جست‌وجوی استاد و پژوهشگر

Haoxiang Langمشاهده پروفایل

دانشیار

• Mechatronics
• Autonomous Robotics
• Visual Servoing
• +۳ مورد دیگر

Haoxiang Lang serves as Department Chair and Associate Professor in the Department of Automotive and Mechatronics Engineering at Ontario Tech University's Faculty of Engineering and Applied Science. His laboratory, GRASP Lab, focuses on advanced robotics and intelligent systems research. Education: PhD in Mechanical Engineering, University of British Columbia (2013) MASc in Mechanical Engineering, University of British Columbia (2008) BSc in Marine Engineering, Ningbo University (2003) Dr. Lang's research spans mechatronics, autonomous robotics, visual servoing, and machine learning. His work focuses on developing advanced control systems for robotic manipulation, autonomous vehicle navigation, and sensor fusion techniques. Recent projects include vision-based combat vehicle control, deep learning for aerial image processing, and thermal management for robotic systems. His publications demonstrate strong emphasis on robotic perception-control integration, with recurring themes in visual servoing, sensor fusion, autonomous navigation, and machine learning applications in robotics. Recent works increasingly incorporate deep neural networks for complex tasks like object manipulation and environment mapping. Dr. Lang leads the GRASP Lab, which develops experimental platforms including autonomous combat vehicles and robotic manipulators. The lab focuses on practical implementations of control theories and collaborates on industrial applications.

• Programming Languages
• Concurrency
• Embedded Systems
• +۴ مورد دیگر

Dr. Emil Sekerinski is a Professor in the Department of Computing and Software at McMaster University. He holds the position of Undergraduate Advisor for Computer Science (Jan 1, 2025 – June 30, 2025). His expertise spans programming logic (verification, refinement), concurrency (components, implementation), embedded systems (modelling, analysis), and programming languages (run-time, compilation). He is actively involved in research clusters focused on Digital & Smart Systems. His research interests emphasize formal methods in computer science, concurrency theory, parallel algorithms, and embedded systems design. He has contributed to advancing methodologies for software quality assurance and system reliability through formal specifications and automated verification techniques. Recent work includes efficient synchronization mechanisms for object-oriented programming languages and formal development of delay-tolerant multicast protocols for wireless sensors. Dr. Sekerinski has organized and participated in major conferences such as FMTea'24 (Co-Chair) and ICTAC'24 (Program Committee). His teaching includes advanced courses like CAS 781 Advanced Topics: Advanced Compiler Design and Optimization and SFWR ENG 3BB4 Concurrent System Design . He maintains an active presence in academic outreach through LinkedIn, Instagram, and other platforms. His work bridges theoretical foundations with practical applications, including environmental sensor data analysis and holistic embedded system development. Despite no explicit mention of awards, his contributions to formal methods education and research are widely recognized in his field.

Gabriela Nicolescuمشاهده پروفایل

استاد

• Cybersecurity for Avionics
• Security by Design for Cyber-Physical Systems
• Advanced Programming Models for Avionic Multi and Many-Core Systems
• +۱۱ مورد دیگر

Gabriela Nicolescu is a Professor at Polytechnique Montréal , affiliated with the Department of Computer Engineering and Software Engineering and the LAMA-WeST (Web, Semantics and Text) research group. Her work focuses on design methodologies , programming , and security for advanced systems integrating 3D SoCs , optical networks , and heterogeneous technologies . Education: B.Sc. and M.Sc., Politehnica Bucharest Ph.D., Institut National Polytechnique de Grenoble (2002), awarded Best Thesis in Microelectronics Research Interests include cybersecurity for avionics , security by design for cyber-physical systems , advanced programming models for multi-core systems , and modeling/simulation of heterogeneous systems . She has contributed to fields like optical networks on chip , real-time embedded systems , and edge computing security . Recent Publications highlight her leadership in all-optical neural networks , formal verification , and security for avionic systems . Her work spans machine learning-driven intrusion detection , photonic integrated circuits , and ontology-based cybersecurity frameworks . Scientific Awards: Best Thesis in Microelectronics, Institut National Polytechnique de Grenoble (2002) Student Supervision encompasses doctoral and master’s theses in areas such as multi-core RTOS , security in autonomous systems , AI-based penetration testing , and performance optimization in avionics . Current advisees include Felipe Magalhaes , Julien Carayol , and Kacem Khaled , while completed supervision includes A. S. Shahnejat Bushehri and Rami Zrelli . She is actively involved in collaborative research projects addressing national cybersecurity priorities and safer aircraft design , as highlighted in media outlets like The Globe and Mail and Science-Presse .

Aastha Mehtaمشاهده پروفایل

استادیار

• Systems Security
• Data Privacy
• Operating Systems
• +۳ مورد دیگر

Aastha Mehta is a Tenure-Track Assistant Professor in the Computer Science Department at the University of British Columbia (UBC), affiliated with the Systopia Lab and the Security and Privacy Group. Her research focuses on systems security, data privacy, and distributed systems, particularly in cloud and edge platforms. She teaches courses such as CPSC 538M (Systems Security) and CPSC 317 (Introduction to Computer Networking). Research Interests: Her work spans systems security, network privacy, ICS security, and privacy-preserving technologies. Key projects include compliance tools for serverless applications (Growlithe), network side-channel mitigation (NetShaper), and epidemic risk mitigation systems leveraging Bluetooth beacons. Received NCC Funding for ICS security research (2024) Faculty Teaching Awards (UBC) Advising: Currently supervises PhD and MSc students like Yayu Wang, Angela DeMarco, and Satvik Vemuganti. Collaborates with researchers from MPI-SWS, UofT, and UBC ECE on security and privacy projects. Service: Serves on program committees for CCS 2025, ASPLOS 2025, and Oakland S&P. Organized mentoring programs at SOSP and OSI.

Garnet Hertzمشاهده پروفایل

دانشیار

• DIY culture
• Electronic art
• Critical making
• +۷ مورد دیگر

Garnet Hertz is an Associate Professor at Emily Carr University of Art + Design, holding the Canada Research Chair in Design and Media Arts. His work bridges critical making, electronic art, and interdisciplinary design research through the Studio for Critical Making. With a global presence in venues like Ars Electronica, SIGGRAPH, and DEAF, Hertz has shaped discourse on DIY culture and sociotechnical critique since the 2000s. Canada Research Chair (Tier 2) in Design and Media Arts Director of Studio for Critical Making (Room C4246) Teaching at Emily Carr since 2014 with 0/1 teaching load His research establishes critical making as a framework for material inquiry through projects like Slow Game (2018 CHI paper), Disobedient Electronics (2017), and Art + DIY Electronics (MIT Press, 2023). This work interrogates technological temporality, corporate blackboxing, and post-optimal design across 15+ countries. Recent collaborations include doctoral advising at RMIT (School of Design), RPI (Electronic Arts), and committee roles at Technical University of Berlin. His 2008 Oscar Signorini Award in robotic art recognizes technical-artistic innovation, while publications in Current , INC Readers , and Aarhus Conference Proceedings establish theoretical foundations for material speculation in design research. Studio activities at Emily Carr include: Graduate Design Studio III (GSMD 600) - 2025 Critical Making (DESN211) - 2016 Open Studio events - Speculative Design (2018), Post-Optimal (2016)

Andrew Boutrosمشاهده پروفایل

استادیار

• Reconfigurable Computing
• Field-Programmable Gate Array (FPGA) Architecture
• Computer-Aided Design (CAD) Tools
• +۴ مورد دیگر

Andrew Boutros is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Waterloo. His research focuses on reconfigurable computing architectures, FPGA design, and domain-specific acceleration for deep learning and datacenter workloads. He holds a PhD from the University of Toronto (2024) and has worked at Intel Labs and MangoBoost. Education: PhD, Electrical and Computer Engineering, University of Toronto, Canada (2024) MASc, Electrical and Computer Engineering, University of Toronto, Canada (2018) BSc, Electronics Engineering, German University in Cairo, Egypt (2016) Research Interests: Developing efficient FPGA architectures and CAD tools for reconfigurable hardware, domain-specific acceleration, and application/hardware co-design. Specific areas include 3D-stacked acceleration devices, graph neural network acceleration, and FPGA-based smart NICs for AI training. Publications: Over 30 papers in top venues like IEEE FPL, FCCM, and FPGA conferences, with 4 best paper awards. Recent work includes 3D-stacked architectures, graph neural network inference, and FPGA optimization for deep learning. Awards: Best Paper Award (FPT 2023) Best Paper Award (ICM 2021) Stamatis Vassiliadis Best Paper Award (FPL 2018) Advising/Grants: Currently accepting graduate students. Collaborations include 84 co-authors and industry partnerships with Intel and MangoBoost.

Hai Wangمشاهده پروفایل

استاد

• Big Data
• Business Analytics
• Business Intelligence
• +۵ مورد دیگر

Hai Wang serves as Professor in the Department of Finance, Information Systems, and Management Science at Saint Mary's University's Sobey School of Business, with an additional Adjunct Professor appointment in Dalhousie University's Faculty of Computer Science. His career progression from Assistant Professor (2004) to Associate Professor (2010s) to current full Professor demonstrates sustained academic impact within Canadian business education. His educational foundation includes: B.Sc. in Computer Science, University of New Brunswick (1995) M.Sc. in Computer Science, University of Toronto (1997) Ph.D. in Computer Science, University of Toronto (2004) Professor Wang's research integrates technical data science with business applications , featuring core interests in Big Data, Business Intelligence, and Knowledge Management. His work emphasizes practical implementation frameworks for enterprises, particularly focusing on Shared Services optimization and SME data analytics , while maintaining strong connections to Machine Learning and Database Management methodologies. This interdisciplinary approach bridges computer science rigor with business decision-making needs. Analysis of his recent publications (2020-2023) reveals three dominant trajectories: (1) Knowledge Management applications in cybersecurity and small business contexts, (2) Pedagogical innovations in database and business analytics education (including no-code approaches), and (3) Predictive analytics for shared services operations. His work consistently demonstrates translational value from academic research to business practice. His scientific recognition includes: Decision Sciences Journal of Innovative Education Best Teaching Brief Award (2011) Research funding from NSERC supports his ongoing work, while his teaching portfolio spans foundational courses like Business Applications Programming and advanced topics including Business Intelligence and Data Visualization. His mentorship extends through course-based projects and textbook authorship, though formal doctoral advisees aren't documented in public profiles. Current course offerings (2023-2024) confirm active classroom engagement across undergraduate, MBA, and Master's programs. Professor Wang maintains significant scholarly output through textbook development and conference leadership, with research increasingly addressing real-world challenges in business data ecosystems while advancing information systems education methodologies.

Amine Mezghaniمشاهده پروفایل

استادیار

• Wireless Communication
• Digital Signal Processing
• Communication Systems
• +۱۲ مورد دیگر

Dr. Amine Mezghani is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Manitoba's Price Faculty of Engineering. His research focuses on wireless communication systems, signal processing, and antenna engineering, particularly under hardware constraints like 1-bit quantization and phase-only DACs. Education: PhD (2015) in Electrical Engineering, Technical University of Munich; Dipl.-Ing. (Electrical Engineering), Technical University of Munich; Diplôme d'Ingénieur, École Centrale Paris His work explores sustainable wireless infrastructure, with emphasis on reconfigurable intelligent surfaces (RIS), millimeter-wave Massive MIMO, V2X communications, and physically consistent modeling combining Shannon and Maxwell theories. Publications include over 100 papers on hardware-constrained systems. Scientific contributions have earned him the 2021 IEEE Signal Processing Society Best Paper Award and the 2016 Rohde & Schwarz Outstanding Dissertation Award . He serves as an Associate Editor for IEEE Transactions on Wireless Communications . Current research includes electromagnetic consistency in multi-band MIMO, unlabeled compressed sensing, and VAMP-based algorithms. He advises graduate students in wireless communications and signal processing at the Intelligent Communication and Sensing Lab .

Mark Eramianمشاهده پروفایل

استاد

• Image Processing
• Medical Image Analysis
• Image Segmentation
• +۵ مورد دیگر

Mark Eramian is a Professor in the Department of Computer Science at the University of Saskatchewan. His research focuses on image processing, medical image analysis, and computer vision, with significant contributions to plant phenotyping and medical diagnostics. He leads a lab emphasizing equity, diversity, and inclusivity, outlined in the Lab Code of Conduct . His work spans applications in agriculture, such as automated plant trait analysis using UAV and multispectral imaging, and medical imaging, including thyroid nodule classification and ovarian follicle segmentation. He collaborates with organizations like the Canadian Food Inspection Agency and is part of the Plant Phenotyping and Imaging Research Centre (P2IRC), funded by the Canada First Research Excellence Fund. Eramian teaches courses in image processing, computer vision, and algorithms. His lab actively develops open-source tools for image analysis, including texture enhancement methods and segmentation algorithms. Current projects include self-supervised learning for phenotyping and semi-automated seed classification. Prospective students are encouraged to apply through the university's graduate program, selecting 'Computer Vision and Image Processing' as their focus area. His lab emphasizes collaboration, learning, and ethical conduct, with ongoing work in phenomics and medical imaging technologies.

Sebastian Magierowskiمشاهده پروفایل

دانشیار

• Semiconductors
• Electronics
• Bioinformatics
• +۵ مورد دیگر

Sebastian Magierowski is an Associate Professor in the Department of Electrical Engineering & Computer Science at York University's Lassonde School of Engineering. He holds a P.Eng designation and has held faculty positions at the University of Calgary (2004–2012) and York University since 2013. His research focuses on semiconductor systems, bioinformatics, and artificial intelligence, with particular emphasis on energy-efficient analog/digital chips, CAD/EDA tools, and applications like mobile DNA sequencing and DNA-based data storage. Dr. Magierowski's academic journey includes a PhD in Electrical Engineering from the University of Toronto (2004). His work bridges electrical engineering and biomedical innovation, with contributions to CMOS-based nanopore DNA sequencing systems, capacitive sensors for DNA storage monitoring, and FPGA-accelerated bioinformatics tools. His research also explores hardware acceleration for embedded systems and novel sensing technologies for point-of-care diagnostics. Key research themes include: Development of semiconductor-based biosensors (e.g., CMOS nanopore systems) Design of compact, portable diagnostic devices Integration of AI and machine learning in bioinformatics pipelines Advances in DNA data storage and retrieval mechanisms His recent publications highlight innovations in low-power circuits for DNA sequencing, thermo-FET sensing systems, and embedded RISC-V multicore architectures for miniature devices. While no awards are explicitly listed, his extensive publication record reflects impactful contributions to interdisciplinary engineering and biomedical research. Dr. Magierowski's work emphasizes cross-disciplinary collaboration, with applications spanning healthcare, environmental monitoring, and data storage. His research group focuses on translating theoretical advancements into practical, scalable technologies for emerging biomedical and computational challenges.

Mostafa Tahaمشاهده پروفایل

استادیار

• Security of Embedded Systems
• Internet of Things (IoT)
• Implementation Attacks and Countermeasures
• +۳ مورد دیگر

Mostafa Taha is an Assistant Professor at Carleton University's Systems and Computer Engineering Department within the Faculty of Engineering and Design. He holds a PhD from Virginia Tech (2014) and has held postdoctoral roles at Worcester Polytechnic Institute and Western University. His research focuses on securing embedded systems and IoT against implementation attacks, including side-channel analysis, leakage-resilient designs, and cryptographic algorithm hardening. He has contributed to projects like LR-Keymill, a keystream generator with inherent SCA resistance, and has served on program committees for conferences such as DATE and SPACE. Education includes a PhD in Computer Engineering (Virginia Tech), M.Sc. in Electrical Engineering (Assiut University), and B.Sc. in Electrical Engineering (Assiut University). His work spans hardware security, cryptographic implementations, and vehicular networks. Awards include Egyptian Government and Assiut University scholarships for his PhD and M.Sc., respectively. Research interests emphasize quantifying side-channel resistance, leakage-resilient architectures, and lightweight cryptographic implementations. Notable publications include work on AES S-box optimization, threshold implementations for SIMON, and quantifying masking strength in software. He teaches courses on cryptographic implementations, computer systems design, and digital systems at Carleton. Publications reflect a focus on hardware-oriented security, with contributions to conferences like CHES, FDTC, and HOST. His work on LR-Keymill demonstrates a novel approach to SCA resistance without explicit countermeasures, achieving 67.9-bit post-attack complexity. Awards and industrial connections highlight his impact in embedded system security.

Xiaodong Liangمشاهده پروفایل

استاد

• Power systems dynamics
• Renewable energy integration
• Synchrophasor technology and smart grids
• +۴ مورد دیگر

Dr. Xiaodong Liang is a Professor in the Department of Electrical and Computer Engineering at the University of Saskatchewan, holding the prestigious Canada Research Chair in Technology Solutions for Energy Security in Remote, Northern, and Indigenous Communities. His research focuses on critical power system challenges with direct applications to remote community energy security. Education: Bachelor of Engineering (BEng) Master of Engineering (MEng) Master of Science (MSc) Doctor of Philosophy (PhD) Dr. Liang's research spans power systems dynamics, renewable energy integration, synchrophasor technology, smart grids, power electronics applications, and protection systems. His work addresses grid stability in renewable-rich environments, microgrid operation in remote areas, and corrosion issues in transmission infrastructure. Recent publications emphasize practical solutions for northern and indigenous communities, including inverter-based microgrid protection and machine learning applications for fault detection. Analysis of his 2023-2025 publications reveals dominant themes in microgrid protection (particularly for inverter-based systems), renewable integration challenges, advanced control techniques, and corrosion modeling. His work consistently targets remote community applications, with strong emphasis on real-time simulation validation using OPAL-RT platforms and practical field implementation. Scientific Awards: Canada Research Chair in Technology Solutions for Energy Security in Remote, Northern, and Indigenous Communities As a Canada Research Chair holder, Dr. Liang leads significant federally funded research addressing energy security gaps in remote communities. His program involves collaborations with indigenous groups, northern communities, and industry partners to develop deployable technology solutions. His lab utilizes advanced real-time simulation capabilities for testing microgrid control strategies and protection schemes under realistic conditions. Dr. Liang's research group operates within the University of Saskatchewan's Engineering Building, utilizing specialized power systems laboratories equipped with OPAL-RT real-time simulators for hardware-in-the-loop testing of microgrid components and protection systems. Current work focuses on enhancing resilience of remote microgrids through AI-driven control and novel protection architectures.

Ulrike Stegeمشاهده پروفایل

دانشیار

• Computational Biology
• Parameterized Complexity
• Design of Heuristics
• +۴ مورد دیگر

Ulrike Stege is an Associate Professor and Director of the Master of Engineering in Applied Data Science (MADS) at the University of Victoria's Faculty of Engineering and Computer Science. She holds a PhD from the Swiss Federal Institute of Technology (ETH Zurich). Her research spans computational biology, parameterized complexity, algorithm design, graph theory, and cognitive psychology. She leads initiatives in quantum computing frameworks and educational tools, including projects like SCOOP (quantum optimization) and QGrover (quantum algorithm visualization). Her work also addresses RNA pseudoknot structure prediction and the integration of quantum computing into combinatorial optimization software. Key educational contributions include developing browser-based quantum learning tools (e.g., QNotation and QuantumCrypto) and promoting computational thinking in K-12 education. Her research bridges theoretical computer science with practical applications in biology and quantum systems, emphasizing algorithmic innovation and interdisciplinary collaboration. Her publications focus on advancing quantum computing frameworks, optimizing bioinformatics algorithms, and creating accessible educational resources. Recent work addresses quantum annealing for constrained optimization, structural biochemistry of viral RNA, and hybrid quantum-classical problem-solving methods.

• Machine Learning
• Computer Vision
• Artificial Intelligence
• +۵ مورد دیگر

Eugene Belilovsky is an Assistant Professor in the Department of Computer Science and Software Engineering at Concordia University. His research focuses on machine learning, computer vision, optimization algorithms, and their applications in medical imaging and federated learning. He explores topics such as continual learning, model merging, and bias mitigation in deep learning systems. His work bridges theoretical advancements with practical applications, including healthcare diagnostics and efficient training strategies for large models. Belilovsky's recent publications emphasize innovative methods like MuLoCo for inner optimizer design, FairDropout for enhancing minority group generalization, and techniques to scale multi-task learning with sparse masks. His studies on federated learning explore incentive mechanisms for decentralized systems and robust pre-training of large language models. His contributions to 3D reconstruction and motion prediction highlight interdisciplinary applications in computer graphics and generative models. His research trends reflect a strong emphasis on improving model efficiency, fairness, and adaptability across diverse domains. While no scientific awards are explicitly listed, his prolific publication record underscores his impactful contributions to AI and machine learning.

Thomas Fevensمشاهده پروفایل

استاد

• Deep Learning
• Medical Image Processing
• Computer-Aided Medical Diagnosis
• +۵ مورد دیگر

Thomas G. Fevens is a Professor in the Department of Computer Science and Software Engineering at Concordia University, and an Adjunct Professor in the Department of Surgery at McGill University. He holds a PhD in Computing and Information Science from Queen's University (1999), with prior degrees in Astrophysics and Physics. His research focuses on medical imaging applications of deep learning, computational geometry, and surgical software innovation. Notable work includes computer-aided diagnosis systems for breast cancer using cytological images, 3D surgical planning algorithms, and motion tracking frameworks for ACL injury assessment. Education: PhD in Computing and Information Science, Queen's University (1999) MSc in Computing and Information Science, Queen's University (1994) MSc in Physics (General Relativity), Queen's University (1993) BSc in Astrophysics (Honours), Queen's University (1990) Research emphasizes interdisciplinary applications of AI in healthcare, including: Medical image segmentation using U-Net architectures Deep learning for tumor localization Kinect-based motion analysis systems Generative adversarial networks (GANs) for medical imaging Publications demonstrate contributions to both core computer science (mobile ad hoc networks, computational geometry) and clinical applications (surgical planning, cancer diagnosis). Active in industry collaboration through the ML-MVP lab, focusing on translating AI research into clinical tools.