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Ethan MeyersView profile

Visiting Professor

• Neural Coding
• Computational Neuroscience
• Visual Processing
• +3 more

Ethan Meyers holds dual academic positions as a Visiting Professor in the Department of Statistics and Data Science at Yale University and an Associate Professor of Statistics at Hampshire College. He also maintains a research affiliation with the Center for Brains, Minds and Machines at MIT. His academic work bridges neuroscience, statistics, and computational methods. Dr. Meyers' research focuses on understanding how information is coded in neural activity, with particular emphasis on high-level visual and cognitive brain regions. His work combines experimental neuroscience with computational approaches, developing sophisticated tools to analyze high-dimensional neural recordings. His research spans neural population coding, working memory systems, face recognition mechanisms, and object perception. Analysis of his publication record from 2004-2018 reveals a consistent trajectory in computational neuroscience, with increasing focus on population-level neural dynamics. His work shows strong interdisciplinary connections between neuroscience, machine learning, and statistics, particularly in developing methods for decoding neural activity. Key themes include dynamic population coding, neural representations of visual information, and the development of computational tools for neural data analysis. Dr. Meyers has developed the Neural Decoding Toolbox, a significant contribution to the field that enables researchers to analyze neural population activity. His work with primate visual systems, particularly the face patch system, has provided insights into how neural information is processed and represented. His research demonstrates expertise in both experimental design and computational analysis of neural data.

Juana MorenoView profile

Professor

• Condensed Matter Physics
• Quantum Transport
• Strongly Correlated Electron Systems
• +5 more

Juana Moreno is the Patricia Hewlett Bodin Distinguished Professor of Physics at Louisiana State University's Department of Physics & Astronomy, part of the College of Science. She holds a Ph.D. from Rutgers University (1997). Her research focuses on transport and magnetic properties of correlated electron systems, including diluted magnetic semiconductors, heavy fermion compounds, and low-dimensional materials. She utilizes computational methods like dynamical mean-field theory and density matrix renormalization group approaches. Recent work includes nanoscale quantum dots, orbital degrees of freedom in heavy-fermion modeling, and first-principles-based simulations. Key research themes include Anderson localization, quantum criticality in Hubbard models, and applications of machine learning (e.g., quantum convolutional neural networks). She has published extensively in Phys. Rev. Lett. , Phys. Rev. B , and interdisciplinary journals. Her work bridges computational physics with experimental relevance, addressing both fundamental and applied challenges in materials science. Awards include the prestigious Patricia Hewlett Bodin Distinguished Professor title. Her educational contributions include developing computational thinking curricula for high schools and teacher training programs. Recent projects involve automatic grading systems for computational courses and integrating culturally relevant pedagogy in coding education. Research collaborations span LSU's Hearne Institute for Theoretical Physics and interdisciplinary initiatives like GeauxDock for protein functional annotation.

Yandi ShenView profile

Assistant Professor

• Nonparametric Statistics
• Semiparametric Statistics
• High-Dimensional Inference
• +6 more

Yandi Shen is an Assistant Professor in the Department of Statistics and Data Science at Carnegie Mellon University (CMU), part of the Dietrich College of Humanities and Social Sciences. He holds a Ph.D. in Statistics from the University of Washington (2021), advised by Fang Han and Daniela Witten. Prior to joining CMU, he was a Kruskal Instructor in Statistics at the University of Chicago (2021–2023) and a postdoctoral researcher at Yale University under Zhou Fan. His research focuses on nonparametric and semiparametric statistics, high-dimensional inference, applied probability, and optimization algorithms. Education: Ph.D. in Statistics (University of Washington, 2021); Postdoctoral work at University of Chicago and Yale University. Research Interests: Nonparametric and semiparametric methods High-dimensional statistical inference Applied probability and stochastic processes Empirical Bayes methods Optimization and algorithm design Statistical machine learning Teaching Experience: University of Washington: Teaching Assistant for advanced inference courses (STAT 581-583), probability (STAT 394-395), and applied statistics (STAT 528). University of Chicago: Instructor for courses like STAT 22400 (Applied Regression Analysis) and STAT 33611 (Gaussian Processes). Recent Research Trends: His publications emphasize theoretical advancements in high-dimensional statistics, including empirical Bayes estimation, gradient flows, and optimization in overparameterized models. Key areas include analyzing phase transitions in spline regression, universality in regularized estimators, and nonparametric mixture models under optimal transport distances. Labs/Teams: While no specific lab is named, his collaborative work spans statistical theory and applications, often involving interdisciplinary teams in data science and machine learning.

• Atomic-resolution electron microscopy
• Semiconductor heterostructures
• Nanostructures
• +7 more

David Smith is Regents' Professor in the Department of Physics at Arizona State University and concurrently a Distinguished Global Futures Scientist within the Global Futures Scientists and Scholars initiative. Since 1984 he has directed ASU’s Center for High Resolution Electron Microscopy and served as Principal Investigator for the NSF National User Facility for High Resolution Electron Microscopy. His leadership roles also include Associate Chair for Space & Infrastructure in Physics (2013–present), Director of the John M. Cowley Center (1991–2006) and President of the Microscopy Society of America (2009). Education D.Sc., University of Melbourne, Australia (1988) Ph.D., University of Melbourne, Australia (1978) B.Sc. (Hons.), University of Melbourne, Australia (1970) Research Overview David Smith’s research is devoted to the development and application of atomic-resolution electron microscopy . Over four decades he has pioneered instrumentation and techniques that allow direct visualization of atomic arrangements in solids, enabling breakthrough insights into semiconductor heterostructures , nanostructures , oxide/semiconductor interfaces , magnetic multilayers and quantum-confined systems . Recent emphasis targets wide-band-gap nitrides for optoelectronics, two-dimensional electron gases at oxide interfaces, and nanoscale dopant distributions critical for next-generation electronic devices. His work integrates aberration-corrected TEM , off-axis electron holography , in-situ microscopy and quantitative image simulation to correlate structure with functional properties at the sub-ångström scale. Selected Scientific Awards & Honors Helmholtz International Fellowship Award, Helmholtz Foundation (2014) Distinguished Physical Scientist Award, Microscopy Society of America (2014) Harold Rose Distinguished Lectureship Award, German Microscopy Society (2019) Fellow, Materials Research Society Fellow, American Physical Society Fellow, Microscopy Society of America Fellow, Institute of Physics (U.K.) Grants & Doctoral Advising Smith has served as PI or co-PI on numerous federal and industry grants exceeding tens of millions of dollars. Current and recent funding includes: DOD-AFOSR – “Modulation-Doped Heterovalent Structures for High-Speed Electronic Device Applications” (2015–2018) UT-Austin – “Charge Transfer at Metal Dielectric Interfaces under Extreme Environments” (2014–2017) WYLE LABS – “Advanced Nanostructural Techniques for Nitride Device Operation” (2013–2016) NSF-MPS-PHY – “SusChEM: FRG: Molecular routes to new classes of polar/non-polar alloy semiconductors” (2013–2016) DOD-ARMY-ARO – “Advanced Microscopy and Analytical Studies for Hg-based Infrared Detector Materials and Substrates” (2013–2016) He routinely supervises Ph.D. dissertations and M.S. theses; course offerings include PHY 799 Dissertation , MSE 554/555 Electron Microscopy II & Lab , and PHY 792 Research . Laboratories & Teams Smith directs operations within the John M. Cowley Center for High Resolution Electron Microscopy , one of the premiere university-based microscopy facilities in the United States, housing multiple aberration-corrected TEMs, environmental TEMs, and dedicated specimen-preparation suites. The center supports interdisciplinary teams spanning physics, materials science, chemistry, electrical engineering and geoscience, and hosts national and international visiting researchers via the NSF user facility program.

• Algorithms
• Machine Learning
• Deep Learning
• +6 more

David Li serves as Program Director for Data Analytics and Visualization at the Department of Graduate Computer Science and Engineering, part of the Katz School of Science and Health at Yeshiva University. His research focuses on algorithmic modeling, machine learning, and interdisciplinary applications in fields such as computational chemistry, finance, and ecology. He has published extensively on topics including MapReduce optimization, reinforcement learning frameworks, and ecological forecasting. Notable work includes the Best Regional Paper Award-winning Dual-Path Deep Learning Framework for Video Quality Assessment presented at IEEE ICCE 2025. Education: Details not provided in current text. Courses Taught: Deep Reinforcement Learning, Numerical Methods, Data Acquisition & Management, Independent Study. Grants & Collaborations: Collaborates with students on projects across algorithm design, machine learning, and interdisciplinary applications. His work bridges theoretical computer science with practical challenges in diverse domains, emphasizing scalable solutions for complex systems.

Jim E. GriffinView profile

Professor

• Bayesian Nonparametric Methods
• Regression Modelling with High-Dimensional Data
• Time Series Modelling in Econometrics and Finance
• +4 more

Jim E. Griffin is a Professor of Statistical Science at University College London's Department of Statistical Science. His research focuses on Bayesian Nonparametric Methods, Regression Modelling with High-Dimensional Data, and Time Series Modelling in Econometrics and Finance. He leads multiple collaborative projects including Bayesian Modelling of Macroeconomic Data (with Maria Kalli), Athletic Performance Passport for anti-doping detection (with James Hopker), and Efficient Computational Methods for Bayesian Variable Selection (with Sam Livingstone and Xitong Liang). Key research areas include applications in genomics, environmental science (via eDNA modelling with Eleni Matechou), and computational methods for model uncertainty visualization. Preprints and code for projects like Cartesian credible sets and tensor vector autoregression models are available via arXiv.org and GitHub repositories maintained by collaborators. His work bridges theoretical advancements in Bayesian statistics with practical applications in finance, ecology, and sports science. Collaborations span disciplines, with notable contributions to statistical methodologies for high-dimensional problems and model interpretability frameworks.

• Algorithms
• Computational Geometry
• Optimization
• +3 more

Prof. Bernd Gärtner is a Lecturer at the Department of Computer Science of ETH Zürich. His research focuses on algorithms, computational geometry, optimization, and theoretical computer science. He has contributed significantly to the study of unique sink orientations, combinatorial algorithms, and algorithm design. Gärtner teaches courses such as 'Algorithms, Probability, and Computing' and 'Geometry: Combinatorics and Algorithms,' reflecting his expertise in foundational computer science topics. His work bridges discrete mathematics and algorithmic theory, addressing challenges in linear programming, combinatorial optimization, and geometric algorithms. His recent research explores the realizability of structures in unique sink orientations, optimization techniques for symbolic visibility, and the analysis of opinion dynamics in networks. He has published extensively on topics including ARRIVAL game complexity, sampling algorithms, and high-dimensional learning models. His contributions also extend to the development of efficient algorithms for geometric problems and the study of cellular automata systems. Teaching: Courses include Algorithms, Probability, and Computing (252-0209-00L), Linear Algebra (401-0131-00L), and Geometry: Combinatorics and Algorithms. Research Interests: Algorithms, computational geometry, optimization, combinatorics, and theoretical computer science. Labs/Teams: Affiliated with the Institute of Theoretical Computer Science at ETH Zürich.

Wolf SingerView profile

Professor

• Neuroscience
• Cognitive Science
• Neural Dynamics
• +3 more

Wolf Singer is a renowned neuroscientist and Professor serving as Department Director at the Max Planck Institute for Brain Research in Frankfurt am Main. His research focuses on understanding the neuronal basis of higher cognitive functions, particularly the role of oscillatory dynamics and synchronization in cortical networks. Key areas include how the brain integrates sensory information, processes attention, and forms predictive representations through high-dimensional nonlinear dynamics. His lab investigates population dynamics in the visual cortex of trained monkeys using chronically implanted microelectrode arrays, emphasizing the interplay between bottom-up sensory input and top-down expectations. Singer has contributed significantly to theories of neural synchrony in disorders like schizophrenia and has explored philosophical questions about consciousness and free will. Notable achievements include the 2015 Ehrenplakette der Stadt Frankfurt am Main. His work bridges experimental neuroscience with computational models, often collaborating with researchers across disciplines. The Singer Lab hosts a team including researchers like Igor Dubinin and Andreea Lazar, focusing on topics such as predictive coding, neural plasticity, and EEG/MEG studies of perceptual processes. Singer has authored over 200 publications, with key contributions in journals like PNAS, Neuron, and Trends in Cognitive Sciences. Media engagements include interviews on topics like animal research ethics and the limits of artificial intelligence. His research underscores the importance of nonlinear dynamics and recurrent network architectures in understanding brain function.

Andrew GelmanView profile

Professor

• Bayesian Statistics
• Political Methodology
• Statistical Inference
• +6 more

Andrew Gelman is the Higgins Professor of Statistics and Professor of Political Science at Columbia University, where he also serves as director of the Applied Statistics Center. His dual appointments reflect his interdisciplinary approach to research and teaching, bridging statistical methodology with political science applications. Gelman earned his Ph.D. from Harvard University in 1990. His educational background established the foundation for his influential career that combines rigorous statistical methodology with substantive social science inquiry. He has maintained a strong presence in both academic communities throughout his career, contributing to the development of statistical methods while applying them to pressing questions in political science and public policy. Gelman's research spans an exceptionally wide range of topics at the intersection of statistics and social science. His work addresses fundamental questions in voting behavior, electoral systems, and political representation, while simultaneously advancing methodological innovations in Bayesian statistics, multilevel modeling, and data visualization. He has made significant contributions to understanding why it is rational to vote, why campaign polls are variable despite predictable elections, and how redistricting affects democracy. His methodological work spans statistical inference challenges in diverse contexts from toxicology to medical imaging, from arsenic exposure in Bangladesh to radon levels in homes, and from police practices to social network analysis. His development of multilevel regression and poststratification (MRP) has become a standard technique in survey analysis and small-area estimation. Analysis of Gelman's recent publications reveals a continued focus on foundational statistical methodology with applications across multiple domains. His work demonstrates consistent attention to practical implementation challenges in Bayesian computation, causal inference, and survey methodology. A strong theme throughout his recent work is addressing the replication crisis through improved statistical practice, with particular emphasis on model checking, transparent reporting of uncertainty, and the integration of Bayesian methods with machine learning approaches. His research increasingly focuses on the practical implementation challenges of advanced statistical methods in real-world settings. Outstanding Statistical Application award from the American Statistical Association Best article published in the American Political Science Review Council of Presidents of Statistical Societies award for outstanding contributions by a person under the age of forty As director of Columbia's Applied Statistics Center, Gelman oversees a hub for interdisciplinary statistical research and collaboration. He has mentored numerous students and researchers through the Center's activities, fostering a community that applies advanced statistical methods to real-world problems across various domains. His teaching includes graduate courses in quantitative political research and applied regression methods, reflecting his commitment to training the next generation of researchers in robust statistical practice. Gelman has taught courses including Principles of Quantitative Political Research, Quantitative Political Research, and Quantitative Methods II: Applied Regression. Gelman leads research teams focused on developing and applying advanced statistical methods to social science questions. His work often involves collaboration across disciplines, bringing statistical expertise to address substantive questions in political science, public health, and social policy. The Applied Statistics Center under his direction serves as a nexus for methodological innovation and application, connecting statisticians with domain experts to tackle complex data challenges while promoting best practices in statistical analysis and communication.

Brun ChristopheView profile

Associate Professor

• Geophysical Fluid Dynamics
• Turbulence Modeling
• Katabatic Winds
• +3 more

Christophe Brun is an Associate Professor at Laboratoire des Écoulements Géophysiques et Industriels (LEGI) , Université Grenoble Alpes, specializing in geophysical fluid dynamics and turbulence modeling. His research focuses on katabatic winds, atmospheric boundary layers, and Görtler instability through field experiments and numerical simulations. Key Research Areas: Katabatic Flow Dynamics Large Eddy Simulation (LES) Stably Stratified Turbulence Mountain Meteorology Recent Publications (2024): Analysis of turbulent boundary layers in alpine katabatic flows Wave turbulence evidence and Bolgiano spectra 3D velocity measurement techniques Technical Affiliation: Member of the MEIGE team at LEGI, working with advanced rotating platforms like Coriolis for geophysical flow experiments.

• Quantum Computing
• Machine Learning
• Quantum Algorithms
• +4 more

Michael Kastoryano is an Associate Professor in the Machine Learning group at the University of Copenhagen (DI KU). His research focuses on quantum computing, quantum algorithms, and their intersections with machine learning and theoretical physics. He explores topics such as quantum-inspired differential equation solvers, tensor network simulations, and quantum thermal state preparation. His work also delves into quantum error correction, quantum chemistry simulations, and the expressive power of neural network models for quantum systems. Key research interests include quantum algorithms for complex systems, quantum Gibbs samplers, and the development of efficient computational methods for quantum phenomena. His recent publications highlight advancements in low-rank adapters for quantized pretraining, coarse-to-fine tensor representations, and the evaluation of quantum advantage in computational chemistry. Dr. Kastoryano's contributions span theoretical frameworks and practical implementations, with a particular emphasis on bridging quantum computing and machine learning. His articles reflect a deep engagement with both foundational quantum mechanics and applied computational challenges. No scientific awards are listed. His work has been supported through collaborations within the University of Copenhagen's research infrastructure, focusing on machine learning and quantum technologies.

Roger WheateView profile

Professor

• Geographic Information Systems (GIS)
• Remote Sensing
• Cartography
• +7 more

Roger Wheate is a Professor and GIS Coordinator in the Department of Geography, Earth, and Environmental Sciences at the University of Northern British Columbia (UNBC), Faculty of Environment. He holds a PhD and BSc Honours from the University of St. Andrews and an MA from Queen's University at Kingston. He previously served as Graduate Program Chair for the Natural Resources and Environmental Studies (NRES) program. Education: PhD, University of St. Andrews MA, Queen's University at Kingston BSc Honours, University of St. Andrews Dr. Wheate's research focuses on the integration of geomatic sciences, particularly GIS and remote sensing, in natural resource and environmental studies. His expertise includes cartography, map design, terrain visualization, mountain cartography, and glacier mapping using satellite imagery. He specializes in extracting topographic and environmental features from digital elevation models and satellite data for applications in ecology, conservation, and hazard assessment. His publication record shows a consistent focus on advancing geospatial techniques in mountainous and remote environments. Key trends include the development of methods for relief depiction, shaded relief analysis, principal component transformation for satellite data, and tactile map creation from GIS outputs. His work spans both theoretical cartographic innovation and applied environmental mapping, particularly in Canadian mountain systems. Professional Affiliations: Canadian Cartographic Association Friends of Nose Hill UNBC GIS Lab Dr. Wheate has supervised several graduate students in projects related to wildlife habitat modeling, vegetation phenology, and grizzly bear telemetry. His courses include GEOG 205 (Cartography and Geomatics), GEOG 300 (Geographical Information Systems), GEOG 440 (Internship), and GEOG 499 (Independent Studies). He is actively involved in research and education, with no indication of part-time status or retirement.

• Rocket Propulsion
• Hypersonics
• Cyberphysical Integration
• +5 more

Kazuki Maeda is an Assistant Professor in the School of Aeronautics and Astronautics at Purdue University. He holds a Ph.D. from the California Institute of Technology (2018), an M.S. from Caltech (2014), and a B.S. from The University of Tokyo (2013). His research focuses on complex flow dynamics, rocket propulsion, hypersonics, and cyberphysical integration, combining physics-based modeling, high-performance computing, and machine learning. He leads the Maeda Research Group, which develops advanced frameworks for simulating and optimizing engineering systems. Key awards include the Richard Bruce Chapman Memorial Award and Stanford-CTR Postdoctoral Fellowship. Research interests emphasize propulsion systems, high-speed flows, and computational methods. Publications span bubble dynamics, reactive shock waves, and neural network applications in flow analysis. The group collaborates on heterogeneous computing frameworks for combustion and fluid dynamics simulations. Prospective students and postdocs are encouraged to apply through Purdue’s AAE programs. Awards: Richard Bruce Chapman Memorial Award, Funai Foundation Scholarship, Stanford-CTR Fellowship Labs/Teams: Maeda Research Group (Complex Flow & Cyber-physical Laboratory) Grants: Not explicitly listed, but research is supported by institutional and collaborative initiatives.

Sabre KaisView profile

Professor

• Quantum Chemistry
• Quantum Computing
• Quantum Information Theory
• +4 more

Sabre Kais is a Distinguished Professor of Chemistry and Professor of Chemical Physics at Purdue University, with courtesy appointments in Computer Science and Physics. He joined Purdue in 1994 as an Assistant Professor and became a full professor in 2002. His research focuses on quantum information, quantum computing for complex systems, and quantum machine learning applications in chemistry and materials science. He holds leadership roles such as Research Director of the theory group at Qatar Environment and Energy Research Institute and External Research Professor at the Santa Fe Institute. Education: BS, MS, and PhD in Chemical Physics from the Hebrew University (1983–1989), followed by a postdoc at Harvard University (1989–1994). Research Interests: Quantum algorithms for quantum chemistry, entanglement in chemical reactions, quantum machine learning for drug discovery, and quantum simulation of spin liquids. His work bridges quantum information science with practical applications in chemistry and physics. Notable contributions include quantum annealing for factorization, entanglement-based chemical reaction analysis, and quantum computing with polar molecules. Publications: Over 230 peer-reviewed articles, including influential works on quantum machine learning for electronic structure calculations and quantum simulation of avian compass dynamics. Recent focus areas include quantum algorithms for many-body systems and quantum state tomography techniques. Awards: NSF Career Award (1997), Guggenheim Fellowship (2005), APS Fellow (2007), and the Herbert Newby McCoy Award (2019). His work has been recognized for advancing quantum technologies in chemistry. Labs/Teams: Leads research groups at Purdue and QEERI, collaborating on projects like quantum information for quantum chemistry and quantum computing applications in energy research.

Bingni Wen BruntonView profile

Professor

• Neuroscience
• Applied Mathematics
• Data Science
• +5 more

Bingni Wen Brunton is a Professor of Biology and Richard & Joan Komen University Chair at the University of Washington , with adjunct roles in Applied Mathematics, Computer Science & Engineering, and affiliations at the eScience Institute and Institute of Neuroengineering. She holds a B.S. in Biology from Caltech (2006) and a Ph.D. in Neuroscience from Princeton (2012). Her research focuses on data-driven approaches to neuroscience , including neural decoding, sparse sensing, and dynamical systems modeling. She has pioneered tools like Anipose for 3D pose estimation and developed methods for analyzing high-dimensional neural data. Brunton is recognized for interdisciplinary work bridging biology, engineering, and computer science, supported by grants from NSF, NIH, DOD, and the Weill Neurohub. She mentors a diverse lab of postdocs and students in computational neuroscience and neuroengineering. Education : Ph.D. in Molecular Biology & Neuroscience, Princeton University, 2012 B.S. in Biology, California Institute of Technology (Caltech), 2006 Research Interests : Data-driven models of neural systems Sparse sensor placement for control Quantifying natural behaviors Neural-inspired machine learning Brain-computer interfaces Awards : Alfred P. Sloan Fellowship (2016) UW Innovation Award (2017) Air Force Young Investigator (2018) Weill Neurohub Investigator (2020) Lab & Mentorship : Advises over 10 students/postdocs across Biology, Neuroscience, and Engineering programs. Lab alumni hold faculty positions at Bryn Mawr College, University of Nevada Reno, and industry roles at Amazon/Meta. Collaborates widely on grants involving neuroengineering, sensor design, and computational methods. Labs & Teams : Leads the Brunton Lab at UW, part of a network including the eScience Institute and Neuroengineering programs.