جست‌وجوی استاد و پژوهشگر

Barry C. Sandersمشاهده پروفایل

استاد

• Quantum Computing
• Quantum Information Theory
• Quantum Algorithms
• +۳ مورد دیگر

Barry C. Sanders is a Professor and Director of the Institute for Quantum Science and Technology at the University of Calgary. He holds additional roles as Lead Investigator of the Alberta Major Innovation Fund Project on Quantum Technologies, a Thousand Talents Chair at the University of Science and Technology China, and Vajra Visiting Faculty at the Raman Research Institute. His research focuses on quantum computing, quantum algorithms, and optical implementations of quantum information tasks. Sanders has a PhD from Imperial College London (1987) and has held faculty positions at Macquarie University and the Australian National University. Education: BSc (University of Calgary, 1984), PhD (Imperial College London, 1987) Affiliations: Institute for Quantum Science and Technology, Alberta Major Innovation Fund Project His research interests include quantum-limited measurement, nonclassical light, quantum cryptography, and quantum algorithm development. He has pioneered work on Schrödinger cat states, qudit systems, and quantum error correction. Awards include Fellowships from the Institute of Physics, Optical Society of America, and Royal Society of Canada. His work bridges theory and application, with contributions to quantum technology commercialization and interdisciplinary collaborations. Editor-in-Chief, New Journal of Physics Past leadership roles: President of the Australian Optical Society, Founding Co-Chair of the Canadian Association of Physicists Division His recent publications explore quantum speedup in machine learning, topological quantum walks, and fault-tolerant quantum computing architectures.

Gul N. Khanمشاهده پروفایل

استاد

• Embedded Systems
• Hardware/Software Co-design
• Network-on-Chip (NoC)
• +۷ مورد دیگر

Dr. Gul N. Khan is a Professor in the Department of Electrical, Computer and Biomedical Engineering at Toronto Metropolitan University (formerly Ryerson University). He has held academic positions at the University of Saskatchewan, Nanyang Technological University, RMIT University, and Quaid-i-Azam University. His career spans over three decades with a focus on embedded systems , network-on-chip (NoC) , and heterogeneous computing . Education: Ph.D. (Imperial College, 1989), M.Sc. (Syracuse University, 1982), B.Sc. (UET Lahore, 1979) Dr. Khan’s research interests include hardware-software co-design , CPU-GPU systems , fault-tolerant computing , and smart RFID systems . His work has led to over 125 refereed publications and three US patents. His recent publications highlight advancements in GPU auto-tuning , NoC synthesis , and digital time interpolators . Despite being listed in a Google Scholar block with unrelated public health topics, these appear to be errors, as his core expertise remains in computer engineering. Dr. Khan has supervised numerous graduate projects in embedded systems and SoC design . He served as Program Director for Computer Engineering from 2004–2015 and leads the Microsystems Research Lab at Toronto Metropolitan University.

Kalikinkar Mandalمشاهده پروفایل

دانشیار

• Cryptography
• Cybersecurity
• Privacy
• +۷ مورد دیگر

Dr. Kalikinkar Mandal is an Associate Professor in the Faculty of Computer Science at the University of New Brunswick (UNB), Fredericton, Canada. He holds the prestigious NB Power Cybersecurity Research Chair for smart grid security and privacy, a position supported by $500,000 in funding from NB Power for a five-year term. Dr. Mandal is also a member of the Canadian Institute for Cybersecurity (CIC), an ACM member, and a member of the International Association for Cryptologic Research (IACR). Education: PhD in Electrical and Computer Engineering from the University of Waterloo (2013) MTech in Computer Science from the Indian Statistical Institute, Kolkata (2009) Additional Master's degree in Mathematics Dr. Mandal's research broadly focuses on cryptography, cybersecurity, and privacy, with specific expertise in lightweight cryptography, privacy-preserving computation, security and privacy in smart grids and Internet of Things (IoT), trusted computing, and high-speed cryptography. His work addresses critical challenges in securing emerging technologies, particularly in energy infrastructure where cybersecurity threats can have severe consequences for essential services. His research bridges theoretical cryptography with practical applications in real-world systems. Analysis of Dr. Mandal's recent publications reveals a consistent focus on cryptographic techniques for resource-constrained environments, particularly for smart grid and IoT applications. His work spans theoretical foundations of cryptographic primitives, practical implementations of lightweight ciphers, and innovative privacy-preserving protocols for emerging technologies. A notable trend in his research is the development of efficient cryptographic solutions that balance security requirements with performance constraints in critical infrastructure systems. Scientific Awards: NB Power Cybersecurity Research Chair ($500,000 funding) Contributor to multiple cryptographic algorithms (ACE, SPIX, SpoC, WAGE) that reached Round 2 of NIST Lightweight Cryptography standardization Dr. Mandal actively mentors graduate students in cybersecurity research, currently supervising three students working on cryptographic protocols for cyber-physical systems, cybersecurity in advanced metering infrastructure, and privacy for electric vehicles. His NB Power Cybersecurity Research Chair supports research that provides training opportunities for both graduate and undergraduate students, preparing them as future cybersecurity leaders. Through direct applied research, knowledge dissemination, and student training, his work addresses critical challenges in power and security infrastructure. Dr. Mandal is actively involved in several research initiatives related to lightweight cryptography. He is part of the development teams for ACE, SPIX, SpoC, and WAGE - all of which were Round 2 candidates in the NIST Lightweight Cryptography standardization project. His GitHub repository (comsec-lwc) contains reference and optimized implementations of these cryptographic algorithms. He also contributes to the Canadian Institute for Cybersecurity at UNB, focusing on practical applications of cryptographic techniques in critical infrastructure security.

Nariman Sepehriمشاهده پروفایل

استاد

• Fluid Power Systems
• Robotics and Teleoperation
• Control Systems
• +۴ مورد دیگر

Dr. Nariman Sepehri is a Professor in the Department of Mechanical Engineering at the Price Faculty of Engineering, University of Manitoba, Canada. He has held significant administrative roles including Department Associate Head (Graduate Studies), Associate Dean of Engineering (Undergraduate Programs), and Acting Dean of Engineering. His research focuses on fluid power systems, robotics, and control with applications in rehabilitation and heavy machinery. Education: Post-Doctorate, Electrical & Computer Engineering, University of British Columbia, Canada (Tele-Robotics, Mechatronics) PhD, Mechanical Engineering, University of British Columbia, Canada (Control, Fluid Power Systems, Robotics) MSc, Mechanical Engineering, University of British Columbia, Canada (Computer-Aided Manufacturing Planning) BSc, Mechanical Engineering, Sharif University of Technology, Iran (Machine Design) Dr. Sepehri's research interests span Fluid Power Systems and Technology , Robotics and Teleoperation , Control Systems , Condition Monitoring , and Mechatronics of Rehabilitation Devices . His work integrates advanced control theory with practical applications in hydraulic and pneumatic systems, aiming to improve energy efficiency and reliability in robotics, manufacturing, aerospace, and healthcare. Notably, he has developed innovative rehabilitation devices using game-based interfaces for stroke and cerebral palsy patients. His recent publications (2022-2025) demonstrate a strong trend towards energy-efficient hydraulic systems, fault detection using machine learning, and the development of soft robotic actuators for rehabilitation. Key areas include electro-hydrostatic actuators, pump-controlled circuits, and the application of advanced algorithms for condition monitoring and control. Scientific Awards: Dean of Engineering’s Award for Superior Academic Performance University of Manitoba Rh Award for outstanding contributions to scholarships and research in Applied Sciences Fellow of the Canadian Academy of Engineering (CAE) Fellow of the American Society of Mechanical Engineers (ASME) Fellow of the Canadian Society for Mechanical Engineering (CSME) Dr. Sepehri has supervised over 100 graduate and postdoctoral students, contributing significantly to the field of fluid power and robotics. His research has been supported by major grants from the Natural Sciences and Engineering Research Council of Canada (NSERC) and other sources, enabling the establishment of the Fluid Power Research Laboratory. This lab features state-of-the-art equipment including a human-robot-in-the-loop simulator and hardware-in-the-loop test facilities for condition monitoring. The Fluid Power Research Laboratory at the University of Manitoba, under Dr. Sepehri's leadership, is a hub for innovation in fluid power technology. The lab collaborates internationally with researchers in USA, Brazil, China, Hungary, Romania, Denmark, Sweden and France, and has developed interdisciplinary projects bridging engineering with healthcare applications.

Stephen Pistoriusمشاهده پروفایل

استاد

• Medical Physics
• Medical Imaging
• Image Reconstruction
• +۴ مورد دیگر

Stephen Pistorius is a Professor in the Department of Physics and Astronomy at the University of Manitoba's Faculty of Science. He serves as Associate Head and Director of the Medical Physics Program, focusing on interdisciplinary research at the intersection of medical physics, biomedical engineering, and artificial intelligence. Role : Professor, Associate Head, Director of Medical Physics Program Contact : Office 205 Allen Building, Stephen.Pistorius@umanitoba.ca , +1 204-474-6205 Research Interests Medical Imaging : Development and optimization of radar-based microwave sensing systems for breast cancer detection. Image Reconstruction : Innovation in computational algorithms for PET and microwave imaging. Artificial Intelligence : Application of machine learning to tumor detection and signal analysis. Publication Trends His recent work emphasizes microwave imaging hardware (antenna arrays, phantom materials), machine learning integration for tumor detection, and image quality metrics across modalities. Key subfields include radar systems , stochastic optimization , and clinical translation of microwave sensing . Additional Contributions He contributes to radiation oncology via EPID-based positioning automation and dose verification systems, while also addressing technical challenges in 3D-printed phantom modeling and microwave propagation in biological tissues.

Werner Dietlمشاهده پروفایل

دانشیار

• Programming Languages
• Static Analysis
• Software Security
• +۴ مورد دیگر

Werner Dietl is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Waterloo. His work focuses on programming languages, static analysis, software security, and formal verification techniques. He has contributed to type system design, low-power computing, and approximate data types through projects like EnerJ. His research also addresses challenges in compiler design, cryptographic protocol validation, and runtime enforcement mechanisms. Key research areas include: Type systems for imperative and domain-specific languages Static analysis of implicit control flow (e.g., Java reflection, Android intents) Approximate computing and energy-efficient computation Formal verification of security properties Publications span topics from unit measurement type inference to ownership-based security models, reflecting a focus on practical formal methods. His work emphasizes scalability and precision in type systems while addressing real-world software engineering challenges.

Andriy Miranskyyمشاهده پروفایل

دانشیار

• Applied Machine Learning
• Big Data
• Blockchain
• +۴ مورد دیگر

Dr. Andriy Miranskyy is an Associate Professor in the Department of Computer Science at Toronto Metropolitan University. His research focuses on applied machine learning, quantum computing, cloud computing, and software engineering, with notable contributions to anomaly detection in cloud systems and quantum software engineering. He leads the AMiR Lab, exploring risk mitigation and software development challenges in emerging technologies. Education: Ph.D. in Computer Science from The University of Western Ontario (2011). Research Interests: He investigates quantum software engineering methodologies, cloud-native systems governance, and big data applications. His work bridges theoretical advancements with industrial-scale implementations, such as IBM Db2 quantum safety case studies and cloud monitoring tools like CloudHeatMap. He emphasizes practical solutions for flakiness detection in quantum programs and sustainable software development practices. Awards: Recognitions include the Rogers Cybersecure Catalyst Fellowship (2023-2024), IBM CAS Best Project (2021), and a Guinness World Record for pioneering a 3Pb data warehouse (IBM DB2 team). Teaching: Teaches courses like CPS 840 (Quantum Computing), CPS 847 (Software Tools for Startups), and CPS 731 (Software Engineering). Labs/Teams: Directs the AMiR Lab, focusing on risk-aware software engineering in quantum and cloud domains.

• Robotics
• Human-Robot Interaction
• Control Systems
• +۵ مورد دیگر

Brandon J. DeHart is an Adjunct Assistant Professor at the University of Waterloo. He is affiliated with the university's academic faculty and can be contacted at brandon.dehart@uwaterloo.ca or at location E5 3108. His research focuses on robotics, particularly humanoid robots, control systems, and roboethics. He has contributed to advancements in dynamic balance, gait metrics, and human-robot interaction. His work spans topics like legged mechanism design, sensor networks in art installations, and automated content generation systems. His research interests include the development of humanoid robots capable of child-friendly interactions, robust control strategies for uncertain environments, and ethical considerations in robotic design. He has explored technical challenges like foot placement estimators for balance recovery and momentum-based gait generation in bipedal systems. Notable contributions include methodologies for tracking visitor engagement in large art installations and frameworks for interconnecting cellular automata across hardware and software. His publications reflect a blend of robotics engineering, AI applications, and interdisciplinary approaches to technology design.

Frederic Nabkiمشاهده پروفایل

دانشیار

• MEMS
• RF Integrated Circuits (RFICs)
• Phase-Locked Loops
• +۴ مورد دیگر

Prof. Frederic Nabki is an Associate Professor of Electrical Engineering at the École de technologie supérieure (Québec, Canada). He holds a B.Eng. (Honors) and Ph.D. in Electrical Engineering from McGill University. His primary research focuses on MEMS integration with CMOS systems, RF integrated circuits (RFICs), and ultra-wideband (UWB) systems. He leads the Microtechnology and Microsystems Laboratory (Micro²) and co-directs the CoFaMic Research Centre at UQAM, exploring novel MEMS fabrication processes and hybrid RF-MEMS systems. Education: B.Eng. (McGill, 2003), Ph.D. (McGill, 2010) Affiliations: IEEE Montreal Section Secretary Labs: CoFaMic (UQAM), Micro² (ETS) His research interests span MEMS resonators, phase-locked loops, and UWB transceivers. Notable contributions include patents in MEMS-CMOS integration and book chapters on MEMS resonators. He has secured funding from NSERC, CFI, and ReSMiQ, focusing on miniaturization, energy efficiency, and system-level integration challenges. Recent work emphasizes energy-efficient UWB systems, terahertz modulation, and deep learning-enhanced ultrasonic sensors. His interdisciplinary approach bridges microelectromechanical and electronic systems for applications in healthcare, aerospace, and industrial IoT. Awards: Governor General of Canada’s Academic Bronze Medal (2025). Grants and collaborations include NSF-funded projects on MEMS fabrication and partnerships with Quebec’s Microsystems Strategic Alliance. He advises on emerging trends in hybrid RF-MEMS systems and low-power sensor networks.

Sash Vaidمشاهده پروفایل

دانشیار

• Political Economy of Marketing
• Dual-use Industrial and Cyber Technologies
• Exogenous Shocks
• +۳ مورد دیگر

Dr. Sash Vaid is a tenured Associate Professor of Marketing at McMaster University's DeGroote School of Business and an Affiliate Faculty member with the School of Computational Science and Engineering. He also holds a Research Affiliate position at the University of Michigan, specializing in econometrics and quantitative marketing of dual-use industrial and cyber technologies. His educational background includes: Ph.D. from the University of Houston MBA from Duke University Graduate studies at Rice University Graduate studies at the Institute for the Study of Business Markets, Penn State University Dr. Vaid's research revolves around a 'Vaid Venn Diagram' intersecting political economy of marketing, dual-use industrial and cyber technologies, and exogenous shocks. He investigates regulatory contexts impacting firms, defense-derived technologies (AI, cybersecurity, geodata) repurposed for commercial use, and macro/micro-level shocks affecting marketing interfaces. His methodology leverages field experiments and programming languages (SQL, Python, Java, C++, CSS, R) to quantify cyber-driven consumption. Analysis of his 2020-2025 publications reveals strong interdisciplinary trends spanning marketing analytics, executive turnover dynamics, AI applications, and pandemic modeling. His work bridges business, computational science, and public health, with significant focus on B2B executive transitions, lead generation technologies, and Covid-19 infection rate estimation using AI. No scientific awards were mentioned in the provided text. Dr. Vaid advises students across undergraduate, graduate, and doctoral programs. He teaches Marketing Analytics (the first undergraduate business course integrating dual-use technology industry), Synthetic Consumer Analytics in the Master of Management in AI and Analytics program, and doctoral courses in Causal Inference. His research is supported by grants including SSHRC funding, and he co-leads development of Canada's 1st Sales & Data Science Lab. He directs the Vaid Lab for Dual-use Technologies and co-leads the Sales & Data Science Lab at the McLean Centre for Collaborative Discovery, providing students with training in high-performance computing, data visualization, deep learning hardware, and collaborative data analysis systems.

Athula Dayanart Rajapakseمشاهده پروفایل

استاد

• Power Systems Protection
• Renewable Energy Integration
• HVDC Grid Protection
• +۳ مورد دیگر

Dr. Athula Rajapakse is a Professor in the Department of Electrical and Computer Engineering at the Price Faculty of Engineering, University of Manitoba, Canada. He leads the Intelligent Power Grid Laboratory and holds Fellow status with Engineers Canada, alongside senior membership in IEEE. His research addresses critical challenges in modern power systems, particularly renewable energy integration and grid resilience. His academic foundation includes: B.Sc. (Eng) First Class Honours in Electrical Engineering from the University of Moratuwa, Sri Lanka (1990) M.Eng. in Energy Technology from the Asian Institute of Technology, Bangkok, Thailand (1993) Ph.D. in Quantum Engineering and Systems Science from the University of Tokyo, Japan (1998) Dr. Rajapakse's research centers on power system protection in evolving grid environments, with specialized expertise in transient-based protection schemes , HVDC grid security , and machine learning applications for fault detection. His work bridges theoretical innovation and industry implementation through synchrophasor technology and wide-area monitoring systems, addressing vulnerabilities introduced by inverter-based renewable resources. Analysis of his 2023-2025 publications reveals three dominant thrusts: (1) Machine learning-driven voltage stability prediction using real-time monitoring, (2) Novel protection architectures for HVDC grids and microgrids requiring sub-cycle fault clearance, and (3) Standardized electro-thermal designs for scalable DC circuit breakers in medium-voltage networks. These reflect the industry's urgent need for adaptive protection in grids with high renewable penetration. His recognition includes: Fellow of Engineers, Canada Dr. Rajapakse serves as Associate Editor for the International Journal of Electric Power and Energy Systems (Elsevier) and Technology and Economics of Smart Grids and Sustainable Energy (Springer), while convening the Cigre/IEEE Joint Working Group C4/C2.62 on synchrophasor applications. Though not accepting new graduate students currently, his editorial leadership and working group contributions significantly shape power systems protection standards. His research has secured industry partnerships focused on real-world grid modernization. The Intelligent Power Grid Laboratory under his direction develops hardware-in-the-loop validation platforms for protection schemes, collaborating with Manitoba Hydro and international grid operators to test solutions for renewable-rich systems and remote microgrids.

• Computer Science Education
• Parallel Programming
• System Software
• +۳ مورد دیگر

Celina Berg is an Associate Teaching Professor in the Department of Computer Science at the University of Victoria, affiliated with the Faculty of Engineering and Computer Science. She also serves as Outreach, Recruitment, and Retention Coordinator, focusing on enhancing student engagement and diversity in computer science education. Her research interests span parallel programming, system software design, aspect-oriented programming, and educational tool development. Dr. Berg’s work emphasizes bridging gaps between theoretical concepts and practical implementation, particularly in improving accessibility to complex computer science topics for undergraduate students. She has contributed to frameworks such as Ice for binary analysis, eMOTE for online collaboration, and visualization tools like HiLPR for pattern representation. Her publications (2004–2011) explore concurrency solutions, abstraction layers, and educational innovations. Notable areas include parallel pattern languages, virtual machine architecture, and automation strategies in system design. While no specific awards are listed, her contributions to teaching and infrastructure software reflect impactful academic engagement. In outreach, she coordinates initiatives like SPARCS (Supporting Sustainable and Integrated Outreach Activities) to inspire young minds in STEM fields. Her advising and grant activities are not explicitly detailed in available records.

Kui Wuمشاهده پروفایل

استاد

• Computer Networks
• Wireless and Mobile Networking
• Mobile Computing
• +۵ مورد دیگر

Prof. Kui Wu is a Professor in the Department of Computer Science at the University of Victoria, affiliated with the Faculty of Engineering and Computer Science. His research focuses on computer networks, wireless and mobile networking, mobile computing, and network security. He is part of the Parallel, Networking and Distributed Computing (PANDA) research group. Key areas of expertise include distributed learning frameworks, autonomous systems, IoT anomaly detection, and edge computing architectures. His work integrates machine learning techniques with network optimization, addressing challenges in real-time systems, security, and resource allocation. Notable contributions include advancements in federated learning, privacy-preserving distributed systems, and UAV-based monitoring solutions. Prof. Wu's research also explores edge computing innovations, such as smart contract-aided IoT resource sharing and energy-efficient edge data centers. He has contributed to over 50 peer-reviewed publications, with recent work emphasizing AI-driven network design, anomaly detection in IoT, and reinforcement learning applications in autonomous driving safety. His research has practical implications for improving the reliability and efficiency of next-generation communication and computing infrastructures.

Yasaman Amannejadمشاهده پروفایل

دانشیار

• Machine Learning
• Data Analytics
• Cloud Computing
• +۷ مورد دیگر

Yasaman Amannejad is an Associate Professor in the Department of Mathematics and Computing at Mount Royal University (MRU), Faculty of Science. She holds a PhD in Software Engineering from the University of Calgary (2017) and an MSc/BSc in Computer Information Technology from Amirkabir University of Technology (2011/2008). Her research focuses on applying machine learning to healthcare diagnostics, performance analysis of cloud and edge computing systems, and addressing social challenges like domestic violence and homelessness. Her work has been funded by NSERC, Petro-Canada, and the New Frontiers in Research Fund. Education: PhD in Software Engineering, University of Calgary (2017) MSc in Computer Information Technology, Amirkabir University of Technology (2011) BSc in Computer Information Technology, Amirkabir University of Technology (2008) Research Interests: Machine Learning for Healthcare (e.g., tropical disease diagnosis, Multiple Myeloma cancer) Performance Optimization in Cloud/Edge Systems Resource-Constrained Device Learning (wearables, IoT) Social Impact Technologies (domestic violence detection, homelessness solutions) Awards & Grants: NSERC Discovery Grant (2020) New Frontiers in Research Fund-Exploration (2020) Petro-Canada Young Innovator Award (2019) Multiple Teaching Awards (2015-2016) Teaching & Industry: Incorporates industry experience into courses, emphasizing real-world applications Outstanding Teaching Performance Award (2016) Over 5 years of industry experience in software engineering Labs/Teams: Part of interdisciplinary teams at MRU's Faculty of Science Collaborates on NSERC-funded projects

Akramul Azimمشاهده پروفایل

دانشیار

• Real-Time Systems
• Embedded Software
• Safety-Critical Systems
• +۵ مورد دیگر

Dr. Akramul Azim is an Associate Professor in the Department of Electrical, Computer and Software Engineering at Ontario Tech University, Faculty of Engineering and Applied Science. His research focuses on real-time systems, embedded software, safety-critical systems, IoT, software verification/validation, and applied machine learning. He leads the Real-Time Embedded Software Research (RTEMSOFT) Lab and has contributed to over 50 peer-reviewed publications. His work spans cybersecurity for automotive systems, edge computing optimization, and AI-driven software testing. Education: PhD (Electrical and Computer Engineering), University of Waterloo (2014) MSc (Computer Science and Engineering) (2009) BSc (Computer Science and Information Technology) (2006) Research Interests: Dr. Azim explores the intersection of real-time systems and machine learning, particularly in embedded environments. Key areas include: Autonomous vehicle safety Edge/IoT security Optimized task scheduling ML-enhanced software testing FPGA vulnerability mitigation Recent Research Trends: His publications (2023-2025) highlight advancements in LLM-based code optimization, lock contention mitigation in Java, energy-efficient vehicular networks, and safety-critical AI applications. Notable contributions include frameworks for situation-aware adaptation in ITS and reinforcement learning for edge task scheduling. Labs/Teams: Directs the RTEMSOFT Lab, collaborating on projects like AD-CGAN for anomaly detection and ESOps agile pipeline development.