جست‌وجوی استاد و پژوهشگر

Arun Kumar Kuchibhotlaمشاهده پروفایل

استادیار

• Causal Inference
• Statistical Machine Learning
• High-Dimensional Statistics
• +۵ مورد دیگر

Arun Kumar Kuchibhotla is an Assistant Professor in the Department of Statistics & Data Science at Carnegie Mellon University (CMU), within the Dietrich College of Humanities and Social Sciences. His research focuses on foundational aspects of statistical inference, machine learning, and high-dimensional data analysis. Education : While specific details are not listed here, his academic trajectory aligns with roles in top-tier institutions. His research interests span Causal Inference , Statistical Machine Learning , High-Dimensional Statistics , and Nonparametric Methods . He explores theoretical guarantees for algorithms, fair machine learning applications, and algorithmic forecasting of extreme events. Notable areas include: Development of robust statistical methods for online algorithms and M-estimation. Innovations in conformal prediction, confidence regions, and self-normalization techniques. Advancements in fair risk assessments in criminal justice through optimal transport and conformal prediction. Recent work emphasizes dimension-agnostic adaptive inference and censored time-to-event analysis , with applications to climate science and policy. His publications in 2025 highlight breakthroughs in concentration inequalities, extreme heat wave forecasting, and doubly robust calibration methods. Advising & Grants : No specific advisees or grants are listed here, but his research aligns with CMU’s emphasis on interdisciplinary projects. He collaborates with groups like the CSAFE and DELPHI labs, focusing on applied statistics and societal impact. Labs/Teams : Active contributor to CMU’s CSAFE Lab Group (statistical forensics) and DELPHI Lab Group (epidemiological forecasting).

Larry Wassermanمشاهده پروفایل

استاد

• nonparametric inference
• causal inference
• machine learning
• +۶ مورد دیگر

Larry Wasserman is a UPMC University Professor at Carnegie Mellon University, jointly appointed in the Department of Statistics and Data Science and the Machine Learning Department. He received his Ph.D. from the University of Toronto in 1988 and is recognized as one of the leading statisticians of his generation. His research spans theoretical and applied statistics, with core interests in: Foundational inference : Nonparametric methods, asymptotic theory, causal frameworks Modern applications : Machine learning, high-dimensional statistics, astrostatistics Interdisciplinary domains : Bioinformatics, genomics, physical sciences via the STAMPS group His recent publications demonstrate strong emphasis on causal methodology, optimal transport, and robust inference, with applications ranging from particle physics to genomic analysis. Articles frequently develop novel nonparametric techniques with minimax optimality guarantees. Award highlights include: COPSS Presidents' Award (1999) - Top honor for statisticians under 40 CRM-SSC Prize (2002) - Landmark contributions to statistics Fellowships: American Statistical Association, Institute of Mathematical Statistics, AAAS He leads the Statistical Machine Learning Theory Group and founded STAMPS (Statistical Methods for Physical Sciences). His textbooks All of Statistics and All of Nonparametric Statistics are widely used in graduate programs globally.

• Statistical Machine Learning
• High-Dimensional Statistics
• Nonparametric Methods
• +۷ مورد دیگر

Ann Lee is a Professor and Co-Director of the Ph.D. Program in the Department of Statistics & Data Science at Carnegie Mellon University, with a joint appointment in the Machine Learning Department. She has been at CMU since 2005, after serving as the J.W. Gibbs Assistant Professor of Applied Mathematics at Yale University. Her academic journey began with a PhD in Physics from Brown University in 2002, preceded by BSc and MSc degrees in Engineering Physics from Chalmers University of Technology in Sweden. Dr. Lee's educational background includes: PhD in Physics, Brown University MSc/BSc in Engineering Physics, Chalmers University of Technology, Sweden Her research focuses on developing statistical methodology for complex data and problems in the physical sciences, with particular emphasis on trust-worthy scientific inference and reliable uncertainty quantification. She works at the intersection of classical statistics and machine learning, developing methods for simulation-based inference and experimental design. Dr. Lee is especially interested in likelihood-free inference, calibrated probabilistic forecasting, and interpretable diagnostics of generative models, with applications in astronomy and hurricane intensity guidance involving satellite imagery and large surveys. Dr. Lee co-founded the STAMPS (STAtistical Methods for the Physical Sciences) research group with Mikael Kuusela in 2018, which is transitioning to a CMU Research Center in Fall 2024. The group hosts public colloquia-style webinars and weekly research meetings for students and faculty at CMU and UPitt. Her notable scientific contributions have been recognized through: Her paper "Detecting Distributional Differences in Labeled Sequence Data with Application to Tropical Cyclone Satellite Imagery" being selected for The Best of AOAS session at the 2023 Joint Statistical Meeting Her student Luca Masserano winning the ASA Best Student Paper Award at the 2023 Joint Statistical Meeting Her student Alex Shen winning a Poster Award at the Machine Learning and the Physical Sciences Workshop, NeurIPS 2023 Dr. Lee has advised numerous PhD students to completion, with research spanning statistical machine learning, high-dimensional statistics, and applications in physical sciences. She has secured research funding supporting her work on statistical methods for physical sciences and has collaborated extensively with researchers across disciplines. Her current research projects focus on advancing likelihood-free inference methods and applying them to challenging problems in climate science and astronomy. She leads the STAMPS research group, which is becoming a CMU Research Center in Fall 2024, and organizes workshops including the PHYSTAT-SBI workshop on "Simulation Based Inference in Fundamental Physics" and the Hammers & Nails workshop on "Frontiers in Machine Learning in Cosmology, Astro & Particle Physics."

Thang Buiمشاهده پروفایل

مدرس

• Probabilistic Modelling
• Machine Learning
• Monte Carlo Methods
• +۳ مورد دیگر

Thang Bui is a Lecturer (equivalent to tenure-track Assistant Professor) in Machine Learning at the School of Computing, Australian National University (ANU) since July 2022. Previously, he was a Lecturer at the University of Sydney (2018–2022) and spent two years (2019–2020) at Uber AI. He holds a PhD from the Cambridge Machine Learning Group at the University of Cambridge, supervised by Richard Turner and advised by Carl Rasmussen. Research Interests: His work focuses on probabilistic modeling and inference, Monte Carlo methods, distributed/continual learning, and model-based reinforcement learning. Current projects include uncertainty estimation in Gaussian processes and neural networks, adaptive models for changing environments, and interpretable machine learning techniques. Awards: Best Paper Award, ACL Workshop on Information Extraction from Scientific Publications (2023) Best Paper Award, NeurIPS workshop on Deep Learning through Information Geometry (2020) Best Paper Award, NIPS Workshop on Advances in Approximate Bayesian Inference (2015) Advising & Grants: Actively recruiting PhD/MPhil students and postdoctoral fellows. His research has been supported by grants focusing on Bayesian methods and scalable machine learning. He has advised students on topics ranging from Gaussian processes to federated learning and continual learning. Labs/Teams: Leads a research group exploring uncertainty quantification, adaptive learning systems, and probabilistic AI at ANU's College of Engineering and Computer Science.

• Cosmology
• Intergalactic Medium Studies
• Astrophysics
• +۳ مورد دیگر

Frederick Davies is the MPG Research Group Leader leading the Reionization and Intergalactic Medium (REIGM) Group at the Max Planck Institute for Astronomy in Heidelberg. His research focuses on unraveling the cosmic reionization epoch, the evolution of the intergalactic medium (IGM), and the origins of early supermassive black holes. Key goals include measuring reionization topology via quasar damping wings, constraining black hole accretion timescales, and tracing IGM thermal fluctuations before/during reionization. His work leverages high-redshift quasar spectra from facilities like JWST and Euclid, employing advanced numerical simulations and statistical methods. Research interests span cosmological structure formation, radiative transfer processes, and the interplay between galaxies/quasars and the IGM. Davies' group develops novel techniques to analyze Lyman-α forest data and proximity zones, aiming to quantify ionization histories and cosmic heating. His affiliations include the REIGM Group and the MPIA's Galaxies and Cosmology department. Publications emphasize precision cosmology, quasar evolution, and multi-scale IGM modeling. While no formal advisees are listed, his research team actively contributes to large-scale surveys like ASPIRE and XQR-30. The group's efforts bridge observational data with theoretical frameworks to address fundamental questions about the Universe's early evolution.

Bikramjit Banerjeeمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Distributed Artificial Intelligence
• +۳ مورد دیگر

Dr. Bikramjit Banerjee is a Professor of Computer Science at the School of Computing Sciences & Computer Engineering, University of Southern Mississippi. He holds a PhD from Tulane University (2006) and an MS from the University of Tulsa (2000). His research focuses on distributed artificial intelligence, multi-agent systems, and reinforcement learning applications in robotics and autonomous systems. He has served on editorial boards of journals like Autonomous Agents and Multi-Agent Systems (2014–2023) and organized workshops such as the 2006 ICML Workshop on Structural Knowledge Transfer. His professional affiliations include the Association for the Advancement of Artificial Intelligence (AAAI). Education Background: PhD in Computer Science, Tulane University, 2006 MS in Computer Science, University of Tulsa, 2000 Bachelor’s in Computer Science & Engineering, Jadavpur University (India), 1998 Research Interests: Dr. Banerjee’s work centers on advancing reinforcement learning techniques for multi-agent systems, with applications in swarm robotics, multi-robot coordination, and decision-making under uncertainty. His recent publications emphasize theoretical bounds in reinforcement learning (e.g., PALO bounds) and practical challenges like occlusion in inverse reinforcement learning (I2RL framework). These studies bridge foundational AI theory with real-world robotic deployments. Academic Contributions: He has contributed to IEEE Intelligent Systems as a guest editor (2017) and served on NSF panels. His teaching spans courses like Artificial Intelligence, Robotics, and Advanced Algorithms. Though no explicit grants are listed, his NSF panel roles suggest involvement in funding evaluation. No student advisees are listed in the provided texts. Labs/Teams: His research aligns with the university’s robotics and AI initiatives, though specific lab names are not mentioned in the data.

Fabrizio Leisenمشاهده پروفایل

استاد

• Bayesian Inference
• Bayesian Nonparametric Methods
• Objective Bayesian Analysis
• +۳ مورد دیگر

Fabrizio Leisen is a Professor of Statistics at King’s College London, Department of Mathematics, within the Faculty of Natural, Mathematical & Engineering Sciences. Previously, he held positions at the University of Nottingham (Professor), University of Kent (Reader), and institutions in Spain and Italy. He earned a PhD in Mathematics from the Università di Modena e Reggio Emilia, specializing in Probability. His research focuses on Bayesian inference, nonparametric methods, objective Bayesian analysis, and foundational statistical theory, with notable contributions to predictive constructions, knockoff procedures, and stochastic processes. He serves as an Associate Editor for Bayesian Analysis , Statistics and Probability Letters , and Statistical Methods and Applications . Leisen’s work bridges theoretical and applied statistics, emphasizing Bayesian nonparametric priors for complex data structures and model selection. His recent articles address survival analysis, prior elicitation without subjective inputs, and algorithmic advancements in Bayesian computation. He collaborates globally, including co-supervising a PhD student at the Università di Bologna. His teaching includes Computational Statistics and Probability and Statistics Skills sessions. Research interests span Bayesian foundations, model selection via knockoffs, stochastic processes, and interdisciplinary applications like genomic and biostatistical analysis. His lab is affiliated with King’s Statistics group, focusing on MCMC methods, Bayesian nonparametrics, and experimental design.

• Cognitive Psychology
• Computational Modeling
• Neuroscience
• +۴ مورد دیگر

Trisha Van Zandt is a tenured Professor in the Department of Psychology and holds a courtesy appointment in the Department of Statistics at The Ohio State University. Her research integrates cognitive psychology with advanced computational methodologies. Primary affiliation: Department of Psychology, College of Arts and Sciences Courtesy appointment: Department of Statistics Research Focus: Specializing in computational modeling of cognitive processes, her work addresses: Bayesian analysis of chronometric data Signal detection and response time modeling Cognitive mechanisms in ADHD and decision-making Computational neuroscience approaches to attention Mathematical psychology foundations Publication Trends: Over 15 years of research demonstrates expertise in: Bayesian hierarchical modeling Cognitive architecture and decision-making Neurocomputational methods Attention and response inhibition Methodological Contributions: Includes innovations in: Approximate Bayesian computation Hidden Markov modeling Chronometric data analysis Perceptual conflict tasks

Alessio Lachiمشاهده پروفایل

استادیار

• Epidemiological Modeling
• Medical Statistics
• Global Sensitivity Analysis
• +۳ مورد دیگر

Alessio Lachi is an Assistant Professor (Junior RTD A) at Saint Camillus International University of Health and Medical Sciences in Rome, specializing in Medical Statistics (MEDS-24/A). He holds a background from the University of Florence and focuses on mathematical and statistical analysis of contagion processes, epidemiological data modeling, and global sensitivity analysis applied to health policy. His research bridges statistical methodologies with public health challenges, addressing topics like smoking dynamics, gambling behavior, and environmental health risks. Education: Medical Statistics specialization from University of Florence Collaborations: ISPRO, University of Florence (UNIFI), National Research Council (CNR) His work emphasizes interdisciplinary approaches, such as compartmental models for disease transmission and Bayesian inference techniques. Recent studies include analyses of canine tumor epidemiology, alcohol-breast cancer links, and regional gambling prevalence. Despite prolific output, no awards or grants are explicitly mentioned. No student advising records were found in the provided data.

Veronika CZELLARمشاهده پروفایل

استاد

• Financial Econometrics
• Robust Statistics
• Simulation-Based Estimation
• +۴ مورد دیگر

Veronika CZELLAR is a Professor in Econometrics and Data Science at SKEMA Business School, France. She holds a PhD in Econometrics and Statistics from the University of Geneva (2006) and has held academic positions at institutions including EDHEC, EM Lyon, HEC Paris, and the University of Washington. Her research focuses on simulation-based estimation, financial econometrics, and robust statistics, with publications in top journals such as the Journal of Financial Economics and Journal of Econometrics . She teaches courses on Portfolio Management, Financial Econometrics, and Data Science. Education: PhD in Econometrics and Statistics, University of Geneva, 2006 Master in Econometrics, University of Geneva, 2002 Research Interests: Her work spans simulation-based estimation, financial econometrics, robust statistics, and applications in asset pricing and M&A analysis. Recent studies include multifractal cryptocurrency dynamics and the impact of M&A rumors on transaction values. Key Contributions: Her methods address challenges in indirect inference and robust filtering, with applications in volatility modeling and market participation analysis. She actively presents at conferences such as the Society for Financial Econometrics and SKEMA Finance Seminars. Teaching & Mentorship: Courses include Portfolio Management, Financial Econometrics, R/VBA Programming, and Data Science. While her profile does not explicitly list students, her academic roles suggest involvement in PhD/Master’s advising.

Fazlu Rahmanمشاهده پروفایل

پژوهشگر

• Cosmic Microwave Background (CMB)
• Early Universe Physics
• Galactic Emissions Modelling
• +۳ مورد دیگر

Fazlu Rahman is a Postdoctoral Research Associate at the Mitchell Institute for Fundamental Physics and Astronomy, Texas A&M University , working under Prof. Kevin Huffenberger. His research focuses on observational cosmology, particularly Cosmic Microwave Background (CMB) analysis , Galactic emission modelling , and random field statistics in astrophysics . He develops advanced component separation pipelines for CMB experiments and explores novel statistical tools for cosmological data. His work includes multifractal analysis of astrophysical structures like the Crab Nebula and statistical characterization of galactic synchrotron emissions. He contributes to large-scale collaborations like COSMOGLOBE and BEYONDPLANCK , aiming to refine CMB data processing and cosmological parameter estimation. His research bridges theoretical astrophysics with cutting-edge data analysis techniques, addressing fundamental questions about the early universe and galactic dynamics. Key projects include Bayesian end-to-end CMB analysis frameworks, isotropic cosmic birefringence constraints, and multi-frequency comparisons of galactic foregrounds. Rahman's interdisciplinary approach integrates cosmology, mathematical physics, and computational methods to solve complex observational challenges.

Ullrich Kötheمشاهده پروفایل

دانشیار

• Machine Learning
• Neural Networks
• Explainable AI
• +۶ مورد دیگر

Prof. Dr. Ullrich Köthe is an Associate Professor and group leader in the Visual Learning Lab Heidelberg at the University of Heidelberg . He focuses on Explainable Machine Learning , leveraging Invertible Neural Networks to enhance transparency and utility in image analysis and medical applications . He also maintains the widely used VIGRA image analysis library. Education: PhD in Informatics, University of Hamburg, 2000 Habilitation in Informatics, University of Hamburg, 2008 His research interests center around machine learning , image analysis , and scientific computing , particularly the development of robust algorithms for medical imaging , computer vision , and life sciences . His work on invertible neural networks and parameter-free segmentation has led to significant advancements in the field. Recent publications highlight his contributions to Bayesian inference , neural network interpretability , and stochastic modeling , with applications ranging from disease outbreak dynamics to connectomics . Key trends include generative models , parameter-free segmentation , and likelihood-free inference . Scientific Awards: DAGM 2003 Main Prize DAGM Best Paper Award 2008 He has supervised numerous Master and Bachelor theses in machine learning and image analysis , with teaching roles in Advanced Machine Learning and Explainable AI . His collaborative research grants include funding from HARMAN International (2024). He leads the Explainable Machine Learning subgroup and has contributed to open-source software projects like ilastik and VIGRA , which are critical tools in bioimage analysis .

Jean-Jacques Forneronمشاهده پروفایل

استادیار

• Econometrics
• Macroeconometrics
• Industrial Organization
• +۳ مورد دیگر

Jean-Jacques Forneron is an Assistant Professor of Economics at Boston University, specializing in econometrics and computational methods. He holds a PhD from Columbia University (2018), an MA in Economics and Statistics from ENSAE Paris (2012), and an MSc in Management from HEC Paris (2012). His research focuses on advancing econometric theory, particularly in dynamic models, moment condition estimation, and stochastic optimization techniques. He explores topics like model misspecification, optimal transportation approaches, and robust estimation under noisy conditions. His recent work emphasizes methodological innovations in simulation-based estimation, including sieve estimators, scrambled methods of moments, and likelihood-free sampling. These contributions aim to address challenges in parameter identification, computational efficiency, and statistical inference under non-standard conditions. Forneron teaches advanced econometrics courses at both MA and PhD levels, including Advanced Econometrics 2 and Advanced Topics in Econometrics , often co-instructing these courses. His office is located in SSW 415D on Boston University's campus.

Olivia Feldmanمشاهده پروفایل

دانشیار

• Mathematical Biology
• Infectious Disease Dynamics
• Computational and Applied Mathematics
• +۴ مورد دیگر

Olivia Feldman is an Associate Professor in the Department of Mathematics at the University of Tennessee. Her research focuses on the intersection of mathematics and biology, particularly in developing mathematical models to understand infectious disease dynamics. Key areas include vector-borne diseases, drug resistance, and spatial transmission patterns. She holds a Ph.D. from the University of Florida and has been supported by grants such as the NSF CAREER award for designing optimal sampling strategies in epidemiological models. Her work integrates pharmacokinetics/pharmacodynamics with population-level disease transmission, explores within-vector parasite diversity, and examines spatial heterogeneity's role in disease spread. Recent grants include DMS-1816075 (collaborative research on vector-borne diseases and drug resistance) and DMS-2045843 (CAREER grant on sampling strategies). Education: Ph.D., University of Florida Grants: NSF CAREER (2021-2026), Collaborative Research on Drug Resistance (2018) Publications span topics like malaria dynamics, pandemic modeling, and optimal sampling methods. She emphasizes bridging mathematical models with real-world data through methodologies addressing model identifiability challenges.

• Genomics Data Analysis
• Macroevolution
• Epidemiology
• +۳ مورد دیگر

Sasha Gavryushkina is a Lecturer in Data Science and Statistics at the School of Computer and Mathematical Sciences, part of the Faculty of Sciences, Engineering and Technology at the University of Adelaide. Her research focuses on developing statistical and computational methods for genomics data analysis, with applications in macroevolution, epidemiology, and cancer research. She specializes in reconstructing evolutionary histories from genetic data to infer population dynamics parameters and enhancing single-cell sequencing analysis for cancer development insights. Her work involves extending birth-death models for phylodynamic inference and developing likelihood-free inference techniques for phylogenetic analysis. She also explores machine learning applications for sequencing data pre-processing and genotype-phenotype association studies. Sasha is eligible to supervise Masters and PhD students as a Co-Supervisor, focusing on topics such as evolutionary genomics and computational biology.