies comparing different production methods including HSC, EDM, and generative processes Work on mechatronic tool holders for process monitoring Applications in the ophthalmic industry for precision machining of spectacle lenses Prof. Fiedler teaches multiple courses at THM including Factory Planning/Ergonomics, Handling and Assembly Technology, Innovative Manufacturing Processes, and Machine Tools at the bachelor's level, and Learning Factory 1 and 2 at the master's level. He leads current research projects including Klag-Robotics (2023-2025), Loewe Project OST (2018-2021), and GrünSpan (2014-2015), demonstrating sustained research activity across multiple manufacturing domains.

Santa Slokenbergaمشاهده پروفایل

دانشیار

• Medical Law
• Administrative Law
• Biobanking
• +۷ مورد دیگر

Santa Slokenberga, LL.D., serves as an Associate Professor in Medical Law and Senior Lecturer in Administrative Law at Uppsala University's Department of Law within the Faculty of Law. She maintains an active research profile focusing on the intersection of law, medicine, and technology, with particular expertise in biobanking, genetic privacy, and healthcare data governance. Her research interests span several critical areas in contemporary medical law, including the human genome and biobanking, AI applications in healthcare, privacy and personal data protection frameworks, quality of pediatric healthcare, and governance of rare diseases. Slokenberga's work demonstrates a strong focus on European regulatory frameworks and their implementation across different jurisdictions, particularly examining how GDPR interacts with medical research and biobanking practices. Analysis of her recent publications reveals a clear trajectory toward increasingly complex issues at the intersection of law, technology, and healthcare. Her scholarship consistently addresses regulatory challenges in genomic data sharing, European health data spaces, and the ethical implications of emerging technologies in medicine. Notably, she has contributed significantly to understanding how European data protection law applies to medical research contexts. Slokenberga holds leadership positions as a board member of EAHL and the Nordic Permed Law, reflecting her recognized expertise in health policy at both national and international levels. Her collaborative research projects often involve multinational teams, demonstrating her engagement with global health law challenges. Her academic supervision and teaching activities focus on preparing future legal professionals to navigate the complex landscape of medical law, administrative law, and data protection regulations as they apply to healthcare systems and biomedical research.

Chaopeng Shenمشاهده پروفایل

استاد

• Hydrology
• Deep Learning
• Physics-Informed Machine Learning
• +۹ مورد دیگر

Chaopeng Shen is a Professor in the Department of Civil and Environmental Engineering at Pennsylvania State University. His research bridges hydrology with state-of-the-art deep learning and differentiable modeling techniques, focusing on advancing our understanding of hydrologic cycles and their interactions with ecosystems, energy, and carbon cycles. He leads the Multi-scale Hydrology, Processes and Intelligence group (MHPI) and has developed the Process-based Adaptive Watershed Simulator (PAWS) for large-scale hydrologic modeling. Shen's work emphasizes physics-informed machine learning , where deep learning components are integrated with process-based equations through differentiable modeling. This approach enables training neural networks using big data while respecting physical laws, leading to improved generalizability and robustness. His group has demonstrated advantages of differentiable models in rainfall-runoff prediction, routing, ecosystem modeling, and water quality studies. Notably, his team's deepLDB project addresses landslide prediction using AI and big datasets. Recent publications highlight his contributions to global water modeling (grid-LSTM, differentiable Muskingum-Cunge routing), extreme flood forecasting (probabilistic diffusion models), and hydrologic uncertainty quantification . Shen actively engages in interdisciplinary collaborations through the PRISM Cooperative Institute, which aims to integrate multi-domain data for systemic risk assessment. His group has advised students including Dapeng Feng, Wen-Ping Tsai, Kuai Fang, Xinye Ji, and Tasnuva Mahjabin. Shen's research is supported by the National Science Foundation (NSF), Department of Energy (DoE), USGS, Google.org, and the Gates Foundation. He serves as Editor for the Journal of Geophysical Research - Machine Learning & Computation and Chief Editor for Frontiers in Water: Water & AI. His open-source software tools like PAWS and deepLDB are available through dedicated project websites.

• Psychological Safety
• Teaming
• Organizational Learning
• +۳ مورد دیگر

Amy C. Edmondson is the Novartis Professor of Leadership and Management at Harvard Business School, holding a chaired position dedicated to human interactions in successful enterprises. She has been consistently ranked among Thinkers50's top management thinkers since 2011, achieving #1 status in 2021 and 2023. Her research focuses on psychological safety, teaming, and organizational learning, with significant contributions to understanding how organizations learn from failure. Key publications include The Fearless Organization (2019) and Right Kind of Wrong (2023), the latter winning the Financial Times and Schroders Best Business Book of the Year award. Her work spans organizational behavior, healthcare management, and innovation science, with articles in top journals like Administrative Science Quarterly and Harvard Business Review . Recent research trends show increasing focus on psychological safety dynamics in constrained environments, cross-boundary teaming, and the science of intelligent failure. Her 15 most recent articles (2021-2025) demonstrate expanding applications from healthcare to general management contexts, with growing emphasis on temporal dimensions of team learning and data-driven decision pitfalls. Major awards include: Thinkers50 #1 Management Thinker (2021, 2023) Thinkers50 Breakthrough Idea Award (2019) Financial Times Best Business Book Award (2023) Accenture Award for California Management Review (2003) Edmondson advises on organizational transformation through numerous Harvard Business School cases, including culture change initiatives at Microsoft, LEGO, and Cleveland Clinic. Her research has secured funding for studies on teaming in pharmaceutical development, smart city projects, and healthcare innovation. Current work examines psychological safety erosion in new hires and cross-boundary team dynamics in complex innovation projects.

Job Boekhovenمشاهده پروفایل

دانشیار

• Synthetic Chemistry
• Biophysics
• Biochemistry
• +۷ مورد دیگر

Prof. Dr. Job Boekhoven is an Associate Professor at the Department of Bioscience , TUM School of Natural Sciences , Technical University of Munich . His research focuses on synthetic life , chemically fueled self-assembly , and supramolecular materials , aiming to synthesize life from scratch. Research Interests include creating synthetic cells that compete for resources, replicate, and undergo Darwinian evolution . His lab designs molecules like lipids , peptides , and nucleic acids that self-assemble into active compartments regulated by chemical energy. These systems exhibit life-like hallmarks such as emergence , self-division , and controllable lifetimes . Scientific Awards include: ERC Consolidator Grant (2024) Lecturer Award by Association of the Chemical Industry (2024) ERC Starting Grant (2019) Volkswagen Foundation 'Life?' Grant (2019) Max Planck Fellow (2019) VCI Dozentenpreis (2021) Thieme Chemistry Journal Award (2017) Rubicon Postdoctoral Fellowship (2013) Publications highlight trends in nonequilibrium materials , dynamic combinatorial libraries , and protocell engineering . His work bridges synthetic chemistry with biophysics to explore life's origins and applications in materials science .

Steven L. Yaffeeمشاهده پروفایل

استاد

• Environmental Policy
• Sustainability
• Collaborative Governance
• +۷ مورد دیگر

Steven L. Yaffee serves as Professor of Environmental Policy and Planning at the University of Michigan's School for Environment and Sustainability (SEAS), where his work centers on collaborative decision-making for complex environmental challenges and ecosystem-scale conservation strategies. His academic journey includes a PhD in Environmental Policy and Planning from the Massachusetts Institute of Technology, complemented by an MS in Natural Resource Policy and BS in Resource Planning and Conservation from the University of Michigan. Professor Yaffee's research pioneers collaborative governance frameworks for: Landscape-scale conservation and sustainable natural resource management Marine and coastal ecosystem protection through ecosystem-based approaches Biodiversity policy implementation on public lands Energy transition governance and stakeholder negotiation His on-the-ground work with nonprofits and foundations has shaped practical dispute resolution methodologies and adaptive management systems for conservation initiatives worldwide. His publication trajectory reveals a three-decade evolution from foundational works on collaborative natural resource management toward contemporary marine ecosystem governance, consistently emphasizing stakeholder engagement, institutional innovation, and practical implementation pathways across diverse ecological contexts. Yaffee directs the Ecosystem Management Initiative at SEAS, leading externally funded research projects including the California Marine Life Protection Act Initiative (Resources Legacy Fund Foundation, 2013-2017), Kinship Fellows Program evaluation (Kinship Foundation, 2014-2016), and Packard Foundation-supported marine ecosystem management case studies (2008-2013), demonstrating sustained impact on conservation practice. As an educator, he develops skill-based curricula in political analysis, negotiation, and mediation through courses like NRE 562: Environmental Policy, preparing next-generation practitioners for complex sustainability challenges while maintaining active engagement with conservation media and policy discourse through outlets like MIT Technology Review.

• Functional Biogeography
• Plant Ecology
• Biodiversity Research
• +۳ مورد دیگر

Jens Kattge is an Independent Research Group Leader at the Max Planck Institute for Biogeochemistry in Jena, Germany, specializing in Functional Biogeography. His career spans roles from Research Associate (2002–2005) to Senior Scientist (2010–2012) and current group leadership since 2013. He focuses on plant functional traits, their role in terrestrial ecosystems, and their integration into Earth system models. He coordinates the global TRY Initiative , a plant trait database for biodiversity research, and contributes to the German Centre for Integrative Biodiversity Research (iDiv). His research spans functional biogeography, trait-climate relationships, ecosystem modeling, and biodiversity-ecosystem functioning. Key projects include developing the rtry R package for trait data preprocessing and analyzing global datasets like CoRRE Trait Data and sPlotOpen. Recent publications investigate leaf cuticle thickness, drought responses in tree species, trait controls on biomass, global biodiversity patterns, and the integration of citizen science with Earth observation data. His work emphasizes trait data representativeness and future-proofing ecological science.

Matias E. Margulisمشاهده پروفایل

دانشیار

• Global Governance of Food Security
• Agricultural Trade Liberalization
• Large-Scale Land Acquisitions
• +۴ مورد دیگر

Matias E. Margulis is an Associate Professor at the University of British Columbia, jointly appointed between the Faculty of Land and Food Systems and the School of Public Policy and Global Affairs. His research explores the intersection of global governance, food security, and international trade, with a focus on regime complexity and power dynamics in multilateral institutions. Ph.D. in International Relations, McMaster University (2012) M.A. in Public Policy and Politics, University of Toronto (2003) B.A. (Honours) in Economics and Political Science, University of Toronto (2000) Postdoctoral Fellow, Max Planck Institute (2012-2013) His academic work investigates how food insecurity stems from human-made social, economic, and political systems, analyzing the role of international organizations like the WTO, UN, and EU in shaping trade and food policy. Recent publications examine regime entanglements, human rights in trade negotiations, and climate crisis impacts on food governance. Matias has received multiple accolades, including the Distinguished Academic Award (Early in Career, 2014) and the Cadieux-Léger Fellowship (2010-2011). He previously held academic positions at the University of Edinburgh, University of Stirling, and Max Planck Institute, and served as a Canadian representative to the WTO, OECD, and FAO. His expertise extends to advising the UN Special Rapporteur on the Right to Food and consulting for NGOs and the Brookings Institution.

• Climate Science
• Hydrology
• Biogeochemistry
• +۳ مورد دیگر

Prof. Dr. René Orth is a Professor at the University of Freiburg, leading the Modelling of Biogeochemical Systems group. He focuses on land-atmosphere interactions, climate change impacts on vegetation and water systems, and Earth system modeling, with a particular emphasis on soil moisture-vegetation-climate feedbacks and extreme weather events. Education: MSc and BSc in Atmospheric and Climate Science from ETH Zurich (2010), BSc in Earth Science (2008), Erasmus exchange at University of Leeds. His research integrates biophysical and societal perspectives to improve climate projections and hydroclimatic forecasts. Key projects include the EU H2020 MYRIAD multi-hazard framework and the DFG Emmy Noether Group on Hydrology-Biosphere-Climate Interactions. His publications highlight vegetation responses to climate change, drought impacts, and the robustness of modeling approaches. He has supervised over 15 BSc, MSc, and PhD theses and actively reviews manuscripts and proposals for leading journals and funding agencies. Scientific awards include the ETH Excellence Scholarship, SNF Advanced Postdoc Mobility Grant, and DFG Emmy Noether Group Grant. René Orth is affiliated with the Max Planck Institute for Biogeochemistry (Jena), Die Junge Akademie, and the institute's graduate school (IMPRS-gBGC), contributing to science-policy interfaces and advancing climate science through interdisciplinary research.

Matthew K. Tamمشاهده پروفایل

دانشیار

• continuous optimisation and its applications
• monotone operator theory
• variational and convex analysis
• +۳ مورد دیگر

Matthew K. Tam is an Associate Professor at the School of Mathematics and Statistics, The University of Melbourne, specializing in Operations Research. He is also an investigator at the Melbourne Centre for Data Science and an associate investigator in the ARC Training Centre OPTIMA. PhD in Mathematics (2016) from University of Newcastle under Jonathan Borwein Postdoctoral research at University of Göttingen with RTG-2088 and Alexander von Humboldt Foundation Junior Professor at University of Göttingen (2017-2020) His research focuses on continuous optimization, monotone operator theory, and variational analysis, with applications in wavelet construction and inverse problems. Key trends include distributed algorithms, resolvent splitting, and convergence analysis for feasibility problems. Discovery Early Career Researcher Award (DECRA) Alexander von Humboldt Fellowship He collaborates with institutions like ANZIAM, Springer, and IEEE, with publications spanning mathematical optimization, harmonic analysis, and computational mathematics. His work emphasizes algorithmic design for complex data systems and real-world applications in imaging and industrial modeling.