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Hector McKimmView profile

Researcher

• Bayesian Statistics
• Astrostatistics
• Monte Carlo Methods
• +3 more

Hector McKimm is a Researcher in the Department of Statistics at Imperial College London, where he collaborates with the California-Harvard Astrostatistics Collaboration (CHASC) to apply Bayesian statistics to astronomical data. His work focuses on advancing computational methods like Monte Carlo techniques and Markov processes for astrostatistical analysis. He completed his PhD at the University of Warwick under the Oxford-Warwick Statistics Programme (OxWaSP), with a thesis on 'Monte Carlo Methods based on Novel Classes of Regeneration-enriched Markov processes.' Prior to his PhD, he studied Mathematics at Durham University as part of St Mary's College. McKimm's research interests span Bayesian methodology, astrostatistics, and computational statistics. He has contributed to pandemic modeling during the COVID-19 crisis, co-authoring influential papers on school reopening impacts and UK exit strategies. His recent conference activities include presentations on Hamiltonian Jump Processes and Adaptive Simulation techniques at events such as the CIRM Bayesian Statistics conference. He has been involved with The Alan Turing Institute through its Engage@Turing program and contributed to a data analytics project on weather's impact on ASDA sales. Teaching experience includes tutoring first-year Probability modules at Warwick.

Rohan HitchcockView profile

Researcher

• Machine Learning
• Computational Physics
• Statistical Mechanics
• +3 more

Rohan Hitchcock is a Researcher in the Department of Electrical and Electronic Engineering, focusing on interdisciplinary research at the intersection of machine learning and computational physics. His work emphasizes developing physically informed ML architectures to accelerate particle-based models, as reflected in his thesis titled 'Beyond the Black Box.' He is supervised by Prof Jonathan Manton. His research interests span machine learning, computational physics, statistical mechanics, and algebraic topology. Recent articles highlight contributions to stochastic processes, neural network physics simulators, and theoretical frameworks in category theory. Rohan's publications reflect a blend of applied and theoretical work, including studies on stochastic gradient Langevin dynamics (SGLD), entropy in thermodynamic systems, and advanced mathematical constructs like Landau-Ginzburg models and matrix factorizations.

Malgorzata O'ReillyView profile

Associate Professor

• Applied Probability
• Stochastic Modelling
• Operations Research
• +4 more

Malgorzata O'Reilly is an Associate Professor in Mathematics at the University of Tasmania's School of Natural Sciences. Her expertise lies in applied probability, stochastic modelling, and matrix-analytic methods (MAMs), with applications in healthcare systems optimization, evolutionary biology, and operations research. She leads the multidisciplinary team 'Designs for a Better Patient Journey' focused on healthcare system modeling. Her career includes roles from Lecturer (2005–2014) to Senior Lecturer (2014–2019) before her current position since 2019. Education: MSc in Mathematics and Education, Wroclaw University (1987) PhD in Applied Probability, University of Adelaide (2002) Research Interests Her research integrates theoretical and applied aspects of stochastic processes, including Markov-modulated models, healthcare system efficiency, and evolutionary gene duplication dynamics. She collaborates internationally on projects like MAM10 conference leadership and healthcare optimization with Tasmania's Department of Health. Key Contributions O'Reilly has pioneered work on two-dimensional and time-varying Markov-modulated models, contributing to algorithms for transient/stationary performance measures and numerical methods. She chairs MAM conferences and edits related proceedings, emphasizing interdisciplinary applications. Grants & Collaborations Her funding includes ARC Discovery and Linkage grants. Collaborations span healthcare modeling (e.g., emergency department wait times) and evolutionary genomics. She co-authored influential studies on GP co-payments' impact on hospital wait times and stochastic niche-based ecological models. Labs & Teams Leads the 'Designs for a Better Patient Journey' team, focusing on healthcare system modeling. Active in the ARC Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS).

Rudesindo Núñez QueijaView profile

Professor

• Queueing Theory
• Stochastic Modeling
• Operations Research
• +7 more

Rudesindo Núñez Queija is a Professor at the Korteweg-De Vries Institute, Faculty of Science, University of Amsterdam (UvA), and a Scientific Staff Member in the Stochastics Department at Centrum Wiskunde & Informatica (CWI), Amsterdam. His work bridges theoretical stochastic modeling and practical applications in transportation and network systems. University: University of Amsterdam Research Institute: Centrum Wiskunde & Informatica (CWI) Department: Stochastics Role: Professor and Scientific Staff Member His research focuses on queueing theory, stochastic models, and dynamic systems, particularly in the context of transportation networks and service systems. He investigates problems such as dynamic routing under uncertainty, traffic congestion modeling, optimal dispatching in queues, and performance analysis of polling and processor-sharing systems. His methodological toolkit includes Markov decision processes, fluid approximations, heavy-traffic analysis, and perturbation techniques. The 15 most recent publications highlight a strong trend in applying stochastic modeling to real-world transportation challenges—such as intelligent routing, bottleneck dynamics under uncertainty, and urban traffic control—while also advancing fundamental queueing theory. His work frequently appears in high-impact journals like Transportation Research Part B , Queueing Systems , and Operations Research Letters . Notable scientific recognition includes: Applied Probability Trust Award for his PhD thesis on Processor-Sharing Models (2000) He has co-supervised several PhD students, including Daphne van Leeuwen, Peter Kovacs, and Nikki Levering, and has been actively involved in major research grants, such as the NWO Networks program and the NWO Sustainable Logistics project 'Dynafloat'. He teaches courses such as 'Statistics for Forensic Science', demonstrating a broad educational impact. His research is supported by funding from the Netherlands Organisation for Scientific Research (NWO), and he collaborates extensively within the Dutch academic and research community. He is affiliated with research groups at both CWI and UvA, particularly within the Stochastics and Networks domains, contributing to large-scale interdisciplinary projects focused on modeling and optimizing complex systems.

• Econometrics
• Time Series Analysis
• Copula Models
• +9 more

Brendan Kinnane Beare is a Professor of Econometrics at the University of Sydney , affiliated with the School of Economics under the Faculty of Arts and Social Sciences. His research focuses on econometric theory, time series analysis, copula modeling, and stochastic processes. Education: PhD in Economics (2007) from Yale University Research areas: Time series, copulas, Markov modulation, unit root testing, financial econometrics Recent work includes stochastic arbitrage with options, Pareto exponents in economic models, and tests for copula symmetry Beare's publication trends span econometric theory, applied statistics, and financial modeling, with a focus on mathematical rigor and empirical relevance. He teaches undergraduate and postgraduate econometrics courses and participates in international seminars on unit roots and cointegration.

Jon CockayneView profile

Lecturer

• Statistical Computing
• Bayesian Computation
• Probabilistic Numerical Methods
• +3 more

Dr Jon Cockayne is a Lecturer in Statistics at the University of Southampton's Mathematical Sciences Department. His research focuses on probabilistic numerical methods, Bayesian computation, and statistical computing, particularly in numerical analysis and uncertainty quantification. He co-leads the second-year Statistical Modelling module (MATH2010). Education: Bachelor's in Mathematics from Imperial College London. PhD in Statistics at the University of Warwick under Prof. Mark Girolami, titled Bayesian Probabilistic Numerical Methods . Research Interests: Jon develops probabilistic numerical methods that integrate statistical principles into computational algorithms for uncertainty quantification. His work spans Bayesian linear solvers, radiative transfer modeling, and calibration of machine learning procedures. He collaborates on EPSRC-funded projects like Unifying Probabilistic Computation for PDEs and Linear Systems . Grants & Projects: EPSRC grant for Unifying Probabilistic Computation for PDEs and Linear Systems . Postdoctoral research at the Alan Turing Institute (2017–2019). Advising: Supervises PhD students Zoe Abbott (iPhD AI for Sustainability) and Disha Hegde (Mathematical Sciences). Labs & Teams: Member of the Statistics group and the Statistical Sciences Research Institute (S3RI) at Southampton.

• Autonomous Systems
• Bayesian Statistics
• Big Data
• +7 more

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.

Jenny BrynjarsdottirView profile

Associate Professor

• Bayesian Statistics
• Environmental Statistics
• Uncertainty Quantification
• +4 more

Jenny Brynjarsdottir is an Associate Professor at Case Western Reserve University's Department of Mathematics, Applied Mathematics, and Statistics, within the College of Arts and Sciences. She specializes in Bayesian statistics, environmental modeling, and uncertainty quantification. Her work integrates spatio-temporal processes and model discrepancy analysis to address complex scientific challenges, such as climate data interpretation and energy system optimization. Her research also extends to applications in public health, crowd dynamics, and renewable energy, reflecting a multidisciplinary approach. She maintains an active research website at http://sites.google.com/case.edu/jennybrynjarsdottir/ and can be reached at jenny.brynjarsdottir@case.edu. She is based at two locations on campus: 2145 Adelbert Rd. and Sears 5th Floor, 2083 Martin Luther King Jr Dr, Cleveland, OH 44106-7058. Her articles highlight advancements in Bayesian hierarchical modeling, satellite-based environmental measurements, and data-driven solutions for photovoltaic systems. While no formal awards or grants are listed, her contributions emphasize methodological innovations in statistics with practical environmental and engineering applications.

Mark JerrumView profile

Professor

• Combinatorics
• Computational Complexity
• Theoretical Computer Science
• +7 more

Mark Jerrum is a Professor of Mathematics at Queen Mary University of London, part of the School of Mathematical Sciences. His research focuses on combinatorics, computational complexity, and stochastic processes, particularly in the design and analysis of randomized algorithms. He explores the mixing times of Markov chains and computational complexity of counting problems, including partition functions and generating functions, often motivated by statistical physics, constraint satisfaction, and graph polynomials. Notable grants include an EPSRC-funded project on Sampling in Hereditary Classes (EP/S016694/1, 2019–2023). He has contributed to teaching, serving as module organiser for MTH4213 (Numbers, Sets and Functions) in 2023–24. His work bridges theoretical computer science and discrete mathematics, with applications in algorithmic design and probabilistic analysis. Research highlights include advancements in perfect sampling algorithms, approximation algorithms for counting problems, and foundational work on the interplay between statistical physics models and computational complexity. He is affiliated with the Centre for Combinatorics, Algebra and Number Theory, reflecting his interdisciplinary approach to combinatorial and algorithmic challenges.

Bernard WongView profile

Professor

• Actuarial Science
• Risk Management
• Insurance
• +3 more

Professor Bernard Wong is the Head of the School of Risk and Actuarial Studies at the University of New South Wales, Australia. As a Fellow of the Institute of Actuaries of Australia and a Fulbright Scholar, he has significantly advanced actuarial science through research and leadership. His academic credentials include a PhD from the Australian National University and a BCom (Hons) in Actuarial Studies from Macquarie University. PhD (Australian National University) BCOM (Hons) in Actuarial Studies (Macquarie University) His research focuses on AI/ML-enhanced actuarial methods and capital modeling under climate change , with applications to insurance and risk management. He co-leads the Innovations in Risk, Insurance, and Superannuation (IRIS) Knowledge Hub and serves as a chief investigator in the UNSW Institute of Climate Risk and Response. His work also drives the Business AI Lab’s actuarial innovations. Recent publications analyze data breach trends, stochastic loss reserving with neural networks, and multivariate count processes. He has secured substantial Australian Research Council grants , including projects on extreme value theory and claim dependencies. Awards include the Hachemeister Prize (2023, 2017), Taylor-Fry Silver Prize (2018), and Melville Practitioner Prize (2000). Professor Wong actively contributes to actuarial governance as a Board Member of ASTIN and former participant in the Actuaries Institute Data Analytics Practice Committee. His teaching encompasses actuarial data science and enterprise risk management courses.

David BertoinView profile

Associate Professor

• Representation Learning
• Computer Vision
• Reinforcement Learning
• +2 more

David Bertoin is a Research Engineer at Photoroom, focusing on training diffusion models for image editing. He concurrently serves as a part-time Associate Professor at INSA Toulouse, where he teaches and conducts research. Previously, he worked as a Research Scientist at IRT Saint-Exupéry, contributing to the DEEL and ANITI research programs. His research interests center on representation learning, computer vision, and reinforcement learning, with a particular emphasis on improving generalization and robustness in these domains. Bertoin actively participates in academic conferences, presenting work such as Local Feature Swapping at ICLR 2022 and studies on ReLU activation functions at Neurips 2021. His GitHub repositories reflect his engagement in projects like CLOP (Local Feature Swapping) and teaching modules for M2 MAPI3. He maintains an active presence in open-source contributions related to deep learning and reinforcement learning frameworks. Education details are not explicitly provided in the text, but his professional trajectory suggests advanced qualifications in computer science or artificial intelligence. His research outputs span theoretical advancements in neural networks and practical applications in autonomous systems, cybersecurity, and drone technology. Collaborations within ANITI and DEEL highlight his involvement in interdisciplinary AI initiatives.

Laura CarnevaliView profile

Associate Professor

• Formal Methods
• Real-Time Systems
• Performance Evaluation
• +4 more

Laura Carnevali is an Associate Professor of Computer Science at the Department of Information Engineering, University of Florence, where she leads research in formal methods and performance evaluation as a member of the Software Technologies Lab. She teaches Software Engineering for Embedded Systems, Quantitative Evaluation of Stochastic Models, and Foundations of Computer Programming at the School of Engineering. Her research focuses on formal methods for real-time software development and performance evaluation of non-Markovian models, with significant contributions to the ORIS Tool for quantitative system analysis. Key areas include: Stochastic modeling of critical infrastructure Formal verification of real-time systems Reliability engineering methodologies Performance evaluation of cyber-physical systems Her publications show strong emphasis on applied formal methods, with recent work focusing on workflow analysis, infrastructure reliability, and transportation systems modeling. Research consistently integrates theoretical frameworks with practical applications in engineering domains. Awards include: Best Paper Award at ICPE 2021 She actively contributes to academic service as program committee member for leading conferences including FORMATS, QEST, and EPEW, and serves as reviewer for IEEE Transactions on Software Engineering, Reliability, and other prestigious journals.

Pascal MaillardView profile

Professor

• Probability Theory
• Mathematical Physics
• Statistical Mechanics
• +3 more

Pascal Maillard is a Professor at the Department of Mathematics at Université Toulouse III - Paul Sabatier, affiliated with the Institut de Mathématiques de Toulouse (CNRS UMR5219). He has been a Junior member of the Institut Universitaire de France since October 2021. His research focuses on probability theory, particularly branching random walks, multiplicative cascades, and random energy models, with applications in statistical mechanics and mathematical physics. Maillard has coordinated the ANR-DFG funded project REMECO (2021-2024), investigating extreme value distributions, partition functions at complex temperatures, and optimization algorithms in random energy models. He has also co-organized the annual 'Les probabilités de demain' conference (2016–2019) to support early-career researchers in probability. His teaching spans advanced modules in stochastic modeling, probability theory, and mathematical statistics at both undergraduate and graduate levels. He is currently developing lecture notes on branching random walks and multiplicative cascades based on his Master's course at Université Paris-Sud. Research Interests: Extremal processes in branching systems, random energy landscapes, stochastic optimization, and applications to statistical physics. Awards: Junior member of Institut Universitaire de France (2021–present). Advising: Supervised three PhD students and multiple research projects in probability theory, including studies on SLE, Markov chain mixing times, and Erdős-Rényi graphs. Labs/Teams: Co-lead of REMECO project with Lisa Hartung, involving institutions in Toulouse and Mainz.

• Neuroscience
• Neuromodulation
• Transcranial Magnetic Stimulation
• +4 more

Vittorio Pizzella holds the position of Visiting Professor in the Department of Neuroscience and Biomedical Engineering . His research focuses on the intersection of neuroscience , neuroengineering , and biomedical signal processing , with a particular emphasis on transcranial magnetic stimulation (TMS) and electroencephalography (EEG) integration for studying brain network dynamics. Key areas of investigation include: State-Dependent Neuromodulation : Using concurrent EEG-TMS with hidden Markov models to analyze brain responses. Control Theory in Neuroscience : Bridging neuromodulation techniques with engineering principles. Cross-Frequency Coupling : Developing bicoherence methods to study multi-dimensional neural interactions. Recent publications highlight advancements in multi-locus TMS for spatiotemporal brain network modulation and functional connectivity analysis during motor tasks. Collaborations span institutions in Europe, with co-authorship involving experts in clinical neurophysiology , neuroimaging , and computational neuroscience .

• Soft Robotics
• Robotic Manipulation
• Biomimetic Design
• +5 more

Kai Junge is a robotics researcher at École polytechnique fédérale de Lausanne (EPFL), affiliated with CREATE-LAB in the School of Engineering. His work focuses on developing advanced robotic systems with emphasis on compliant interactions, biomimetic design, and applications in agriculture and food systems. Dr. Junge's research interests span several cutting-edge domains in robotics: Soft robotics and compliant manipulation systems Biomimetic design of anthropomorphic robotic hands Variable stiffness actuation for improved dexterity Agricultural robotics with focus on delicate fruit harvesting Tactile sensing and multi-modal perception for contact-rich tasks Human-robot interaction and imitation learning His recent publications demonstrate a strong trend toward developing robotic systems that leverage compliance and biomimetic principles to achieve human-like or even superior manipulation capabilities. The work spans from fundamental research on compliance in robotic hands to practical applications in agriculture and food safety. A notable pattern is the development of the ADAPT Hand, which incorporates spatially distributed compliance to enhance robustness in manipulation tasks, achieving 93% success rate across 24 objects with varying geometries. His research also extends to agricultural applications where soft robotic grippers have demonstrated 95.4% picking success rate for blackberries while improving shelf life by 30-150%. Dr. Junge has been involved in several research projects funded by various organizations including the European Union's Horizon 2020 program, the Japan Science and Technology Agency, and Berry Gardens Ltd. His collaborative work with Josie Hughes and other researchers has resulted in numerous publications in top robotics conferences and journals. He is actively involved with CREATE-LAB at EPFL, where his team develops innovative robotic systems for manipulation, agricultural applications, and food safety testing. The lab's work on the ADAPT Hand, closed-structure soft grippers for agriculture, and robotic swallowing simulators demonstrates a commitment to translating robotics research into practical applications that address real-world challenges.