جست‌وجوی استاد و پژوهشگر

Karl Henrik Johanssonمشاهده پروفایل

استاد

• Networked and distributed control
• Cyber-physical systems
• Cyber-secure control systems
• +۴ مورد دیگر

Karl Henrik Johansson is a Professor at the School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology in Stockholm, Sweden, where he also serves as the Founding Director of Digital Futures. He is a Fellow of both IEEE and the Royal Swedish Academy of Engineering Sciences, and has held leadership positions including Immediate Past President of the European Control Association and IEEE Control Systems Society Vice President Diversity, Outreach & Development. Dr. Johansson earned his MSc in Electrical Engineering and PhD in Automatic Control from Lund University. His academic journey includes visiting positions at prestigious institutions such as UC Berkeley, Caltech, and NTU. His research focuses on networked control systems and cyber-physical systems with applications in transportation, energy, and automation networks. His work investigates fundamental challenges in connecting physical world systems through communication networks, exploring how wireless communication and sensor technology can enhance system robustness, reliability, energy efficiency, and safety. Current research directions include security of cyber-physical systems, distributed optimization, multi-agent systems, and applications to intelligent transportation and energy networks. Analysis of his recent publications reveals a strong focus on distributed optimization algorithms, secure networked control, multi-agent systems, and applications to transportation and energy networks. His work increasingly integrates machine learning techniques with traditional control theory, addressing challenges in privacy-preserving distributed computation, resilient state estimation, and resource allocation in complex networked systems. IEEE Control Systems Society Hendrik W. Bode Lecture Prize (2024) Swedish Research Council Distinguished Professor (2018-2027) Wallenberg Scholar (2009-2026) IFAC Young Author Prize IEEE CSS Distinguished Lecturer (2017-2019) IFAC Outstanding Service Award IEEE Fellow Dr. Johansson has supervised over 100 postdocs and PhD students, with many now holding prominent positions at institutions worldwide. His research has been supported by significant grants including the Swedish Research Council Distinguished Professor Grant (2018-2027), multiple Wallenberg Foundation grants, and numerous EU and national research projects. He has directed major research centers including ACCESS Linnaeus Centre (2009-2016) and Strategic Research Area ICT TNG (2013-2020). His research group operates within the Digital Futures initiative and maintains strong connections with industry partners through projects like the Integrated Transport Research Lab (supported by Scania and Ericsson) and Smart Mobility Lab. The group actively collaborates with international institutions and participates in major EU-funded projects addressing challenges in cyber-physical systems, transportation, and energy networks.

Il Memming Parkمشاهده پروفایل

استاد

• Computational Neuroscience
• Neural Dynamics
• Machine Learning
• +۳ مورد دیگر

Il Memming Park is a Professor and Group Leader at the Centre for Restorative Neurotechnology within the Champalimaud Research division of the Champalimaud Foundation in Lisbon, Portugal. His work bridges computational neuroscience, machine learning, and statistical modeling to understand neural dynamics and computation. Dr. Park's research focuses on developing statistical and machine learning methods for analyzing neural time series data. His lab investigates the appropriate language for neural dynamics that can explain and generate specific predictions on neural data and behavior. He builds on foundations of dynamical systems and stochastic processes to create models of neural computation tightly tied to biology. His publications reveal a strong emphasis on developing methods like variational latent Gaussian processes and exponential family dynamical systems to extract meaningful patterns from complex neural recordings. His work spans both theoretical developments in computational methods and their application to real neural data from areas like visual cortex, parietal cortex, and other brain regions involved in perception and decision making. Dr. Park has previously held positions at Stony Brook University and the University of Texas at Austin, where he was affiliated with departments of Neurobiology and Behavior, Applied Mathematics and Statistics, Psychology, and Neuroscience. His lab at Champalimaud includes multiple PhD students, postdoctoral researchers, and research staff working collaboratively on various aspects of neural data analysis and modeling. The team employs an interdisciplinary approach combining neuroscience, statistics, machine learning, and dynamical systems theory.

Mathias Drtonمشاهده پروفایل

استاد

• Graphical models
• Algebraic statistics
• Causal inference
• +۳ مورد دیگر

Prof. Mathias Drton holds the Chair of Mathematical Statistics at the Technical University of Munich (TUM), within the Department of Mathematics and School of Computation, Information and Technology. His research focuses on graphical models, algebraic statistics, causal inference, and multivariate data analysis. He has authored numerous publications in top-tier journals and conferences, including work on conditional independence, sparse factor analysis, and causal discovery in linear models. Drton has supervised a large number of theses, mentoring students in areas like high-dimensional statistics, graphical models, and causal inference. He is actively involved in teaching advanced courses such as 'Graphical Models in Statistics' and 'Fundamentals of Mathematical Statistics.' His academic contributions span theoretical developments in statistical methodology and computational tools, including R packages like SEMID and symRC . Drton collaborates internationally, contributing to projects like the TUM-ICL Mathematical Sciences Hub and Exzellenzcluster MCQST. His work bridges algebraic methods with statistical challenges, addressing identifiability in latent variable models and robust graphical modeling under non-Gaussian assumptions. Recent research emphasizes causal structure learning under partial homoscedasticity, distribution-free independence tests, and multi-domain causal representation learning. Drton’s lab actively explores applications in genomics, epidemiology, and machine learning, leveraging both theoretical rigor and practical computational methods.

• Mathematical Statistics
• Model Selection
• Robust Estimation
• +۳ مورد دیگر

Yannick BARAUD is a Full Professor in Mathematics at the University of Luxembourg, leading the group 'Developing Contemporary Mathematical Statistics' within the Department of Mathematics. He holds the ERA-Chair 'SanDAL' in Mathematical Statistics and Data Science, funded by the European Commission. His research focuses on robust estimation, model selection, hypothesis testing, and nonparametric methods. He serves as the Study Programme Director for the Master in Data Science and has held academic positions at the University of Nice Sophia Antipolis and CNRS. His career includes roles as a researcher at École Normale Supérieure (Paris) and a lecturer at the same institution. He earned his PhD from Université Paris-Sud and studied at École Normale Supérieure de Cachan. His work emphasizes rigorous statistical methodologies, including rho-estimation and robust Bayes-like approaches. Notable contributions include advancements in density estimation under shape constraints and robust regression techniques. He has published extensively on topics such as loss functions, empirical processes, and statistical inference, with applications in epidemiology and data science. His leadership in the SanDAL initiative underscores his commitment to bridging mathematical statistics and practical data science challenges. Collaborations and grants further highlight his role in advancing interdisciplinary research.

Pragya Surمشاهده پروفایل

استادیار

• High-dimensional Statistics
• Machine Learning
• Artificial Intelligence
• +۳ مورد دیگر

Pragya Sur is an Assistant Professor of Statistics at Harvard University and currently on leave as a Visiting Professor at MIT’s Laboratory for Information and Decision Systems (LIDS). Her research focuses on high-dimensional statistics, machine learning, and artificial intelligence, particularly in overparametrized models, causal inference, and learning under distribution shifts. She has held postdoctoral positions at Harvard’s Center for Research on Computation and Society (hosted by Cynthia Dwork) and was a Simons Institute Long-Term Participant at UC Berkeley. She earned her Ph.D. in Statistics from Stanford University under Emmanuel Candès, and completed her B.Stat and M.Stat at the Indian Statistical Institute, Kolkata. Sur’s work has been supported by NSF awards, the Eric and Wendy Schmidt Fund, and the William F. Milton Fund. She was named an International Strategy Forum (ISF) Fellow (2023) and led the Institute of Mathematical Statistics (IMS) New Researchers Group (2022–2024). She serves as an Associate Editor for Statistical Science and Guest Co-Editor for a special issue on AI and statistics. Her research contributions span theoretical guarantees for machine learning, transfer learning, and debiasing techniques in high-dimensional inference. Awards: NSF CAREER Award, Theodore W. Anderson Dissertation Award, Ric Weiland Fellowship Education: Ph.D. in Statistics (Stanford, 2019); M.Stat (ISI Kolkata, 2014); B.Stat (ISI Kolkata, 2012) Professional Roles: Associate Editor, Statistical Science ; ISF Fellow (2023); former IMS New Researchers Group Lead Her current research emphasizes statistical theory for modern machine learning, including foundational work on overparametrized models and robust inference across heterogeneous environments.

Piotr Zwiernikمشاهده پروفایل

دانشیار

• Statistical Computing
• Theoretical Statistics
• High-dimensional Statistics
• +۷ مورد دیگر

Piotr Zwiernik is an Associate Professor in the Department of Statistical Sciences at the University of Toronto's Faculty of Arts and Science, with a cross-appointment in the Department of Mathematics. Currently on leave from the University of Toronto, he is based in Barcelona following his return in July 2025. His academic journey includes a PhD in Statistics from the University of Warwick (2011), research positions at prestigious institutions including Mittag Leffler Institute, IPAM, TU Eindhoven, UC Berkeley, and the University of Genoa, and an Assistant Professorship at Universitat Pompeu Fabra in Barcelona (2016-2021). His research spans the intersection of statistics, mathematics, and computational methods, with particular emphasis on graphical models, covariance matrix estimation, convex analysis, tensors, and algebraic and combinatorial methods in statistics. Zwiernik's work demonstrates a consistent focus on high-dimensional statistics, mathematical statistics, and elegant theoretical frameworks that bridge abstract mathematics with practical statistical applications. His recent publications reveal a deepening exploration of tensor analysis, algebraic statistics, and the geometric properties of statistical models. Zwiernik serves as an associate editor for leading journals including Biometrika, Scandinavian Journal of Statistics, and Algebraic Statistics. His research program includes the development of the GOLAZO R package for asymmetric regularization of log-likelihood in Gaussian graphical models. As an academic leader, he has served as Associate Chair for Research in his department and actively participates in numerous international conferences and workshops, reflecting his significant standing in the statistical community. His recent publications show a strong trend toward algebraic and geometric approaches to statistical problems, with increasing focus on tensor methods, positivity constraints in statistical models, and the theoretical foundations of graphical models. The work demonstrates remarkable continuity in exploring the mathematical structures underlying statistical models while adapting to emerging challenges in high-dimensional data analysis. Zwiernik is committed to mathematical accessibility and education, guided by Federico Ardila's four axioms which emphasize equitable distribution of mathematical potential, joyful mathematical experiences, mathematics as a malleable tool, and treating every student with dignity and respect. He actively seeks PhD students with strong mathematical backgrounds for research at UPF or the Institute of Mathematics of UPC.

Harald Tauchmannمشاهده پروفایل

استاد

• Health Economics
• Empirical Health Economics
• Health Insurance
• +۳ مورد دیگر

Prof. Dr. Harald Tauchmann is a Professor of Health Economics at Friedrich-Alexander University Erlangen-Nuremberg (FAU), where he has held a faculty position since 2013. He is affiliated with the School of Business, Economics and Social Sciences, specifically within the Department of Economics. Prof. Tauchmann also participates in multiple research focus areas at FAU, including 'Insurance and Risk' and 'Work in Transition,' demonstrating his interdisciplinary approach to health economics. Prof. Tauchmann received his education at Heidelberg University and the University of Manchester, UK, where he studied economics, political science, and sociology. He graduated in 1998 and completed his doctorate at the Interdisciplinary Institute for Environmental Economics (University of Heidelberg) in 2003. Prior to joining FAU, he worked as a research associate at the Rhineland-Westphalian Institute for Economic Research (RWI) in Essen from 2003 to 2012 and headed a junior research group at the health economics research center CINCH at the University of Duisburg-Essen. His research expertise lies in empirical health economics, with a particular emphasis on health insurance choice and competition, as well as individual health behavior. He has made significant contributions to understanding obesity, health shocks, mental health care payment systems, and thyroid diagnostics through his extensive publication record. His methodological work includes developing specialized Stata modules for econometric analysis, which have been widely adopted by researchers in the field. Prof. Tauchmann's scholarly work demonstrates a consistent focus on applying rigorous econometric methods to pressing health policy questions. His research portfolio shows particular strength in causal analysis of health behavior, especially regarding obesity interventions, health insurance market dynamics, and the economic consequences of health shocks. His recent publications (including several forthcoming in 2025) indicate continued scholarly productivity and relevance to current health policy debates. From March 2021 to April 2022, Prof. Tauchmann served as chairman of the German Society for Health Economics, highlighting his leadership and recognition within the national health economics community. His email contact is harald.tauchmann@fau.de for professional inquiries.

Halina Frydmanمشاهده پروفایل

استاد

• Survival Analysis
• Markov Processes
• Stochastic Modeling
• +۷ مورد دیگر

Halina Frydman is a Professor in the Department of Statistics and Operations Research at the Leonard N. Stern School of Business, New York University, where she has been a faculty member since 1978. Her academic work bridges statistical theory and real-world applications in finance and labor economics. Institution: New York University School: Leonard N. Stern School of Business Department: Department of Statistics and Operations Research Academic Rank: Professor Email: hf2@stern.nyu.edu Education: Ph.D. in Mathematical Statistics, Columbia University, 1978 M.A. in Mathematical Statistics, Columbia University, 1974 B.S. in Physics and Mathematics, Cooper Union, 1972 Research Interests: Professor Frydman specializes in survival analysis and Markov processes , with a strong focus on their applications in financial modeling and labor market dynamics . Her work explores mixture models of Markov chains to capture heterogeneity in longitudinal data, particularly in the context of corporate credit rating migrations and employment/unemployment transitions. She also contributes to methodological advances in stochastic modeling and statistical inference for time-to-event data. Publication Trends: Her recent research, reflected in reconstructed articles, demonstrates a consistent focus on developing and applying advanced statistical models—particularly survival models, Markov chains, and mixture models—to problems in finance and economics. There is a clear progression toward more complex, data-driven models incorporating Bayesian methods, high-dimensional estimation, and time-varying effects. Scientific Awards: No awards explicitly mentioned in the source text. Advising and Grants: While specific advisees and grant funding are not listed in the available text, Professor Frydman's long-standing research program and publications in premier journals such as the Journal of the American Statistical Association and The Journal of Finance suggest a significant scholarly impact and likely history of research sponsorship. She teaches core courses including Regression & Forecasting Models , Stochastic Processes I , and Stochastic Models in Finance , indicating active engagement in graduate education. Labs and Research Teams: No specific laboratories or research groups are mentioned in the provided content. However, her research aligns with interdisciplinary efforts in financial statistics and econometric modeling, potentially involving collaboration within NYU’s broader quantitative research community.

Iain Murrayمشاهده پروفایل

استاد

• Machine Learning
• Probabilistic Modeling
• Density Estimation
• +۳ مورد دیگر

Iain Murray is Professor of Machine Learning and Inference at the School of Informatics, University of Edinburgh. His research focuses on developing flexible probabilistic models applicable across diverse domains including cosmology, neuroscience, perception, speech, sports, and text. Program Chair for ICLR (2018) Publications Chair for ICML (2017, 2018) Area Chair for AISTATS, ICLR, ICML, NeurIPS, and UAI Amazon Scholar (2018-2024), first appointed in Europe Murray's research interests center on probabilistic reasoning using machine learning, with specific expertise in density estimation and Markov chain Monte Carlo methods. His work spans theoretical foundations and practical applications, with significant contributions to neural autoregressive distribution estimation (NADE), real-valued NADE (RNADE), and pseudo-marginal slice sampling techniques. His research has enabled advances in flexible probabilistic modeling across multiple domains. His publications show consistent focus on advancing probabilistic modeling techniques, with recent work emphasizing neural autoregressive models, density estimation methods, and efficient sampling algorithms. The research trajectory demonstrates progression from foundational work on NADE to increasingly sophisticated deep learning approaches for density estimation and inference. Notable Paper Award for NADE work Amazon Scholar (2018-2024) Murray has supervised numerous PhD students who have gone on to prominent positions at Google DeepMind, NYU, stability.ai, and other leading institutions. His teaching responsibilities include the Machine Learning and Pattern Recognition course and project supervision. His research group focuses on developing tractable probabilistic models with applications across multiple scientific domains.

• Statistical signal processing
• Robust statistical methods
• Array processing
• +۶ مورد دیگر

Esa Ollila serves as Associate Professor in the Department of Signal Processing and Acoustics at Aalto University, Finland, and holds an adjunct professorship in Statistics at the University of Oulu. His academic appointments include Academy of Finland Research Fellow (2010-2015) and prior senior research/lecturing roles at both institutions. His educational background features: M.Sc. in Mathematics, University of Oulu (1998) Ph.D. in Statistics (with honors), University of Jyväskylä (2002) D.Sc.(Tech) in Signal Processing (with honors), Aalto University (2010) Professor Ollila's research centers on statistical signal processing and robust statistical methodologies , with significant contributions to array processing, high-dimensional data analysis, and covariance matrix estimation. His work bridges theoretical statistics with practical applications in radar systems, wireless communications, and big data analytics, emphasizing robustness against outliers and computational efficiency in modern data-intensive environments. Current focus areas include compressed sensing, sparse approximation, and blind source separation techniques. Analysis of his 15 most recent publications (2024-2025) reveals three dominant trends: (1) robust covariance learning for massive random access systems, (2) integrated sensing and communications (ISAC) for 6G networks using advanced beamforming, and (3) geometric approaches to elliptical distributions in statistical inference. His work increasingly incorporates deep learning (GANs, graph neural networks) while maintaining strong foundations in classical signal processing theory. Key recognitions include: Academy of Finland Postdoctoral Fellowship (2004-2007) Academy of Finland Research Fellowship (2010-2015) His research has been supported through prestigious Academy of Finland grants totaling over a decade of continuous funding. Professor Ollila currently leads an active research group at Aalto University, supervising doctoral candidates and collaborating internationally with institutions including Princeton University (where he served as Visiting Post-doctoral Research Associate during 2010-2011). He maintains strong ties with the University of Oulu through his adjunct professorship and has contributed to EURASIP's Special Area Team on Theoretical and Methodological Trends in Signal Processing. The Esa Ollila Research Group focuses on cutting-edge challenges in statistical signal processing, with current projects spanning robust DOA estimation under non-Gaussian noise, covariance matrix learning for massive MIMO systems, and machine learning-enhanced radar-communication integration. The group actively develops open-source tools like the fitHeavyTail R package for heavy-tailed distribution modeling and maintains collaborations with industry partners in wireless communications.

Robert Picheمشاهده پروفایل

استاد

• Kalman Filters
• Global Positioning Systems (GPS)
• Signal Processing
• +۵ مورد دیگر

Robert Piche is a Professor at the Computing Sciences Mathematics Research Centre, specializing in advanced signal processing, positioning systems, and sensor fusion. He holds a Doctor of Science (Technology) and Master of Science from the University of Waterloo, Canada (1986 and 1982, respectively). His research focuses on Kalman filters, Global Positioning Systems (GPS), particle filters, and indoor positioning technologies. He has contributed extensively to fields like satellite orbit prediction, non-line-of-sight (NLoS) positioning, and machine learning applications in biomechanics and robotics. Dr. Piche has authored over 230 publications and received recognition through an invitation/ranking in a 2014 competition. He actively participates in academic activities, including conference presentations and peer-review roles. His work bridges theoretical advancements and practical applications, with contributions to autonomous systems, sensor data analysis, and wearable technology. Collaborations span international institutions, reflecting his global impact in engineering and computer science disciplines.

Jose Israel Rodriguezمشاهده پروفایل

استادیار

• Applied Algebraic Geometry
• Algebraic Statistics
• Nonlinear Algebra
• +۵ مورد دیگر

Jose Israel Rodriguez is an Assistant Professor in the Department of Mathematics at the University of Wisconsin-Madison, with additional affiliations in the Department of Electrical & Computer Engineering and the Institute for Foundations of Data Science. He joined UW Madison in Fall 2020 after completing postdoctoral positions at the University of Chicago (with Lek-Heng Lim) and Notre Dame (with Jonathan Hauenstein). Rodriguez earned his PhD in 2014 from UC Berkeley under the supervision of Bernd Sturmfels. His research focuses on applied algebraic geometry and algebraic methods for statistics, with particular interests in nonlinear algebra and nonlinear eigenvalue problems, algebraic statistics and nearest point problems, and applications of monodromy and Galois groups. Rodriguez has made significant contributions to numerical algebraic geometry, particularly in solving polynomial systems, maximum likelihood estimation, and Euclidean distance degree calculations. His work bridges theoretical mathematics with practical computational methods. Rodriguez's recent publications demonstrate a strong trend toward developing numerical methods for solving complex algebraic problems with applications in statistics, optimization, and engineering. His research shows increasing sophistication in handling decomposable systems, multiparameter eigenvalue problems, and braid group computations, often implementing these methods in software tools like Macaulay2. His work connects abstract algebraic geometry with concrete computational approaches. NSF Postdoctoral Fellow Provost's Postdoctoral Scholar Rodriguez currently advises PhD students Julia Lindberg (expected graduation May 2022, joint with B. Lesieutre) and Zinan Wang. He has organized numerous seminars and conferences including SIAM_SAGA, Algebra in Statistics and Computation Seminar, and Applied Algebra Seminar. His research has been supported by various grants that enable his work in numerical algebraic geometry and its applications. Rodriguez is actively involved in the algebraic geometry and statistics communities, organizing several seminars and minisymposia at major conferences. He has developed several software tools including implementations for decomposable sparse polynomial systems, multiregeneration, algebraic optimization, Galois groups, and maximum likelihood obstruction functions. His work connects theoretical mathematics with practical computational applications across various domains.

Alan Huangمشاهده پروفایل

مدرس ارشد

• Biostatistics
• Mathematical Sciences
• Probability theory
• +۶ مورد دیگر

Dr. Alan Huang is a Senior Lecturer at the School of Mathematics and Physics, University of Queensland. He holds a PhD in Statistics from the University of Chicago (McCormick Fellowship) and an Honours degree in Science (Advanced Mathematics) from the University of Sydney. His academic career includes lecturing roles at the University of Wisconsin-Madison and the University of Technology Sydney before joining UQ. Research Focus: Biostatistics, nonparametric methods, and statistical modeling for dispersed counts. Key Projects: Bayesian methods for agricultural data, trend analysis of pesticide concentrations in the Great Barrier Reef, spectral water quality analysis. Article Trends: His work spans generalized linear models, count data analysis, and environmental statistics, with recent emphasis on Conway-Maxwell-Poisson regression and time-series modeling. Collaborations include environmental science applications. Awards: McCormick Fellowship (University of Chicago). Supervision: Currently advising PhD research on count data methods. Past supervision includes topics in geotechnical uncertainty and rock mechanics. Collaborates with Queensland Department of Environment and Science on water quality projects.

• Sublinear-time algorithms
• Error-correcting codes
• Computational complexity
• +۳ مورد دیگر

Elena Grigorescu is a Professor at the University of Waterloo, Department of Computer Science. She holds a Ph.D. from the Massachusetts Institute of Technology (2010), an M.S. from MIT (2006), and a B.A. from Bard College (2004). Her research focuses on sublinear-time algorithms, error-correcting codes, computational complexity, and learning theory. She explores foundational aspects of algorithms with constraints on time/space, privacy-preserving computation, and applications in graph theory and optimization. Her work includes advancements in spanner algorithms for network design, differential privacy in sublinear-time settings, and learning-augmented approaches for online optimization. Recent publications address trace reconstruction, privacy-utility trade-offs, and combinatorial optimization techniques. Grigorescu is actively involved in conferences like APPROX/RANDOM and IEEE Foundations of Computer Science, contributing to algorithmic theory and practical implementations. Her research emphasizes theoretical rigor while addressing real-world challenges in data analysis and distributed systems. No awards or formal advisees are explicitly listed in the provided information.

• Finance
• Financial Economics
• Quantitative Finance
• +۳ مورد دیگر

Ke Xu is an Assistant Professor at the Department of Finance, Faculty of Business and Economics, University of Victoria. His research bridges finance, econometrics, and cryptocurrency, focusing on market microstructure, high-frequency trading, and price discovery mechanisms. He has extensively studied Bitcoin ETFs, fractional cointegration models, and machine learning applications in financial markets. Key Research Areas: Market Microstructure High-Frequency Trading Cryptocurrency Dynamics Price Discovery Machine Learning in Finance Financial Econometrics Article Trends: Xu’s work spans empirical analyses of Bitcoin ETFs, volatility modeling (e.g., affine GARCH), and algorithmic trading strategies. His recent papers explore mini flash crashes using machine learning, regulatory impacts on market quality, and sustainable crypto portfolios.