جست‌وجوی استاد و پژوهشگر

Steven Shechterمشاهده پروفایل

استاد

• Stochastic Optimization
• Simulation
• Medical Decision Making
• +۳ مورد دیگر

Steven Shechter is a Professor of Business Administration and holds the WJ VanDusen Chair in the Operations and Logistics Division at the University of British Columbia's Sauder School of Business. He holds a BS in Mathematics from Loyola University Chicago, an MS in Operations Research from Georgia Tech, and a PhD in Industrial Engineering from the University of Pittsburgh. His research focuses on stochastic optimization, simulation methodologies, and healthcare operations, with particular emphasis on medical decision-making and healthcare system efficiency. His academic contributions include groundbreaking work in multi-objective optimization (e.g., electoral apportionment models), patient monitoring systems, and surgical capacity allocation. He teaches advanced decision modeling courses to MBA and MBAN students, including Simulation Modeling and Optimal Decision Making modules. Key research trends in his work include applying stochastic processes to healthcare challenges (e.g., alarm fatigue management in patient monitoring systems), optimizing surgical workflows, and developing adaptive treatment protocols using Bayesian methods. His recent studies address pressing issues like kidney transplantation decision models and pediatrician scheduling in healthcare facilities. While no formal awards are listed, his extensive publication record reflects sustained impact in operations research and healthcare analytics. His work often bridges theoretical models with practical healthcare applications, emphasizing system efficiency and patient-centered outcomes.

Hien Quoc Ngoمشاهده پروفایل

دانشیار

• Massive MIMO
• Cell-Free Massive MIMO
• Cooperative Communications
• +۴ مورد دیگر

Dr. Hien Quoc Ngo is a Reader at Queen's University Belfast and a UKRI Future Leaders Fellow. He specializes in wireless communications, particularly in massive MIMO, cell-free massive MIMO, and cooperative systems. His research focuses on improving spectral efficiency, security, and energy efficiency in next-generation networks. Education: B.S., Electrical Engineering, Ho Chi Minh City University of Technology (2007) M.S., Electronics and Radio Engineering, Kyung Hee University (2010) Ph.D., Communication Systems, Linköping University (2015) Research Interests: Dr. Ngo's work spans massive MIMO systems, cell-free architectures, physical layer security, and millimeter-wave technologies. He has pioneered studies on channel estimation, power allocation, and interference management in distributed networks. Awards & Recognition: IEEE ComSoc Stephen O. Rice Prize (2015) IEEE ComSoc Leonard G. Abraham Prize (2017) Best PhD Award from EURASIP (2018) UKRI Future Leaders Fellowship (2019) Multiple AMiner Most Influential Scholar Awards (2022-2024) Grants & Projects: Lead on the Future Communications Hub in All-Spectrum Connectivity (UKRI-funded) Principal Investigator for Cell-Free Massive MIMO for ISAC Labs & Teams: He leads the Wireless Communications Research Group at Queen's University, focusing on 5G/6G technologies and intelligent systems.

Theodore Kypraiosمشاهده پروفایل

استاد

• Bayesian Inference
• Statistical Methodology
• Infectious Disease Modeling
• +۵ مورد دیگر

Theodore Kypraios is a Professor of Statistics at the School of Mathematical Sciences, University of Nottingham, United Kingdom. He serves as Course Director for the MSc in Statistics and MSc in Statistics with Machine Learning programmes, and as Head of the Statistics and Probability Section. Kypraios joined the University of Nottingham in September 2006 as a Research Fellow and was appointed as Lecturer in 2008. His research focuses on developing novel statistical methodology for Bayesian inference and model selection for high-dimensional complex data, with particular emphasis on stochastic epidemic models and infectious disease outbreak data. Kypraios has made significant contributions to Bayesian nonparametric methods, as evidenced by his PNAS publication on heterogeneously mixing infectious disease models which enables more data-driven approaches to understanding transmission mechanisms without strict parametric assumptions. Kypraios has been actively involved with the Royal Statistical Society, serving as Chair of the Computational Statistics and Machine Learning Section until December 2020, and currently sits on the Academic Advisory Group committee. He has presented his work at major conferences including the International Symposium for Bayesian Analysis and the Bayesian Inference for Stochastic Processes Workshop. As an educator, Kypraios has taught Statistical Inference and Data Analysis and Modelling modules at Nottingham, and has been an instructor for the 'MCMC II for Infectious Diseases' module at the Summer Institute in Statistics and Modeling in Infectious Diseases since 2010. His teaching emphasizes both classical and Bayesian approaches to statistical inference with applications to real-world problems. He has supervised PhD students including Dr. Rowland Seymour, whose thesis work formed the basis of the PNAS publication. His research has practical implications for understanding disease transmission mechanisms, as demonstrated by his analysis of the 2001 UK foot and mouth disease outbreak, and contributes to the development of more effective disease control strategies.

George Streftarisمشاهده پروفایل

استاد

• Bayesian Statistics
• Epidemiology
• Actuarial Science
• +۳ مورد دیگر

Professor George Streftaris is a faculty member at Heriot-Watt University within the Actuarial Mathematics and Statistics department under the School of Mathematical and Computer Sciences . His academic career spans over two decades, including roles as associate professor and lecturer at Heriot-Watt University (2004-2019) and post-doctoral positions at BioSS and Heriot-Watt (2001-2004). He serves on the Board of Examiners for the Institute and Faculty of Actuaries and acts as an external examiner for multiple institutions. Professional memberships include Fellow of the Royal Statistical Society , member of the International Society for Bayesian Analysis , and the Greek Statistical Institute . Education: PhD in Statistics (University of Edinburgh) MSc in Statistics and OR (University of Essex, Distinction) BSc in Statistics and Actuarial Science (University of Piraeus, Greece) Research Interests: Streftaris specializes in Bayesian stochastic modeling , inference, and assessment at the intersection of statistics, epidemiology, and actuarial science. His work addresses critical illness insurance, longevity risk, and health-related insurance through predictive modeling and statistical machine learning. Key themes include disease transmission dynamics, model diagnostics, and uncertainty quantification in epidemic systems. Collaborations extend to life and biomedical sciences. Recent Publications: Recent articles focus on COVID-19 pandemic impacts on breast cancer mortality using semi-Markov models, neural network approaches for admission rate prediction, and Bayesian modeling of epidemic systems. Notable projects involve machine learning for multi-asset strategies, model uncertainty in insurance pricing, and stochastic frameworks for disease spread. Research Projects: Centers of Actuarial Excellence (SOA, 2019-2023): Predictive modeling for medical morbidity risk SCOR Foundation of Science (2022-2024): Breast cancer life insurance impact ARC Project (IFoA, 2016-2022): Longevity and morbidity risk management The Data Lab (2017-2018): Machine learning for multi-asset strategies Advising: Supervises ongoing PhD students in Bayesian and neural network modeling in epidemiology, with completed students working on topics like critical illness insurance, disease transmission, and stochastic mortality. Collaborations include researchers in the UK, USA, and international institutions.

Jamie Caineمشاهده پروفایل

مدرس ارشد

• Digital Strategy
• Digital Transformation Maturity Assessments
• Business-IT Alignment
• +۳ مورد دیگر

Dr. Jamie Caine is a Senior Lecturer in Information Systems & Business Modelling at the School of Computing and Digital Technologies, part of the College of Business, Technology and Engineering at Sheffield Hallam University. His work bridges academic research with industry practice in digital transformation strategy, focusing on strategic alignment between business and IT. He holds a PhD in strategic management digital technology alignment, alongside PGCE and BSc qualifications. His research explores digital capabilities, strategy execution, and ontology-driven models to close the strategy-implementation gap. Key projects include digital maturity assessments for global standard bodies and cloud strategy architectures for Bayer. He teaches modules like IT Strategy, Business Architecture, and Cloud Technologies at both undergraduate and postgraduate levels. Industry contributions include leading digital transformation projects across 170+ countries and designing corporate training programs. He is a sought-after speaker on Industry 4.0, leadership, and personal development, with active involvement in East African digital capacity-building initiatives. Previously served as a Group Scout Leader to foster youth leadership. Editorial roles include Measuring Ontologies in Value seeking Environments (MOVE). Research outputs span CEUR Workshop Proceedings, Springer journals, and edited volumes, with a focus on strategy ontologies and digital transformation frameworks.

• Probability Theory
• Stochastic Processes
• Applied Probability
• +۲ مورد دیگر

Dr. Robin Stephenson is a Lecturer at the School of Mathematical and Physical Sciences, University of Sheffield. His research focuses on Probability Theory, Stochastic Processes, and Applied Probability, with a particular emphasis on random trees, fragmentation processes, and Markov additive processes. His publications span topics like scaling limits of random graphs, self-similar fragmentation, and bivariate Markov chain convergence. He teaches the Probability Modelling module (MAS275) and is based in the Hicks Building (I18), Sheffield, S3 7RH, UK. His work reveals trends in theoretical probability, including critical Galton-Watson trees, directed graph scaling, and the analysis of Markov additive processes. Research applications extend to random maps and self-similar fragmentation dynamics.

Alex Hagen-Zankerمشاهده پروفایل

دانشیار

• Geographical Information Science
• Spatial modelling and simulation
• Infrastructure systems
• +۴ مورد دیگر

Dr. Alex Hagen-Zanker is an Associate Professor in Infrastructure Systems at the University of Surrey's School of Sustainability, Civil and Environmental Engineering. His research focuses on quantitative analysis and simulation modelling of land use, transport, and urban activities, addressing challenges in urbanization, environmental pressures, and social equity. He leads initiatives on spatial modelling, infrastructure systems, and nature-based solutions. He holds roles as Director of Learning and Teaching and Module Leader in the School. His teaching contributions include Geographical Information Science, Numerical Methods, and Environmental Engineering modules. His work integrates geospatial technologies with environmental and transportation systems to inform sustainable urban development. Research interests include geographical information science, spatial simulation, urbanization dynamics, and landscape processes. He has collaborated on projects funded by ESRC (JPI Urban Europe / NSFC), focusing on multifunctional landscapes and urban growth scenarios. Recent work explores machine learning applications in urban planning and policy-relevant decision frameworks. His publications span over two decades, addressing topics from bridge scour risk under climate change to the socio-ecological impacts of urban expansion. His interdisciplinary approach bridges engineering, environmental science, and policy, emphasizing data-driven solutions for resilient infrastructure systems.

Vincenzo Carnevaleمشاهده پروفایل

دانشیار

• Statistical Physics
• Machine Learning
• Protein Structure-Function Relations
• +۳ مورد دیگر

Vincenzo Carnevale is an Associate Professor in the Department of Biology at Temple University's College of Science and Technology. He holds a B.Sc. and M.Sc. in Physics from the University of Pisa and a Ph.D. from SISSA in Trieste, Italy. His research group focuses on statistical physics and machine learning to study sequence-structure-function relations in proteins, epistasis in molecular evolution, and self-organization in biomolecules. Key themes include how interactions drive collective phenomena and emergent behaviors in biological systems, with a long-term goal of understanding electrical signaling in excitable cells. Recent work spans computational approaches in medical applications (e.g., AI in obstetrics-gynecology diagnostics), lipid-ion channel interactions, and genotype imputation algorithms. His methods include molecular dynamics simulations, Monte Carlo techniques, and deep learning frameworks. Carnevale has no listed scientific awards, but his work emphasizes interdisciplinary collaborations at the interface of physics, biology, and computer science. He contributes to programs like Molecular and Cellular Biology and maintains a lab focused on theoretical/computational biology.

• Computational Neuroscience
• Cognitive Decision Making
• Social Neuroscience
• +۳ مورد دیگر

Wako Yoshida is Professor of Clinical Neuroscience at the University of Oxford's Nuffield Department of Clinical Neuroscience, leading research at the intersection of computational neuroscience, decision theory, and social cognition. Her work focuses on neural mechanisms of belief construction during partially observable decision-making and social interactions, with particular emphasis on prefrontal cortex function. Her research interests include: Computational modeling of uncertainty resolution in cognitive decision-making Neural basis of Theory of Mind during cooperative social interactions Pain neuroscience and aversive learning mechanisms Application of hyper-scanning fMRI for group decision-making studies Brain-machine interfaces for pain control through co-adaptive learning Analysis of her 15 most recent publications (2016-2024) reveals three dominant trends: (1) Neural mechanisms of hierarchical belief inference in spatial navigation and social contexts, (2) Uncertainty processing in pain modulation involving prefrontal-periaqueductal circuits, and (3) Development of computational frameworks for brain-machine interfaces targeting chronic pain management. Her work consistently bridges machine learning concepts with human neuroimaging. Scientific awards: No awards mentioned in source material. Advising and grants: Source text provides no details about students, advisees, or grant funding. Yoshida directs the Pain and Aversive Learning research group at Oxford, collaborating with Ben Seymour and others to investigate computational and neural mechanisms underlying pain, aversion, and decision-making using fMRI, hyper-scanning, and computational modeling approaches.

Tadesse Ghirmaiمشاهده پروفایل

استاد

• Statistical Signal Processing
• Communications
• Bayesian Methods
• +۳ مورد دیگر

Tadesse Ghirmai is a Professor and Division Chair of Electrical Engineering at the University of Washington Bothell, affiliated with the School of Science, Technology, Engineering & Mathematics. He has worked at the university since 2009 and holds a Ph.D. in Electrical Engineering from Stony Brook University (2007), an M.S. in Electrical Engineering from the University of South Florida, and a B.S.E.E. from Addis Ababa University, Ethiopia. His research focuses on statistical signal processing , Bayesian analysis , and Monte Carlo methods for applications in wireless communication, sensor networks, system modeling, and biomedical signals. He has developed advanced algorithms for data detection, channel estimation, and adaptive filtering in dynamic communication environments. Key trends in his 15 most recent publications (2012–2017) include innovations in distributed particle filtering Laplace autoregressive modeling non-invasive biomedical monitoring systems reduced-complexity precoding for MIMO-OFDM Bayesian methods in relay-based communication multi-band signal processing. Scientific recognition includes the Best Paper of the Year Award from IEEE Signal Processing Magazine (2007) for co-authoring a seminal work on particle filtering in wireless communications. His teaching portfolio includes courses on digital signal processing, wireless communication, and statistical signal processing at undergraduate and graduate levels.

Anahita Khojandiمشاهده پروفایل

دانشیار

• Decision Making Under Uncertainty
• Reinforcement Learning
• Markov Decision Processes
• +۹ مورد دیگر

Anahita Khojandi is an Associate Professor in the Department of Industrial and Systems Engineering at the University of Tennessee, Knoxville, where she also holds the Heath Endowed Faculty Fellow in Business & Engineering title. She is also a Joint Associate Professor at the Bredesen Center for Interdisciplinary Research and Graduate Education and served as the Director of the Reliability and Maintainability Engineering Program from 2021 to 2024. Her interdisciplinary research spans healthcare, transportation, environmental engineering, and advanced manufacturing. Education: PhD in Industrial Engineering, University of Pittsburgh MS in Industrial Engineering, University of Pittsburgh BS in Industrial Engineering, Sharif University of Technology, Tehran, Iran Her research focuses on decision-making under uncertainty, reinforcement learning, Markov decision processes, and predictive analytics. These methodologies are applied to critical domains such as sepsis prediction, Parkinson’s disease treatment using wearable sensors, intelligent transportation systems, nuclear safety, and green infrastructure resilience. She integrates machine learning with stochastic modeling to improve real-time decision-making in complex systems. The recent publications highlight a strong trend toward AI-driven solutions in healthcare and infrastructure, with emphasis on explainability, anomaly detection, and real-time adaptation. Her work frequently appears in high-impact journals such as Management Science , IEEE Transactions , and Nuclear Technology , reflecting both methodological rigor and practical relevance. Scientific Awards and Honors: AAAS Rapid Response AI Fellow (2024) NIH AIM-AHEAD Fellowship in Leadership (2023) First Place, Harvey J. Greenberg Research Award (INFORMS, 2022) Best Paper Award, OMEGA Journal (2020, 2021) INFORMS Senior Member (2024) Teaching Award, IISE DAIS Division (2024) Khojandi has advised numerous graduate students and postdoctoral researchers, many of whom are co-authors on her publications. Her research has been supported by the National Science Foundation (NSF), National Institutes of Health (NIH), Department of Energy (DOE), and other federal and institutional grants. She actively contributes to professional service, including leadership roles in INFORMS, where she served as Chair of the Diversity, Equity, and Inclusion Committee and currently serves as Vice President of Membership and Professional Recognition. She leads research initiatives at the intersection of industrial engineering and data science, often in collaboration with the Health Innovation Technology & Simulation (HITS) Lab and the Center for Transportation Research. Her lab focuses on developing AI-enabled decision support tools for clinical and operational environments, with future work targeting autonomous systems, personalized medicine, and climate-resilient infrastructure.

• Auditory Attention
• EEG Decoding
• Cochlear Implants
• +۵ مورد دیگر

Tom Francart is a Professor at the Department of Neuroscience, Faculty of Medicine, KU Leuven. He leads research in auditory attention, EEG decoding, and speech processing, with affiliations to iSi Health, LBI (KU Leuven Brain Institute), and Leuven.AI. His research focuses on: Neural decoding of auditory attention using EEG Objective measures for aphasia and hearing impairment Brain-computer interfaces for hearing aids Audiovisual integration in speech perception Machine learning applications in auditory neuroscience Recent publications analyze neural tracking of speech in aphasia, transformer-based EEG decoding, and audiovisual context integration. He supervises projects on cochlear implants, attention decoding algorithms, and post-stroke recovery.

Shu Liمشاهده پروفایل

دانشیار

• Risk Theory
• Ruin Theory
• Stochastic Modeling
• +۴ مورد دیگر

Shu Li is an Associate Professor in the Department of Statistical and Actuarial Sciences at Western University's Faculty of Science. Their research focuses on risk theory, ruin theory, stochastic modeling, and predictive analytics in insurance and finance. Recent work includes advancements in generalized drawdown processes, active learning applications for variable annuity portfolios, and generalized Poisson regression models for insurance claims. Publications frequently appear in top-tier journals like Insurance: Mathematics and Economics and ASTIN Bulletin. Scientific awards include professional designations as Associate of the Society of Actuaries (ASA) and Associate of the Canadian Institute of Actuaries (ACIA). They supervise graduate students in research areas related to financial risk management and contribute to Canada’s Financial Wellness Lab.

Hector McKimmمشاهده پروفایل

پژوهشگر

• Bayesian Statistics
• Astrostatistics
• Monte Carlo Methods
• +۳ مورد دیگر

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 Hitchcockمشاهده پروفایل

پژوهشگر

• Machine Learning
• Computational Physics
• Statistical Mechanics
• +۳ مورد دیگر

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.