جست‌وجوی استاد و پژوهشگر

Xuemin Shenمشاهده پروفایل

استاد

• 6G Networks
• AI-Driven Communications
• Digital Twin Technology
• +۵ مورد دیگر

Xuemin (Sherman) Shen is a University Professor at the University of Waterloo, specializing in advanced communication systems and AI-driven network optimization. His research focuses on 6G networks, digital twin technology, vehicular systems, and generative AI applications in wireless communications. He leads initiatives in edge computing, federated learning, and secure physical layer communications. Key research areas include 6G architecture design, intelligent transportation systems, and AI integration into network management. His work bridges theoretical advancements with practical implementations in areas like OTFS modulation, MIMO equalization, and hybrid-field antenna systems. Recent projects emphasize digital twin frameworks for network orchestration, immersive communications via edge computing, and trustworthy semantic communication systems. His contributions span both foundational studies and applied solutions in wireless security, distributed learning, and terahertz communication systems.

Liang-Liang Xieمشاهده پروفایل

استاد

• Network Information Theory
• Wireless Relay Networks
• Adaptive Control
• +۶ مورد دیگر

Liang-Liang Xie is a Professor in the Department of Electrical and Computer Engineering at the University of Waterloo. His research focuses on network information theory, wireless relay networks, adaptive control systems, and systems biology. He holds a B.S. in Mathematics from Shandong University (1995) and a Ph.D. in Control Theory from the Chinese Academy of Sciences (1999). He completed postdoctoral research at Linköping University (Sweden) and the University of Illinois at Urbana-Champaign (USA). His work spans theoretical advancements in wireless communication protocols, including relay frameworks, compression techniques, and network coding. Recent articles emphasize omnidirectional relaying, throughput optimization in ad hoc networks, and information-theoretic scaling laws. He has advised over 25 graduate students, many co-supervised with senior faculty members. Current students include Shuyue Wang and Yuxi Zhang. Teaching responsibilities include courses in signals and systems, probability theory, and network information theory. His research has contributed to foundational understanding of transport capacity, channel coding, and multi-user wireless systems.

David Yevickمشاهده پروفایل

استاد

• Photonics
• Optical Systems Design
• Machine Learning Applications in Optics
• +۳ مورد دیگر

David Yevick is a Professor at the University of Waterloo, affiliated with the Photonics and Atomic, Molecular, & Optical Physics research group. He leads the Advanced Optical Systems Lab and specializes in integrating machine learning with photonics, nonlinear optics, and quantum systems. His work spans optical communication systems, fiber nonlinearity mitigation, and quantum state analysis. Research interests include applying neural networks and deep learning to optical device performance prediction, material science simulations, and signal processing. Recent trends in his publications focus on combining ML techniques with optical systems, such as using random forests for CNT TFET analysis and variational autoencoders for phase transition modeling. His lab explores cutting-edge topics like self-phase modulation compensation in WDM systems and entropy-regulated data balancing. The Advanced Optical Systems Lab also develops novel photonic crystal designs and fiber compensation algorithms.

• Astrophysics
• Quantum Mechanics
• Cosmology
• +۳ مورد دیگر

Dr. Tim Beadle is a Senior Lecturer in the Department of Physics and Astronomy at the University of Birmingham. He holds a PhD in Theoretical Physics from the University of Cambridge (2005) and a Master's degree in Physics from the University of Oxford (2002). His research focuses on the intersection of cosmology, quantum mechanics, and high-energy physics, with particular emphasis on dark matter, gravitational waves, and quantum gravity phenomena. He leads the Cosmology and Astrophysics Group at Birmingham and oversees a research portfolio valued at £2.5 million from the Science and Technology Facilities Council (STFC). Education: PhD Theoretical Physics (Cambridge, 2005), MPhys (Oxford, 2002) Dr. Beadle's research interests include astrophysical applications of quantum mechanics, cosmological structure formation, and observational tests of dark energy. He has pioneered studies on dark matter distribution in galaxy clusters and gravitational wave signals from primordial black holes. His work bridges theoretical modeling with cutting-edge observational data from facilities like the Square Kilometre Array and the Event Horizon Telescope. Key research trends in his publications include: 1) Exploring quantum effects in cosmological contexts, 2) Developing new methods for detecting dark matter via gravitational lensing, 3) Investigating gravitational wave signatures of early universe processes, and 4) Testing modified gravity theories against galaxy survey data. His recent work emphasizes multi-messenger astronomy and quantum foundations in expanding spacetimes. Royal Society University Research Fellowship (2020) Institute of Physics Early Career Physicist Award (2018) Marie Skłodowska-Curie Actions Fellowship (2015) Dr. Beadle currently supervises 10 PhD students and has mentored over 20 early-career researchers. He manages a £2.5M STFC grant exploring quantum gravity signatures in cosmological data and leads the Birmingham node of the UK National Cosmology Supercomputing Facility. His research group collaborates internationally with teams at CERN, the European Southern Observatory, and NASA's Jet Propulsion Laboratory. He directs the Cosmology and Astrophysics Group, which operates state-of-the-art computational facilities for cosmological simulations. Future work includes developing novel techniques for detecting cosmic neutrino backgrounds and refining theoretical models of dark energy dynamics using upcoming Euclid mission data.

Jeffrey Paiمشاهده پروفایل

استاد

• Risk management
• Derivatives statistical modeling
• Term structure of interest rates
• +۳ مورد دیگر

Jeffrey Pai is a Professor at the Warren Centre for Actuarial Studies and Research within the Asper School of Business at the University of Manitoba. With over 30 years of academic experience spanning multiple countries, he specializes in actuarial science, risk modeling, and financial derivatives. His research examines livestock/crop insurance, weather derivatives, and Bayesian statistical methods for risk assessment. Research encompasses quantitative risk management frameworks, stochastic modeling of insurance products, and econometric analysis of financial instruments. Recent work focuses on parametric insurance for natural disasters, mortality catastrophe modeling, and agricultural risk transfer mechanisms using advanced statistical approaches. Publications demonstrate consistent focus on practical applications of actuarial science, particularly in agricultural insurance and weather-related financial instruments. Recent trends show increased emphasis on Bayesian methods, catastrophe modeling, and developing market applications. Awards: Students' Teacher Recognition Awards (2021, 2002) CAS Best Paper Award (2016) Literati Network Outstanding Paper Award (2011) Featured in MACLEAN's Guide to Canadian Universities (2004) Extensive teaching includes courses in life contingencies, risk theory, and actuarial modeling. Secured research funding from international agencies including SSHRC (Canada), Society of Actuaries (USA), and UK Development Partnerships.

Nicholas Watierمشاهده پروفایل

استاد

• Threat Perception
• Visual Psychophysics
• Face Perception
• +۳ مورد دیگر

Dr. Nicholas Watier is a faculty member at Brandon University's Department of Psychology, specializing in experimental psychology with focuses on threat perception, visual psychophysics, and face perception. He holds a Ph.D. in Psychology from the University of Ottawa (2012) and a BA (Hons) in Psychology from Laurentian University (2006). His research explores visual cues underlying threat appraisals, using experimental and computational methods. He teaches courses such as Fundamentals of Psychological Research, Advanced Statistics, and Perception, emphasizing the conjectural nature of knowledge. Key research areas include threat perception in media and art (e.g., horror posters, metal album covers), face identification, and cognitive mechanisms. He has received a Canadian Psychological Association award and government grants. His work integrates visual artifacts and experimental paradigms to uncover how humans infer danger from visual stimuli. Teaching responsibilities span undergraduate and advanced courses, with a focus on fostering critical thinking and statistical literacy. Collaborations include studies on holistic face processing, directed forgetting, and mindfulness effects on cognition. His lab integrates psychophysical and computational approaches to address perceptual and cognitive questions. Phone: (204) 727-9731 Office: Room 1-09, John R. Brodie Science Centre

Sahar Darvish Mollaمشاهده پروفایل

استادیار مدعو

• Medical Physics
• Radiation Oncology
• Detector Development
• +۴ مورد دیگر

Sahar Darvish Molla is an Adjunct Assistant Professor in Interdisciplinary Science at McMaster University, with expertise spanning medical physics, radiation oncology, and biomedical engineering. Her work bridges advanced detector technologies and clinical radiation applications. Ph.D. in Medical Physics (2016), McMaster University M.Sc. in Medical Physics (2012), McMaster University Postdoctoral Fellow (2016-2017), McMaster University Postdoctoral Researcher (2018-2019), Nova Scotia Health Authority Medical Physics Resident (2019-2023), Juravinski Cancer Centre Her research focuses on radiation dosimetry , medical imaging , and detector development , particularly for applications in dual-energy x-ray imaging, THGEM detectors, and Monte Carlo simulations. She has contributed to innovations in dose distribution measurement and cancer detection via x-ray fluorescence. Recent publications highlight her work in high-dose-rate brachytherapy , 7 Tesla MRI , and multi-input signal processing systems , reflecting interdisciplinary approaches to radiation science and biomedical engineering challenges. She has served as Instructor for Research Methods in Medical Radiation Sciences (MEDRADSC 3X03) in 2024-2025, indicating active involvement in academic training.

Habib Hamamمشاهده پروفایل

استاد

• Optics
• Biomedical Engineering
• Wireless Communication
• +۷ مورد دیگر

Habib Hamam is a Professor in the Department of Electrical Engineering at the University of Moncton's Faculty of Engineering. His research spans optics, biomedical engineering, wireless communication, and AI applications. Specializations: Diffractive elements, optical interconnections, biomedical engineering, hybrid fiber/wireless systems, and AI-driven optimization. Contact: Email habib.hamam@umoncton.ca | Phone (506) 858-4762 Recent publications highlight his focus on AI integration across healthcare, energy systems, and education, combined with blockchain for security and transparent data management. Key subdomains include medical imaging, renewable energy optimization, and IoT scalability. Notable research trends involve deep learning for diagnostics (e.g., brain tumors, diabetic retinopathy), blockchain applications in smart grids and fisheries, and quantum-inspired algorithms for energy forecasting.

• Space Physics
• Ionospheric Physics
• Magnetosphere-Thermosphere-Ionosphere Coupling
• +۴ مورد دیگر

Daniel Billett is a space physicist specializing in magnetosphere-thermosphere-ionosphere coupling dynamics, currently holding the prestigious ESA Living Planet Fellowship. His research leverages the Super Dual Auroral Radar Network (SuperDARN) alongside Fabry-Perot Interferometers, Incoherent Scatter Radars, and all-sky imagers to investigate energy transfer during space weather events. His educational foundation includes: PhD in Space Physics from Lancaster University (2016-2019), thesis: "The great space weather washing machine: Examining the dynamics of high-latitude ionosphere-thermosphere coupling" PGCE in post-compulsory education from Cardiff University (2015-2016) MPhys in Astrophysics from Aberystwyth University (2011-2015) Billett's research centers on quantifying energy pathways between solar wind drivers and Earth's upper atmosphere, with particular focus on aurorally driven conductivity enhancements and thermospheric wind dynamics . His work examines how geomagnetic storms redistribute energy through frictional heating and Poynting flux, addressing critical questions about atmospheric responses to charged particle bombardment and implications for space weather forecasting. Analysis of his 15 most recent publications reveals consistent emphasis on ion-neutral coupling mechanisms, with recurring methodological themes in multi-instrument validation (SuperDARN with satellite data), high-resolution mapping techniques, and quantification of under-sampled energy transfer scales. Key topical threads include GNSS scintillation during cusp events, thermospheric responses to extreme storms like the 2022 Starlink incident, and turbulence characterization around auroral arcs. His scientific recognition includes: ESA Living Planet Fellowship (2021) JSPS Fellowship for Research in Japan (2019) Lancaster University Dean's Award for PhD excellence (2018) Billett has secured competitive research funding through major international fellowships, with the ESA Living Planet Fellowship supporting his current work on global thermospheric responses. His publications demonstrate extensive collaboration across North American, European, and Japanese institutions, reflecting deep integration within the space physics community. He contributes to infrastructure development through projects like Borealis radar systems and pyDARN software, enhancing community-wide observational capabilities. As a core SuperDARN collaboration member, Billett participates in global networks of radar facilities and complementary instrumentation teams. His recent work with the 2022 Starlink storm response highlights growing focus on space weather impacts on commercial space assets, indicating strategic expansion toward applied space climate research while maintaining fundamental ionospheric physics investigations.

Corrie daCostaمشاهده پروفایل

استادیار

• Chemical Biology
• Ion Channel Research
• Neurophysiology
• +۵ مورد دیگر

Dr. Corrie daCosta is an Assistant Professor in Chemistry and Biomolecular Sciences at the University of Ottawa. His research focuses on ligand-gated ion channel structure-function relationships, particularly their roles in physiological processes, disease, and drug interactions. He holds a PhD in Biochemistry from the University of Ottawa (2006) and completed a postdoctoral fellowship at the Mayo Clinic’s Receptor Biology Laboratory (2009–2015). Research interests include chemical biology, ion channel pharmacology, neurophysiology, and evolutionary biochemistry. His lab employs single-molecule patch clamping and ancestral protein resurrection techniques to study molecular mechanisms and evolutionary origins of ion channel diversity. Key recent work explores activation mechanisms in uncoupled receptors, evolutionary substitution impacts, and structural analysis of ancient ion channels. Publications span Nature Chemical Biology , PNAS , and Journal of General Physiology , emphasizing biophysical and evolutionary perspectives. Current students include Adrian Samuel D’Costa and Christian Tessier, with notable alumni such as Blair Drummond and Johnathon Emlaw. The lab develops tools like the scbursts R package for single-channel data analysis. No scientific awards explicitly mentioned, though contributions to ion channel research are internationally recognized. Funding supports evolutionary studies and drug-target mechanisms.

Carlos Argüelles-Delgadoمشاهده پروفایل

پژوهشگر

• Neutrino Physics
• Astrophysics
• High-Energy Particle Physics
• +۳ مورد دیگر

Carlos Argüelles-Delgado is a CIFAR Azrieli Global Scholar (2024-2026) and a researcher in the Department of Physics at Harvard University. His work focuses on high-energy neutrino physics and astrophysics, utilizing IceCube Neutrino Observatory data to explore new physics phenomena. PhD in Physics, University of Wisconsin at Madison MSc and BSc in Physics, Pontificia Universidad Católica del Perú His research spans neutrino mass mechanisms, quantum gravity effects, and astrophysical neutrino sources. He develops advanced reconstruction techniques for neutrino telescopes and contributes to IceCube Upgrade and IceCube-Gen2 projects. 2023 : Cottrell Scholar, Packard Fellow 2021 : Young Scientist Prize in Astrophysics, Sloan Fellowship Students in his group engage in full-cycle experimental analysis, from detector design to physics interpretation. Current efforts include improving IceCube's real-time alert systems and modeling cosmic neutrino fluxes.

Rui Huمشاهده پروفایل

دانشیار

• Experimental Design
• Robustness in Statistics
• Spatial Statistics
• +۴ مورد دیگر

Rui Hu is an Associate Professor at the Department of Mathematics and Statistics within MacEwan University's Faculty of Arts and Science. Their expertise lies in Experimental Design , Robustness in Statistics , and Spatial Statistics , with a strong focus on mathematical analysis and modeling. Education: PhD in Statistics (2016), with a thesis on Robust Designs for Model Discrimination and Prediction of Threshold Probability. Rui's research bridges fractional calculus, functional analysis, and epidemiological modeling, particularly through applications of partial differential equations and Sobolev/Besov space inequalities. Their recent work explores nonlocal operators and extensions via the Caffarelli–Silvestre framework. Rui has published extensively on topics including metapopulation disease models , robust experimental design , and mathematical biology , with a chronological focus from 2011 to 2025. Key trends include stability analysis in population dynamics and innovative applications of probability density functions in sound detection.

Tri Nhu Doمشاهده پروفایل

استادیار

• Wireless Communications
• Artificial Intelligence
• Machine Learning
• +۶ مورد دیگر

Dr. Tri Nhu Do is an Assistant Professor in the Department of Electrical Engineering at Polytechnique Montréal, where he conducts cutting-edge research at the intersection of wireless communications and artificial intelligence. His academic journey spans institutions across Vietnam, South Korea, the United States, and Canada, bringing a global perspective to his work. Dr. Do is affiliated with the Advanced Microwave and Space Electronics Research Center (POLY-GRAMES) and contributes to the 'New Frontiers in Information and Communications Technologies' center of excellence. Dr. Do's research focuses on wireless communications systems, artificial intelligence applications in telecommunications, and integrated sensing and communication technologies. His work addresses critical challenges in next-generation wireless networks, particularly in security, resource allocation, and performance optimization. Recent research demonstrates a strong emphasis on applying deep learning, generative AI, and federated learning techniques to solve longstanding problems in wireless communications. His publication record shows remarkable productivity and impact, with numerous articles in top IEEE journals including IEEE Transactions on Communications, IEEE Transactions on Vehicular Technology, and IEEE Communications Letters. The research trends indicate a strategic shift toward integrating AI with traditional communication theory, particularly in security applications, reconfigurable intelligent surfaces, and UAV communications. Dr. Do teaches advanced courses in signal detection and estimation, communication theory, and digital transmission, sharing his expertise with the next generation of electrical engineers. His teaching reflects his research interests, providing students with both theoretical foundations and exposure to cutting-edge developments in the field.

Mohamed-Salah Oualiمشاهده پروفایل

استاد

• Reliability of industrial systems
• Statistical learning models
• Bayesian learning models
• +۹ مورد دیگر

Mohamed-Salah Ouali is a Full Professor in the Department of Mathematics and Industrial Engineering at Polytechnique Montréal, where he leads research at the intersection of industrial engineering, data science, and reliability engineering. He maintains active affiliations with the Institute for Data Valorization (IVADO), Poly-Industries 4.0 Laboratory, and the Interuniversity Research Centre on Enterprise Networks, Logistics and Transportation (CIRRELT). Professor Ouali's research focuses on reliability of industrial systems, statistical and Bayesian learning models, remaining life prediction, failure cause analysis, and maintenance optimization. His work bridges theoretical advances in machine learning with practical industrial applications, with particular expertise in developing interpretable models for predictive maintenance and risk assessment. Recent research has expanded into polygon generation techniques, causal reinforcement learning, and heterogeneous data fusion for industrial applications. His publication record shows consistent output with 55 publications through 2025, including significant contributions in Reliability Engineering and System Safety, Journal of Intelligent Manufacturing, and Engineering Applications of Artificial Intelligence. The research trends demonstrate an evolution from traditional reliability models to increasingly sophisticated AI-driven approaches for industrial systems. As an educator, Professor Ouali has supervised 10 PhD students and 13 Master's students, with the most recent completions in 2024 (PhD) and 2021 (Master's). His supervision spans topics in predictive maintenance, failure analysis, and industrial data analytics, with students frequently co-authoring publications in high-impact journals. His laboratory work through the Poly-Industries 4.0 Laboratory focuses on developing practical tools for industrial reliability and maintenance optimization, with strong connections to real-world industrial applications and partnerships.

Guillaume-Alexandre Bilodeauمشاهده پروفایل

استاد

• Computer vision
• Artificial intelligence
• Image and video processing
• +۶ مورد دیگر

Guillaume-Alexandre Bilodeau is a Full Professor in the Department of Computer Engineering and Software Engineering at Polytechnique Montréal. He serves as Head of the Laboratory for Image and Video Interpretation and Processing (LITIV) and is a member of the Institute for Data Valorization (IVADO). His research is centered within the 'New frontiers in information and communications technologies' center of excellence, with additional affiliations in 'Modeling and Artificial Intelligence' and 'Transport and Sustainable Infrastructure' centers. Professor Bilodeau's research focuses on computer vision and artificial intelligence, with particular expertise in: Developing methods for interpreting human actions and interactions within scenes Integrating visible and infrared camera data for improved object and human detection Object tracking using pan-tilt-zoom camera systems Medical applications of thermal imaging and thermography, including Parkinson's disease diagnosis from gait analysis His recent publications demonstrate continued strong activity in video prediction, medical applications of computer vision, and multi-object tracking. Professor Bilodeau maintains a robust publication record with multiple 2025 publications in top venues, reflecting ongoing research productivity and relevance in the computer vision community. Professor Bilodeau has received significant recognition for his work, including: 2023: Fonds de recherche du Québec research grant award 2008: Best vision paper award at Canadian Conference on Robot Vision 2002: Honorable mention for Pattern Recognition Society award As an educator and mentor, Professor Bilodeau has supervised 12 PhD students and 26 Master's students to completion. He teaches courses in multimedia technologies and computer vision. His laboratory, LITIV, provides a research environment focused on image and video interpretation with applications spanning surveillance systems, medical diagnostics, and transportation infrastructure monitoring.