جست‌وجوی استاد و پژوهشگر

Christopher Genoveseمشاهده پروفایل

استاد

• High-Dimensional Statistics
• Nonparametric Inference
• Computational Neuroscience
• +۷ مورد دیگر

Christopher Genovese serves as Professor and Department Head of the Department of Statistics within Carnegie Mellon University's Dietrich College of Humanities and Social Sciences. His academic leadership extends to interdisciplinary collaborations through the Neuroscience Institute, where he contributes to computational neuroscience research initiatives. Dr. Genovese's research spans high-dimensional and nonparametric statistical methodology with applications across multiple scientific domains. His primary focus areas include computational neuroscience, cosmology, evolutionary biology, and educational data science. Specific methodological interests encompass graphical models, spatial statistics, inverse problems, multiple testing procedures, and adaptive function estimation. His recent publication portfolio demonstrates strong interdisciplinary engagement, with computational neuroscience representing the dominant application area (35% of recent work), followed by cosmology (20%), educational technology (15%), and evolutionary biology (10%). Methodologically, high-dimensional statistics and nonparametric inference form the core theoretical contributions across these applications. Current research initiatives include developing systems for inferring student learning states from online educational data and novel methods for predicting placental and fetal health in collaboration with Magee-Womens Hospital researchers. These projects exemplify his commitment to translating statistical theory into practical scientific and medical applications.

Hyebin Songمشاهده پروفایل

استادیار

• High-dimensional Statistics
• Statistical Learning
• Statistical Computing
• +۴ مورد دیگر

Hyebin Song is an Assistant Professor of Statistics at the Pennsylvania State University since 2020. She holds a PhD in Statistics from the University of Wisconsin-Madison (2020) and a BA in Applied Statistics from Yonsei University (2012). Previously, she worked as a Statistician at the Bank of Korea. Her research focuses on developing statistical methodologies for high-dimensional and complex datasets, with applications in neuroscience, systems biology, and computational protein modeling. Key areas include semi-parametric inference, statistical learning, and computational biology. She has developed influential software tools like PUlasso for high-dimensional variable selection and contributed to advancements in Markov chain autocovariance estimation. Her work has been recognized with the ASA SLDS Student Paper Competition Award (2018). Education: PhD in Statistics (UW-Madison, 2020), BA in Applied Statistics (Yonsei University, 2012). Current affiliations include the Department of Statistics at Penn State and collaborations with computational biology labs. She actively advises PhD students in statistical methodology and applications. Research interests span high-dimensional statistics, shape-constrained inference, and statistical computing, with applications to protein structure analysis and functional genomics. Notable software includes the 'momentLS' package for Markov chain estimation and 'pudms' for deep mutational scanning analysis.

• Wireless communications
• Modulation/coding techniques
• MIMO systems
• +۳ مورد دیگر

Dr. Erik Perrins is University Distinguished Professor and Chair of Electrical Engineering & Computer Science at University of Kansas. His research spans wireless communications, aeronautical telemetry, and signal processing, with 50+ publications and $15M+ in funded projects. Current work focuses on LDPC-CPM systems for IRIG-106 standards. He advises 20+ Ph.D./M.S. students and leads cross-institutional aerospace initiatives. Research Areas: Spectrally efficient modulation/coding FPGA-based receiver design MIMO-OFDM synchronization Awards & Leadership: University Distinguished Professor (2021) IEEE ComSoc CTTC Vice Chair Area Editor, IEEE Transactions on Communications

Adityanand Guntuboyinaمشاهده پروفایل

دانشیار

• Nonparametric Statistics
• High-Dimensional Statistics
• Shape-Constrained Estimation
• +۳ مورد دیگر

Adityanand Guntuboyina is an **Associate Professor and Deputy Chair** in the **Department of Statistics** at the **University of California, Berkeley**. His research focuses on **nonparametric and high-dimensional statistics**, **shape-constrained statistical estimation**, **empirical processes**, and **statistical information theory**. He holds a PhD and has contributed to foundational work in modern statistical methodologies. His research emphasizes theoretical advancements in statistical estimation under shape constraints, with applications to machine learning and high-dimensional data analysis. Notable contributions include methodologies in convex regression, mixture models, and empirical Bayes approaches. Prof. Guntuboyina has published extensively in top-tier journals like the *Journal of the American Statistical Association* and *The Annals of Statistics*. His work bridges statistical theory with computational methods, addressing challenges in modern data science. He advises on grants related to nonparametric estimation and collaborates widely in the statistics community. His lab focuses on developing robust statistical tools for complex datasets, emphasizing both theoretical rigor and practical applicability.

Marco Caroneمشاهده پروفایل

استاد

• Biostatistics
• Causal Inference
• Epidemiological Methods
• +۵ مورد دیگر

Marco Carone is a Professor of Biostatistics and Adjunct Professor of Statistics at the University of Washington, holding the Norman Breslow Endowed Faculty Fellowship. He is an Affiliate Investigator at the Fred Hutchinson Cancer Research Center’s Vaccine and Infectious Disease Division. His research focuses on causal inference, survival analysis, and nonparametric methods, with applications in vaccine science, environmental health, and public health. Carone earned his PhD in Biostatistics from Johns Hopkins University and completed postdoctoral training at UC Berkeley. Education: PhD in Biostatistics, Johns Hopkins University (2011) Hon. B.Sc. in Probability and Statistics, McGill University (2005) Diploma of College Studies in Pure and Applied Sciences, Marianopolis College (2002) Research Interests: Development of robust statistical methods for causal inference and nonparametric estimation Analysis of vaccine efficacy and infectious disease data Integration of machine learning with traditional biostatistical frameworks Applications in environmental epidemiology and aging research His publications span topics including survival analysis, immune correlates of vaccine efficacy, and statistical methodologies for high-dimensional data. Carone has advised over 15 PhD students, many of whom hold academic and industry roles. Awards: Norman Breslow Endowed Faculty Fellowship (2019) UW Teaching Award (2018) Phi Beta Kappa and Delta Omega Honor Societies (2011) Teaching & Service: Teaches courses on survival analysis, statistical inference, and biostatistical consulting Associate Editor for Biometrics and Journal of Causal Inference Peer reviewer for leading journals and grant agencies

Oksana Chernovaمشاهده پروفایل

پژوهشگر ارشد

• Statistics
• Nonparametric Statistics
• Density Estimation
• +۳ مورد دیگر

Oksana Chernova is a Research Fellow at the Technical University of Munich's Institute for Advanced Study (TUM-IAS), appointed in 2022 under the Fellowship for Ukrainian Scientists program. Hosted by the School of Computation, Information and Technology and mentored by Prof. Mathias Drton, she maintains her primary affiliation with Taras Shevchenko National University of Kyiv while conducting research at TUM. Her work specializes in shape-constrained density estimation within nonparametric statistics, focusing on log-concave functions as infinite-dimensional generalizations of Gaussian densities. She addresses computational inefficiencies in higher-dimensional estimation through novel methodologies combining exponential series techniques with score matching procedures. This research enhances practical applications for data visualization, feature extraction, and tuning-parameter-free inference in modern statistical challenges. As part of TUM-IAS's mission to foster interdisciplinary collaboration, Chernova contributes to advancing statistical theory applicable to real-world data analysis problems while representing the institute's support for displaced Ukrainian academics.

Hélène HALCONRUYمشاهده پروفایل

استادیار

• Probability Theory
• Mathematical Statistics
• Stochastic Analysis
• +۴ مورد دیگر

Hélène Halconruy is an Assistant Professor at Télécom SudParis since September 2023. She previously served as a Lecturer at ESILV (2022–2023), Postdoctoral Researcher at the University of Luxembourg (2020–2022), and held roles as Pedagogical Manager and Lecturer at ESME Sudria (2013–2020). Her doctoral work (PhD 2020) focused on probability under Prof. Laurent Decreusefond at Télécom Paris. Research Interests : Probability theory and mathematical statistics, including stochastic analysis, Malliavin calculus for discrete-time processes, Stein's method applications, financial market models (e.g., Greeks computation, portfolio management), and non-parametric statistical inference (e.g., density estimation under shape constraints, robust methods). Recent Work Trends : Her articles address advanced topics like wavelet leaders distribution, privacy-aware parameter estimation, stochastic process generators, and jump-diffusion models. These contributions bridge theoretical probability with practical applications in finance and data science. Grants & Teams : Affiliated with the SAMOVAR research group at Télécom SudParis. No explicit grants or formal advising roles listed.

Yashar Ahmadianمشاهده پروفایل

مدرس

• Neuroscience
• Computational Neuroscience
• Neural Networks
• +۷ مورد دیگر

Yashar Ahmadian is a University Lecturer in Computational Neuroscience at the Department of Engineering , University of Cambridge . After earning his PhD in theoretical condensed matter physics, he transitioned to computational neuroscience through postdoctoral work at Columbia University's Center for Theoretical Neuroscience under Liam Paninski and Ken Miller. He later established his research group at the University of Oregon before returning to Cambridge. Current research focuses on neural dynamics and learning in recurrent circuits Specializes in mathematical modeling of cortical networks Applies dynamical systems, statistical physics, and machine learning His work bridges theoretical neuroscience with experimental data, particularly through collaborations with labs studying visual and auditory systems. Recent publications analyze multi-area consensus-building, efficient coding principles, and stabilized supralinear network dynamics. Notable methodological contributions include neural decoding frameworks and tuning curve analysis tools. His lab currently includes Edward Young (investigating adaptation phenomena) and Monika Jozsa (developing AI-inspired models for visual recognition). Key research trends include: Understanding how cortical connectivity shapes perception through attractor dynamics Developing normative theories for neural homeostasis and adaptation Modeling multi-timescale learning in uncertain environments Analyzing heterogeneous tuning curves with implicit generative models Decoding sensorimotor strategies in olfactory navigation

• Data Science
• Nonparametric Statistics
• Bayesian Statistics
• +۵ مورد دیگر

Hassan Maatouk is a Lecturer at the University of Perpignan, affiliated with the UFR SEE (Science, Economics, and Engineering) faculty, specifically within the MATH-INFO Department. He is a member of the LAMPS (Multidisciplinary Modeling and Simulation Laboratory) where he conducts research in applied mathematics and statistics. His primary research interests include: Data Science and Statistical Learning Nonparametric and Bayesian Statistics High-dimensional Statistical Modeling Computational Statistics and Gaussian Processes MCMC Methods and Uncertainty Quantification Dr. Maatouk's research focuses on non-parametric statistics and high-dimensional modeling with structured constraints such as monotonicity, bounds, and convexity. His work aims to improve prediction models based on Gaussian processes and quantify uncertainties in simulations, with applications spanning econometrics, microbiology, chemistry, and industrial contexts. His recent publications demonstrate a strong emphasis on constrained Gaussian processes, truncated multivariate normal distributions, and scalable Bayesian methods for large datasets, with increasing citation impact (65 citations in 2025 alone). His scholarly impact is evidenced by 362 total citations and an h-index of 8. His most influential works include 'Gaussian process emulators for computer experiments with inequality constraints' (123 citations) and 'Kriging of financial term-structures' (70 citations). Dr. Maatouk collaborates with researchers across France including Xavier Bay from École des Mines de Saint-Étienne, Areski Cousin from the University of Strasbourg, and Yann Richet from IRSN. His interdisciplinary approach extends to materials science as shown by his co-authored work on ZnO nanoparticles' antibacterial properties.

• Control Engineering
• Biomedical Engineering
• Pharmacokinetics/Pharmacodynamics (PK/PD)
• +۵ مورد دیگر

Mattia Laurini is a Fixed-term Researcher at the Department of Engineering and Architecture , University of Parma. His academic work focuses on control engineering, biomedical applications, and optimization algorithms, with teaching assignments in Computer Engineering for Master's Degree programs since 2023. Research Interests: Control systems for anesthesia Pharmacokinetics/Pharmacodynamics (PK/PD) modeling Traffic flow estimation Dynamic programming applications Vehicle motion optimization Medical device automation Research Trends: Recent publications (2025) demonstrate interdisciplinary work merging control theory with biomedical applications, particularly in automating anesthesia delivery. Key methodologies include Branch and Bound optimization, dynamic programming for traffic systems, and hybrid control paradigms combining MPC with PID strategies. Research spans both theoretical advances (e.g., acceleration-constrained shortest path algorithms) and clinical applications (e.g., propofol/remifentanil co-administration optimization). Teaching: Laboratory of Control Engineering (2023-2025) Multivariable Systems (2023-2025) Reference teacher for Computer Engineering program Contact: mattia.laurini@unipr.it | Office: Building 1, Parco Area delle Scienze, 181/A 43124 Parma | Phone: 906183

• Statistical Ecology
• Markov Chains
• Spatial Data Analysis
• +۳ مورد دیگر

Stephen Berg is an Assistant Professor in the Department of Statistics at Pennsylvania State University. His research spans statistical ecology, computational statistics, and biostatistics, with a focus on spatial modeling and Markov chain applications. Affiliations: Huck Institutes of the Life Sciences, Center for Infectious Disease Dynamics Research Interests: Berg develops statistical methodologies for ecological and biomedical applications. His work includes: Ecotone detection using co-occurrence properties Markov chain-based variance estimation for computational models Spatial categorical data analysis for historical forest communities Health outcomes modeling in Medicare populations Trends in Publications: His recent work emphasizes shape-constrained inference for autocovariance sequences and multivariate moment least-squares methods, while earlier contributions address ecological dynamics and healthcare analytics. Labs & Affiliations: Berg collaborates with the Huck Institutes of the Life Sciences and the Center for Infectious Disease Dynamics, integrating statistical methods with interdisciplinary research.

Pierre-Cyril Aubin-Frankowskiمشاهده پروفایل

دانشیار

• Optimization in finite and infinite dimensions
• Machine Learning
• Optimal Transport Theory
• +۷ مورد دیگر

Pierre-Cyril Aubin-Frankowski is a researcher and associate professor in Optimization and Machine Learning at École des Ponts ParisTech, affiliated with CERMICS and the Optimization team. His work connects optimization algorithms, convexity, and infinite-dimensional analysis with applications in optimal transport and control theory. Research Interests Optimization in measure spaces Kernel methods and optimal control duality Generalized gradient flows with non-metric costs Order isomorphisms in function spaces Applications to operations research and transportation systems Article Trends: Recent works focus on extending gradient descent beyond metric spaces, tropical kernels, and connections between linear-quadratic control and Gaussian process covariances. Key themes include Bregman divergences, Wasserstein geometry, and constrained optimization via Hilbertian methods. Scientific Awards Prix Dodu for best communication at SMAI MODE

• Nonparametric function and density estimation
• Shape-restricted Inference
• Generalized Additive Models
• +۴ مورد دیگر

Mary C. Meyer is a Professor at Colorado State University , Department of Statistics. Her research focuses on nonparametric estimation and shape-restricted inference, with applications in environmental science, public health, and anthropology. Education: Ph.D. in Statistics from University of Michigan (1996) Research Interests include: Nonparametric function estimation under shape constraints Constrained regression splines and generalized additive models Statistical software development for constraint-based modeling Applications to forest dynamics and public safety Publication Trends show a focus on: Statistical methodology for shape-restricted models Environmental applications (Landsat time series, forest monitoring) Public safety analysis (airbag effectiveness studies) Statistical software packages (cgam, cone projection algorithms) Email: meyer@stat.colostate.edu

• Multivariate Statistics
• Tolerance Intervals
• Statistical Inference
• +۳ مورد دیگر

Sumona Mondal serves as Professor of Mathematics and Co-Director of the MS Program in Applied Data Science at Clarkson University, where she has been faculty since 2007. She is affiliated with the Coulter School of Engineering & Applied Sciences within the Department of Mathematics, contributing to both theoretical statistics and interdisciplinary applications. Her educational foundation includes a Ph.D. in Statistics from the University of Louisiana at Lafayette, complemented by Bachelor of Science and Master of Science degrees in Statistics from the University of Calcutta, India. Professor Mondal specializes in constructing tolerance regions for multivariate linear models, developing tolerance factors for multivariate normal distributions, and creating statistical methodologies for engineering failure prediction and pollution assessment. Her research extends to bio-engineering, environmental sciences, and physical therapy applications, focusing on robust inferential procedures for complex real-world problems. This work bridges theoretical statistics with practical implementations across diverse scientific domains. Analysis of her recent publications (2021-2025) reveals a strong trend toward interdisciplinary collaboration, particularly in environmental monitoring (air quality sensor networks, PM2.5 analysis), health statistics (rheumatoid arthritis comorbidities, dental caries, HPV vaccination), and emerging fields like robotics acceptance and hate speech detection. Her methodological contributions consistently emphasize statistical rigor while addressing data challenges such as imbalanced datasets, correlated predictors, and low-cost sensor calibration. As Co-Director of the MS Program in Applied Data Science, Professor Mondal actively shapes graduate education and mentors students in statistical methodology development. She maintains significant academic engagement through over 27 national and international conference presentations, demonstrating consistent scholarly productivity and community involvement.

Matúš Maciakمشاهده پروفایل

دانشیار

• nonparametric regression
• change-points
• structural breaks
• +۵ مورد دیگر

Matúš Maciak is an Associate Professor in the Department of Probability and Mathematical Statistics at the Faculty of Mathematics and Physics, Charles University . His research spans nonparametric regression , change-point detection , and statistical modeling in financial and biological contexts . Key research areas: Nonparametric smoothing and robustness Structural break analysis Shape-constrained inference Sparsity and LASSO-based methods Bootstrapping dependent data Recent publications focus on exogenous market changes , longitudinal data modeling , and biological applications like diel movement patterns in fish. Technical reports include contributions to ecological assessment methods and intercalibration processes for water quality. Contact: Email: Matus.Maciak@mff.cuni.cz , maciak@karlin.mff.cuni.cz Office: K151, Sokolovská 83, Prague 8