جست‌وجوی استاد و پژوهشگر

Mihai Nicaمشاهده پروفایل

استادیار

• Deep Neural Networks (DNNs)
• Probability Theory
• Stochastic Processes
• +۴ مورد دیگر

Prof. Mihai Nica is an Assistant Professor in the Department of Mathematics and Statistics at the University of Guelph, affiliated with the CARE-AI institute and Vector Institute. His research focuses on probability theory, stochastic processes, and their applications to machine learning, particularly deep neural networks (DNNs). He explores scaling limits of DNNs, numerical methods using neural networks, and phase transitions in high-dimensional learning problems. Education: B.Math in Pure & Applied Math with Physics Option, University of Waterloo PhD in Mathematics, Courant Institute of Mathematical Sciences, New York University Postdoctoral Fellow at University of Toronto (supervised by Jeremy Quastel) Research Interests: His work bridges mathematical theory and practical AI applications, emphasizing topics like the neural tangent kernel, KPZ universality class, and stochastic processes in machine learning. Notable contributions include studies on neural network dynamics, random matrices, and directed polymers. Publications: Over 15 peer-reviewed articles in journals like Communications in Pure and Applied Mathematics and Electronic Journal of Probability , with a focus on theoretical foundations of AI and stochastic systems. Recent work explores infinite-width limits of neural networks and their connections to differential equations. Labs/Teams: Affiliated with CARE-AI (bridging mathematics, engineering, and philosophy) and the Vector Institute, fostering interdisciplinary collaborations.

André Longtinمشاهده پروفایل

استاد

• Nonlinear Dynamics
• Stochastic Dynamics
• Theoretical Biophysics
• +۶ مورد دیگر

André Longtin is a Full Professor in the Department of Physics at the University of Ottawa, affiliated with the Faculty of Science. He co-directs the Centre for Neural Dynamics & Artificial Intelligence and holds cross-appointments in Cellular and Molecular Medicine, Mathematics and Statistics. His research focuses on nonlinear dynamics, stochastic systems, and computational neuroscience, with applications to sensory processing, neural coding, and thermodynamic principles in biological systems. Education: B.Sc. (Honours Physics, Université de Montréal), M.Sc. (Physics, Université de Montréal), Ph.D. (Physics, McGill University, 1989). Postdoctoral training at Los Alamos National Laboratory. Research Interests: Theoretical biophysics, neural modeling, entropy production in biological systems, delayed dynamical systems, and interdisciplinary applications of nonlinear science. His group explores computations in hippocampus and zebrafish pallium, epilepsy mechanisms, neural coding in electrosensory systems, and autonomous stochastic rhythms. Recent Work: Articles highlight entropy dynamics in humans, myelin plasticity effects on neural networks, and reservoir computing with delayed loops. Awards include APS Fellowship (2003) and the NSERC Brockhouse Prize (2017). Editorial Roles: Biological Cybernetics, Frontiers in Computational Neuroscience, and others. Collaborations span systems neuroscience, clinical specialties, and engineering.

Fady Alajajiمشاهده پروفایل

استاد

• Information Theory
• Communications
• Coding Theory
• +۵ مورد دیگر

Fady Alajaji is a Professor of Mathematics and Engineering at Queen's University, with a cross-appointment in the Department of Electrical and Computer Engineering. He holds a B.E. from the American University of Beirut, and M.Sc. and Ph.D. from the University of Maryland, College Park. His research focuses on information theory, coding for communication networks, probability models (e.g., Polya urns, contagion processes), and applications of information theory to machine learning (e.g., generative adversarial networks, data privacy). He has served as Associate Editor for the IEEE Transactions on Information Theory and has received awards for research and teaching. His work spans theoretical foundations (e.g., Shannon limits) and practical coding techniques for wireless systems. Education: B.E. (1988), M.Sc. (1990), Ph.D. (1994) in Electrical Engineering from the University of Maryland. Roles: Professor of Mathematics and Engineering, Cross-appointment in Electrical and Computer Engineering. Research Interests: Information theory, coding for communication networks, stochastic processes (network epidemics, Polya urn models), machine learning applications (information bottleneck, GANs), and data privacy. Recent work includes optimal signaling schemes for sensor networks, privacy-aware estimation, and curing models for contagion networks. Publications: Over 100 journal/conference papers, including foundational work on joint source-channel coding, hybrid digital-analog coding, and theoretical bounds for communication systems. Recent trends focus on information-theoretic machine learning and network science. Awards: Premier's Research Excellence Award (2001), Golden Apple Teaching Award (2015). Grants/Advising: Supervised postdoctoral fellow Jian-Jia Weng. Active in conference organization and editorial roles. Labs/Teams: Member of the Mathematics and Engineering Communications and Information Theory Group at Queen's University.

En-Hui Yangمشاهده پروفایل

استاد

• Information Theory
• Multimedia Compression
• Digital Communications
• +۳ مورد دیگر

Dr. En-Hui Yang is University Professor in Electrical and Computer Engineering at the University of Waterloo and founding Director of the Leitch-University of Waterloo Multimedia Communications Lab. A world-renowned expert in information theory, he co-developed the Yang-Kieffer algorithm for lossless compression and invented soft decision quantization technology used in smartphones and web browsers. His research spans multimedia compression, digital communications, and deep learning. Education: Ph.D. Electrical Engineering, University of Southern California (1996) Ph.D. Probability and Statistics, Nankai University (1991) B.Sc. Applied Mathematics, HuaQiao University (1986) His transformative work in data compression has impacted millions globally through technologies accelerating data transmission efficiency. Articles focus on optimization of video/image compression standards (HEVC/H.264), channel coding theorems, and novel compression algorithms. Scientific Awards: IEEE Eric E. Sumner Award (2021) Canada Research Chair - Tier 1 (2010, 2017) Fellow of the Royal Society of Canada (2009)

Vahab Khoshdelمشاهده پروفایل

استادیار

• Machine Learning
• Deep Learning
• Computer Vision
• +۶ مورد دیگر

Vahab Khoshdel is an Assistant Professor in the Department of Electrical and Computer Engineering at the Price Faculty of Engineering, University of Manitoba. His research bridges machine learning, deep learning, computer vision, robotics, and medical imaging, with applications in rehabilitation robotics, microwave/ultrasound imaging, stored grain monitoring, and precision agriculture. Education: 2021, Ph.D. Biomedical Engineering, University of Manitoba 2017, Ph.D. Mechanical Engineering, Ferdowsi University of Mashhad 2013, M.Sc. Mechatronic Engineering, University of Shahrood 2011, B.Sc. Robotics Engineering, University of Shahrood Research Interests: Khoshdel specializes in applying generative AI, neural networks, and optimization techniques to medical imaging and robotics. His work includes microwave/ultrasound breast imaging, impedance control for rehabilitation robots, and AI-driven agricultural monitoring systems. Publication Trends: His recent articles emphasize machine learning workflows for medical diagnostics, deep learning in multimodal imaging, and neural networks in rehabilitation robotics. Key subfields include 3D imaging, inverse scattering, tissue classification, and sEMG signal analysis. Contact: Vahab.Khoshdel@umanitoba.ca

Andrew Jirasekمشاهده پروفایل

استاد

• Medical Physics
• Radiation Oncology Physics
• Raman Spectroscopy
• +۳ مورد دیگر

Andrew Jirasek is a Professor at the Irving K. Barber Faculty of Science , University of British Columbia Okanagan , and serves as the Associate Dean for Graduate and Postdoctoral Training . He leads the Analytics in Medical Sciences (AiMS) Institute with a focus on Medical Physics and Radiation Oncology Physics , particularly utilizing Raman spectroscopy and 3D radiation dosimetry for cancer treatment verification. PhD from University of British Columbia His research involves developing polymer gel dosimeters for 3D radiation dose verification in complex therapies like volumetric modulated arc therapy (VMAT) , collaborating across physics, oncology, and engineering . He also investigates optical technologies to monitor biological responses during radiotherapy using Raman spectroscopy with machine learning for data analysis. Recent publications highlight advancements in 3D gel dosimetry , Raman spectroscopy for metabolic profiling , and iterative image reconstruction algorithms . His work spans dosimeter technology development , radiation therapy quality assurance , and clinical applicability studies for novel treatment verification methods.

Nicolás Quesadaمشاهده پروفایل

دانشیار

• Quantum Information
• Quantum Computing
• Quantum Optics
• +۷ مورد دیگر

Nicolás Quesada is an Associate Professor in the Department of Engineering Physics at Polytechnique Montréal, where he holds the MEI Chair in Quantum Photonics. He serves as Director of COPL (Centre d'optique, photonique et laser) and is a member of INTRIQ (Institut transdisciplinaire d'informatique quantique). His research program focuses on quantum information, quantum computing, and quantum optics, with particular emphasis on photonic implementations of quantum technologies. Dr. Quesada earned his B.Sc. in Physics from Universidad de Antioquia in 2010, followed by M.Sc. and Ph.D. degrees in Physics from the University of Toronto. During his doctoral studies, he was awarded both Vanier and Stoicheff scholarships. Prior to joining Polytechnique Montréal, he worked at Xanadu Quantum Technologies as lead developer of the Strawberry Fields and The Walrus software libraries, where he led theoretical efforts demonstrating photonic quantum advantage. His research interests span quantum photonics, quantum computing, and quantum optics, with specific focus on Gaussian Boson Sampling, squeezed light generation, non-Gaussian light sources, and quantum benchmarking techniques. His group develops theoretical frameworks and computational tools for next-generation quantum light sources needed for fault-tolerant quantum computers, quantum communication networks, and quantum sensors. His work bridges theoretical quantum information science with practical photonic implementations. Analysis of his recent publications reveals a strong focus on advancing Gaussian Boson Sampling as a platform for quantum advantage, developing mathematical frameworks for quantum optics, and engineering practical photonic quantum devices. His work spans from fundamental quantum optics to applied quantum computing, with increasing emphasis on verification and benchmarking of quantum computational advantage. Dr. Quesada has received notable recognition including the Vanier Canada Graduate Scholarship and the Stoicheff Scholarship during his doctoral studies. His research has attracted significant funding, including a $6 million grant for quantum projects at Polytechnique Montréal announced in January 2025 and involvement in a $1.91 million NSERC quantum sensing project led by Professor Denis Seletskiy. He has supervised two Master's students to completion in 2024: Dalbec-Constant, N. who worked on photon counting from transition-edge sensors, and Zhao, J. who researched optimal pumps for spontaneous parametric down-conversion. His research group collaborates extensively with both academic and industry partners in the quantum technology sector. As Director of COPL, he oversees one of Canada's leading photonics research centers, facilitating interdisciplinary research across quantum optics, classical optics, and laser technologies.

Geoff Horsmanمشاهده پروفایل

دانشیار

• Natural Products Biosynthesis
• Enzyme Mechanisms
• Biocatalysis
• +۳ مورد دیگر

Geoff Horsman is an Associate Professor in the Faculty of Science at Wilfrid Laurier University. His research focuses on understanding enzyme mechanisms, natural products biosynthesis, and biocatalytic applications. Key areas include the study of phosphonate biosynthesis, enediyne antibiotics, and metabolic pathway engineering. He utilizes advanced spectroscopic techniques and computational tools like R for enzyme kinetics analysis. Research interests span diverse topics such as C-P bond formation, nucleotidyl activation in bacterial systems, and novel enzyme discovery. His work often involves heterologous expression systems and synthetic chemistry approaches to study enzymatic processes. Recent studies include the discovery of glyphosate oxidase enzymes and advancements in NAD+ biosynthesis pathways. No specific scientific awards are listed, though his contributions to enzyme mechanism studies and metabolic engineering are notable. Student opportunities are available in his lab, though specific advisee names are not provided. His work bridges fundamental biochemistry with applied biocatalysis and synthetic biology, contributing to both academic and industrial applications.

James Forbesمشاهده پروفایل

دانشیار

• Dynamics and Control
• Robotics
• Nonlinear Control
• +۵ مورد دیگر

James Forbes is an Associate Professor in the Department of Mechanical Engineering at McGill University. He holds the title of William Dawson Scholar and is affiliated with the Dynamics Estimation & Control of Aerospace & Robotics Systems research group. His primary research focus is on Dynamics and Control, with emphasis on navigation, guidance, and control (GNC) techniques for robotic systems. He teaches courses such as MECH 309 (Numerical Methods), MECH 412 (System Dynamics), and advanced topics in control systems. Forbes earned his Ph.D. in Aerospace Science and Engineering from the University of Toronto, following an M.A.Sc. from the same institution and a B.A.Sc. in Mechanical Engineering from the University of Waterloo. His research interests include nonlinear state estimation (batch methods, filtering), control synthesis via optimization (LQR, LMI approaches), and data-driven modeling using Koopman operator techniques. Applications span unmanned aerial vehicles (UAVs), autonomous underwater vehicles (AUVs), and SLAM systems. He has developed the navlie Python package for state estimation on Lie groups. Notable awards include the William Dawson Scholar distinction. His recent work focuses on multi-UAV localization, robust control algorithms, and sensor fusion techniques. He collaborates on projects involving UWB-based positioning and inertial navigation systems.

Christophe Grovaمشاهده پروفایل

استاد

• Biomedical Engineering
• Neuroscience
• Epilepsy Research
• +۵ مورد دیگر

Christophe Grova , Professor in the Department of Physics at Concordia University and scientific lead of the PERFORM physiology platform , is a multidisciplinary researcher at the intersection of Biomedical Engineering , Neuroscience , and Medical Physics . He directs the Multimodal Functional Imaging Laboratory , focusing on advanced neuroimaging techniques like EEG/MEG , fMRI , and fNIRS to study brain mechanisms in epilepsy and sleep . His work emphasizes multimodal data fusion to uncover neurovascular coupling and functional connectivity patterns, with software developments such as MEM and NIRSTORM enhancing analysis precision. Key Research Areas : Epilepsy, Sleep Studies, Neuroimaging, Computational Neuroscience, Multimodal Data Fusion Affiliations : Full Professor at Concordia University (Physics), Adjunct Professor at McGill University (Biomedical Engineering, Neurology and Neurosurgery). Scientific Awards : Not explicitly listed in the provided text. Software Contributions : Developed MEM (EEG/MEG source localization) and NIRSTORM (fNIRS analysis) for open-source platforms like Brainstorm .

Dawn MacIsaacمشاهده پروفایل

دانشیار

• Biomedical Engineering
• Software Engineering
• Knowledge Engineering
• +۱ مورد دیگر

Dr. Dawn MacIsaac is an Associate Professor in the Department of Computer Science at the University of New Brunswick’s Faculty of Computer Science, where she has served for over 14 years. She holds a PhD and Master of Science in Engineering from the same institution. Her research focuses on Biomedical Engineering, Software Engineering, Knowledge Engineering, and Signal Processing, particularly in the context of myoelectric control systems and biomedical signal analysis. Dr. MacIsaac has authored 49 peer-reviewed publications and supervised 25 graduate students. Her work emphasizes improving the performance and robustness of myoelectric prosthetics through advanced machine learning techniques, signal processing algorithms, and adaptive control strategies. Notable contributions include developing the Myosim 2.0 EMG simulation tool and pioneering self-supervised learning approaches for pattern recognition in unclear-label environments. She currently serves on the Editorial Board of the Journal of Electromyography and Kinesiology and is a Professional Engineer (PEng) registered with the Association of Professional Engineers and Geologists of New Brunswick (APEGNB). Her research bridges engineering and healthcare, addressing challenges in signal quality assessment, fatigue monitoring, and human-machine interface design. Key themes in her publications include enhancing EMG signal analysis for medical applications, optimizing control systems for prosthetic devices, and advancing methodologies for automated biosignal evaluation. Her interdisciplinary work impacts rehabilitation technology, clinical diagnostics, and biomedical instrumentation.

Nick Virgilioمشاهده پروفایل

استاد

• Thermodynamics
• Multiphase systems
• Surfaces, interfaces and thin layers
• +۲ مورد دیگر

Nick Virgilio is a Full Professor in the Department of Chemical Engineering at Polytechnique Montréal . His research focuses on soft matter interfaces, polymer blends, and advanced hydrogel systems for biomedical and catalytic applications. Director, Research Laboratory on Surfaces, Interfaces and Soft Matter Member, Research Center for High-Performance Polymer and Composite Systems (CREPEC) Research interests include interfacial phenomena in multiphase systems, self-assembly of soft materials, nanoparticle-hydrogel composites, Pickering emulsions, and polymer microstructure engineering. Scientific awards include the 2010 Canadian Macromolecular Science Thesis Prize and the 2004 Polytechnique Montréal Master's Thesis Award. Recent publications highlight his work in macroporous hydrogels for cancer cell capture, nanoparticle synthesis in soft matrices, and interfacial control of polymer blends. His studies frequently appear in high-impact journals like ACS Applied Materials & Interfaces , Green Chemistry , and Macromolecules . Students under his supervision have explored topics from biofilm mechanics to lunar environment polymer systems across 4 PhD and 6 Master’s theses completed or ongoing.

Hina Shaheenمشاهده پروفایل

استادیار

• Bio-statistics
• Neuroscience Data Analysis
• Computational Modelling
• +۹ مورد دیگر

Hina Shaheen is an Assistant Professor in the Department of Statistics , Faculty of Science, University of Manitoba. Her research integrates statistical methods with computational neuroscience and neurodegenerative disease modeling. Email: Hina.Shaheen@umanitoba.ca Lab: NeuroStats Lab Research Interests: Shaheen specializes in neurodegenerative disorders like Alzheimer’s and Parkinson’s disease, with a focus on: Multiscale and network modeling of brain dynamics Bio-statistical and machine learning approaches BAYESIAN inference and STOCHASTIC processes Calcium signaling and PROTEIN dynamics in disease pathology Publication Trends: Recent work combines data-driven stochastic modeling with connectomic insights to study neurodegenerative mechanisms. Key themes include: Integration of machine learning and BAYESIAN frameworks in brain network analysis Applications to Alzheimer’s (amyloid-beta/calcium interactions, exosomal spread) and Parkinson’s (DBS treatment, neural dynamics) Development of multiscale co-simulation techniques for clinical data interpretation Academic Engagement: Shaheen actively mentors graduate students and collaborates across disciplines. She participated in the V AMMCS International Conference (2019) and maintains affiliations with computational neuroscience communities.

Arash Habibi Lashkariمشاهده پروفایل

دانشیار

• Cybersecurity
• Network Security
• Threat Hunting
• +۷ مورد دیگر

Dr. Arash Habibi Lashkari is an Associate Professor and Canada Research Chair in Cybersecurity at York University's School of Information Technology. He holds a PhD from the University of Technology Malaysia and completed a Post-Doc at the University of New Brunswick. With over 25 years of teaching experience, he specializes in cybersecurity risk management, malware analysis, and threat hunting. He pioneered Canada’s first cybersecurity Capture the Flag (CTF) competition for post-secondary students and has authored 10 books and over 110 academic articles. Research Interests: Cybersecurity, Network Security, Threat Hunting, Malware Analysis, Cybersecurity Risk Management, and Blockchain Security. He leads the Behaciour-Centric Cybersecurity Center (BCCC), focusing on vulnerability detection technologies and cybersecurity dataset generation. Awards: Mitacs 150 Top Researchers (2017), University of New Brunswick Teaching Innovation Award (2020), Gold Medal at 2020 Canadian Online Publishing Awards, and multiple international security competition awards. His work has been featured in CBC, CIC, and global cybersecurity conferences. Key Projects: Development of intrusion detection datasets (e.g., CIRA-CIC-DoHBrw-2020), malware analysis frameworks, and AI-driven cybersecurity tools. He actively collaborates with organizations like Aviva and NICT on cybersecurity resilience strategies. Teaching: Offers courses in Digital Forensics, Network Security, and Cybersecurity Management. His 'Think-Que-Cussion' teaching methodology emphasizes interactive learning.

Kemal Tepeمشاهده پروفایل

استاد

• Vehicular Networks
• Cognitive Radio
• Wireless Communication
• +۶ مورد دیگر

Professor Kemal Tepe is a faculty member in the Faculty of Engineering at the University of Windsor, specializing in wireless communication and information processing. His research focuses on vehicular networks, cognitive radio systems, and smart grid technologies. He leads the Wireless Communication and Information Processing Lab , where he develops solutions for autonomous driving systems, cybersecurity in vehicle-to-infrastructure communication, and spectrum sensing techniques. In 2016, he was awarded the Medal of Excellence by the Faculty of Engineering for his dedication and service. Tepe’s work bridges theoretical advancements and practical applications, addressing challenges in autonomous systems, machine learning for anomaly detection, and IoT security. His contributions include pioneering methods for detecting adversarial behavior in vehicular networks and improving spectrum utilization through probabilistic modeling. Collaborations with industry partners like Ford Motor Company and involvement in initiatives such as the Perspective Magazine automotive research highlight his industry-relevant research. His research interests span a wide range of domains including: Autonomous vehicle safety and communication protocols Cognitive radio networks and spectrum management Machine learning for network security and anomaly detection Wireless sensor networks and energy-efficient protocols Smart grid integration and communication architectures Tepe’s publications emphasize practical implementations, such as real-time routing protocols for wireless sensor networks and hardware designs for cognitive radio systems. His lab’s innovations have been showcased in industry-relevant platforms, demonstrating the real-world impact of his work.