جست‌وجوی استاد و پژوهشگر

Shanshan Lvمشاهده پروفایل

استادیار

• Statistical Inference
• Fiducial Inference
• Small Sample Methods
• +۳ مورد دیگر

Shanshan Lv is an Assistant Professor in the Department of Statistics at Truman State University. She holds a Ph.D. in Applied Statistics (2019) from the University of Louisiana at Lafayette, with additional master’s degrees in Applied Statistics (2017), Pure Mathematics (2015), and a bachelor’s in Applied Mathematics (2012) from Guizhou University of Finance and Economics. Her research focuses on fiducial inference for discrete distributions, statistical computing, and small sample inference methods. Current work emphasizes fiducial inference applications, with plans to expand into continuous distributions and machine learning domains. She also explores prediction intervals for discrete distributions and confidence interval methodologies for population size estimation using capture-recapture data. Her publications span topics like hypothesis testing for continuous distributions, ratio estimation for normal means, and finite population proportion estimation. Research trends show a strong emphasis on developing robust statistical methodologies with practical applications in ecology, engineering, and computational statistics. No scientific awards or grants are explicitly mentioned in the provided text. She has no listed advisees, and no specific labs/teams are referenced in her profile.

Kate Devittمشاهده پروفایل

استاد مدعو

• robotics
• autonomous systems
• artificial intelligence
• +۷ مورد دیگر

Kate Devitt is an Adjunct Professor and Research Leader in robotics, autonomous systems and artificial intelligence (RAS-AI) at Queensland University of Technology (QUT), specializing in ethical, lawful, and assured RAS-AI systems for industry, government, and academic applications. She serves as a Global Expert on good data practices and is recognized as a strategic thinker and collaborative enabler skilled at aligning diverse stakeholder goals. Her research spans multiple disciplines: Cognitive and computational psychology Philosophy, particularly epistemology and ethics Law related to autonomous systems Decision science for human-AI collaboration Devitt's recent work demonstrates strong interdisciplinary integration between technical AI development and philosophical, ethical, and legal considerations. Her research focuses on ethical frameworks for AI in defense contexts, meaningful human control of autonomous weapons systems, and cognitive factors affecting the adoption of autonomous agriculture. She has developed practical methods for ethical AI implementation and frameworks for trustworthy human-AI networks across multiple sectors including defense, agriculture, and humanitarian aid. Her publications reveal a consistent trajectory toward establishing robust ethical frameworks for high-stakes AI deployment. The 2024 publication 'Meaningful Human Command' represents a significant contribution to military AI ethics, while her work on 'good data' principles provides foundational support for ethical AI systems across multiple domains. Devitt's collaborative approach bridges theoretical concerns with practical applications, making her a valuable contributor to both academic discourse and real-world AI implementation challenges.

Pär Österholmمشاهده پروفایل

استاد

• Macroeconomics
• Financial Economics
• Monetary Policy
• +۴ مورد دیگر

Pär Österholm is a Professor of Financial Economics at Örebro University since 2016. He holds a PhD in Economics from Uppsala University (2004) and was an Associate Professor there until 2016. His research focuses on macro-finance, monetary policy, and forecasting, particularly analyzing interest rate markets, central bank communication, and the economic impact of the 2008 financial crisis. Österholm has held roles at the Riksbank (Sweden's central bank), the International Monetary Fund, and the National Institute of Economic Research. He has advised institutions like Kommuninvest and contributes to economic policy debates through media engagements. His research explores how unconventional monetary policies affect financial markets and the real economy. Key topics include the natural rate of interest, inflation dynamics, and the stability of economic relationships post-crisis. Österholm collaborates internationally, with visiting positions at the Federal Reserve and University of Sydney. His work often emphasizes empirical analysis using Bayesian methods and large datasets, with publications in journals like Journal of Monetary Economics and International Journal of Central Banking .

KOUNTOURIS Mariosمشاهده پروفایل

استاد

• Communication Theory
• Machine Learning for Wireless Communications
• Low Latency Networking
• +۳ مورد دیگر

Marios Kountouris is a Professor at EURECOM in the Department of Communication Systems. He holds a Ph.D. in Electrical Engineering from Telecom ParisTech (France), an M.S. from Telecom ParisTech, and a Diploma in Electrical & Computer Engineering from the National Technical University of Athens (Greece). His career includes roles as Principal Scientist at Huawei Technologies (France), Assistant/Associate Professor at CentraleSupélec (France), Adjunct Professor at Yonsei University (South Korea), and Post-doctoral researcher at the University of Texas at Austin (USA). His research focuses on communication theory, machine learning for wireless communications, low latency networking, and stochastic modeling. Notable contributions include work on ultra-reliable low-latency communication (URLLC), network densification, and semantic communication frameworks. Distinctions: IEEE Communication Society Young Author Best Paper Award 2020 2016 IEEE ComSoc Communication Theory Technical Committee Early Achievement Award 2013 IEEE ComSoc Outstanding Young Researcher Award (EMEA Region) Recent research trends emphasize semantic-aware resource allocation, federated learning in wireless networks, and next-generation waveforms like AFDM. His articles explore optimization frameworks, real-time systems, and AI-driven telecom innovations.

• Machine Learning
• Gaussian Processes
• Bayesian Inference
• +۳ مورد دیگر

Paul Chang is a Visiting Professor in the Department of Computer Science at Aalto University, associated with the Professorship Solin A. He holds a Doctor of Technology (Tekn. toht.) degree in Computer Science from Aalto University, awarded in December 2024. His research focuses on machine learning, Gaussian processes, Bayesian inference, and sequential learning, with applications in neural networks and optimization. His work contributes to UN Sustainable Development Goals related to education and innovation through advancements in probabilistic modeling and computational methods. Chang's academic background includes a doctoral thesis titled Rethinking Inference in Gaussian Processes: A Dual Parameterization Approach (2024), exploring novel methods for improving efficiency and scalability in Gaussian process models. His research has been published in leading conferences such as ICML and ICLR, with notable contributions to sequential learning algorithms, Bayesian optimization, and memory-based approaches in Gaussian processes. Key research interests include dual parameterization techniques, sparse representations in neural networks, and the integration of simulation-based inference with diffusion models. His work bridges theoretical advancements in machine learning with practical applications in optimization, active learning, and temporal data analysis. Collaborations span international institutions, reflecting his global academic engagement. Chang's publications highlight advancements in function-space parameterization, memory-augmented Gaussian processes, and amortized probabilistic conditioning. These contributions address challenges in scalable Bayesian methods and sequential decision-making systems. His research emphasizes computational efficiency, robustness, and applicability to real-world problems in machine learning.

• Multi-target Tracking
• Bayesian Filtering
• Sensor Fusion
• +۳ مورد دیگر

Professor Daniel Clark is a member of the University of Southampton's academic staff, specializing in multi-target tracking, Bayesian methods, and sensor fusion. His research focuses on advancing algorithms for aerospace systems, robotics, and statistical inference. He supervises PhD students in electronic engineering and is affiliated with the Vision, Learning and Control Centre for Robotics. His work bridges theoretical foundations with practical applications in tracking systems and cybersecurity. Recent contributions include advancements in covariance intersection, adversarial risk formulations, and sensor data fusion techniques. Research Interests: His expertise spans multi-object tracking, stochastic guidance systems, and information-theoretic bounds. He explores applications in maritime surveillance, robotics navigation, and secure algorithm design. Advising & Grants: Currently supervising Chuanhang Qiu (PhD in Electronics). His research has been published in top-tier journals like IEEE Transactions on Aerospace and Electronic Systems. Labs & Teams: Active member of the Vision, Learning and Control Centre for Robotics, contributing to interdisciplinary projects in autonomous systems and data fusion.

• Graphical Models
• Bayesian Nonparametrics
• Bayesian Variable Selection
• +۳ مورد دیگر

Yabo Niu is an Assistant Professor and Presidential Frontier Faculty in the Department of Mathematics at the University of Houston, with a joint appointment in the Department of Health Systems & Population Health Sciences within the Tilman J. Fertitta Family College of Medicine. They hold a Ph.D. in Statistics from Texas A&M University (2019) and a B.S. in Statistics from Nankai University, China (2013). Their research focuses on Bayesian statistical methods, including graphical models, nonparametric approaches, variable selection, and tree-based regression/classification techniques. Recent contributions include the 2024 JASA Reproducibility Award-winning paper on covariate-assisted Bayesian graph learning for heterogeneous data. Dr. Niu has developed novel methodologies in network modeling and hybrid estimation techniques (e.g., EPSOM-Hyb). Their work bridges statistical theory with applications in health systems, neuroscience, and genomics. In 2024, they introduced a new Bayesian Statistics course (MATH 6397) at the University of Houston. Education: Ph.D. in Statistics, Texas A&M University (2014–2019) B.S. in Statistics, Nankai University (2009–2013) Research Highlights: Developed robust high-dimensional network modeling frameworks Advanced Bayesian approaches for heterogeneous data integration Investigated astrocyte structural plasticity using machine learning Teaching: Bayesian Statistics course (Spring 2024) Current research emphasizes integrating omics data and advancing reproducible statistical practices. Dr. Niu’s group includes postdoctoral researchers like Ji Shi (jointly supervised with Demetrio Labate and Peng Zhao).

Andres Masegosaمشاهده پروفایل

دانشیار

• Machine Learning
• Bayesian Networks
• Data Streaming
• +۴ مورد دیگر

Andres Masegosa is an Associate Professor at the Department of Computer Science, Aalborg University (AAU), within The Technical Faculty of IT and Design. He is actively involved in the DarkScience project (2022–present), focusing on metagenomic binning and microbial dark matter analysis. His research interests span Bayesian networks, machine learning, probabilistic graphical models, and educational methodologies in computer science instruction. Key research contributions include advancements in PAC-Bayes theory, genome representation learning, and cold posterior effects in Bayesian models. He has published extensively in top venues like Advances in Neural Information Processing Systems and Transactions on Machine Learning Research. His work often bridges theoretical contributions with practical applications in genomics and education. Masegosa leads the development of tools like InferPy for probabilistic modeling and has contributed to open-source projects such as the AMIDST toolbox. His educational research explores learning styles and active learning strategies, emphasizing live coding and programming exercises. Collaborations include interdisciplinary projects with microbiologists and data scientists, reflecting his expertise in computational methods for complex biological systems. His research portfolio demonstrates a strong focus on scalable probabilistic methods and their real-world applications.

Jaya Bishwalمشاهده پروفایل

دانشیار

• Probability
• Stochastic Processes
• Statistical Inference
• +۲ مورد دیگر

Dr. Jaya Bishwal is an Associate Professor in the Mathematics & Statistics Department at the University of North Carolina at Charlotte. Her research expertise centers on Probability and Stochastic Processes, with applications in mathematical finance and statistical inference. Her extensive publication record demonstrates deep specialization in fractional processes, stochastic differential equations, and statistical estimation methods. Recent work focuses on advanced econometric applications including Levy process modeling, fractional Ornstein-Uhlenbeck systems, and volatility estimation in financial mathematics. Methodological innovations include developments in quasi-likelihood estimation, Kolmogorov distance analysis for estimators, bootstrap methods for financial derivatives, and asymptotic theory for nonergodic systems. Her research bridges theoretical probability and practical applications in quantitative finance.

Lennart Hoogerheideمشاهده پروفایل

دانشیار

• Econometrics
• Bayesian Analysis
• Monte Carlo Methods
• +۶ مورد دیگر

Lennart Hoogerheide is an Associate Professor at the Department of Econometrics, School of Business and Economics, Vrije Universiteit Amsterdam, and a Research Fellow at the Tinbergen Institute. He earned his PhD from Erasmus University Rotterdam in 2006. His research focuses on Bayesian econometrics, Monte Carlo integration methods, and computational statistics, with applications in financial econometrics and instrumental variable models. Hoogerheide has developed influential R packages like MitISEM and AdMit for Bayesian analysis and importance sampling. He has received awards including the 'Best Senior Lecturer' (2015-2016) and 'Lecturer of the Year' (2009-2010). His work explores complex posterior distributions, non-elliptical contours, and robust inference techniques in econometric models. His recent research includes time-varying factor models, Bayesian mode inference for discrete distributions, and adaptive mixture methods. He has been awarded the VENI grant (NWO, 2009) and is recognized for contributions to computational Bayesian methods in economics and finance.

Catherine Forbesمشاهده پروفایل

استاد

• Bayesian Statistics
• Econometrics
• Financial Econometrics
• +۵ مورد دیگر

Catherine Forbes is a Professor and Deputy Head of the Department of Econometrics and Business Statistics (EBS) at Monash University, where she also serves as Director of Education. She holds an adjunct professorship at Sunway University, Malaysia, and is an Associate Editor of Applied Stochastic Models in Business and Industry . Her research focuses on Bayesian statistics, econometrics, and computational methods for analyzing financial risks and complex time series. She has led multiple research grants, including ARC and NHMRC-funded projects, and contributed to policy reforms, such as the Victorian Children, Youth and Families Act 2005. Her research interests span Bayesian modeling, financial econometrics, robust statistical methods, and nonparametric techniques. She has supervised 20 PhD students and 20 Honours projects, emphasizing mentorship in statistical methodologies. Notable collaborations include work with the Centre of Excellence in Child and Family Welfare on predictive analytics for youth outcomes. Dr. Forbes has led over 20 research projects, including grants focused on Bayesian empirical likelihood, financial risk dynamics, and health care analysis. Her recent work includes advancements in cross-validation methods and familial inference techniques, with applications in housing affordability and mental health linkages.

• Artificial Intelligence
• Machine Learning
• Multi-agent Systems
• +۶ مورد دیگر

Leandro Soriano Marcolino is a Lecturer (Assistant Professor) in Data Engineering at Lancaster University's Computing and Communications Department. He holds a PhD from the University of Southern California (USC), advised by Milind Tambe, and completed his master's in Japan under a Monbukagakusho Scholarship and his undergraduate studies at Universidade Federal de Minas Gerais in Brazil, where he graduated with top honors. His research focuses on multi-agent systems, machine learning, and robotics, emphasizing teamwork, online learning, and real-world applications in domains like swarm robotics, computer games, and architectural design. Education: PhD in Computer Science, University of Southern California (USC), 2016 Master's Degree in Japan (Monbukagakusho Scholarship) Undergraduate Degree in Computer Science, Universidade Federal de Minas Gerais (Brazil), 2009 Research Interests: Multi-agent teamwork and coordination Online learning and planning Robotics (swarm robotics, real-time strategy games) Applications in computer vision, social networks, and bioinformatics Highlights: Recipient of the Best Dissertation and Best Research Assistant Awards at USC (2016, 2015) Best Paper Nomination at AAMAS 2011 and Best Undergraduate Paper in Brazil (2009) Active in PhD supervision, mentoring students in AI, multi-agent systems, and robotics Co-founder of the COLAB research group at Lancaster University Labs/Teams: Lancaster Intelligent, Robotic and Autonomous Systems Centre (LIRAS), LIRA - Fundamentals, SCC (Data Science).

• Gravitational-Wave Detection
• Pulsar Timing Arrays
• Bayesian Modeling
• +۱۰ مورد دیگر

Rutger van Haasteren is a Group Leader in the Observational Relativity and Cosmology division at the Albert Einstein Institute (AEI) in Hannover, Germany. His research focuses on data analysis methods for pulsar timing arrays (PTAs) and gravitational-wave detection. He previously held positions at Caltech/Jet Propulsion Lab and the Albert Einstein Institute, and worked in industry at Microsoft as a Senior Data Scientist. His research interests lie at the intersection of astrophysics and advanced statistical methods. He specializes in Bayesian modeling , sampling methods , and Gaussian processes applied to PTA data. His work contributes to foundational methodologies used in major collaborations such as NANOGrav. He also has significant experience in machine learning , including large language models, classification, and reinforcement learning from his time in industry. The available publications reflect a strong trend in developing inference methods for gravitational-wave astronomy, particularly using pulsar timing data. The focus is on robust statistical frameworks and computational techniques for detecting low-frequency gravitational waves. NSF Einstein Fellowship Rutger van Haasteren has been involved in mentoring and collaborative research, particularly within the NANOGrav collaboration during his fellowship. While no formal students are listed, his role as a Group Leader at AEI suggests active supervision and team leadership. He has not received public grant mentions in the text, but his Einstein Fellowship indicates competitive external funding. He leads a research group at the Albert Einstein Institute focused on advancing data analysis techniques for pulsar timing arrays, working closely with Bruce Allen. The team aims to improve sensitivity and reliability in gravitational-wave detection using innovative statistical and computational approaches.

• Machine Learning
• Computer Vision
• Signal Processing
• +۳ مورد دیگر

Chenguang Liu is a researcher at Institut Polytechnique de Paris , France, with a focus on computational methods, machine learning, and control systems. His work bridges theoretical and applied research across multiple domains including computer vision, signal processing, and operations research. Key research areas: Machine Learning, Computer Vision, Computational Physics, Control Theory Notable publication trends: Develops novel algorithms for parallel computing, peridynamic modeling, and real-time systems Recent contributions include Bayesian neural networks for gas-bearing prediction, multi-agent reinforcement learning for UAV swarms, and domain adaptation techniques in object detection. He collaborates extensively with researchers in electrical engineering and applied mathematics disciplines.

Dhafer SAIDANEمشاهده پروفایل

استاد

• Sustainable Finance
• Islamic Finance
• Banking
• +۴ مورد دیگر

Dr. Dhafer SAIDANE is a full Professor at SKEMA Business School (France), holding a HDR (Paris 1 Sorbonne) and French university qualification (CNU: Professeur des universités). He specializes in finance, with a focus on sustainable finance, Islamic banking, and development economics. He leads the MSc in Corporate Financial Management - FinTech & Digital Finance in Suzhou, China, and coordinates programs across SKEMA's global campuses. He advises Tunisia's Prime Minister and chairs the African Finance Network, promoting financial integration in Africa. His research explores governance, CSR, and systemic risk in banking, with over 150 publications. Awards include the 2012 TURGOT Prize and 2024 Emerald Literati Award. Education Doctorat en sciences économiques (1991), HDR (1997), Paris 1 Panthéon-Sorbonne Maitrise, Licence en sciences économiques (1985-1986), Paris 1 Research Interests His work bridges finance and development, addressing sustainability, banking governance, and African economic challenges. Key themes include Islamic finance models, CSR-banking linkages, and public debt impacts on credit supply. He advocates for integrated reporting frameworks and sustainable banking practices. Professional Roles Advisor to the Tunisian Prime Minister's Economic Council Member, Club of African Bankers Editorial Board Member, Journal of Risk and Financial Management Organized major conferences like the African Finance Network summits Grants & Awards 2024 Fintech for Tomorrow Challenge Prize 2024 Emerald Literati Outstanding Paper 2012 TURGOT Prize for Sustainable Finance Advisees & Leadership Supervised 12+ PhD students, serving as director or jury member. Leads SKEMA's global programs and chairs African finance networks, fostering academic-industry collaboration. Labs & Initiatives Director, International Observatory of Sustainable Finance; co-founded African Finance Network promoting financial innovation and regional integration.