جستجوی استاد راهنمای دانشگاهی

Erik B. Sudderthمشاهده پروفایل

استاد

• Machine Learning
• Computer Vision
• Statistical Inference
• +۳ مورد دیگر

Erik B. Sudderth is a Professor of Computer Science and Statistics and Chancellor's Fellow at the University of California, Irvine (UCI). He leads the Learning, Inference, & Vision Group and directs multiple research centers, including the UCI Center for Machine Learning and Intelligent Systems and the HPI Research Center in Machine Learning and Data Science. He previously served as an Associate Professor at Brown University. Education: B.S. (summa cum laude) in Electrical Engineering from UC San Diego (1999), M.S. and Ph.D. in EECS from MIT (2002, 2006). His research focuses on statistical methods for scalable machine learning, Bayesian nonparametrics, probabilistic graphical models, and applications in computer vision, AI, and environmental science. Key areas include nonparametric clustering, deep generative models, and particle-based inference algorithms. Research interests span diverse topics: advancing Bayesian nonparametric models for medical time series, scalable variational inference, and AI ethics. Notable contributions include the NET-VISA seismic monitoring system (ISBA Mitchell Prize, 2014), the BNPy toolbox (NSF CAREER Award), and work on diverse particle max-product algorithms for continuous inference. Scientific awards include the NSF CAREER Award, ISBA Mitchell Prize, and recognition as one of "AI's 10 to Watch" (IEEE). He has served as editor for top journals (JMLR, IEEE PAMI) and conference chairs (NeurIPS, CVPR). His work bridges theory and practice, with applications in robotics, climate science, and healthcare. Labs/Teams: UCI Learning, Inference, & Vision Group; UCI Center for Machine Learning; CREATE Technology Center. Grants include NSF funding for visually impaired collaboration tools and soil biogeochemical modeling.

Ali Vakilianمشاهده پروفایل

دانشگاهی

• Algorithmic Foundations of Machine Learning
• Data Science
• Streaming Algorithms
• +۱۸ مورد دیگر

Ali Vakilian is a Research Assistant Professor at the Toyota Technological Institute at Chicago (TTIC), with a strong academic background in theoretical computer science and algorithms. He will join the Department of Computer Science at Virginia Tech as an Assistant Professor in Fall 2025. His research bridges algorithmic theory and machine learning, focusing on scalable, fair, and efficient algorithms for massive data. Education: Ph.D. in EECS, Massachusetts Institute of Technology (MIT), advisors: Erik Demaine and Piotr Indyk M.S. in Computer Science, University of Illinois at Urbana-Champaign (UIUC), advisor: Chandra Chekuri B.S. in Computer Engineering, Sharif University of Technology Research Interests: Ali Vakilian's work centers on the algorithmic foundations of machine learning and data science. He develops streaming, sketching, and sublinear-time algorithms for massive datasets, and pioneers learning-augmented algorithms that use machine learning predictions to improve performance while maintaining worst-case guarantees. His research in trustworthy ML includes algorithmic fairness, fair clustering, and learning with strategic agents. He also contributes to combinatorial optimization and approximation algorithms for network design, set cover, and low-rank approximation. His recent publications (2023–2025) show a consistent focus on fair clustering (individual and group fairness), streaming graph algorithms , learning-augmented methods , and frequency estimation . These works appear in top venues such as NeurIPS, ICML, SODA, and ICALP, often with recognitions like oral or spotlight presentations. Scientific Awards: Outstanding Student Paper Highlight Award, AISTATS 2024 Notable-top-25% paper, ICLR 2023 Oral presentation, AISTATS 2024 Spotlight presentation, NeurIPS 2023 Advising and Grants: Ali Vakilian mentors several students and interns, including summer interns at TTIC and Fatima Fellows. His research is supported by the National Science Foundation (TRIPODS program), as noted in the press coverage of his work on LearnedSketch. He actively contributes to the academic community through advising, organizing workshops (e.g., Algorithms with Predictions, Learning-Augmented Algorithms), and serving on program committees (e.g., NeurIPS, ICML, AISTATS). Labs and Teams: He is affiliated with the theory and algorithms group at TTIC and collaborates with researchers at MIT, UIUC, and other institutions. His work on learning-augmented algorithms has led to influential workshops and collaborations with leading figures such as Piotr Indyk and Erik Demaine.

Guillaume Lajoieمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Neuroscience
• Machine Learning
• +۳ مورد دیگر

Guillaume Lajoie is an Associate Professor in the Department of Mathematics and Statistics at Université de Montréal and a Core Academic Member of Mila – Quebec Artificial Intelligence Institute. He holds a Canada CIFAR AI Research Chair and a Canada Research Chair in Neural Computation and Interfacing. His research focuses on the intersection of AI and neuroscience, particularly in understanding neural network dynamics and developing brain-machine interfaces for clinical and scientific applications. He is affiliated with the Centre de recherches mathématiques (CRM), the Interdisciplinary Center for Research on the Brain and Learning (CIRCA), and the UNIQUE initiative. Education: PhD in Applied Mathematics from the University of Washington (Seattle), postdoctoral fellowships at the Max Planck Institute for Dynamics and the University of Washington Institute for Neuroengineering. Awards include the FRQS Scholar designation and leadership roles in strategic research initiatives like UNIQUE and CIRCA. Research interests include neural computations, recurrent neural networks, neurotechnology, and responsible AI development. Supervised students include François Paugam (PhD), Giancarlo Kerg (PhD), and others. Key grants include projects on adaptive neuroprosthetics, neural decoding, and Canada Research Chairs funding.

Shengjie Liمشاهده پروفایل

پژوهشگر

• Marine Biogeochemistry
• Microbial Ecology
• Nitrogen Cycling
• +۳ مورد دیگر

Shengjie Li is a Researcher in the Department of Biogeochemistry at the Max Planck Institute for Marine Microbiology (MPI-Bremen), Germany. Their postdoctoral research focuses on microbial ecology and biogeochemical processes in marine oxygen minimum zones, particularly linking particle properties, microbial communities, and nitrogen-loss dynamics. Li holds a PhD from Peking University (2017-2022) and conducted a guest PhD at the University of Calgary (2020-2021). Research interests include microbial responses to environmental change, nitrogen cycling in aquatic systems, and the interplay between organic carbon, sulfur, and iron in driving nitrate reduction. Key projects explore isotopic signatures of N₂O production, methane dynamics in groundwater, and the role of biochar in stress resistance. Publications highlight contributions to understanding N₂O emissions, cold seep nitrogen-loss mechanisms, and microbial adaptations in fluctuating environments. Ongoing work bridges microbial community analysis with biogeochemical modeling to address global climate impacts.

• Soil Biogeochemistry
• Carbon Cycling
• Mineral-Soil Interactions
• +۳ مورد دیگر

Dr. Marion Schrumpf is a Group Leader in the Soil Biogeochemistry research group at the Max Planck Institute for Biogeochemistry, affiliated with the Department of Biogeochemical Processes. Her work focuses on soil carbon dynamics, mineral-organic matter interactions, and climate change impacts on soil systems. Research areas: Soil biogeochemistry, carbon cycling, mineral-soil interactions, nutrient stoichiometry, microbial ecology, and climate modeling. Email: mschrumpf@... Phone: +49 3641 57-6182 Office: B2.015 Her recent publications address themes like mineral control over soil carbon stabilization, drought effects on soil processes, microbial stoichiometric adaptation, and the Jena Soil Model's role in simulating carbon-nutrient interactions. She leads efforts to disentangle the complex relationships between land use, mineralogy, and soil organic matter turnover across diverse ecosystems.

• Extreme Value Theory
• Copulas
• Spatial Statistics
• +۳ مورد دیگر

Professor Jennifer Wadsworth is a Professor in Statistics at the School of Mathematical Sciences, Lancaster University . Her research spans extreme value theory, copulas, spatial statistics, and environmental statistics, focusing on likelihood-based and nonparametric inference methods. Projects include Exploring and exploiting new representations for multivariate extremes (2023-2026) and STORi: Multivariate Extremes for Nuclear Regulation (2019-2023). Current PhD supervision interests: multivariate and spatial extremes, aiming to develop realistic models by relaxing assumptions. Research groups: Extreme Value Theory, STOR-i Centre for Doctoral Training. Her recent work, such as Modeling of spatial extremes in environmental data science (2025), highlights the need to move beyond max-stable processes for more accurate spatial extreme modeling. She supervises PhD students including Ryan Campbell, Kristina Grolmusova, and Lydia Kakampakou at Lancaster University.

• Electrochemical Energy Storage
• Lithium-ion Batteries
• Solid-State Electrolytes
• +۴ مورد دیگر

Candace K. Chan is a Professor in the School for Engineering of Matter, Transport and Energy at Arizona State University , with a joint appointment in the School of Molecular Sciences graduate faculty. Her research bridges materials science , electrochemistry , and energy storage , with additional focus on water treatment technologies and photocatalysis . She leads the NSF Nanosystems Engineering Research Center for NEWT water treatment projects and holds multiple patents in solid-state electrolytes and clathrate battery materials . Education: Ph.D. in Physical Chemistry (Stanford University), B.S. in Chemistry (Rice University) Expertise: Inorganic/Materials Chemistry, Nanoscience, Renewable Energy Her research group specializes in solid-state battery materials , photocatalytic water treatment , and clathrate electrochemistry , with recent work on flexible battery mechanics and metal oxide photocatalysts . Publications focus on garnet-type electrolytes , Si/Ge clathrates , and LDH nanocomposites . Key scientific awards include: NSF CAREER Award (2016) Alexander von Humboldt Fellowship (2016–2018) ECS Battery Division Travel Grant (2014) ASU Fulton Exemplar Faculty (2017–2018) Advising: Mentored 12 Ph.D. students (e.g., Andrew Dopilka , Ting Yang ), 8 M.S. students, and 6 Barrett Honors Thesis students. Collaborates with international researchers from Max-Planck-Institut , Universität Göttingen , and Universidade Federal de Itajubá . Grants: Currently leads 3 active NSF/industry projects on solid electrolyte interphases , clathrate synthesis , and selective nano-sorbents . Completed 8 projects including SRP-funded selenium removal studies and ACS Petroleum Research Fund grants.

Anders Rønn-Nielsenمشاهده پروفایل

دانشیار

• Applied Probability Theory
• Lévy-Based Modelling
• Statistical Methods in Efficiency Analysis
• +۳ مورد دیگر

Anders Rønn-Nielsen is an Associate Professor at the Department of Finance and Center Coordinator at the Center for Statistics at Copenhagen Business School (CBS). He holds a M.Sc. in Statistics from University of Copenhagen and a PhD in Statistics and Probability Theory from Aarhus University. His research focuses on applied probability theory, particularly Lévy-based spatial models, and statistical efficiency analysis. His academic credentials include: M.Sc. in Statistics, University of Copenhagen PhD in Statistics and Probability Theory, Aarhus University Research interests span: Lévy processes and spatial stochastic modeling Extreme value theory applications in finance and natural sciences Nonparametric production frontier analysis Efficiency measurement methodologies His publications (18+ articles) emphasize theoretical probability and statistical applications in efficiency analysis. He has served as external examiner at Aarhus University and Copenhagen University for master’s and PhD examinations (2017–2019). His teaching responsibilities include advanced probability theory courses and statistical methods training for economics students.

Kanwarpal Singhمشاهده پروفایل

پژوهشگر

• Biomedical Optics
• Optical Coherence Tomography
• Endoscopic Imaging
• +۳ مورد دیگر

Kanwarpal Singh serves as Group Leader and Head of the Microendoscopy Research Group at the Max Planck Institute for the Science of Light (MPL) in Erlangen, Germany. His research focuses on developing and applying advanced optical imaging techniques, particularly Optical Coherence Tomography (OCT) and related technologies, for biomedical applications. As part of the Max Planck Society, one of Germany's premier research organizations, his work bridges fundamental optical physics with clinical medicine. Dr. Singh's research interests center on biomedical optics and imaging, with particular expertise in endoscopic OCT, optical elastography, and polarization-sensitive imaging techniques. His work spans from developing novel optical systems and probes to applying these technologies in clinical settings for disease diagnosis and monitoring. Key areas include gastrointestinal imaging, dermatological applications, and neurological tissue characterization. His research demonstrates a consistent trajectory from fundamental optical engineering to translational medical applications, with particular emphasis on improving imaging depth, resolution, speed, and clinical usability. Analysis of Dr. Singh's recent publications (2021-2025) reveals a strong focus on overcoming technical limitations in biomedical imaging. His work addresses critical challenges including motion artifacts in in vivo measurements, depth of focus limitations, polarization sensitivity issues, and the development of portable, clinically practical systems. The research shows increasing clinical relevance, with applications spanning inflammatory bowel disease monitoring, esophageal tissue analysis, skin biomechanics, and central nervous system regeneration studies. Dr. Singh leads the Microendoscopy Research Group within the MPL's research structure. While specific lab details aren't provided in the text, his numerous publications describing novel probe designs and imaging systems suggest an active laboratory focused on optical system development, with strong connections to clinical collaborators for in vivo and patient studies. His research appears to involve both theoretical modeling and practical implementation of optical technologies.

Jan Ratajمشاهده پروفایل

استاد

• Integral Geometry
• Geometric Measure Theory
• Stochastic Geometry
• +۲ مورد دیگر

Jan Rataj is a Professor at the Faculty of Mathematics and Physics , Charles University , specializing in Integral Geometry , Geometric Measure Theory , and Stochastic Geometry . He has been affiliated with Charles University since 1991 and has held visiting positions at universities in Jena, Karlsruhe, Ulm, and Aarhus. Research interests focus on geometric probability, convex geometry, and measure theory Teaches courses on Measure and Integration Theory , Geometry , and advanced topics in geometric measure theory Scientific contributions include coauthoring 2 monographs and ~50 research papers. His work explores: Random measurable sets and perimeter analysis Stochastic modeling of microstructures Geometric functionals and invariants Applications to spatial statistics and probability theory He supervises PhD students and leads the Seminar of Stochastic Geometry , which examines topics like: Max-stable random fields Convex body reconstruction Point process analysis

Martin Hebartمشاهده پروفایل

استاد

• Visual Perception
• Computational Cognitive Neuroscience
• Quantitative Psychiatry
• +۵ مورد دیگر

Martin Hebart is a Professor for Computational Cognitive Neuroscience and Quantitative Psychiatry at Justus Liebig University Giessen and an Independent Max Planck Research Group Leader at the Max Planck Institute for Human Cognitive and Brain Sciences in Leipzig, Germany. His work bridges cognitive neuroscience, computer science, and psychology to explore visual perception, object recognition, and computational models of brain function. PhD in Psychology from Bernstein Center for Computational Neuroscience Berlin (2014) M.Sc. and B.Sc. in Neuro-cognitive Psychology from Ludwig Maximilian University Munich His research integrates psychophysics , neuroimaging (fMRI, MEG), and machine learning to decode how visual input transforms into stable object representations and how these insights inform psychiatric conditions like hallucinations. Articles highlight his focus on computational models , neural network alignment , and large-scale behavioral-neuroimaging datasets (e.g., THINGS-data). His group’s work spans from basic visual cognition to translational applications in psychiatry. Scientific awards include postdoctoral fellowships from the National Institute of Mental Health (2016) and Alexander von Humboldt Foundation (Feodor Lynen, 2016), alongside doctoral and study scholarships. He leads a multidisciplinary team at the intersection of JLU Giessen’s Medical Department and MPI, mentoring students in visual neuroscience , AI-driven modeling , and clinical applications .

Joachim Kopkaمشاهده پروفایل

استاد پژوهش

• Metabolome Analysis
• Fluxome Analysis
• Plant Physiology
• +۴ مورد دیگر

Dr. Joachim Kopka is a Research Group Leader at the Max Planck Institute of Molecular Plant Physiology in Potsdam, Germany, where he heads the Applied Metabolome Analysis group. His research focuses on developing cutting-edge technologies for metabolome and fluxome analysis, particularly GC-EI-TOF-MS-based metabolite profiling and stable isotope labeling techniques. He investigates plant physiological responses to environmental stimuli, with emphasis on root metabolism and stress adaptation mechanisms. Kopka completed his Staatsexamen (1988) and PhD (1992) at Wilhelms University of Münster, followed by postdoctoral research in the USA and Germany. He co-founded Metanomics GmbH before joining MPI-MP in 1996, where he established his research group in 2001. He received his Habilitation from the University of Potsdam in 2009. His research integrates metabolomics, isotope labeling, and computational biology to study: Metabolic plasticity in response to environmental changes Development of GC-MS and data analysis pipelines (e.g., TagFinder software) In vivo stable isotope labeling for flux phenotyping Plant-microbe interactions and stress physiology Kopka's recent publications demonstrate strong emphasis on metabolic adaptations to abiotic stresses (drought, cold), plant-microbiome interactions, and advanced analytical methodologies. His work frequently combines metabolomics with proteomics and transcriptomics to unravel complex plant physiological processes. He leads infrastructure development for metabolome analysis and maintains collaborative projects across plant science disciplines. His group contributes to public resources like the Golm Metabolome Database (GMD), facilitating metabolite identification and data sharing in the scientific community.

• Biogeochemical Cycles
• Soil Organic Matter
• Carbon Cycling
• +۴ مورد دیگر

Gerd Gleixner serves as a Research Group Leader at the Max Planck Institute for Biogeochemistry in Jena, Germany, and holds an Associate Professor position at Friedrich Schiller University Jena. His primary affiliation is with the Department of Biogeochemical Processes (BGP), where he leads research on molecular-scale biogeochemical cycles. His research focuses on molecular biogeochemistry , particularly the origin, turnover, and stability of soil organic matter, carbon transport in plant metabolism, and biodiversity's role in biogeochemical cycles. Key projects include the Amazon Tall Tower Observatory (ATTO) and Aqua Diva, examining ecosystem processes across spatial and temporal scales. His work integrates isotopic techniques with molecular analyses to understand carbon cycling in terrestrial ecosystems. Analysis of his 2021-2023 publications reveals strong emphasis on plant-soil-microbe interactions , paleoclimate reconstruction using biomarkers, and dissolved organic matter characterization . Methodologically, his work bridges field observations with advanced analytical techniques including mass spectrometry and isotope ratio measurements. 2022 Top Scientists (Environmental Sciences; #527 worldwide, #36 nationally) 2016 Thuringian Research Prize for Basic Research Gleixner actively mentors through the International Max Planck Research School for Global Biogeochemical Cycles, which he has led as Spokesperson since 2017. His research involves significant international collaborations, particularly in Tibetan Plateau studies and Amazon basin research. Current laboratory work centers on molecular-level analyses of organic matter using state-of-the-art facilities at the Max Planck Institute.

Eugene A. Vaganovمشاهده پروفایل

استاد

• Dendrochronology
• Climate Change
• Forest Ecology
• +۳ مورد دیگر

Eugene A. Vaganov is a Scientific Adviser at Siberian Federal University and a full member of the Russian Academy of Sciences since 1997. He has held leadership roles including Rector of Siberian Federal University (2006–2018) and Director on Research at Sukachev Institute of Forest. His work focuses on dendrochronology, climate change impacts on boreal forests, and biogeochemical cycles. Biophysics (Krasnoyarsk State University, 1971) Ph.D. in Biological Sciences (1975) D.Sc. in Biological Sciences (1984) His research integrates tree-ring analysis with climate modeling to study: Climate reconstruction using dendroclimatic records Carbon balance in Siberian forests and bogs Stable isotope analysis in larch tree rings Permafrost forest ecosystem dynamics Global Change Biology in boreal regions Recent publications highlight expertise in: Millennial climate trends Isotopic signature interpretation Process-based dendroecological models Forest-steppe climate interactions Observatory-based ecological monitoring Scientific awards include: Order of Friendship (2017) Alexander Humboldt Prize (2003) Golden Sign Diploma (2001) RAS Sukachev Prize (2004) He has mentored over 40 students and researchers, including A. Kirdyanov and O. Sidorova, while collaborating with institutions like Max Planck Institute, University of Arizona, and Stockholm University.

Richard A. Davisمشاهده پروفایل

استاد

• Applied Probability
• Time Series Analysis
• Stochastic Processes
• +۴ مورد دیگر

Richard A. Davis is a Professor of Statistics at Columbia University and a Hans Fischer Senior Fellow at the Technical University of Munich's Institute for Advanced Study (TUM-IAS). He holds the Howard Levene Professorship in Statistics. His research focuses on applied probability, time series analysis, stochastic processes, and extreme value theory. Davis has contributed to nonlinear time series models, spatial modeling, and financial econometrics. He has held academic positions at Colorado State University and MIT, and has been recognized with awards including the Koopmans Prize, IBM Faculty Award, and fellowships from the Institute of Mathematical Statistics and American Statistical Association. His education includes a B.A. and Ph.D. in Mathematics from the University of California, San Diego. He co-authored influential textbooks like Time Series: Theory and Methods and has advised numerous papers on topics ranging from count time series to spatial extremes. Current research explores machine learning applications in time series and environmental risk modeling through collaborations like the STARMAP program.