جست‌وجوی استاد و پژوهشگر

Partha Sarathi Deyمشاهده پروفایل

دانشیار

• Probability Theory
• Statistical Physics
• First Passage Percolation
• +۷ مورد دیگر

Partha Sarathi Dey serves as Associate Professor in both Mathematics and Statistics at the University of Illinois at Urbana-Champaign, joining the faculty in 2014 after postdoctoral appointments at NYU's Courant Institute and the University of Warwick. His academic credentials include: Ph.D. in Statistics from UC Berkeley (2010) Undergraduate and Masters degrees from Indian Statistical Institute, Kolkata, specializing in Mathematical Statistics and Probability Dr. Dey's research bridges Probability Theory and Statistical Physics, with expertise in First/Last Passage Percolation, Random Growth Models, Stein's Method, Concentration Inequalities, Spin Glasses, Random Graphs, and Random Matrices. His work develops rigorous probabilistic frameworks for physical systems. Recent publications (2023-2025) demonstrate consistent focus on disordered systems and phase transitions, examining random walks on discrete tori, nonlinear Schrödinger equations in higher dimensions, critical-strip path structures, monomer-dimer models, and spin glasses under external fields. These studies reveal deep connections between probabilistic fluctuations and thermodynamic behavior. His distinguished recognitions include: Simons Fellowship Harrison Early-Career Fellowship Though specific advising records and grants are unreported, his extensive co-authorship network reflects active collaboration across international research communities in mathematical physics.

• Algorithmic Fairness
• Responsible AI
• Deep Learning
• +۵ مورد دیگر

Golnoosh Farnadi is an Assistant Professor at McGill University's School of Computer Science , an Adjunct Professor at Université de Montréal , and a Visiting Faculty Researcher at Google Research . She holds the Canada CIFAR AI Chair and is a core academic member of Mila – Quebec AI Institute . Her research focuses on algorithmic fairness , responsible AI , and optimization . She founded the EQUAL Lab (EQuity & EQuality Using AI and Learning algorithms) to address bias and discrimination in AI systems. Key publications explore fairness in kidney exchange programs, generative model geometry, multilingual LLM de-biasing, and prototype-based recommender systems. Her work bridges causal inference, adversarial robustness, and ethical AI. Google Award for Inclusion Research (2023) Women in AI Awards North America Finalist (2023) Facebook Privacy Enhancing Technologies Award (2021) IVADO Postdoctoral Fellowship (2018–2021) She has supervised over 15 PhD and Master's students, including Prakhar Ganesh (McGill) and William St-Arnaud (Université de Montréal). Her teaching includes Responsible AI and Machine Learning courses at McGill and HEC Montréal.

Jun Yangمشاهده پروفایل

استادیار

• Computational Statistics
• Machine Learning
• Bayesian Statistics
• +۳ مورد دیگر

Jun Yang is a Tenure Track Assistant Professor at the Department of Mathematical Sciences, University of Copenhagen. His research spans computational statistics and machine learning, with a focus on high-dimensional inference, time series analysis, and Monte Carlo methods. Current Position: Tenure Track Assistant Professor, University of Copenhagen (2023–present) Previous Role: Florence Nightingale Bicentennial Fellow, University of Oxford (2020–2023) Education: Ph.D. in Statistics, University of Toronto (2020), advised by Daniel M. Roy and Jeffrey S. Rosenthal Research Interests: Jun’s work addresses the intersection of computational statistics and machine learning, including: - High-dimensional Markov chain Monte Carlo (MCMC) algorithms - Bayesian variable selection in complex models - Spectral inference for nonlinear time series - Quantitative bounds and complexity analysis for MCMC Publications: His publications highlight advancements in high-dimensional sampling, time series analysis, and algorithm design. Key contributions include: - Dimension-free mixing results for Bayesian variable selection - Stereographic projection techniques for MCMC - State-domain change point detection in nonlinear regression Awards: Florence Nightingale Bicentennial Fellow, University of Oxford (2020–2023) Collaborations: Jun collaborates with researchers like K. Łatuszyński, G.O. Roberts, and J.S. Rosenthal, advancing statistical theory and applications in econometrics, machine learning, and stochastic processes.

• Fixed Point Theory
• Nonlinear Functional Analysis
• Operator Theory
• +۷ مورد دیگر

Jesus Garcia Falset is a Full Professor in the Department of Mathematical Analysis at the Faculty of Mathematics, University of Valencia, Spain. He has been an active researcher since the early 1990s, with a primary focus on nonlinear functional analysis and fixed point theory in Banach spaces. Doctorate: University of Valencia (1990), thesis on Banach spaces and fixed point properties. Position: Catedrático de Universidad (Full Professor). Department: Mathematical Analysis. Faculty: Faculty of Mathematics. Email: jesus.garcia@uv.es. Homepage: https://www.uv.es/garciaf/ . His research lies at the intersection of functional analysis, operator theory, and differential equations. Key interests include fixed point theory for nonexpansive and generalized nonexpansive mappings, geometric properties of Banach spaces (such as uniform nonsquareness and normal structure), measures of noncompactness, accretive operators, and nonlinear semigroups. He has made significant contributions to the understanding of conditions under which Banach spaces possess the fixed point property. His work often involves the application of topological and geometric methods to prove existence results for differential and integral equations in infinite-dimensional spaces. The 15 most recent articles highlight a consistent research trajectory in nonlinear functional analysis. They explore fixed point theorems for various classes of mappings (pseudocontractive, multivalued, generalized nonexpansive), the role of measures of noncompactness, and applications to differential equations in Banach spaces, including models for cell population growth. There is a strong emphasis on existence, uniqueness, and well-posedness of solutions using abstract functional-analytic frameworks. While specific scientific awards are not mentioned in the provided text, his extensive publication record (66 publications, including a book), high citation count (over 800 citations in zbMATH), and long-standing professorship indicate significant recognition within the mathematical community. Dr. Garcia Falset has supervised or collaborated with several researchers, including notable co-authors like Enrique Llorens-Fuster, David Ariza-Ruiz, Khalid Latrach, and Simeon Reich. His collaborative work spans topics in fixed point theory, differential equations, and operator theory. No specific grants or funding sources are mentioned in the provided information. He is associated with the research group in Mathematical Analysis at the University of Valencia. His work contributes to the theoretical foundations used in mathematical biology and dynamical systems, particularly through the analysis of evolution equations. His homepage and presence in zbMATH, MGP, and Wikidata reflect an active academic profile.

Zhengyuan Zhouمشاهده پروفایل

استادیار

• Machine Learning
• Reinforcement Learning
• Stochastic Optimization
• +۱۲ مورد دیگر

Zhengyuan Zhou is an Assistant Professor in the Department of Technology, Operations, and Statistics at the Leonard N. Stern School of Business, New York University. He is also associated faculty at the Department of Computer Science and Engineering, Tandon School of Engineering, and affiliated with the NYU Center for Data Science. He joined NYU Stern in 2020 after serving as an IBM Goldstine Research Fellow and a Visiting Scholar at NYU Stern during 2019–2020. Education: Ph.D., Electrical Engineering, Stanford University, 2019 Master’s in Computer Science, Stanford University Master’s in Statistics, Stanford University Master’s in Economics, Stanford University B.A., Mathematics, UC Berkeley B.S., Electrical Engineering and Computer Sciences, UC Berkeley His research centers on the intersection of machine learning, stochastic optimization, control theory, and game theory, with a focus on data-driven decision-making. He develops algorithms for reinforcement learning, contextual bandits, and policy learning under uncertainty, with applications in inventory control, revenue management, and auction bidding. His work emphasizes sample efficiency, computational tractability, and robustness. The recent publications reflect a strong trend in distributionally robust learning , offline reinforcement learning , and multi-agent systems , particularly in settings with delayed feedback, adversarial environments, and adaptive data collection. His articles span top journals in operations research, machine learning, and control theory. Scientific Awards and Honors: IBM Goldstine Fellowship (2019–2020) INFORMS Nicholson Award Finalist (2017, 2018) NSF and ONR grants (multiple, 2021–2027) NYU Research Catalyst Prize (2023) Horizon Robotics, Bain, and JP Morgan faculty awards (2021) CRA Outstanding Undergraduate Researcher (2013) Zhou advises PhD students in operations management and has served on dissertation committees at Georgia Tech and Tsinghua University. He has received over $1.8 million in research funding from NSF, ONR, and industry partners. He is actively involved in editorial roles as Associate Editor for Management Science , Operations Research , and Mathematics of Operations Research , and as Area Chair for NeurIPS, ICML, and ICLR. He also mentors high school students through logic and cryptography programs at Stanford’s Pre-Collegiate Summer Institute.

Lionel Truquetمشاهده پروفایل

مدرس

• Time Series Analysis
• Statistics for Dependent Data
• Markov Chains
• +۳ مورد دیگر

Lionel Truquet is a Lecturer-Researcher in Statistics and Director of Research at ENSAI (École Nationale de la Statistique et de l'Analyse de l'Information). His research focuses on advanced statistical methodologies, including time series analysis, Markov chains, and ecological data modeling. He has contributed to multivariate autoregressive binary models, compact space time series models, and nonstationary count processes. Research Interests: His work emphasizes statistical theory applied to dependent data, with a strong focus on ecological applications. Key areas include ergodic properties of Markov chains, mixing properties of time series, and modeling presence-absence data. His methods address challenges in high-dimensional and nonstationary environments. Publications: Recent work includes influential contributions such as the TJALLING C. KOOPMANS ECONOMETRIC THEORY PRIZE-winning paper on iterations of dependent random maps. His publications span top journals like Bernoulli, the Annals of Applied Probability, and the Journal of Time Series Analysis, reflecting his expertise in both theoretical and applied statistics. Teaching: He teaches advanced courses such as Asymptotic Statistics and Dependence at the M2 level, reflecting his commitment to training future statisticians in cutting-edge methodologies.

Jop Briëtمشاهده پروفایل

پژوهشگر

• Theoretical Computer Science
• Quantum Information Theory
• Combinatorics
• +۳ مورد دیگر

Jop Briët is a Researcher at the Department of Algorithms and Complexity at Centrum Wiskunde & Informatica (CWI) in the Netherlands. His work focuses on theoretical computer science, quantum information theory, combinatorics, and tensor analysis. He has held grants including the Veni Innovational Research Grant from NWO and a Rubicon fellowship. He has authored over 50 publications in leading venues, exploring topics such as Grothendieck inequalities, quantum computing, and additive combinatorics. His research interests span the interplay between combinatorics and computational complexity, with particular emphasis on tensor analysis, probabilistic methods, and algorithm design. Recent work includes studies on Szemerédi’s theorem with random differences and the application of quantum query algorithms to entanglement-based problems. Awards: Outstanding paper award TQC (2020), Andreas Bonn medal (2013), Stieltjesprijs (2011). Professional Activities: Editor for ERCIM News, Board Member of Koninklijk Wiskundig Genootschap, and frequent invited speaker at workshops on quantum computing and combinatorics. Grants: Veni Grant (2014), Rubicon Fellowship (2012). Current teaching includes courses on Additive Combinatorics and Quantum Information Processing, reflecting his commitment to bridging foundational theory with advanced applications in computing and mathematics.

Zhenke Wuمشاهده پروفایل

دانشیار

• Bayesian Statistics
• Precision Medicine
• Latent Variable Models
• +۵ مورد دیگر

Zhenke Wu is an Associate Professor (with tenure) in the Department of Biostatistics at the University of Michigan School of Public Health. He holds affiliate appointments at the Michigan Institute for Data and AI in Society (MIDAS) and leads the Michigan Statistics for Individualized-healthcare Lab (MiSIL). His research bridges statistical methodology and public health applications, with particular focus on precision medicine. Educational background includes: PhD in Biostatistics from Johns Hopkins University (2014) BS in Mathematics from Fudan University (2009) Dr. Wu's methodological research focuses on: Structured Bayesian latent variable models for disease subtyping and clustering Causal inference methods for sequential interventions in mobile health studies Reinforcement learning frameworks for personalized health interventions Scalable computation for high-dimensional biomedical data His applied work spans infectious diseases, mental health, autoimmune disorders, and cancer through collaborations with multiple research consortia including the Intern Health Study and UZIMA-DS project in Africa. Recent publications demonstrate strong focus on Bayesian methods, causal inference, and reinforcement learning applications in digital health. Article themes include mobile health interventions, synthetic EHR development, fair machine learning algorithms, and novel approaches for longitudinal and survival data analysis. Methodological innovations consistently address challenges in precision medicine and individualized health decision-making. Dr. Wu leads several collaborative initiatives including the Precision Health Use Case for mental health treatment (PROMPT) and partners with the Rogel Cancer Center. He advises multiple PhD students and postdoctoral researchers in statistical methodology development and health applications.

• Signal Processing
• Statistical Signal Analysis
• High-Dimensional Data Analysis
• +۳ مورد دیگر

Pascal Vallet is an Associate Professor at University of Bordeaux, affiliated with IMS Bordeaux (Integration Laboratory from Material to System) where he conducts research in the Signal and Image Processing group and Spectral team. His work focuses on theoretical and applied aspects of statistical signal processing with emphasis on high-dimensional data analysis. Vallet's research interests center on statistical signal processing methodologies, particularly in high-dimensional settings. His work addresses fundamental problems in covariance matrix analysis, spectral coherence, and change detection within Gaussian models. He develops theoretical frameworks for analyzing complex signals with applications in areas such as SAR imaging and multivariate time series analysis. His approach combines rigorous mathematical statistics with practical signal processing applications, focusing on scenarios where dimensionality challenges traditional statistical methods. Analysis of Vallet's recent publications reveals a consistent research trajectory focused on high-dimensional statistical signal processing. His work primarily addresses covariance structure analysis in Gaussian low-rank models, with applications to SAR imaging and multivariate time series. The publications demonstrate increasing sophistication in handling high-dimensional asymptotic regimes where both sample size and dimensionality grow large. His research shows strong theoretical foundations combined with practical validation through synthetic and real-world data experiments, particularly in radar imaging applications. Vallet maintains active research collaborations with several colleagues including Rémi Beisson, Audrey Giremus, Guillaume Ginolhac, and Philippe Loubaton, as evidenced by his co-authored publications across multiple years. His research contributes to advancing theoretical understanding of statistical signal processing in high-dimensional settings while maintaining relevance to practical applications in remote sensing and signal analysis.

• Information Theory
• Coding Theory
• Algorithm Design
• +۳ مورد دیگر

Sidharth Jaggi is a Professor at the School of Mathematics, University of Bristol, with over 19 years of experience in Information and Data Sciences through the lens of Information Theory. His work emphasizes fundamental performance limits and algorithm design for systems under adversarial threats. Education: B.Tech, M.Phil, PhD Research interests focus on adversarial communication, information-theoretic security, coding theory, and sparse data estimation. He leads the CAN-DO-IT team (Codes, Algorithms, Networks – Design and Optimization for Information Theory), integrating theoretical tools into practical applications like secure distributed computing and robust data storage. Recent publications highlight advancements in adversarial channels , group testing , and privacy-preserving coding . Trends include covert communication under spectral constraints, causal feedback benefits, and efficient algorithms for high-dimensional problems. Current projects include "Information Theory for Interactive Distributed AI" (2024–2029), exploring interactive systems under adversarial constraints.

• Computational Neuroscience
• Neurobiology
• Visual Cortex
• +۳ مورد دیگر

Mario Dipoppa is an Assistant Professor in the Department of Neurobiology at the University of California, Los Angeles. His research focuses on computational neuroscience, cortical adaptation, and neural circuit dynamics. Position: Assistant Professor, Neurobiology Email: mdipoppa@g.ucla.edu Research Interests: Mario's work explores how neural populations in the visual cortex adapt to sensory input, with a particular emphasis on the interplay between neural oscillations, synchrony, and cognitive functions like working memory. His recent studies investigate optimal coding strategies in visual adaptation, contextual modulation mechanisms, and the role of transcriptomic diversity in cortical interneuron function. Publications Trends: His research spans computational modeling of cortical networks, visual neuroscience, and neurogenetic analyses of brain circuits. Early work (2013-2016) focused on working memory mechanisms and neural oscillations, while recent studies (2022-2025) emphasize visual cortex adaptation, population coding, and cross-species circuit comparisons.

Ramana V Davuluriمشاهده پروفایل

استاد

• Machine Learning
• Cancer Data Science
• Gene Regulation
• +۴ مورد دیگر

Ramana V Davuluri serves as Professor in the Department of Biomedical Informatics at Stony Brook University's Renaissance School of Medicine. With over 20 years of experience in bioinformatics and computational genomics, he leads research at the intersection of machine learning and cancer genomics, focusing on translating high-dimensional -omic data into clinically actionable insights through statistically rigorous methodologies. Dr. Davuluri's research spans Machine Learning applications in Cancer Data Science , isoform-level gene regulation , and precision-medicine development. His lab pioneers bioinformatics solutions for genomic data interpretation, with emphasis on developing machine learning algorithms that convert NextGen sequencing outputs into experimentally testable discovery models. A core focus involves creating rapid biomarker identification systems from human tissue and blood samples through integrated computational-experimental approaches in systems biology. Analysis of his 2023-2025 publications reveals a dominant trend toward genomic foundation models (e.g., DNABERT variants), multi-omic cancer subtyping , and time-dependent therapeutic strategies for pediatric brain tumors and ovarian cancer. His work consistently bridges computational innovation with biological validation across diverse cancer types including glioma, lung adenocarcinoma, and high-grade serous carcinoma. As Principal Investigator for multiple multi-investigator and multi-site projects, Dr. Davuluri directs research integrating high-throughput experimental procedures with advanced data-mining techniques. His laboratory maintains strong collaborations across oncology, neuroscience, and immunology domains while developing genomics-based decision support systems for clinical translation. The Davuluri Lab employs a systems biology framework to develop novel informatics tools for precision oncology, with particular emphasis on translating genomic discoveries into clinical applications through biomarker discovery and therapeutic strategy optimization.

Shie Mannorمشاهده پروفایل

استاد

• Machine Learning
• Reinforcement Learning
• Game Theory
• +۸ مورد دیگر

Shie Mannor is a Professor at the Technion - Israel Institute of Technology in the Department of Electrical Engineering. He also holds a visiting professorship at Cornell-Tech in New York City and is affiliated with the Technion Machine Learning Center and the Grand Technion Energy Program . Key Research Interests: Machine Learning: Theory, algorithms, and applications to high-dimensional data and dynamics modeling. Reinforcement Learning and Markov Decision Processes: Adaptive control in large stochastic systems. Learning and control under uncertainty: Robust/stochastic optimization frameworks. Game Theory: Stochastic, dynamic, and network games applied to power markets and resource allocation. Multi-agent systems: Online learning and designing economic systems with optimal equilibria. Power Grid: Data-driven reliability, pricing, and decision-making in smart grids (e.g., EU-funded GARPUR project). Applications: Communication network optimization, mobile health, LDPC codes, and large-scale optimization problems. He actively seeks postdocs, graduate, and undergraduate students with strong mathematical or programming skills for projects in mobile phone programming and complex system optimization. Contact: shie.mannor@ee.technion.ac.il | Phone: ++972-4-829-3284

• Computational Molecular Systems Biology
• Stochastic Analysis and Modelling
• Dynamical Systems in Applications
• +۳ مورد دیگر

Vahid Shahrezaei is a Professor of Biomathematics at Imperial College London's Department of Mathematics (Faculty of Natural Sciences). He holds affiliations with the Biomathematics Group, Centre for Synthetic Biology, and Mathematics in Medicine. His research focuses on Computational Molecular Systems Biology, studying cellular robustness under stochasticity and environmental noise using computational and analytical methods. Notable contributions include methods for single-cell RNA-sequencing analysis and simulation-based inference of biochemical networks. Education: PhD in Physics from Simon Fraser University (Canada), BSc/MSc in Physics from Sharif University (Iran). Career highlights include a sabbatical at the Crick Institute (2023-2024) and roles such as Diversity Champion for the Faculty of Natural Sciences. Awards include the Imperial College President Medal for Research Supervision (2017). He has led interdisciplinary grants, including a Leverhulme-funded study on noise in gene expression with Samuel Marguerat. Research Interests: Stochastic modeling, gene expression dynamics, systems biology applications Key Projects: Development of bayNorm for single-cell data normalization, studies on mycobacterial cell size control Professional Roles: BBSRC expert panel member, co-organizer of systems biology conferences His lab integrates mathematical modeling with experimental data, addressing questions in developmental biology, cancer metabolism, and microbial systems. Recent work includes agent-based modeling of environmental policy adoption and novel visualization techniques for multi-omics data.

• Functional Analysis
• Artificial Intelligence
• Foundations of Computational Mathematics
• +۷ مورد دیگر

Anders C. Hansen is Professor of Mathematics at the University of Cambridge (Faculty of Mathematics, Department of Applied Mathematics and Theoretical Physics) and Professor II at the University of Oslo. He leads the Applied Functional and Harmonic Analysis group and holds a Royal Society University Research Fellowship. His research bridges pure mathematics and cutting-edge applications in AI, computational harmonic analysis, inverse problems, and compressed sensing. Education: PhD from the University of Cambridge, MA from UC Berkeley, and BA from the Norwegian University of Science and Technology. Research Interests: Hansen's work centers on foundational challenges in computational mathematics, including the Solvability Complexity Index hierarchy for classifying computational problems, instability phenomena in deep learning, and theoretical advances in compressed sensing. His group develops rigorous frameworks for high-dimensional data analysis, medical imaging, and AI safety, often exposing paradoxes in algorithmic reliability. Publication Trends: Recent articles focus on the limits of deep learning (e.g., Smale's 18th problem, instability in image reconstruction), mathematical foundations of AI (trustworthiness, feature selection, LLMs), and advanced compressed sensing (asymptotic incoherence, spectral computations). His work consistently intersects functional analysis with computational feasibility. Awards: PROSE Award Finalist (2022) Whitehead Prize (2019) IMA Prize in Mathematics and Applications (2018) Leverhulme Prize (2017) Royal Society University Research Fellow (2012) Advising & Leadership: Hansen has supervised 17 PhD students and 8 postdocs. He leads the Applied Functional and Harmonic Analysis group, coordinating interdisciplinary projects in mathematical data science. His editorial roles include SIAM Journal on Imaging Sciences and Proceedings of the Royal Society A .