جست‌وجوی استاد و پژوهشگر

• High-dimensional statistics
• Statistical learning theory
• Online learning
• +۳ مورد دیگر

Jaouad Mourtada is an Assistant Professor in the Department of Statistics at ENSAE Paris, a founding member of the Institut Polytechnique de Paris, and a permanent member of CREST (Center for Research in Economics and Statistics). Since September 2020 he has held this faculty position, after completing a post-doctoral fellowship at the University of Genoa and earning his PhD from École Polytechnique. Education PhD in Statistics, École Polytechnique (2016–2019) MSc in Mathematics, "Probability and Random Models", Université Pierre et Marie Curie (2016) MSc in Mathematics, "Fundamental Mathematics", Université Pierre et Marie Curie (2015) BSc in Mathematics, Université Pierre et Marie Curie & École Normale Supérieure (2013) Student at École Normale Supérieure (2012–2016) Research Interests Mourtada’s work lies at the intersection of statistics and learning theory, with a focus on understanding the fundamental complexity of prediction and estimation tasks. His interests span: High-dimensional statistics and minimax theory Statistical learning theory and generalization bounds Online learning, regret minimization, and expert aggregation Density estimation and robust statistics Random forests, kernel methods, and convex optimization Research Output Trends Across more than fifteen recent publications, Mourtada has systematically advanced the understanding of statistical and computational limits in learning. His contributions range from exact minimax analyses of linear least squares and novel robust regression guarantees to refined PAC-Bayesian bounds for aggregation and sharp asymptotics for ridge regression. A recurrent theme is the development of estimators that achieve optimal or near-optimal rates while remaining computationally tractable and adaptive to unknown parameters. Scientific Awards & Recognition While the provided materials do not list specific awards, his sustained publication record in top venues such as Annals of Statistics , Journal of Machine Learning Research , Journal of the European Mathematical Society , and leading ML conferences (NeurIPS, COLT, AISTATS) attests to significant peer recognition. Teaching & Mentoring Mourtada has extensive teaching experience at both undergraduate and graduate levels, covering probability, statistics, and machine learning. Courses delivered include: Statistical Learning Theory (M2 Data Science, École polytechnique & ENSAE) Probability Theory (ENSAE) Python for Probability, Statistics, and Machine Learning (École polytechnique) Optimization for Data Science (M2 Data Science, École polytechnique) Laboratories & Collaborations He is affiliated with CREST/ENSAE and has previously collaborated with the Laboratory for Computational and Statistical Learning at the University of Genoa, the Center for Applied Mathematics (CMAP) at École Polytechnique, and maintains ongoing research ties with international scholars in statistical learning and optimization.

Gleb Tikhonovمشاهده پروفایل

پژوهشگر

• Ecology
• Evolutionary Biology
• Statistical Modelling
• +۳ مورد دیگر

Gleb Tikhonov is a postdoctoral researcher at the University of Helsinki , affiliated with the Faculty of Biological and Environmental Sciences and the Organismal and Evolutionary Biology Research Programme . He also holds a postdoctoral position at Aalto University in the Department of Computer Science . His work bridges statistical ecology, machine learning, and computational biology. Research Interests : Development of joint species distribution modelling (JSDM) for ecological communities Integration of statistical and machine learning methods High-performance computing implementations Phenological and dispersal pattern analysis Publications demonstrate expertise in applying advanced statistical frameworks to ecological questions, including fungal dispersal mechanisms, microbiome variation, and climate change impacts. His LIFEPLAN project (funded by the European Commission Joint Research Centre) focuses on planetary biodiversity synthesis using big data. Education : PhD in Biological and Environmental Sciences, University of Helsinki (2018) MS in Applied Mathematics, Lomonosov Moscow State University (2014)

Jason B. Baxterمشاهده پروفایل

استاد

• Photovoltaics
• Semiconductor Materials
• Nanotechnology
• +۴ مورد دیگر

Jason B. Baxter is a Professor in the Department of Chemical and Biological Engineering at Drexel University's College of Engineering. His research focuses on photovoltaics, semiconductor thin films, nanomaterials, and ultrafast spectroscopy, with a strong emphasis on understanding the interplay between material properties, processing, and device performance. Postdoctoral Fellow, Yale University PhD in Chemical Engineering, University of California - Santa Barbara BS in Chemical Engineering, University of Delaware Key research areas include solar energy conversion, carrier dynamics in semiconductors, and sustainability in photovoltaic systems. His work has been funded by the National Science Foundation, Department of Energy, and National Renewable Energy Laboratory. Notable trends in his recent publications include advanced Bayesian inference techniques for semiconductor characterization, antimony-doped CdTe/CdSeTe solar cells, and environmental risk assessments for perovskite photovoltaics. College of Engineering Teaching Excellence Award (2015) NSF CAREER Award (2009) Students and Collaborations: Dr. Baxter has mentored students such as Crystal, Jesse, Ryan, Sherif Khalifa, Tony Truong, and Mohammad. His lab collaborates extensively with other Drexel groups, universities, and industry partners.

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

Ehsan Namjoo serves as a Research Fellow in the Department of Electronics & Computer Engineering at the University of Limerick, with a primary affiliation at Lero – the Irish Software Research Centre. His multidisciplinary work bridges theoretical signal processing, machine learning applications, and hardware implementation for real-world systems. His research spans signal processing (DOA estimation under non-ideal noise conditions, EEG source imaging), machine learning (explainable AI for medical diagnostics, feature selection in cancer detection), and computer engineering (polar code decoders, visible light communication systems). Notable biomedical applications include breast cancer diagnosis and epileptic source analysis, while cybersecurity work focuses on intrusion detection in cloud environments. Recent publications (2021-2025) reveal a consistent trajectory toward developing lightweight, interpretable models for tabular medical data alongside robust signal processing frameworks for communication systems. His work increasingly integrates hardware-aware algorithms, particularly in polar coding implementations, and emphasizes practical validation through experimental demonstrations in visible light networks. As an active Lero researcher, Namjoo participates in Ireland's national software research ecosystem, contributing to interdisciplinary projects that connect theoretical innovations with healthcare, communications, and security applications through collaborative frameworks.

Olivier Cappéمشاهده پروفایل

استاد مدعو

• Machine Learning
• Statistical Signal Processing
• Bayesian Methods
• +۳ مورد دیگر

Olivier Cappé is a CNRS Research Director at the Department of Computer Science of École normale supérieure (DI ENS - CNRS/ENS/Inria) and an Adjunct Professor at Université PSL. He is affiliated with the Centre Sciences des Données (CSD) at ENS and serves as a Chair holder and member of the Executive committee of the Pr[AI]rie-PSAI (Paris School of AI) project. Previously, he served as deputy scientific director at INS2I (2017-2023) and headed the Information Processing and Communication Laboratory (LTCI) from 2013 to 2016. Dr. Cappé's research focuses on statistical signal processing and machine learning. His work spans several areas including Bayesian methods, Markov Chain Monte Carlo, online learning, multi-armed bandit models, and differential privacy for machine learning. Starting in speech and audio processing in the 1990s, he contributed to natural language processing applications in the 2000s, and has focused extensively on online learning and bandit algorithms since 2010. His recent work also addresses privacy issues in machine learning systems. Cappé teaches Reinforcement Learning and Differential Privacy for Machine Learning courses in the IASD master program at Université PSL. His publication record shows consistent output across multiple research areas, with recent work focusing on bandit algorithms, online learning, and privacy-preserving machine learning. His research bridges theoretical foundations with practical applications in digital advertising, recommendation systems, and pandemic response analysis. Grand Prize of the EADS Corporate Foundation (Information Sciences) from the French Academy of Sciences (2013) Co-author of 'Tout comprendre (ou presque) sur l'intelligence artificielle' with Claire Marc Dr. Cappé holds a Supélec engineering degree (1990) and a doctorate from ENST (currently Télécom ParisTech, 1993). He joined CNRS as a researcher in 1996 and has maintained a productive research career spanning over 30 years, with significant contributions to both theoretical and applied aspects of statistical signal processing and machine learning.

Aubrey Poonمشاهده پروفایل

مدرس ارشد

• Applied Macroeconometrics
• Bayesian Estimation
• Mixed-Frequency Methods
• +۳ مورد دیگر

Dr. Aubrey Poon is a Senior Lecturer in Econometrics at the School of Economics, University of Kent, with affiliated researcher positions at Örebro University (Sweden), Centre for Applied Macroeconomic Analysis (Australian National University), and UK Economic Statistics Centre of Excellence. He completed his PhD in Economics from the Australian National University in 2017. His primary research focuses on Applied Macroeconometrics , specializing in Bayesian estimation methodologies including: Mixed-frequency analysis techniques Non-linear state-space modeling Quantile regression frameworks Macroeconomic forecasting systems His work has been featured in prominent outlets including The Economist and New York Times. Publication analysis reveals strong thematic consistency in econometric innovation, with recent works emphasizing: Advanced Bayesian VAR methodologies International financial market interconnections Macroeconomic tail risk quantification Regional economic nowcasting techniques The research demonstrates growing sophistication in handling high-dimensional data structures and missing data problems across global economies. Dr. Poon maintains active research collaborations across multiple international institutions focusing on macroeconomic measurement and policy analysis.

Dankmar Böhningمشاهده پروفایل

استاد

• Medical Statistics
• Meta-analysis
• Capture-Recapture Methods
• +۳ مورد دیگر

Professor Dankmar Böhning holds a Chair in Medical Statistics at the University of Southampton, affiliated with the Southampton Statistical Sciences Research Institute (S3RI) as Deputy Director. His expertise spans mathematical sciences and medicine, with a focus on meta-analysis, capture-recapture methods in health/social sciences, count data analysis, and mixture models. Education: Bielefeld and Berlin universities (Mathematics/Social Sciences), PhD (1981) and Habilitation (1992) from Free University Berlin. Career highlights include roles as Chair in Statistics at University of Reading (2005–2011) and visiting positions at institutions in Vienna, Graz, PennState, Bangkok, and Manila. Research interests include applying statistical methods to healthcare challenges, including epidemiological studies and clinical trials. He leads projects like CLASP Cancer and capture-recapture for contact-tracing efficacy. Active in teaching statistical inference and epidemiology courses at undergraduate and postgraduate levels. Key achievements: Bualuang ASEAN Chair Professor Prize (2022), editorial roles (e.g., Biometrical Journal), and leadership in national/international research collaborations. His recent work focuses on pandemic modeling (Covid-19) and improving diagnostic testing strategies.

Manfred Jaegerمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning
• Bayesian Networks
• +۵ مورد دیگر

Manfred Jaeger is an Associate Professor at the Department of Computer Science, Technical Faculty of IT and Design, Aalborg University. His research focuses on Artificial Intelligence , Bayesian Networks , and Graph Neural Networks , with significant contributions to probabilistic reasoning and relational learning. University: Aalborg University School: Technical Faculty of IT and Design Department: Department of Computer Science Jaeger's research explores inductive and probabilistic reasoning , statistical relational learning , and model checking . His recent work integrates heterogeneous graph neural networks with relational Bayesian network encodings to enhance reasoning capabilities in complex systems. Key trends in his publications include relational deep learning , probabilistic inference , and graph-based modeling . He has contributed to applications in social network community detection , reinforcement learning for MDPs , and latent variable models for graph learning . Jaeger collaborates on projects involving incomplete data analysis , modularization of complex tasks , and probabilistic logic . His datasets on multi-multi-instance learning networks are publicly available for research use.

Longbing Caoمشاهده پروفایل

استاد

• Artificial Intelligence
• Data Science
• Machine Learning
• +۷ مورد دیگر

Professor Longbing Cao is the Distinguished Chair in Artificial Intelligence and Director of the Frontier AI Research Centre at Macquarie University. He holds dual PhDs in Artificial Intelligence and Computing Science, an MSc in Communication, and a Bachelor's degree in Electrical Engineering. His research spans AI, data science, behavior informatics, and enterprise innovation, with over 400 publications and significant industry collaborations. Education: PhD in Pattern Recognition and Intelligent Systems, Graduate University of Chinese Academy of Sciences PhD in Computing Science, University of Technology Sydney MSc in Communication Bachelor's in Electrical Engineering Research Interests: Focuses on AI systems, federated learning, time series forecasting, and humanoid robotics. His work integrates theoretical advancements with real-world applications in finance, healthcare, and smart cities. Recent Articles: Explores cutting-edge topics like generative AI (ProgDiffusion), federated learning (FedSI), and pandemic modeling (waning effect of interventions). These contributions highlight advancements in both foundational AI and applied data science. Awards: Eureka Prize (2019), ACM Distinguished Scientist (2019) Grants & Projects: Leads major initiatives such as the Federated Omniverse Facilities for Smart Digital Futures (2024-2025) and Data Complexity and Uncertainty-Resilient Deep Variational Learning (2024-2027). Prior roles include Founding Director of UTS Advanced Analytics Institute (2011-2014). Labs & Teams: Directs the Frontier AI Research Centre and collaborates with global institutions like Microsoft, Commonwealth Bank, and Shanghai Stock Exchange.

• Autonomous Systems
• Bayesian Statistics
• Big Data
• +۷ مورد دیگر

Professor Simon Maskell is a Professor of Autonomous Systems at the University of Liverpool, affiliated with the School of Electrical Engineering, Electronics and Computer Science and the Faculty of Science and Engineering. He holds degrees including MEng, MA, PhD, and is a Fellow of the Institution of Engineering and Technology (FIET) and Chartered Engineer (CEng). His research focuses on algorithmic solutions at the intersection of Computer Science, Engineering, and Statistics. Key areas include Bayesian statistics for decision-making under uncertainty, particle filters for high-dimensional parameter estimation, and applications in autonomous systems, big data analytics, and data fusion for sectors like defense, healthcare, and maritime surveillance. He leads the Liverpool Big Data Network and contributes to the Stone Soup open-source tracking framework. Maskell has secured grants from the Home Office, European Commission, and EPSRC, addressing challenges like combating cyber threats, improving drug interaction detection, and enhancing sensor technologies. His notable award includes an Honorary Research Fellow title from Imperial College (2010). In teaching, he oversees modules such as Instrumentation & Control and supervises research on topics like Bayesian methods in proteomics, traffic signal control, and antimicrobial resistance. He serves as an Associate Editor for IEEE Transactions on Aerospace and Electronic Systems and IEEE Signal Processing Letters. Professional activities include editorial roles and administrative leadership in interdisciplinary initiatives, emphasizing ethical AI deployment and data-driven healthcare solutions.

Samuel David Jonesمشاهده پروفایل

مدرس

• Computational Neuroscience
• Developmental Psychology
• Cognitive Modeling
• +۷ مورد دیگر

Samuel David Jones is a Lecturer in Psychology at Bangor University's School of Psychology & Sport Science and a member of the Cognitive Neuroscience Institute. His research focuses on computational approaches to understanding individual differences in human cognitive development across the lifespan, with particular emphasis on speech, language, and communication skills. Dr. Jones earned his PhD from Lancaster University in April 2020 and an MA from the University of Nottingham in April 2017. His educational background has equipped him with strong methodological expertise in behavioral experimentation and computational modeling. His research program centers on three key areas: (i) auditory processing, particularly how frequency discrimination relates to developmental language disorders; (ii) the neural population dynamics of neurodivergent learning and cognitive control; and (iii) resource-rational models of neurodivergent information seeking. His work employs computational modeling to develop transdiagnostic theories that can explain patterns across neurodevelopmental conditions including developmental language disorder, dyslexia, dyscalculia, and autism spectrum disorder. Analysis of his 15 most recent publications reveals a clear trajectory from foundational work on auditory processing mechanisms toward more theoretical contributions around rational inattention frameworks. His research increasingly integrates computational modeling with behavioral experimentation to develop unified theories of neurodevelopmental variation. The work spans multiple disciplines including cognitive neuroscience, developmental psychology, and computational cognitive science, with strong implications for clinical assessment and intervention. Dr. Jones has secured significant research funding including a £59,472 grant from The Waterloo Foundation for "The Waterloo Foundation report into transdiagnostic neurodevelopmental assessment" (2025-2026), a £30,896 Royal Society Research Grant for "Enhancing developmental speech perception modelling through head and torso simulation" (2024-2025), and a £7,160 UKRI grant for high-spec computing resources to model the neurodivergent brain. He actively supervises PhD students and has directed the North Wales Speech and Language Exchange since 2023, organizing academic workshops and developing community engagement initiatives. His research group collaborates extensively with Dr. Kami Koldewyn at Bangor University and Professor Gert Westermann, with whom he has co-authored multiple high-impact publications. Dr. Jones maintains an open science approach, with all papers, code, and data available through his Open Science Framework page.

Wouter Kouwمشاهده پروفایل

استادیار

• Probabilistic Machine Learning
• Variational Bayesian Inference
• Active Inference
• +۶ مورد دیگر

Wouter Kouw is an Assistant Professor at the Electrical Engineering department of Eindhoven University of Technology (TU/e) , leading the Bayesian Intelligent Autonomous Systems lab. With a dual PhD in Computer Science (2018, TU Delft) and MSc in Neuroscience (2013, Maastricht University) , he bridges neurobiology and artificial intelligence through variational Bayesian inference and active inference frameworks. Research Focus : Probabilistic machine learning systems using message passing algorithms on factor graphs , applied to mobile robotics and adaptive control Key Projects : CONTACT-AI (contact-rich robot navigation), FEP-walker (active inference-based locomotion), and BayesBrain (hybrid neuro-in-silico computing) His work spans nonlinear system identification , uncertainty quantification , and sensor modeling , with recent publications in IEEE Transactions , Entropy , and Communications in Computer and Information Science . Awards include the Niels Stensen Fellowship (2017) and TU/e Team Science Award nomination (2023) . Collaborations extend to institutions in Germany, USA, and Denmark, with teaching responsibilities in Bayesian Machine Learning and Neuro Computation .

Benoit Chachuatمشاهده پروفایل

استادیار مدعو

• Process Systems Engineering
• Chemical Process Optimization
• Sustainable Energy Systems
• +۵ مورد دیگر

Benoit Chachuat serves as an Adjunct Assistant Professor in the Department of Chemical Engineering at McMaster University. His academic appointment reflects his active engagement in research and scholarly activities within the field of process systems engineering, with particular emphasis on optimization, sustainability, and environmental assessment of chemical processes. Dr. Chachuat's research interests span across multiple domains of chemical engineering with a strong focus on process systems engineering. His work integrates advanced mathematical modeling, optimization techniques, and environmental considerations to address challenges in sustainable energy systems, chemical process design, and bioprocess engineering. Key research areas include real-time optimization, life cycle assessment, sustainable aviation fuels, carbon utilization technologies, and the application of machine learning to industrial process monitoring and control. His research demonstrates a consistent commitment to developing methodologies that balance economic viability with environmental sustainability in chemical engineering applications. Analysis of his recent scholarly output reveals a clear trend toward sustainability-focused research, particularly in sustainable aviation fuels, waste-to-chemicals conversion, and carbon utilization technologies. His work increasingly incorporates environmental life cycle assessment alongside techno-economic analysis, reflecting the growing importance of holistic sustainability metrics in process design. The integration of machine learning tools for process monitoring and control represents another significant trend in his recent publications, demonstrating adaptation to emerging technologies in industrial applications. Dr. Chachuat's scholarly activity demonstrates substantial impact across the chemical engineering community, with numerous publications in high-impact journals and conference proceedings. His research has been referenced in patents, policy sources, and news outlets, indicating practical relevance beyond academic circles. The breadth of his work spans from fundamental mathematical methods in optimization to applied environmental assessments of emerging technologies. His research collaborations extend across multiple domains, including sustainable energy systems, biopharmaceutical manufacturing, and environmental process engineering. Recent work has particularly focused on pandemic-response vaccine manufacturing and supply chain resilience, highlighting the adaptability of process systems engineering methodologies to address urgent global challenges. While specific grant information isn't detailed in the provided text, the scope and impact of his research suggest substantial external funding support.

Emily Mitchellمشاهده پروفایل

مدرس

• Paleobiology
• Community Ecology
• Marine Ecology
• +۶ مورد دیگر

Emily Mitchell is a faculty member in the Department of Zoology at the University of Cambridge, where she conducts research at the intersection of paleobiology and marine community ecology. She is an active supervisor in the Cambridge NERC Doctoral Training Partnerships (CREATES and C-CLEAR DTP), mentoring postgraduate students in quantitative approaches to ecological and evolutionary questions. Her research focuses on two major areas: the paleoecology of Ediacaran early animal communities and the dynamics of modern marine benthic ecosystems. She applies mathematical and statistical methods, including Bayesian network inference and spatial analyses, to understand how ecological processes shape biodiversity across deep time. Her work spans from the origins of animal life over 500 million years ago to contemporary Antarctic, deep-sea, and tropical reef systems. The most recent publications highlight a strong trend in applying advanced computational and network-based methods to both fossil and modern ecosystems. Her research integrates field data, digital modeling, and theoretical frameworks to investigate community structure, resilience, and evolutionary drivers. Key themes include the role of morphology, reproductive strategy, and environmental gradients in shaping ecological dynamics. Emily Mitchell has received no explicitly mentioned scientific awards in the provided text. She is actively involved in advising postgraduate research students and securing research funding through NERC and other sources. Her projects often involve fieldwork, video database analysis (e.g., AWI Pangea), and the development of 3D digital models for community analysis. She encourages student-led project development in areas such as Ediacaran taphonomy, eco-evolutionary modeling, and Antarctic benthic ecology. Her research group focuses on reconstructing community dynamics throughout ecological succession, particularly in extreme environments like the Antarctic and deep sea. She collaborates with institutions such as the British Antarctic Survey and uses extensive photographic and video datasets to model ecosystem interactions and predict future changes under environmental perturbation.

Tom Griffithsمشاهده پروفایل

استاد

• Cognitive Science
• Machine Learning
• Bayesian Statistics
• +۵ مورد دیگر

Tom Griffiths is a Professor of Psychology and Computer Science at Princeton University, where he directs the Computational Cognitive Science Lab and the Princeton Laboratory for Artificial Intelligence. He mentors graduate students across Psychology, Computer Science, and Neuroscience departments, fostering interdisciplinary research at the intersection of human cognition and artificial intelligence. His research develops mathematical models to understand human cognition, emphasizing how human intelligence uniquely operates under constraints of time, computational resources, and communication bandwidth. Using Bayesian statistics, he explains learning from small data; resource rational analysis reveals efficient cognitive strategies; and studies of cultural evolution and language show how humans combine knowledge across minds. This work bridges cognitive science and AI, aiming to build more human-like machines and improve human decision-making through cognitive prostheses. Griffiths leads two prominent research groups: the Computational Cognitive Science Lab investigates computational principles of human thought through theoretical modeling and behavioral experiments, while the Princeton Laboratory for Artificial Intelligence supports innovative AI research. His lab's recent work analyzes large language models using Bayesian frameworks and develops distributed computing models for human collaboration. He actively advises graduate students and has contributed significantly through publications and the best-selling book Algorithms to Live By . His research on cognitive prostheses and metareasoning provides practical tools for enhancing human decision-making in everyday contexts, with applications in behavioral economics and human-AI interaction.