جست‌وجوی استاد و پژوهشگر

Madeleine McPhersonمشاهده پروفایل

دانشیار

• Renewable Energy Systems
• Energy Systems Modelling
• Decarbonization Strategies
• +۳ مورد دیگر

Dr. Madeleine McPherson is an Associate Professor in the Department of Civil Engineering at the University of Victoria (UVic) and Principal Investigator of the Sustainable Energy Systems Integration & Transitions (SESIT) Group. She specializes in energy systems integration, decarbonization pathways, and multi-scale energy modeling. Her work focuses on coordinating infrastructure systems (transport, buildings, electricity, water) to achieve climate goals. Education: BASc in Engineering Science (University of Toronto, 2009) MEL in Clean Energy Engineering (UBC, 2010) PhD in Civil Engineering (University of Toronto, 2017) Research Interests: Variable renewable energy integration Energy systems modeling (CREST, SILVER) Electrification pathways for cities Decarbonization of Canada’s energy system through stakeholder engagement Her recent work explores 100% renewable city feasibility (e.g., Regina), grid flexibility requirements under high renewable penetration, and cross-sectoral decarbonization strategies. She collaborates with the Energy Modelling Hub to inform national policy dialogues. Advising & Grants: Actively recruiting graduate students/postdocs with backgrounds in engineering, computer science, or related fields. Funding available for MASc/PhD candidates. Her lab focuses on open-source modeling tools and capacity-building initiatives. Labs/Teams: SESIT Group (UVic) develops decision-support models for energy transitions, emphasizing participatory approaches with stakeholders and communities.

Kevin Charles Leonardمشاهده پروفایل

استاد

• Electrocatalysis
• Sustainable Energy
• Data Science
• +۳ مورد دیگر

Kevin C. Leonard is Professor of Chemical and Petroleum Engineering at the University of Kansas, with joint appointment at the Center for Environmentally Beneficial Catalysis. He leads research on sustainable electrochemical technologies for fuel and chemical production, with focus areas in electrocatalysis, scanning electrochemical microscopy, CO₂ utilization, and machine learning for catalyst discovery. His research develops novel electrochemical systems for renewable hydrogen production, CO₂ conversion, and sustainable chemical synthesis. Current projects include federated learning approaches for catalyst discovery, CO₂-expanded electrolytes for chemical production, and scanning electrochemical microscopy for catalyst characterization. Professor Leonard has received significant research funding from NSF, Department of Energy, and Cisco Systems. Awards include the ACS Sustainable Chemistry & Engineering Lectureship Award and Army Research Office Young Investigator Award. He co-founded Avium, LLC to commercialize hydrogen production technology. He teaches undergraduate and graduate courses in chemical engineering fundamentals, kinetics, catalysis, and data science. Professor Leonard currently advises 7 graduate students working on electrocatalysis, machine learning applications, and electrochemical characterization techniques.

Yan Yuمشاهده پروفایل

استادیار

• Computational Fluid Dynamics
• Turbulent Mixing
• Uncertainty Quantification
• +۳ مورد دیگر

Yan Yu is an Assistant Professor in the Department of Computational Applied Mathematics at Stony Brook University. Her research focuses on computational fluid dynamics, including the analysis of chaotic flow fields in turbulent mixing, uncertainty quantification, and numerical simulations of fluid interface instabilities such as Rayleigh-Taylor and Richtmyer-Meshkov instabilities. She collaborates with scientists at Los Alamos National Laboratory and Brookhaven National Laboratory and serves as the SPIR coordinator for the department. Her research interests span computational physics and engineering applications, with recent publications exploring quantum networks, edge computing optimizations, and secure distributed systems. Her work demonstrates strong interdisciplinary connections between fluid dynamics, quantum information science, and network optimization. Recent publications show a consistent focus on quantum computing infrastructure and network optimization, with emerging interests in federated learning and blockchain technologies. This reflects a research trajectory expanding from fundamental fluid dynamics toward distributed computing paradigms.

Robin Pemantleمشاهده پروفایل

استاد

• Probability Theory
• Analytic Combinatorics
• Stochastic Processes
• +۴ مورد دیگر

Robin Pemantle is a Professor of Mathematics and affiliated with the Computer and Information Science department at the University of Pennsylvania's School of Engineering and Applied Science. He holds primary appointment in the Department of Mathematics. His research spans probability theory, analytic combinatorics, stochastic processes, and mathematics education. He co-authored influential works such as There is No One Way to Teach Math (2024) and Analytic Combinatorics in Several Variables (2024), emphasizing active learning strategies and combinatorial methodologies. Research interests include asymptotic analysis of generating functions, percolation theory, and applications of probability to biology and networks. He has advised over 20 PhD and Master's students, focusing on topics like random walks, combinatorial models, and education technology. Pemantle leads initiatives like the Penn Calculus Project, redesigning calculus curricula with active learning principles. His work bridges pure and applied mathematics, with contributions to statistical physics (e.g., Ising models) and theoretical computer science. Notable recent studies address invasion percolation on trees, trace reconstruction algorithms, and aggregation methods for probabilistic forecasts.

Dirk Peschمشاهده پروفایل

استاد

• Artificial Intelligence
• Data Analytics
• Algorithmics
• +۹ مورد دیگر

Professor Dirk Pesch is a faculty member at the School of Computer Science and Information Technology , University College Cork. His research focuses on Internet of Things (IoT) , Cyber-Physical Systems , and Smart Manufacturing , with applications in Smart Communities , Health & Wellbeing , and Industrial Automation . As Director of the SFI Centre for Research Training in Advanced Networks for Sustainable Societies and Co-Principal Investigator of the CONNECT Centre , he leads large-scale research initiatives. He has secured over €80M in research funding and authored 250+ scientific publications. His leadership in the Cork Smart Gateway initiative and founding of the Nimbus Research Centre highlight his industry-focused impact. His recent work explores 5G/6G integration with Time-Sensitive Networking (TSN) , machine learning for loneliness detection , and interoperability solutions for IoT ecosystems . He contributes to international conferences as Technical Programme Co-Chair (IEEE SmartComp 2024) and General Chair (IEEE SmartComp 2025).

• Environmental Engineering
• Water Distribution Systems
• Contamination Warning Systems
• +۴ مورد دیگر

James Uber is a Professor in the Department of Civil & Environmental Engineering at the University of Cincinnati , where he has held academic appointments from 2007–2016. His career spans roles as Co-Director for the Center for Sustainable Urban Environments (2007–2009) and Director of the Environmental Engineering & Science Program (2002–2006). Ph.D., Environmental Engineering (1988) M.S., Environmental Engineering (1985) B.S., Civil Engineering (1983) Uber's research focuses on water distribution systems , contamination warning , disinfectant residual dynamics , and public health risk assessment . He has pioneered work in real-time water quality modeling , epidemiological studies of waterborne diseases , and feedback control for water systems . His peer-reviewed articles (1986–2014) emphasize multi-species transport modeling , optimization of booster disinfection , and MAUP bias reduction in health studies. These works bridge environmental engineering with public health and systems analysis . Uber has received prestigious scientific awards , including the 2010 WDSA Best Paper, 2009 AWWA Management Division Best Paper, and 2007 Franz Edelman Award finalist. He led federal and private grants totaling over $4 million for projects like EPA-funded pathogen-biofilm modeling and NSF cyberinfrastructure development. Grants include: EPA: Pathogen-Biofilm Attachment Model ($45,754) National Institute For Hometown Security: Distribution Hydraulics ($387,383) Department of Energy: Real-Time Water Modeling ($391,512) NSF: Real-Time Network Modeling ($313,701) DoD: Water Warning System Performance ($68,949)

Daniel Oppenheimerمشاهده پروفایل

استاد

• Cognitive Science
• Decision-Making
• Psychometrics
• +۵ مورد دیگر

Professor Daniel Oppenheimer is a dual Professor at Carnegie Mellon University’s Dietrich College of Humanities and Social Sciences, holding roles in the Department of Social and Decision Sciences and affiliated with the Department of Psychology. His research focuses on cognitive science, decision-making processes, and their applications in education, policy, and charity. He has authored influential books like *Democracy Despite Itself* and *Psychology: A Cartoon Introduction*. Notable awards include the Ig Nobel Prize for humor and rigorous scholarship, and multiple teaching accolades. Education: B.A. Psychology (Rice University), M.A. and Ph.D. Psychology (Stanford University). Research Interests: Information processing, metacognition, charitable giving, democracy’s psychological foundations, and educational interventions. His lab innovatively integrates humor and creativity into academic work, such as weekly ‘failure’ awards and ice cream bets. Teaching innovations include cartoon-based introductory psychology courses and workshops on pedagogical strategies. Awards: Einhorn Young Investigator Award (Society of Judgment and Decision Making) Beattie Mid-Career Award (European Association for Decision Making) Ig Nobel Science Humor Award (2016) for research on unnecessary complexity. Advising & Grants: While specific grants are not detailed, his work on educational interventions and decision science likely involves interdisciplinary funding. He emphasizes student autonomy and metacognitive training in his advising.

John Astonمشاهده پروفایل

استاد

• Functional Data Analysis
• Time Series Analysis
• Official Statistics
• +۵ مورد دیگر

Professor John Aston serves as the Harding Professor of Statistics in Public Life at the Statistical Laboratory within the Faculty of Mathematics , University of Cambridge . His interdisciplinary research bridges statistical theory with real-world applications in public policy, neuroimaging, and linguistics. Current Roles: Faculty member, research group leader Awards: Adams Prize recipient Research Interests span Functional Data Analysis , Time Series , Public Policy Statistics , and Statistical Neuroimaging . He develops methodologies for analyzing complex data structures in healthcare and social sciences. Notable Contributions include innovations in federated learning for healthcare , seasonal adjustment techniques , and neuroimaging statistical models . His work addresses challenges in hidden community estimation and crisis evidence communication . Scientific Awards: Adams Prize Contact: J.Aston@statslab.cam.ac.uk | Room D1.03, University of Cambridge

Sen Wangمشاهده پروفایل

دانشیار

• Machine Learning
• Deep Learning
• Data Mining
• +۴ مورد دیگر

Sen Wang is an Associate Professor in the School of Electrical Engineering and Computer Science at The University of Queensland (UQ). He holds an ARC DECRA Senior Research Fellowship and has expertise in machine learning, deep learning, data mining, and health informatics. His research focuses on applications such as ICU data analysis, clinical collaborations with hospitals, and developing algorithms for feature selection, semi-supervised learning, and healthcare analytics. Dr. Wang has published over 80 papers in top-tier conferences and journals, including CVPR, AAAI, and IEEE Transactions series. He has secured several grants, including industry collaborations and ARC funding. Education: PhD in Computer Science (UQ, 2014), MSc and BSc in Computer Science (Jilin University and Liaoning Shihua University). He has taught courses in data science, cloud computing, and databases at UQ and Griffith University. Research interests include AI-driven healthcare solutions, multimodal data analysis, and efficient machine learning models. His work addresses challenges in medical data interpretation, patient outcome prediction, and robust model adaptation in dynamic environments. Key achievements include developing the BiteNet model for medical outcome prediction and contributions to EEG-based intention recognition systems. He actively supervises PhD students in areas like zero-shot learning and healthcare informatics.

• Soil Security
• Remote Sensing
• Spectroscopy
• +۲ مورد دیگر

Nicolas Francos serves as a Postdoctoral Research Associate at the University of Sydney's School of Life and Environmental Sciences and Sydney Institute of Agriculture (SIA), focusing on soil security and advanced methods for soil organic carbon sequestration estimation through spectroscopy and remote sensing techniques. His academic foundation includes: Bachelor's degree in Geography from Hebrew University of Jerusalem Master's and PhD from Tel-Aviv University specializing in multi-scale soil spectral modeling Francos' research centers on soil security, spectral-spatial soil variations, and modeling soil characteristics via spectroscopy, remote sensing, and digital soil maps. His work integrates laboratory analyses, field campaigns, and satellite imagery to develop ground-truthed methods for soil property estimation, particularly organic carbon and water-related metrics. Analysis of his 2022-2025 publications reveals consistent application of hyperspectral remote sensing to global soil challenges, with emphasis on organic carbon mapping, classification systems, and water infiltration. Key patterns include international collaborations across Australia, Israel, Italy, and the Czech Republic, alongside methodological innovations in spectral harmonization and UAV-based monitoring. No scientific awards were documented in the source material. Information regarding student advising or research grant management was not provided. Francos operates within the Sydney Institute of Agriculture framework, leveraging international research networks and advanced tools including UAVs, hyperspectral sensors, and soil spectral libraries for comprehensive soil monitoring systems.

Reuben Rideauxمشاهده پروفایل

مدرس ارشد

• Cognitive Neuroscience
• Computational Neuroscience
• Brain Imaging
• +۳ مورد دیگر

Dr. Reuben Rideaux is a Senior Lecturer and NHMRC Emerging Leadership Fellow at the University of Sydney's School of Psychology, and an Honorary Senior Research Fellow at the University of Queensland’s Brain Institute. He holds a PhD from the Australian National University and previously served as a Leverhulme Early Career Fellow at the University of Cambridge. His research focuses on perception and cognition, combining computational modeling, brain imaging (EEG/fMRI/MRS), and psychophysics to explore brain function and dysfunction. Key research areas include bio-inspired AI systems, high-resolution MR spectroscopy, and neural decoding. He has secured over $2.5M in competitive grants and published over 30 scientific papers. Notable awards include being named one of Australia’s Top 20 Under 40 Biomedical Researchers (2022). Dr. Rideaux chairs the Early Career Researcher Committee of the Australian Cognitive Neuroscience Society and engages in public outreach to promote neuroscience education. Recent work emphasizes neurochemical predictors of learning, sensory adaptation mechanisms, and multisensory integration. His grants span topics like AI-driven autism research and dietary bias decoding. He actively supervises graduate students and collaborates across institutions to advance neuroscience methodologies.

• Machine Learning
• Artificial Intelligence
• Reinforcement Learning
• +۳ مورد دیگر

Alexander Ilin is a Visiting Professor and part-time teacher in the Department of Computer Science at Aalto University, specializing in Artificial Intelligence and Machine Learning. He holds roles in both the Computer Science - Artificial Intelligence and Machine Learning (AIML) research area and the Professors of Practice group. His research focuses on Machine Learning, Reinforcement Learning, and Deep Learning applications, with contributions to areas like neural networks, generative models, and healthcare informatics. Dr. Ilin earned a Doctoral degree in Engineering and Technology from Helsinki University of Technology in 2006. His work aligns with UN Sustainable Development Goals, particularly in advancing education and innovation. Key projects include the Finnish Center for Artificial Intelligence (FCAI) and the APPIA project on privacy-aware AI applications. His research explores cutting-edge topics such as diffusion models for dynamical systems, reinforcement learning for robotics, and self-supervised forecasting in healthcare. He has led projects like APPIA (2020–2021) and FCAI (2020–2026), securing grants from the Academy of Finland and Business Finland. His work bridges theory and practice, with applications in autonomous systems, nanotechnology, and medical diagnostics. Notable activities include visiting research at the UK Met Office Hadley Centre and presentations at top conferences like NeurIPS and AAMAS. His lab focuses on scalable AI solutions for real-world challenges, emphasizing ethical AI and privacy-preserving techniques.

Mario Britoمشاهده پروفایل

استاد

• Risk Analysis
• Artificial Intelligence
• Maritime Systems
• +۴ مورد دیگر

Professor Mario Brito is a Professor of Risk Analysis and Risk Management at the University of Southampton, leading the Department as Head. His work bridges statistics, science, and engineering, with a focus on complex systems such as autonomous vehicles, maritime operations, and healthcare systems. He holds a PhD from the University of Bristol and has extensive industry experience, including roles at BAe Systems and the National Oceanography Centre (NOC). Research Interests: Dynamic Probabilistic Risk Assessment of Complex Systems Impact of Organizational Factors on Socio-technical Systems Risk Root Cause Analysis and Accident Investigations Bayesian Methods and Machine Learning in Risk Assessment Key Projects: EU Horizon2020 BRIDGES Project (€1.8M) KTP Marine Autonomous Systems Risk and Reliability (UK£161K) Autonomous Vehicle Risk Analysis in Arctic/Antarctic Environments Teaching: MANG6143 Project Risk Management MANG6549 Quantitative Methods for Risk Management Professional Roles: Editorial Board Member, ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems General Chairman, 33rd European Safety and Reliability Conference (2023)

• Urban Planning
• Housing Policy
• Economic Geography
• +۳ مورد دیگر

Sarah Mawhorter is an Assistant Professor of Housing at the University of Groningen, affiliated with the Faculty of Spatial Sciences and the Department of Economic Geography . Her research focuses on urban planning, housing policy, and their intersections with sustainable development goals, particularly addressing challenges in diverse populations such as older adults and students. Her work emphasizes the complexities of housing transitions, land use regulation, and historic preservation. She has published extensively on topics like public housing in Indonesia, tensions between demolition and preservation in Philadelphia, and rental housing dynamics near U.S. universities. She also explores informal housing markets through administrative data analysis and evaluates the geographic impact of higher education centers. Mawhorter collaborates with institutions and researchers globally, including the Urban Governance Research Network (since 2023). She serves as a Guest Editor for Housing Policy Debate (2024) and has contributed to peer-reviewed journals like the Journal of Planning Education and Research and the Journal of the American Planning Association. Her research highlights both policy implications and socio-economic dimensions of housing, aiming to inform equitable urban development strategies while addressing public health challenges linked to housing conditions.

Robert F Harrisonمشاهده پروفایل

استاد

• Computational Data Modelling
• Machine Learning
• Neural Networks
• +۵ مورد دیگر

Robert F. Harrison is Professor of Computational Data Modelling at the University of Sheffield's Department of Electronic and Electrical Engineering. He joined the department in 1987 and has served twice as Head of Department (2008-09, 2011-12). Since 2014, he has directed the Centre for Doctoral Training in Control & Systems Engineering. Between 2009-2012, he acted as Director of Sheffield's Rail Innovation and Technology Centre, a Network Rail-funded initiative. Harrison's research focuses on applying systems engineering methods to biomedical and industrial challenges, with expertise in: Machine learning and neural networks for clinical decision support Bayesian hierarchical models for aerospace prognostics Kernel methods and support vector machines Rail network optimization and energy management Biomedical applications including trauma prognosis and thyroid analysis His research themes demonstrate strong interdisciplinary integration across medical diagnostics, transportation systems, and aerospace engineering. Recent work shows increasing focus on cybersecurity in critical infrastructure and optimization of rail networks using Bayesian and evolutionary approaches. Professional Activities Visiting Fellowship at Princeton University's Department of Mechanical and Aerospace Engineering Fellowship at Butler College Visiting Fellowship at James Cook University Member of editorial boards for multiple international journals Technical committee roles for International Federation of Automatic Control (IFAC) Harrison has secured over £3M in research funding from organizations including Siemens, EPSRC, Network Rail, and Rolls-Royce for projects on rail optimization, energy systems, and aerospace prognostics.