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Ira WassermanView profile

Professor

• Relativistic Astrophysics
• Neutron Star Astrophysics
• Cosmology
• +5 more

Ira Wasserman is the Kenneth A. Wallace Professor of Astronomy and Professor of Physics at Cornell University, affiliated with the College of Arts and Sciences. He holds dual appointments in the Department of Astronomy and the Department of Physics. His academic journey includes a B.S. from MIT (1974) and a Ph.D. from Harvard University (1978), both in physics. Wasserman's research focuses on relativistic astrophysics, neutron star properties, cosmological inhomogeneities, and cosmic ray origins. He has held roles ranging from postdoctoral fellow to full professor since joining Cornell in 1981. Key research areas include Type II superconducting neutron star cores, r-mode instabilities in neutron stars, dark energy cosmology, and cosmic ray birthplaces. His work integrates theoretical astrophysics with cosmological observations, addressing topics like modified gravity theories and superstring inflation signatures. Wasserman has been recognized with awards such as the NSF Postdoctoral Fellowship, Alfred P. Sloan Foundation Fellowship, and Bok Prize Lecturer honor. His publications span over four decades, addressing magnetar dynamics, cosmological models, and gravitational wave astrophysics. Notable contributions include studies on neutron star r-mode saturation, cosmic string effects on the CMB, and hierarchical Bayesian frameworks for cosmic ray analysis. Wasserman has advised numerous graduate students and contributed to courses in galactic structure and cosmology at Cornell. Education: B.S. MIT (1974), Ph.D. Harvard (1978) Positions Held: Assistant Professor (1981-87), Associate Professor (1987-93), Full Professor (1993-present) Key Collaborations: Worked with researchers like E. E. Flanagan, T. Akgun, and K. Soiaporn on neutron star dynamics and cosmological inhomogeneity Wasserman's research emphasizes interdisciplinary approaches, linking astrophysical phenomena to fundamental physics questions. His current projects explore magnetar crust dynamics, dark energy degeneracy, and cosmic ray source identification through Bayesian clustering methods.

Alexander StrangView profile

Assistant Professor

• Bayesian Inference
• Stochastic Processes
• Random Graph Theory
• +7 more

Alexander Strang is an Assistant Professor in the Department of Statistics at the University of California Berkeley. He earned his Ph.D. in applied mathematics from Case Western Reserve University in 2020 and served as a Kruskal Instructor in Statistics at the University of Chicago until 2023. His research spans interdisciplinary domains including statistical physics, spectral graph theory, optimization, and mathematical biology. Key research areas include: Stochastic processes and modeling in biological systems Structure-dynamics interplay in networks Bayesian inference and inverse problems Random graph theory and multi-agent training His recent publications focus on competitive systems, hierarchical Bayesian models, network analysis, and stochastic optimization. Scientific contributions include the 2022 Suzuki Postdoctoral Fellowship Award and foundational work on: Sparsity-promoting hierarchical Bayesian algorithms Spectral characterization of games Moment closure bounds via algebraic geometry Simplicial cohomology in stochastic processes

Raja SenguptaView profile

Professor

• Transportation Engineering
• Wireless Communications
• Inertial Navigation for Vehicle Systems
• +5 more

Raja Sengupta is a Professor at the Department of Civil and Environmental Engineering, University of California, Berkeley. He has been actively involved in research spanning automated cars, drones, wireless networking, and control theory , with a focus on translating academic work into market-ready solutions through industry collaborations. Education : Ph.D. and M.S. in Systems Engineering from the University of Michigan; B.S. in Electrical Engineering from Jadavpur University, India Research Expertise : Transportation systems, wireless communication protocols, and inertial navigation for autonomous vehicles His recent work emphasizes Urban Air Mobility (UAM) , including vertiport network design and eVTOL fleet dynamics. He has also pioneered autonomous UAV structural inspection systems and developed patented technologies for vehicle-to-vehicle communication with Toyota and BAE Aerospace. Notably, he co-founded the successful startup Automatic.com and currently serves as CEO of Responsiblerobotics.com . Key awards include: USDOT’s Connected Vehicle Technology Award (2011) UC Berkeley Energy and Climate Lectures Innovation Award (2014) His research has been standardized by SAE international as J2945 for car-to-car networking , demonstrating significant industry impact.

• Applied Bayesian Modelling
• Bayesian Computation
• Spatio-Temporal Data Modelling
• +3 more

Professor Sujit Sahu is a renowned statistician specializing in Bayesian modeling and spatio-temporal data analysis at the University of Southampton. Since joining the university in 1999, he has held a professorial position in the Department of Mathematics and Statistics, contributing significantly to research and education in statistics. His work focuses on developing advanced statistical methods for environmental and health applications, including air pollution modeling, disease mapping, and oceanographic studies. Education: PhD in Statistics, University of Connecticut, 1994 Masters in Statistics, Indian Statistical Institute, 1989 Bachelor of Statistics (Honors), Indian Statistical Institute, 1987 Research Interests: Applied Bayesian Modelling and Computation Spatio-Temporal Data Modelling with R Environmental and Climate Science Applications Statistical Software Development (e.g., R package bmstdr ) Selected Projects: Mapping zero-dose populations in conflict zones and remote areas Surveillance of Guinea worm disease using geospatial methods Development of the MetSim tool for hospital demand forecasting Analysis of long-term health impacts of air pollution Publications and Awards: Author of critically acclaimed books: Bayesian Modeling of Spatio-Temporal Data with R (2022) and Introduction to Probability, Statistics & R (2024) Recipient of the Book Authority Best Bayesian Statistics Book Award (2024) Over 60 peer-reviewed articles on spatio-temporal methodologies and applications Teaching and Mentorship: Develops courses on Bayesian modeling and statistical computing Supervised numerous postgraduate students and postdoctoral researchers Founder of the bmstdr R package for spatio-temporal analysis

Tao ChenView profile

Professor

• Product and Process Systems Engineering (P2SE)
• Computer Modelling
• Data Analytics
• +9 more

Professor Tao Chen is an academic and administrative leader at the University of Surrey, serving as Associate Vice-President (International) and Professor in the School of Chemistry and Chemical Engineering. He plays a pivotal role in advancing the university's global engagement strategy, focusing on transnational education (TNE) through collaborative projects with internal stakeholders and external partners. His responsibilities include guiding international partnerships, brand development, student recruitment, research collaboration, and mobility initiatives. He holds a PhD and is a Fellow of the Institution of Engineering and Technology (FIET). His research interests revolve around Digital Chemical Engineering, emphasizing computer modeling of chemical processes using multiscale mechanisms and machine learning. Key application areas include skin penetration, food engineering, radiotherapy dosimetry, and manufacturing processes. He also focuses on model-based chemical product design and process optimization, leveraging interdisciplinary approaches in biomedical engineering, environmental sustainability, and industrial systems. Professor Chen's recent publications highlight interdisciplinary work, including machine learning applications in spectroscopy, advanced process control methodologies, fire safety modeling, drug delivery systems, and sustainable aquaculture optimization. His research often bridges theoretical and applied domains, addressing real-world challenges through innovative engineering solutions. In terms of academic contributions, he supervises doctoral student Anukrati Goel (EPSRC-funded) and research fellow Dr. Yuqing Xia (BEIS-funded). He leads significant grants, such as the EPSRC-funded project 'Soilless cultivation for rapid bioenergy willow' (EP/X013294/1, £453,816, 2023–2025). His teaching includes Year 2 modules in Computation for Chemical Engineers and Transfer Processes, plus an MSc module in Advanced Process Control. He annually oversees 4–6 MSc dissertation projects and 1–2 MEng research projects, integrating experimental work with modeling.

Mark HoView profile

Assistant Professor

• Cognitive Science
• Social Psychology
• Computational Modeling
• +5 more

Mark Ho is an Assistant Professor of Psychology at New York University (NYU), affiliated with the Department of Psychology in the College of Arts & Science. Previously, he served as an Assistant Professor at Stevens Institute of Technology (2023–2024) and held postdoctoral roles at Princeton University and UC Berkeley. He holds a Ph.D. and M.S. in Cognitive Science and Computer Science from Brown University, alongside a B.A. in Philosophy from Princeton University. His research focuses on cognitive, motivational, and social processes underlying human problem-solving, particularly in intentional action interpretation, theory of mind, and the emergence of social phenomena from individual intentions. Using computational modeling and behavioral experiments, his work spans higher-level cognition and social psychology, including planning, pedagogy, and coordination. He also explores applications of computational cognitive science to develop human-interpretable AI systems. Research interests include: cognitive costs in decision-making, representational alignment in teaching, hierarchical planning structures, and the interplay between reinforcement learning and cognitive constraints. His lab's work bridges cognitive science and AI, emphasizing human-AI collaboration and ethical machine learning. Professional affiliations include the NYU Center for Data Science (2022–2023) and multiple academic conferences. His articles frequently address human-AI interaction, decision-making frameworks, and the theoretical underpinnings of social cognition.

Fergus ChadwickView profile

Lecturer

• Statistical Ecology
• Epidemiology Modelling
• Bayesian Methods
• +3 more

Dr Fergus Chadwick is a Lecturer in Statistics at the University of St Andrews, affiliated with the School of Mathematics and Statistics. He holds roles as Early Career Lead for the National Centre for Statistical Ecology (NCSE) and is a member of the Centres for Research into Ecological and Environmental Modelling (CREEM) and Modelling Social Complexity (CFMSC). His research focuses on applying Bayesian statistical methods to address complex observation challenges in ecology and epidemiology, emphasizing interdisciplinary collaboration between scientists and statisticians. Research interests include developing novel methodologies for ecological and epidemiological data analysis, improving communication between disciplines, and synthesizing disparate analytical approaches. Key areas of application involve wildlife population monitoring, disease surveillance, and health outcome modelling. His work has produced influential contributions to understanding observation biases in ecological systems and optimizing public health strategies in low-income settings. Collaborations span academic and applied sectors, with a strong emphasis on real-world problem-solving.

Robert SchickView profile

Academic

• Ecology
• Marine Biology
• Statistics
• +5 more

Dr Robert Schick is an Honorary Senior Research Fellow at the School of Mathematics and Statistics, University of St Andrews, affiliated with the Centre for Research into Ecological and Environmental Modelling (CREEM). His work bridges ecology, statistics, and information visualization, focusing on animal movement dynamics and human behavioral patterns. Key research interests include marine organism vulnerability to human disturbance, ecological connectivity, and a SULSA LEADERS-funded project examining smoking behavior through movement patterns. He has collaborated on projects involving cetaceans, right whales, and turtle ecology, emphasizing statistical modeling and conservation applications. Education details are not explicitly listed, but his career includes roles at CREEM and MASTS (Marine Alliance for Science and Technology for Scotland). Research has addressed topics such as prey dynamics in leatherback turtles, body condition in marine predators, and spatial modeling of habitat mosaics. Collaborations span institutions including Duke University, Stanford, and NOAA. His work contributes to UN Sustainable Development Goals, particularly life below water (SDG 14) and good health and well-being (SDG 3). Projects like MapMySmoke (funded by EPSRC) apply mobile technology to behavioral health interventions. No specific scientific awards are listed, but his contributions to ecological methodology and conservation science are well-documented. He advises on interdisciplinary projects and maintains active research in marine and human movement ecology.

• Computer Vision
• Machine Learning
• Few-Shot Learning
• +6 more

Da Chen is a Lecturer in the Department of Computer Science at the University of Bath, affiliated with the Bath Institute for the Augmented Human and the Centre for Sustainable Energy Systems. He focuses on advanced computer vision and machine learning techniques, particularly in few-shot learning, incremental learning, video understanding, and their applications to urban planning and sustainability. His work bridges AI with real-world challenges like solar energy prediction, urban mobility optimization, and environmental monitoring. Education: He holds a PhD titled 'The Visual Analysis of Complex Natural Phenomena' (2017), supervised by Prof. P. Hall and Prof. M. Brown. Research Interests: Chen’s research spans generative AI, video object detection, solar radiation modeling, and urban scene analysis. He explores how machine learning can address sustainability goals, such as optimizing bike-sharing systems and predicting energy demands through satellite imagery. His methods often involve GANs, convolutional neural networks, and hybrid sequence encoders. Grants & Collaborations: He co-leads a Royal Society-funded project (2024–2026) on improving transient heat transfer experiments using Bayesian statistics and neural networks. His collaborations span institutions globally, focusing on urban-scale AI applications and environmental science. Labs/Teams: Active in the Bath Institute for the Augmented Human, advancing human-centric AI, and the Centre for Sustainable Energy Systems, integrating computational methods for energy efficiency.

Xi ChenView profile

Senior Lecturer

• Bayesian Inference
• Machine Learning
• Deep Learning
• +3 more

Dr. Xi Chen is a Senior Lecturer (Associate Professor) in Machine Learning and Data Science at the Department of Computer Science, University of Bath. He holds affiliations with the UKRI CDT in Accountable AI, Institute for Mathematical Innovation, and the Centre for Mathematics and Algorithms for Data (MAD). His research focuses on Bayesian inference, machine learning, statistical signal processing, and Monte Carlo methods. He has contributed to interdisciplinary applications such as AI for medical imaging and offshore engineering. Dr. Chen completed his Ph.D. in Bayesian inference and statistical signal processing at the University of Cambridge. His teaching includes courses on Artificial Intelligence, Machine Learning, and Bayesian Machine Learning. He is also an Associate Editor for the IET Signal Processing Journal and a Fellow of the UK Higher Education Academy. His research interests span AI applications in collective intelligence, computational methods for wave dynamics, and biomechanics. Recent work includes developing Gaussian Process models for offshore engineering and wearable sensor-based systems for analyzing human movement. His publications emphasize interdisciplinary challenges in AI ethics, model vulnerability, and data-driven decision-making. Dr. Chen leads the A Novel Contour-based Machine Learning Tool for Reliable Brain Tumour Resection project (EPSRC-funded) and collaborates on offshore wind energy research (SEASWALLOWS project). His grants include funding from the National Institute for Health Research for AI-based disease monitoring tools. He supervises students in the UKRI CDT in Accountable AI and contributes to the Bath Institute for the Augmented Human. His labs focus on Bayesian machine learning and AI for science, fostering collaborations across engineering, mathematics, and healthcare domains.

Yan LiView profile

Lecturer

• IoT
• Mobile Computing
• Wireless Sensor Networks
• +7 more

Dr. Yan Li is a Lecturer in IoT and Networking at the School of Computing, Macquarie University. She holds a PhD in Electrical and Electronic Engineering from the University of Melbourne and a Master of Research in Electronic Engineering from the University of Technology Sydney. Previously, she was a Research Fellow at the Telstra-UoW AIoT Hub. Her research focuses on IoT, inertial navigation, mobile computing, wireless sensor networks, and machine learning applications in sensor data analytics. She has been recognized with the Michael Richey Medal and a DIN Pilot Project Grant, and contributed to initiatives like the Future Communications Research Centre and Smart Green Cities Research Centre. Education: Bachelor of Engineering in Aerospace Engineering, Northwestern Polytechnical University PhD in Electrical and Electronic Engineering, University of Melbourne Master by Research in Electronic Engineering, University of Technology Sydney Research interests include: IoT and networking infrastructure Indoor localization and tracking systems Wearable sensor technologies Machine learning for sensor data analytics Healthcare mobility monitoring Smart home situational awareness Notable awards and grants include the Michael Richey Medal (2023), DIN Pilot Project Grant (2022-2023), and the 2023 Faculty of Science and Engineering Award for Inter-School Collaboration. Her work spans over 300 citations and 25 research outputs, emphasizing practical applications in telecommunications and smart environments. Advising and grants: Supervises PhD/Master students in IoT, localization, and machine learning. Active in collaborative projects like the Future Communications Research Centre, focusing on IoT and smart infrastructure solutions. Labs/Teams: Affiliated with Macquarie's Future Communications Research Centre and Smart Green Cities Research Centre, advancing IoT and sustainable urban technologies.

Joseph G. IbrahimView profile

Professor

• Bayesian Inference
• Clinical Trials Design
• Genomics
• +4 more

Dr. Joseph G. Ibrahim is the Alumni Distinguished Professor of Biostatistics at the University of North Carolina at Chapel Hill's Gillings School of Global Public Health. He holds leadership roles as Director of Graduate Studies in Biostatistics, Director of the Laboratory for Innovative Clinical Trials, and Biostatistics Core Director at the Lineberger Comprehensive Cancer Center. His academic tenure includes teaching courses like BIOS 762 (Linear Models) and BIOS 779 (Bayesian Statistics). Education: PhD/MSc in Statistics (University of Minnesota, 1988) and BS in Mathematics (1983). Research Interests: Bayesian methodology, clinical trial design, missing data, medical imaging, and cancer genomics. His work bridges statistical theory and application, with over 360 publications in top journals and two influential books on Bayesian survival analysis and Monte Carlo methods. Key Contributions: Developed statistical methods for joint modeling of longitudinal/survival data, power prior frameworks, and Bayesian approaches for multi-regional trials. His team's innovations include the LEAP prior for historical data integration and the glmmPen R package for high-dimensional models. Awards: Elected fellowships from ASA, IMS, RSS, and ISBA; Janssen Chair in Survival Analysis (2005); and UNC's Alumni Distinguished Professorship (2006). Service: Editor of JASA (Applications/Cases, 2013–2015), associate editor for Biometrics, Lifetime Data Analysis, and Bayesian Analysis. Served on NIH study sections (BMRD, EPIC) and ASA's Bayesian Section leadership. Labs/Teams: Leads the Center for Innovative Clinical Trials (Gillings Innovation Lab) and directs the Cancer Genomics Training Grant. Collaborates on NIH-funded breast cancer SPORE projects and epigenomic analysis tools like Epigrahmm.

Raymond Ka Wai WongView profile

Associate Professor

• Statistical Learning
• Causal Inference
• Functional Data Analysis
• +4 more

Raymond Ka Wai Wong is an Associate Professor and Director of the PhD Program in the Department of Statistics at Texas A&M University. He holds a PhD in Statistics from the University of California, Davis (2014), an MPhil from The Chinese University of Hong Kong (2010), and a BSc with minors in Mathematics and Risk Management Science (2008). His research focuses on causal inference, functional data analysis, low-rank modeling, reinforcement learning, and statistical learning with applications in astronomy, brain imaging, and genomics. Wong's professional roles include Associate Editor for Journal of Computational and Graphical Statistics , Journal of the American Statistical Association , and member of the Research Institute for Foundations of Interdisciplinary Data Science (FIDS). He has secured grants from NSF, NASA, and NIH, including leadership in projects like Virtual Assistant for Spacecraft Anomaly Treatment and phenomic selection in maize hybrids. His awards include Top Reviewer distinctions at NeurIPS (2023) and ICML (2020), and a 2016 Discussion Paper in the Annals of Applied Statistics. He has advised numerous doctoral students, with notable advisees receiving awards such as the ICSA Student Paper Award and Emanuel Parzen Fellowship. Wong’s recent work emphasizes methodological advancements in reinforcement learning (e.g., distributional off-policy evaluation) and matrix/tensor completion under informative missingness. His research bridges theoretical statistics with practical applications in interdisciplinary domains such as neuroscience, agriculture, and space exploration.

Andriy NoretsView profile

Professor

• Bayesian Econometrics
• Dynamic Discrete Choice Models
• Nonparametric Estimation
• +3 more

Andriy Norets is a Professor in the Economics Department at Brown University. His research focuses on Bayesian econometrics, dynamic discrete choice models, and nonparametric estimation methods. He has contributed to advancements in econometric theory, particularly in areas such as posterior consistency, adaptive estimation, and computational methods for complex models. Key research themes include Bayesian nonparametric models, instrumental variable regression, and the development of efficient computational algorithms for variable dimension models. Norets has published extensively in top journals like Econometrica , Journal of Econometrics , and Annals of Statistics . His work emphasizes methodological rigor and practical applications, such as analyzing stock market trading activity and improving inference in nonstandard econometric problems. He also collaborates with co-authors like Ulrich Müller, Justinas Pelenis, and Debdeep Pati on projects spanning from theoretical foundations to applied econometric problems. Norets teaches courses including Undergraduate Econometrics and Graduate Econometric Theory II, demonstrating his commitment to both research and education in economic methodology.

Tingting ZhangView profile

Professor

• Neuroimaging Data Analysis
• Human Brain Mapping
• Bayesian Statistics
• +4 more

Dr. Tingting Zhang is a Professor in the Department of Statistics at the University of Pittsburgh's Dietrich School. She holds a Ph.D. in Statistics from Harvard University (2008), an M.S. in Statistics from Harvard (2005), and a B.S. in Mathematics from Peking University (2003). Her research focuses on neuroimaging data analysis, human brain mapping, and Bayesian statistics, with applications to neurological disorders. She collaborates with clinicians and neuroscientists to develop statistical methods for analyzing multimodal brain imaging data, including fMRI and EEG. Her methodological contributions include Bayesian network models, high-dimensional data analysis, and variational inference techniques. Recent work examines functional connectivity across lifespan stages using HCP datasets and epileptic seizure analysis through directional brain networks. She has developed software tools including the Bayesian Modular and Indicator-based Dynamic Directional Model (BMIDDM) and the Bayesian hierarchical model for state-space stochastic block analysis. Dr. Zhang has taught courses in Bayesian statistics, stochastic processes, and experimental design. Her work bridges statistical theory with clinical applications, emphasizing translational research in neuroimaging. She maintains an active research program with over 30 peer-reviewed publications in top journals like Annals of Applied Statistics and NeuroImage.