جست‌وجوی استاد و پژوهشگر

Yuting Liمشاهده پروفایل

مدرس ارشد

• Sedimentary Geology
• Source-to-Sink Sediment Transport
• Paleoclimatology
• +۷ مورد دیگر

Yuting Li is a Senior Lecturer in the Department of Geography at the University of North Texas (UNT), where she teaches Earth Science, Geology, and Physical Geography. She advises students in the BA/BS in Geography (Physical) program and contributes significantly to undergraduate education in the geosciences. Her research focuses on sedimentary geology and source-to-sink sediment transport processes, investigating how tectonic, climatic, and anthropogenic factors influence continental erosion and sediment deposition. She specializes in interpreting geological records from coastal and marine basins to reconstruct past environmental and climatic conditions, with ongoing research centered on the Indus Submarine Canyon. Dr. Li employs a wide range of analytical techniques including detrital zircon and apatite fission track analysis, Nd and Sr isotopic geochemistry, clay mineral analysis, radiocarbon dating, major- and trace-element geochemistry, grain size analysis, and spectral reflectance. These methods support her work in provenance interpretation and paleoenvironmental reconstruction. Her research trends reflect a strong interdisciplinary approach combining sedimentology, geochemistry, and paleoclimatology to understand Earth surface dynamics over geologic timescales. Ph.D. in Geology, Louisiana State University Visiting Assistant Professor, Purdue University Dr. Li has taught 13 introductory and upper-level courses in geology, geography, and energy-related topics. While no specific grants or advising records are detailed in the provided text, her role as an advisor and experienced instructor highlights her commitment to student mentorship and academic development. She is affiliated with the Department of Geography at UNT, contributing to research and teaching within the College of Science.

Bernd Jaglaمشاهده پروفایل

پژوهشگر

• Bioinformatics
• Single-cell RNAseq analysis
• Cytometry data analysis
• +۷ مورد دیگر

Bernd Jagla is a Bioinformatics Research Scientist at the Pasteur Institute in Paris, France, where he has been working since 2009. Currently affiliated with the Bioinformatics and Biostatistics Hub (C3BI) and the Biomarker Discovery Platform, he previously held positions at Columbia University and Memorial Sloan Kettering Cancer Center in New York City. His educational background includes a PhD in Bioinformatics from the Free University Berlin (1999), a Chemistry Diploma from Free University Berlin & Technical University Munich (1989-1995), and Computer Science studies at Free University Berlin (1990-1991). Jagla's research focuses on bioinformatics applications for high-throughput biological data. His expertise spans single-cell RNAseq analysis, cytometry data processing, function prediction from sequence data, image analysis, mass spectrometry data analysis, and workflow management systems. He has developed several bioinformatics tools including SCHNAPPs for single-cell RNAseq analysis, KNIME extensions for Next Generation Sequencing, and Clean-ngs for adapter removal in sequencing data. His work demonstrates a strong emphasis on data quality assurance and visualization methodologies. His scientific achievements include: caBIG™ Embodying the Vision Award (2007) DFG Fellowship of the Graduiertenkolleg (1997-1999) Jagla actively contributes to bioinformatics training programs at Pasteur Institute, including the Bioinformatics program for PhD students financed by the INCEPTION program. He collaborates across multiple research teams and platforms, providing critical bioinformatics support for cytometry, next-generation sequencing, and microarray data analysis. His work bridges computational methods with biological applications, particularly in immunology and infectious disease research. As a key member of the Bioinformatics and Biostatistics Hub, he supports the Human Immunology Center and contributes to technological developments in bioinformatics infrastructure at the institute, working closely with the Transcriptome and Epigenome platform and the Biomarker Discovery Platform.

Patrice Collمشاهده پروفایل

استاد

• Atmospheric Science
• Environmental Chemistry
• Air Pollution
• +۹ مورد دیگر

Patrice Coll is a Professor at Université Paris-Est Créteil (UPD) and serves as the Director of LISA (Laboratoire Interuniversitaire des Systèmes Atmosphériques). His research spans multiple disciplines including atmospheric science, environmental chemistry, astrobiology, and environmental health. He leads an extensive research program investigating the connections between air pollution and respiratory health, as well as planetary science with a focus on Mars and Titan. Coll's research interests primarily focus on the fate of organic carbon , exobiology and astrochemistry , and oxidative and particulate pollution . His work bridges Earth's atmospheric systems with planetary exploration, particularly investigating organic molecules in extraterrestrial environments and their implications for life detection. He has made significant contributions to understanding atmospheric aerosols, both in urban environments on Earth and in simulated extraterrestrial conditions. Analysis of Coll's recent publications (2021-2025) reveals a strong interdisciplinary focus bridging atmospheric science , environmental health , and planetary exploration . His work demonstrates a consistent pattern of investigating organic molecules in various environments, from Parisian urban atmospheres to simulated Martian conditions. A notable trend is the increasing integration of health impacts with atmospheric chemistry , particularly regarding respiratory diseases like cystic fibrosis and COPD. His team has developed innovative experimental platforms like PolluRisk for studying air quality health impacts and has contributed significantly to Mars exploration science through instrument development and analysis of organic compounds in extraterrestrial environments. As Director of LISA, Coll leads research activities in atmospheric science, desert aerosol cycles, becoming organic carbon, exobiology, astrochemistry, spectroscopy, and pollution studies at multiple scales. His laboratory has developed specialized equipment including miniaturized gas chromatographs for field deployment and atmospheric simulation chambers that produce realistic urban atmospheres for preclinical exposure studies. The research group maintains strong collaborations with international space agencies and participates in major planetary exploration missions.

Stefan Steinerbergerمشاهده پروفایل

استاد

• Analysis
• Applied Mathematics
• Combinatorics
• +۵ مورد دیگر

Stefan Steinerberger is a Professor in the Department of Mathematics at the University of Washington, Seattle. He holds a PhD from the University of Bonn (2013) and specializes in Analysis, with research interests spanning Partial Differential Equations, Spectral Theory, Harmonic Analysis, and applications to Data Science and Mathematical Physics. His work often bridges theoretical and applied mathematics, addressing problems in geometry, combinatorics, and numerical methods. Education: PhD in Mathematics, University of Bonn, 2013 Research Interests: Steinerberger’s research focuses on Analysis, with emphasis on PDEs, Spectral Theory, and Harmonic Analysis. He explores interdisciplinary connections, such as applying analytic techniques to graph theory, optimization, and data science. Specific themes include eigenvalue estimates, dynamical systems, and the interplay between geometry and combinatorial structures. Awards & Recognition: While specific awards are not listed, his extensive publication record and affiliations with prestigious journals indicate significant contributions to the field. His work frequently appears in top-tier journals like Advances in Mathematics , Journal of Functional Analysis , and SIAM Journal on Discrete Mathematics . Teaching & Outreach: He teaches courses in the Department of Mathematics and is involved in outreach initiatives such as the Washington Experimental Mathematics Lab (WXML) and the UW Math Circle. His courses and research often emphasize problem-solving and real-world applications. Labs/Teams: Active in collaborations across mathematical disciplines, including work with researchers in combinatorics, probability, and numerical analysis. He maintains a personal website with resources on his research, teaching, and open problems.

Emma Alexanderمشاهده پروفایل

استادیار

• Computational Imaging
• Bio-Inspired Vision
• Physics-Based Computer Vision
• +۳ مورد دیگر

Emma Alexander is an Assistant Professor of Computer Science at Northwestern University's McCormick School of Engineering, with a courtesy appointment in Electrical and Computer Engineering. Her work bridges computational imaging, biological vision principles, and sustainable AI systems. She leads the Bio Inspired Vision (BIV) Lab, focusing on developing cameras and algorithms inspired by natural vision systems. Key research areas include bio-inspired color sensing, depth estimation, and physics-informed machine learning for scientific imaging. Education: PhD and MS in Computer Science from Harvard University, BS in Physics and Computer Science from Yale University. Research emphasizes reverse-engineering biological vision to advance imaging technologies. Notable projects include hyperspectral colorization, zebrafish optic flow analysis, and metalens-based depth sensors. Awards include Best Student Paper (ECCV 2016) and Best Demo (ICCP 2018). Her lab collaborates with astrophysicists through the SkAI Institute and engages in outreach through courses like Natural and Artificial Vision. Teaching includes graduate Computational Optics and an advanced undergraduate course on vision science. She mentors over 10 students annually, with undergraduates contributing to publications at CVPR and MNRAS.

Vasileios Lamposمشاهده پروفایل

دانشیار

• Machine Learning
• Natural Language Processing
• Artificial Intelligence
• +۳ مورد دیگر

Vasileios (Bill) Lampos is an Associate Professor in the Department of Computer Science at University College London (UCL), affiliated with the AI Centre. His research focuses on machine learning, natural language processing (NLP), and health informatics, with applications in disease surveillance, influenza modeling, and AI-driven solutions for public health challenges. Education includes a PhD and MSc in Computer Science from the University of Bristol (2008–2012). Lampos has held roles including Principal Research Fellow (2018–2021) and Senior Research Fellow (2015–2018) at UCL. He also served as a Research Scientist at Google (2014–2015). His research integrates social media and web search data to predict health trends, such as flu prevalence and vaccine efficacy, and to model transmission dynamics of diseases like COVID-19. Notable projects include the Virus Watch cohort study, supported by Google.org, and collaborations with Public Health England. Key publications highlight innovations in time series forecasting (e.g., DeformTime ), influenza modeling using web trends, and AI applications in autism education. His work has been featured in Nature journals and mainstream media. Lampos supervises PhD students and mentors researchers. He actively seeks collaborations for projects aligning with UCL’s AI and health priorities.

Renming Songمشاهده پروفایل

استاد

• Probability Theory
• Stochastic Analysis
• Markov Processes
• +۳ مورد دیگر

Renming Song is a Professor of Mathematics at the University of Illinois at Urbana-Champaign, with additional affiliations in the Department of Statistics. He holds a Ph.D. from the University of Florida (1993) and has been at UIUC since 1997. His research focuses on stochastic analysis, Markov processes, potential theory, and financial mathematics. Education: B.S. (1983), M.S. (1986) from Hebei University; Ph.D. (1993) from University of Florida. Affiliations: Professor in Mathematics and Statistics, UIUC; former visiting positions at Northwestern University and University of Michigan. Teaching: Currently teaches Math 461 (Probability Theory) and Math 432 (Set Theory and Metric Spaces). Past courses include Math 241 and Math 561. Research Interests: Song specializes in probability theory, with emphasis on Markov processes, branching processes, and their applications. Recent work includes studies on fractional Laplacians, supercritical killings, and asymptotic behaviors in branching systems. His research bridges stochastic analysis and potential theory, with contributions to non-symmetric jump processes and Dirichlet forms. Publications: Over 150 articles, including works in Communications in Mathematical Physics , Electronic Journal of Probability , and Journal of Functional Analysis . Recent topics include heat kernel estimates, tail probabilities, and asymptotic expansions in stochastic systems. Service: Member of NSERC’s Mathematics and Statistics Evaluation Group (2016–2019). Editorial roles at journals like Illinois Journal of Mathematics . Advisor to 11 Ph.D. students, including Hyunchul Park and Andrew Vlasic. Labs/Teams: Engaged in collaborative research on branching processes, stochastic dynamics, and financial mathematics through UIUC’s Mathematics Department and interdisciplinary initiatives.

• Applied and Computational Statistics
• Biostatistics and Bioinformatics
• Robust and Semiparametric Statistical Methods
• +۳ مورد دیگر

Douglas G. Simpson is a Professor of Statistics at the University of Illinois Urbana-Champaign (UIUC) and Affiliate Professor at the Beckman Institute for Advanced Science and Technology. He has held leadership roles, including Chair of the Department of Statistics (2000–2019) and Associate Director of the Institute for Mathematical and Statistical Innovation (2020–2022). His research focuses on applied computational statistics, biostatistics, robust statistical methods, functional data analysis, and quantitative image analysis. Education: BA in Mathematics (Carleton College, 1980), MS and PhD in Statistics (UNC Chapel Hill, 1983 and 1985). He has served on editorial boards for journals like the Journal of the American Statistical Association and Biometrics , and on NIH’s Biostatistical Research and Design Study Section. Awards include Fellowships from the American Statistical Association (2000), Institute of Mathematical Statistics (1998), and AAAS (2017). Recent work emphasizes preterm birth risk prediction via quantitative ultrasound, leveraging interdisciplinary collaborations. His publications explore statistical methodologies for functional data, medical imaging, and clinical decision-making. He advises on external relations for the Statistics Department and maintains active roles in professional societies like the ASA and SIAM. Key contributions include advancing robust statistical techniques, developing algorithms for medical image analysis, and leading institutional initiatives in statistical innovation. His work bridges theory and application, addressing challenges in biomedicine, environmental science, and public health.

• Network Analysis
• Statistical Learning
• Neuroimaging Data Analysis
• +۳ مورد دیگر

Subhadeep Paul is an Associate Professor of Statistics at The Ohio State University, within the Department of Statistics, College of Arts and Sciences. He joined the faculty in 2017 and is affiliated with the Translational Data Analytics Institute (TDAI). His research focuses on statistical inference in complex networks, neuroimaging data analysis, and high-dimensional data. Education: PhD in Statistics from the University of Illinois, Urbana-Champaign (2017). Research interests include network analysis, statistical learning, neuroimaging data analysis, and high-dimensional data analysis. He develops computationally feasible methods such as spectral clustering and matrix factorization for analyzing networks, with applications in neuroimaging and other complex systems. Recent work emphasizes community detection in multi-layer networks, Hawkes process models for temporal dynamics, and spatial transcriptomics analysis. His publications explore topics like peer influence modeling in therapeutic networks, spatial autoregressive frameworks, and virocell metabolic reprogramming. No scientific awards explicitly mentioned. Advising/Grants: No student advisees or grant information provided in the text. Affiliated with TDAI for interdisciplinary data science collaboration.

• Number Theory
• Analytic Number Theory
• L-functions
• +۴ مورد دیگر

James W. Cogdell is a Professor of Mathematics at the Department of Mathematics, Ohio State University. His research focuses on Number Theory, Analytic Number Theory, L-functions, Converse Theorems, and related areas. He holds a PhD from Yale University (1981). His work emphasizes the Langlands Program, functoriality, and automorphic forms. He has collaborated extensively with figures like I.I. Piatetski-Shapiro and F. Shahidi. Research Interests: Cogdell's primary areas include the analytic theory of L-functions for GL(n), converse theorems, and their applications to number theory. He explores topics like exterior and symmetric square L-functions, gamma factors, and the stability of root numbers. His work bridges representation theory and algebraic number theory through the Langlands conjectures. Publications Trends: Recent works focus on Kronecker limit formulas, Weil reciprocity, Mahler measures, and p-adic groups. Earlier contributions addressed functoriality for classical groups, exterior-square L-functions, and the Langlands correspondence. Over 30 papers span foundational theory to applications in complex analysis and algebraic geometry. Collaborations & Mentions: Co-authored major results with Piatetski-Shapiro and Shahidi, including functoriality for classical groups and stability of gamma factors. His 2004 work on functoriality is particularly influential. Active in lecture series (Fields Institute, Park City) and conference proceedings. Teaching: Taught advanced courses like Math 6112 (Spring 2020), focusing on category theory foundations and profinite groups. Syllabi and assignments emphasize abstract algebra and number theory fundamentals.

Cong Maمشاهده پروفایل

استادیار

• Mathematics of Data Science
• Reinforcement Learning
• Transfer Learning
• +۳ مورد دیگر

Cong Ma is an Assistant Professor in the Department of Statistics at the University of Chicago. He holds a PhD from Princeton University (2020), advised by Yuxin Chen and Jianqing Fan, and a BEng from Tsinghua University (2015). Previously, he was a postdoctoral researcher at UC Berkeley under Martin Wainwright. His research focuses on the mathematics of data science, emphasizing reinforcement learning, transfer learning, multi-modal learning, high-dimensional statistics, and nonconvex optimization. Key areas include developing computationally and statistically efficient methods for large-scale data problems, with recent work on contrastive learning, contextual bandits, and robust matrix completion. Education: PhD in Operations Research & Financial Engineering, Princeton University (2020) BEng in Electrical Engineering, Tsinghua University (2015) Awards: SIAM Activity Group on Imaging Science Best Paper Prize (2024) Teaching: STAT 253/317: Introduction to Probability Models (Winter 2025) STAT 28000: Optimization (Winter 2025) Machine Learning (Autumn 2022) His work bridges theoretical insights with practical applications, often addressing challenges in statistical guarantees for optimization algorithms and distributionally robust learning. Recent highlights include contributions to multi-modal contrastive learning and batched contextual bandits.

• Theoretical Physics
• Quantum Gravity
• Holography
• +۳ مورد دیگر

Prof. Jan de Boer is a faculty member at the University of Amsterdam's Faculty of Science, affiliated with the Institute for Theoretical Physics (ITF). He holds the title of Professor and focuses on theoretical physics, particularly in quantum gravity, holography, and string theory. Research Interests: His work explores the intersection of quantum information theory, conformal field theories (CFTs), and gravitational physics. Key areas include black hole microstates, entanglement entropy, AdS/CFT correspondence, and the geometric foundations of spacetime in quantum gravity. Recent Publications: His 2024 research investigates wormholes, random tensor models, and semiclassical gravity principles, while 2023 contributions analyze modular Hamiltonians, Carroll symmetry, and path integral complexity. Earlier works (2022-2011) span topics like replica wormholes, thermalization in CFTs, and exotic branes in string theory.

Jeffrey Caseمشاهده پروفایل

استاد

• Geometric Analysis
• Conformal and CR Geometry
• Einstein and Poincaré-Einstein metrics
• +۳ مورد دیگر

Jeffrey Case is a Professor of Mathematics at The Pennsylvania State University, affiliated with the Department of Mathematics and the Institute for Gravitation and the Cosmos. He holds a Ph.D. from the University of California, Santa Barbara, awarded in 2010. His research focuses on geometric analysis, conformal and CR geometry, smooth metric measure spaces, and global Riemannian and Lorentzian geometry. His work often explores the interplay between differential geometry, partial differential equations, and mathematical physics. Key research interests include the study of Einstein and Poincaré-Einstein metrics, renormalized curvature integrals, and the properties of geometric operators such as the GJMS operators and CR Paneitz operator. His recent publications address topics like the Gauss-Bonnet formula for minimal submanifolds, formal self-adjointness of bidifferential operators, and sharp inequalities in trace-free matrix spaces. Dr. Case’s contributions extend to the development of geometric invariants and the exploration of conformally variational Riemannian invariants. He has investigated boundary value problems, such as the Neumann problem on the Clifford torus, and nonuniqueness results in Yamabe-type problems. His work on the weighted ambient metric and smooth metric measure spaces has advanced understanding of quasi-Einstein metrics and their applications in geometric analysis. His research also engages with CR geometry, including the study of Q-prime curvature in three dimensions and the CR Paneitz operator’s role in stability questions. He frequently employs techniques from global analysis, spectral geometry, and geometric measure theory in his investigations.

Alexei Novikovمشاهده پروفایل

استاد

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

Alexei Novikov is a Professor in the Department of Mathematics at Pennsylvania State University, affiliated with the Eberly College of Science. He holds a Ph.D. from Stanford University (1999). His research focuses on applied analysis, probability, stochastic processes, and their applications in fluid dynamics, imaging science, and materials modeling. Key areas include wave propagation in complex media, sparse signal recovery, and homogenization techniques for heterogeneous systems. His teaching includes advanced courses such as Functional Analysis (Math 503), Stochastic Calculus (Math 519), and Differential Equations (Math 251). He has led NSF-funded research on transport phenomena in heterogeneous media and contributed to algorithm development for imaging in complex environments. Notable works include studies on eddy viscosity in cellular flows, network approximation methods, and fractional kinetic processes. Novikov’s recent work emphasizes interdisciplinary applications, including neural network approaches for super-resolution imaging and stochastic models for knowledge diffusion. His publications span journals like Communications on Pure and Applied Mathematics , SIAM Journal on Mathematical Analysis , and Inverse Problems .

Maggie Niuمشاهده پروفایل

دانشیار

• Network Models
• Latent Variable Models
• Bayesian Statistics
• +۳ مورد دیگر

Maggie Niu is an Associate Professor of Statistics and Director of the Statistical Consulting Center at the Pennsylvania State University. She leads interdisciplinary research in statistical methodology with applications in health and social sciences. Her work emphasizes Bayesian methods, latent variable models, and network models. She actively collaborates with researchers across disciplines through the Statistical Consulting Center, mentoring graduate students in applied projects. Education: Ph.D. in Statistics from the University of Washington (advisor: Peter Hoff), M.S. in Applied Mathematics from University of Minnesota, B.S. in Applied Mathematics from Peking University. Research focuses on developing statistical models for complex data structures, including network dynamics, latent variables, and multivariate analysis. Applications span public health (e.g., HIV epidemiology), social sciences (e.g., voting behavior modeling), and environmental science. Her recent work includes estimating hidden population sizes (e.g., female sex workers in sub-Saharan Africa) and analyzing disease outbreak detection via exported cases. Service contributions include leadership roles in the ASA Section on Statistical Consulting, editorial work for journals like JASA and AOAS, and founding the Chinese Statistical Consulting and Collaboration League. She is actively involved in advancing statistical methodology through workshops like the Workshop on Statistical Network Analysis and Beyond (SNAB). Labs/Teams: Director of Penn State's Statistical Consulting Center, leading a team of researchers and students in collaborative applied projects. Previously directed the Statistical Consulting Center at Tsinghua University (2018-2019).