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Thang BuiView profile

Lecturer

• Probabilistic Modelling
• Machine Learning
• Monte Carlo Methods
• +3 more

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.

Luca MaestriniView profile

Lecturer

• Methodological and Computational Statistics
• Statistical Theory
• Variational Approximations
• +4 more

Dr. Luca Maestrini is a Lecturer of Statistics at the Research School of Finance, Actuarial Studies & Statistics at the Australian National University (ANU), Canberra. His research focuses on methodological and computational statistics, with expertise in variational approximations, generalized linear mixed models (GLMMs), and statistical theory. He previously held postdoctoral positions at ANU (2022–2024) under Prof. Francis Hui and Prof. Alan Welsh, and at the University of Technology Sydney (2018–2022) under Prof. Matt Wand. He completed his PhD in Statistics at the University of Padova, Italy (2015–2019), studying under Prof. Nicola Sartori and co-supervisors Prof. Alessandra Salvan and Prof. Matt Wand. He also served as a Research Fellow at the Australian Research Council Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS). His research interests span computational statistics, Bayesian inference, and applications in forensic science, ecology, and finance. Notable collaborations include work on inverse problems, multivariate abundance data analysis, and stochastic variational inference for GARCH models. He currently chairs the Australian Capital Territory branch of the Statistical Society of Australia. Key research trends in his articles include advancements in variational inference frameworks, asymptotic improvements to GLMMs, and statistical methods for postmortem interval estimation using lipid degradation biomarkers. His work bridges theoretical developments with practical applications in diverse fields. He has advised students through ANU’s research programs and contributed to ACEMS initiatives. His lab focuses on computational methods and applied statistical modeling, emphasizing interdisciplinary collaboration.

Lexing YingView profile

Professor

• Applied Mathematics
• Computational Science
• Quantum Computing
• +7 more

Lexing Ying is a Professor of Mathematics at Stanford University. His research focuses on applied mathematics, computational science, and interdisciplinary areas such as quantum computing, machine learning, and numerical analysis. He has made significant contributions to high-dimensional problems, inverse problems, and the development of efficient algorithms for complex systems. His work spans theoretical advancements and practical applications, including quantum signal processing, tensor-based numerical methods, and probabilistic modeling. Key themes in his research include the analysis of diffusion models, stochastic systems, and optimization algorithms. Recent publications highlight innovations in solving high-dimensional PDEs, improving sampling techniques, and advancing quantum algorithms. Notably, Ying has developed methods for operator learning, functional hierarchical tensors, and robust phase estimation. His research often bridges mathematical theory and computational practice, addressing challenges in areas like statistical inference and numerical simulation.

Ali HashemiView profile

Researcher

• Machine Learning
• Statistics
• Optimization
• +3 more

Ali Hashemi is a Researcher at the Berlin Institute for the Foundations of Learning and Data (BIFOLD) and a Postdoctoral Researcher at Technical University of Berlin. He holds a PhD (Dr. rer. nat.) with distinction from TU Berlin (2022) and an MSc in Electrical Engineering from Sharif University of Technology (2013). His research focuses on machine learning, Bayesian methods, and neuroimaging applications, particularly in inverse problems and explainable AI. He has contributed to advancements in structured noise modeling, robust source localization, and 3D neural networks for brain imaging. His work bridges signal processing and AI to improve biomedical data analysis. Education: PhD in Machine Learning, Technical University of Berlin (2022) MSc in Electrical Engineering, Sharif University of Technology (2013) Research Interests: Machine Learning & Optimization Bayesian Inference Neuroimaging (M/EEG, fMRI) Explainable AI & Diffusion Models Signal Processing for Biomedical Applications Key Achievements: Developed the 'Champagne Algorithm' for noise estimation in brain imaging Published widely in top venues like NeurIPS and NeuroImage Recipient of PhD distinction award for outstanding dissertation Labs & Affiliations: Active in TU Berlin's Uncertainty & Inverse Modeling group and Charité’s Brain and Data Science lab. Collaborates on open-source tools for M/EEG analysis (e.g., contributions to MNE-Python).

Marco StefanucciView profile

Assistant Professor

• Statistical Methodology
• Functional Data Analysis
• Spectroscopy
• +3 more

Marco Stefanucci is an Assistant Professor in Statistics at the Department of Economics and Finance, University of Rome Tor Vergata. Previously, he held positions at the University of Rome La Sapienza, University of Trieste, and served as a postdoctoral researcher at the University of Padova. He earned his PhD from the University of Rome La Sapienza under Professor Pierpaolo Brutti. His research focuses on statistical methodology, functional data analysis, and applications in spectroscopy, mortality trends, and neuroscience. Collaborations include work with Professors Mauro Bernardi, Pierpaolo Brutti, and Laura Sangalli, among others. Research interests include advanced statistical techniques for functional data, compositional data analysis, and their applications in diverse fields such as demography, chemistry, and medical imaging. His recent work emphasizes adaptive regression frameworks, sparse modeling, and classification of complex datasets. He has published extensively on topics like mortality trend analysis, spectroscopic data interpretation, and multimodal imaging methodologies. While no awards or grants are explicitly mentioned, his contributions to statistical theory and applied research are evident through his academic trajectory and collaborative projects. Teaching roles include lecturing on Quantitative Methods and Statistical Learning at undergraduate levels.

John P. CunninghamView profile

Professor

• Machine Learning
• Neuroscience
• Biostatistics
• +3 more

John P. Cunningham is a Professor of Statistics at Columbia University's Faculty of Arts and Sciences and a core member of the Data Science Institute (DSI). His research focuses on machine learning applications in science and industry, particularly leveraging AI to understand biological intelligence and complex systems. He holds affiliations with the Foundations of Data Science, Grossman Center for the Statistics of Mind, Zuckerman Mind Brain Behavior Institute, and Center for Theoretical Neuroscience. Education: B.S. in Computer Science (Dartmouth College), M.S./Ph.D. in Electrical Engineering (Stanford University), and postdoctoral research in Machine Learning at the University of Cambridge. His work bridges computational neuroscience, statistical theory, and scalable machine learning. Research interests include neural decoding, Bayesian optimization, Gaussian processes, and interpretable latent variable models. Recent projects emphasize medical AI, neurotechnology, and foundational ML theory. His lab develops open-source tools for neuroscience analysis, such as Lightning Pose and BehaveNet. Notable contributions include work on neural geometry in motor cortex, scalable Gaussian processes, and AI ethics in clinical applications. Collaborations span academia and industry, focusing on translating theoretical insights into practical solutions for healthcare and neuroscience.

• Wireless Networks
• Mobile Computing
• Smart Grid Systems
• +4 more

Vincent Gauthier is a Professor at Telecom Paris, part of Institut Polytechnique de Paris, with a distinguished research career spanning over two decades in mobile network analysis, urban mobility modeling, and smart infrastructure systems. His research portfolio demonstrates deep expertise in leveraging mobile network metadata for urban population estimation, transportation optimization, and smart grid applications. Dr. Gauthier's research interests center on wireless networks, mobile computing, smart grid systems, urban mobility analysis, and electric vehicle infrastructure. His work bridges theoretical network modeling with practical urban applications, particularly evident in his studies of mobility patterns in Paris and Peru. His research methodology combines mobile network metadata analysis with advanced computational techniques to address urban infrastructure challenges. His recent publication trends show a strategic pivot toward sustainable transportation solutions, with multiple 2024-2025 publications focusing on electric vehicle charging infrastructure optimization. Earlier work established foundational contributions to understanding urban dynamics through mobile network data, with significant publications on population estimation and transport mode detection. His research consistently demonstrates interdisciplinary approaches, connecting telecommunications engineering with urban planning and environmental sustainability. Dr. Gauthier has maintained long-term collaborations with researchers including Monique Becker, Michel Marot, and Hassine Moungla, with whom he has co-authored numerous publications over multiple years. His work shows particular strength in applications to Peru and other LMIC countries, reflecting a commitment to globally relevant research. His research methodology combines theoretical network modeling with practical urban applications, particularly evident in his studies of mobility patterns in Paris and Peru. His work bridges telecommunications engineering with urban planning and environmental sustainability, creating impactful solutions for modern cities.

Loucas Pillaud-VivienView profile

Researcher

• Machine Learning
• Stochastic Processes
• High-Dimensional Statistics
• +3 more

Loucas Pillaud-Vivien is a researcher at the Applied Probability team within CERMICS (École des Ponts ParisTech), specializing in the high-dimensional stochastic dynamics underlying machine learning optimization algorithms. His work bridges statistical theory and computational methods, focusing on understanding algorithmic behavior in modern ML systems. Education : PhD from ENS/Inria Paris (2020) under Francis Bach; postdoctoral research at EPFL, New York University (Courant Institute), and the Simons Foundation (Flatiron Institute). Research interests include stochastic processes, optimization theory, and high-dimensional statistical learning. Key themes involve analyzing gradient-based methods, implicit regularization effects, and the interplay between computational efficiency and statistical accuracy. Recent publications emphasize topics such as gradient flow dynamics, variational inference, and spectral bias in neural networks. His work often addresses fundamental questions about algorithmic convergence and generalization in overparameterized models. Loucas is affiliated with the CERMICS laboratory, contributing to interdisciplinary projects at the intersection of applied mathematics and machine learning.

Lehel CsatóView profile

Professor

• Machine Learning
• Probabilistic Robotics
• Data Analysis
• +4 more

Lehel Csató is a Professor at the Faculty of Mathematics and Informatics of Babeș-Bolyai University in Cluj-Napoca, Romania. His academic journey includes a PhD from Aston University and a postdoctoral position at the Max Planck Institute for Biological Cybernetics. He specializes in Machine Learning, Probabilistic Robotics, and Nonparametric Bayesian Methods, with a focus on Gaussian Processes and their applications in data analysis and robotics. Teaching includes courses in Logic and Functional Programming (Hungarian), Numerical Modelling in Data Analysis (English), and Artificial Intelligence (Hungarian). His research emphasizes sparse Gaussian Process approximations and their use in reinforcement learning, robotics, and medical image analysis. He contributed to the development of the Sparse Online Gaussian Process toolbox. Publications span topics from deep learning architectures to optimization techniques in neural networks. He has organized the ClujUAV drone navigation competition and collaborated on interdisciplinary projects involving robotics, vision, and medical data analysis.

Markus HeglandView profile

Professor

• Computational Mathematics
• High-Dimensional Problems
• Ill-Posed Problems
• +4 more

Markus Hegland is a Professor and Head of the Centre for Mathematics and its Applications (CMA) at the Australian National University (ANU). He holds a PhD from ETH Zurich (1988) and has been affiliated with ANU since 1992, focusing on High-Performance Computing (HPC) and numerical analysis. As a Hans Fischer Senior Fellow at TUM-IAS, his research emphasizes high-dimensional problems, ill-posed systems, and data mining applications. His work bridges computational mathematics with practical domains like systems biology and spectral enhancement. Research interests include sparse grid techniques, regularization methods, and algorithm development for HPC. Notable contributions include the OPTICOM method for stable sparse grid solutions and convergence theory for variable Hilbert scales regularization. He has led projects on fault-tolerant HPC algorithms and collaborated with Fujitsu on HPC applications. Publications span numerical analysis, bioinformatics, and computational physics. His work on the chemical master equation and gyrokinetics showcases interdisciplinary impact. Currently, he explores resilient grid-based solvers and machine learning integration with HPC frameworks. No awards are explicitly listed, but his senior fellowship underscores recognition in his field. Grants and collaborations include ARC-funded research in bioinformatics and HPC resilience. His work on digital twins and algorithm optimization reflects broader interests in advanced computational modeling. He is actively involved in teaching and supervising in computational mathematics and data science at ANU.

Zachary SunbergView profile

Assistant Professor

• Aerospace autonomy
• Partially observable Markov decision processes (POMDPs)
• Game theory
• +5 more

Zachary Sunberg is an Assistant Professor in the Ann and H.J. Smead Aerospace Engineering Sciences Department at the University of Colorado Boulder. He is affiliated with the Research and Engineering Center for Unmanned Vehicles (RECUV) and leads the Autonomous Decision and Control Lab (ADCL). His research focuses on artificial intelligence for autonomous systems, particularly in aerospace autonomy, decision-making under uncertainty (via POMDPs), and human-AI interaction. Sunberg holds a PhD from Stanford University (2018), an MS and BS from Texas A&M University (2013, 2011). His work spans algorithm development for autonomous vehicles, including safe and efficient driving, rotorcraft control, and game-theoretic planning. Notable contributions include the POMCP and POMCPOW algorithms for continuous POMDPs, and open-source frameworks like POMDPs.jl. He has received a NSF CAREER Award (2024) and a NSF Graduate Fellowship (2012). Academic roles include mentoring the AI4ALL program, teaching courses on decision-making under uncertainty, and advising projects at the intersection of robotics and AI. His lab collaborates on applications ranging from weather observation to human-centered autonomy in unmanned systems.

Anna ScampicchioView profile

Researcher

• Intelligent Control Systems
• Model Predictive Control
• Machine Learning
• +3 more

Dr. Anna Scampicchio is a Researcher at ETH Zürich, affiliated with the Professorship for Intelligent Control Systems. Her work focuses on advancing control theory and machine learning methodologies, particularly in data-driven control systems, model predictive control, and Bayesian learning techniques. She has contributed to the development of algorithms for system identification, optimal control, and robust control strategies. Her research integrates theoretical analysis with practical applications in robotics and dynamical systems. Key publications include studies on kernel methods, randomized signatures for learning dynamics, and Bayesian multi-task learning approaches. Dr. Scampicchio holds a Ph.D. (implied by her title) and has published extensively in top-tier journals and conferences, addressing challenges in intelligent control systems and machine learning integration.

• Statistical Inference
• Confidence Intervals
• Model Selection
• +5 more

Dr. Paul Kabaila is an Associate Professor of Statistics at La Trobe University's Mathematics & Statistics school. He holds a PhD from the University of Newcastle and prior degrees from the University of New South Wales. His research focuses on statistical inference methodologies, particularly confidence intervals affected by preliminary model selection, frequentist methods utilizing uncertain prior information, and model-averaged confidence intervals. He has supervised numerous PhD students across diverse fields including biostatistics, bioinformatics, and business analytics. Dr. Kabaila has held visiting appointments at prestigious institutions including Oxford University and the Australian National University. He secured an ARC Discovery Grant (2002–2005) advancing statistical analysis of count data, with applications in epidemiology and finance. He led La Trobe University's first Statistics Program accreditation by the Statistical Society of Australia. His recent work emphasizes high-dimensional data challenges in statistical/machine learning and post-shrinkage strategies. He received the 2020 Distinguished Author Award from the Journal of Time Series Analysis for sustained contributions. His teaching spans all levels of statistical education, including advanced subjects like Statistical Inference and Theory of Statistics .

Aliaksandr HubinView profile

Associate Professor

• Statistics
• Artificial Intelligence
• Econometrics
• +4 more

Aliaksandr Hubin is an Associate Professor of Statistics at Østfold University College (OUC), affiliated with the Section for Research Administration. He provides statistical support to researchers at OUC while conducting methodological research in Bayesian statistics, machine learning, and operations research. His academic background includes a PhD from the University of Oslo (2014–2018), a Master's from Molde University College (2012–2014), and a Specialist degree from Belarusian State University (2008–2013). His research focuses on Bayesian model selection, nonlinear regressions, neural networks, and weak supervision in machine learning. Hubin has held positions at the Norwegian Computing Center (2018–2020) and the University of Oslo as a postdoc (2021–2022). His work spans methodological advancements in MCMC algorithms, variational inference, and applications in epigenetics, econometrics, and healthcare. Key achievements include developing the GMJMCMC algorithm for Bayesian logic regression and the 'skweak' framework for weak supervision in NLP. His awards include the NIMA 2014 award for best MSc graduate, Belarusian Ministry of Education First Award (2013), and recognition at the Graybill 2017 conference. His research has been published in journals like Bayesian Analysis , Neural Computing & Applications , and Scientific Reports , with a focus on model uncertainty quantification and scalable Bayesian methods.

• Bayesian Statistics
• Neuroscience
• Time Series Analysis
• +3 more

Beniamino Hadj-Amar is a Research Fellow in the Department of Statistics at Rice University, specializing in advanced statistical methodologies with applications in neuroscience and biomedical research. His work integrates Bayesian statistical models, time series analysis, and computational techniques to address complex problems in health and cognitive science. Education: PhD in Statistics (University of Warwick, UK), MSc in Statistics (University of Warwick, UK), and BSc in Mathematical Statistics and Data Management (University of Genoa, Italy). Research focuses on Bayesian modeling of neural and physiological systems, including circadian rhythms, neuromodulator dynamics, and gesture phase segmentation. Recent contributions include innovative approaches to improve data quality in neuroimaging and telemetric health monitoring. His interdisciplinary work bridges statistics with neuroscience, genomics, and clinical applications. Publications span 2019–2025, emphasizing methodological advancements in nonstationary time series, autoregressive models, and hidden Markov frameworks. His research addresses both theoretical challenges and practical applications in health informatics and systems biology.