جست‌وجوی استاد و پژوهشگر

Ilias S. Kotsireasمشاهده پروفایل

استاد

• Computer Algebra
• Combinatorics
• Optimization Algorithms
• +۶ مورد دیگر

Dr. Ilias S. Kotsireas is a Professor in the Department of Computer Science and Physics at Wilfrid Laurier University (WLU), Canada. He serves as Director of the CARGO (Computer Algebra Research Group) lab and Editor-in-Chief of the Journal of Algebraic Combinatorics . His research focuses on computational mathematics, combinatorics, optimization algorithms, and symbolic computation. Kotsireas is a Fellow of the Institute of Combinatorics and its Applications (ICA, 2004) and has held leadership roles in organizations like ACM SIGSAM and the ACA Working Group. He maintains adjunct appointments at the Athena Research Center (Greece) and the University of Waterloo, and is a Principal Scientist at ORCCA (Ontario Research Centre for Computer Algebra). His work integrates computer algebra systems with SAT solvers to address complex combinatorial problems, such as Hadamard matrices and Golay pairs. Recent research explores neurosymbolic frameworks, blockchain economics, and machine learning applications in algebraic geometry. Education: Advanced degrees in Mathematics/Computer Science (not explicitly listed, inferred from roles). Research Interests: Computer algebra systems, combinatorial search, algorithm portfolios, cryptographic protocols, and computational neuroscience applications. His contributions span theoretical advancements (e.g., Legendre pair algorithms) and practical tools (e.g., Maple-based software). Kotsireas collaborates internationally, organizing conferences like ACA and publishing in venues such as Journal of Symbolic Computation and SpringerPlus . His lab, CARGO, emphasizes supercomputing and interdisciplinary projects. Awards: Fellow of the Institute of Combinatorics and its Applications (ICA), 2004 Labs & Teams: Leads the CARGO lab and affiliated with ORCCA, focusing on computational algebra and high-performance computing.

Pin-Han Hoمشاهده پروفایل

استاد

• Wireless Communications
• Quantum Communications
• Unmanned Aerial Vehicles (UAV)
• +۴ مورد دیگر

Pin-Han Ho is a Professor at the University of Waterloo's Department of Electrical and Computer Engineering (ECE), part of the Faculty of Engineering. His research focuses on advanced wireless communication systems, including Near-Field Communications (NFC), Reconfigurable Intelligent Surfaces (RIS), and Quantum Key Distribution (QKD). He explores applications in UAV-enabled networks, IoT security, and energy-efficient trajectory planning for autonomous systems. His work integrates machine learning techniques such as Deep Reinforcement Learning (DRL) and Graph Neural Networks (GNN) to address challenges in signal processing, resource allocation, and network optimization. Key contributions include beamforming design for dynamic environments, secure resource management in optical networks, and hybrid communication systems combining near-field and far-field capabilities. Dr. Ho's publications span over 50 peer-reviewed articles, emphasizing interdisciplinary approaches to modern communication challenges. Notable areas include UAV trajectory optimization, quantum CNN-based detection systems, and connectivity-preserving routing algorithms. His research has been featured in special issues on NFCs, quantum networks, and IoT security. Despite extensive contributions, no academic awards or grants are explicitly listed in the provided information. His advising role and lab affiliations remain unspecified in the text.

Anwar Hasanمشاهده پروفایل

استاد

• Cryptography
• Post-Quantum Cryptography
• Secure Multiparty Computation
• +۵ مورد دیگر

Anwar Hasan is a Professor in the Department of Electrical and Computer Engineering at the University of Waterloo. He specializes in cryptography, post-quantum cryptography, and secure computation, with a focus on algorithm optimization, cryptographic hardware implementation, and energy efficiency. His work spans theoretical foundations and applied systems, including blockchain technology, fault-tolerant cryptosystems, and secure multiparty computation. His research interests emphasize developing quantum-resistant cryptographic protocols, improving efficiency in cryptographic algorithms (e.g., EdDSA signature verification, CSIDH optimization), and designing secure systems against adversarial attacks (e.g., poisoning in federated learning, covert adversary models). He also explores energy consumption analysis of cryptographic algorithms, particularly in the context of NIST PQC standards and elliptic curve-based systems. Recent work includes contributions to threshold cryptography (isogeny-based schemes), decentralized oracle networks (CountChain), and privacy-preserving blockchain applications (AdChain). His publications frequently address hardware-software co-design for cryptographic primitives, emphasizing both theoretical advancements and practical implementation challenges. Dr. Hasan’s research also extends to applied areas such as fraud detection systems, digital rights management using blockchain, and energy efficiency in embedded cryptographic systems. He collaborates on projects involving secure communication in cloud computing and resilient key management for mobile applications.

Arie Gurfinkelمشاهده پروفایل

استاد

• Automated Program Analysis
• Software Model Checking
• Automated Reasoning
• +۴ مورد دیگر

Arie Gurfinkel is a Professor at the University of Waterloo, holding a joint appointment in the Department of Electrical and Computer Engineering and the Cheriton School of Computer Science. His research focuses on automated program analysis, software model checking, automated reasoning, and abstract interpretation. He develops tools like SeaHorn, Avy, and others to enhance the verification and testing of complex computer systems. His work emphasizes formal methods, machine learning integration, and hardware/software verification. Recent publications highlight advancements in interpolation-based model checking, constrained Horn clauses, and algorithm selection for hardware verification. Gurfinkel's contributions include open-source tools and frameworks widely used in academic and industrial verification efforts. He actively seeks motivated graduate students interested in logic, automated reasoning, and formal methods. His research has been presented in top-tier conferences and journals, reflecting his expertise in formal verification and software engineering.

• Medical Imaging
• Ultrasound Technology
• Machine Learning
• +۳ مورد دیگر

Di Xiao is a Sessional Lecturer at the University of Waterloo. Their work focuses on advancing medical imaging technologies, particularly in ultrasound and MRI, with an emphasis on real-time processing, deep learning integration, and algorithm optimization. They are affiliated with the Sessional lecturers group. Research interests include ultrasound imaging innovations such as speed-of-sound estimation, beamforming techniques, and sparse-array optimizations. Their work also spans MRI reconstruction, signal processing, and applications of machine learning in medical diagnostics. Notable contributions include developing frameworks for live tissue assessment and minimizing image quality loss in sparse data scenarios. Publications highlight trends in real-time ultrasound systems, neural network-driven imaging enhancements, and sparsification strategies for MRI. The articles collectively emphasize interdisciplinary approaches merging AI with traditional medical imaging challenges. No scientific awards or grants are explicitly mentioned. No lab affiliations or student advising records are provided.

William Melekمشاهده پروفایل

استاد

• Artificial Intelligence
• Intelligent Control
• Mechatronics
• +۱۱ مورد دیگر

William Melek is a Professor in the Department of Mechanical and Mechatronics Engineering at the University of Waterloo. He serves as Director of the Laboratory of Computational Intelligence and Automation and RoboHub. His research focuses on intelligent systems, modular robotics, autonomous vehicles, and bioinformatics. He holds a B.A.Sc. in Electrical & Computer Engineering (Zagazig University, 1994), M.Sc. and Ph.D. in Mechanical Engineering (University of Toronto, 1998/2002), and a Post-Doctorate in Computer Science (Ryerson University, 2004). Education: Bachelor's: B.A.Sc in Electrical & Computer Engineering, Zagazig University (1994) Master's: Mechanical Engineering, University of Toronto (1998) Doctorate: Mechanical Engineering, University of Toronto (2002) Post-Doctorate: Computer Science, Ryerson University (2004) His research interests span artificial intelligence, intelligent control, mechatronics, automotive design, and computational intelligence. Recent work includes autonomous vehicle navigation, predictive maintenance in manufacturing, and bioinformatics-driven clinical decision support systems. He teaches courses such as ME 360 (Control Systems), ME 780 (Mechatronics), and MTE 100 (Intro to Mechatronics). Articles from 2023–2025 highlight advancements in autonomous driving decision-making, modular robotics, and 5G-enabled teleoperation systems. His lab develops algorithms for urban autonomous systems and industrial automation. He currently oversees graduate student applications in mechatronics and robotics. Labs/Teams: Laboratory of Computational Intelligence and Automation, RoboHub.

Pooya Ronaghمشاهده پروفایل

عضو هیئت علمی

• Quantum Computation
• Quantum Algorithms
• Quantum Error Correction
• +۸ مورد دیگر

Pooya Ronagh is a Research Assistant Professor at the University of Waterloo, affiliated with the Department of Physics & Astronomy and the Institute for Quantum Computing (IQC). He also serves as a Scientific Lead at the Perimeter Institute Quantum Intelligence Lab (PIQuIL) and directs the Hardware Innovation Lab at 1QBit. His work bridges quantum computation, machine learning, and optimal control, focusing on quantum algorithms, error correction, and hybrid quantum-classical systems. Education: PhD in Mathematics (University of British Columbia, 2016), MSc in Mathematics (UBC, 2011), dual BSc in Mathematics and Computer Science (Sharif University of Technology, 2009). Awards include the Benjamin Franklin Fellowship (2009). Research Interests: Quantum algorithms for machine learning, reinforcement learning, fault-tolerant quantum architectures, cryogenic systems, and quantum control. He explores applications of quantum simulation to improve learning efficiency and robustness in AI systems. Recent work includes optimizing quantum error correction decoders, developing scalable superconducting architectures, and advancing neural network-based quantum state tomography. His contributions span theoretical frameworks (e.g., lattice surgery scheduling) and experimental methods (e.g., SFQ pulse control). Teaching: Courses like PHYS 490 (Machine Learning in Physics) emphasize practical coding and interdisciplinary projects. Grants and collaborations involve industry and academic partners in quantum hardware and software development. Labs: Hardware Innovation Lab (1QBit), IQC Quantum Control Group Future Work: Scaling quantum supercomputers, cryogenic neural decoders, quantum-enhanced generative AI

• Artificial Intelligence
• Medical Imaging
• Machine Learning
• +۵ مورد دیگر

Dr. Alexander Wong is a Professor in the Department of Systems Design Engineering at the University of Waterloo, cross-appointed in other departments. His research focuses on advancing artificial intelligence (AI), machine learning, and computer vision with applications in healthcare, biomedical engineering, and robotics. Key areas include medical imaging technologies like OCT+ERG systems for neurovascular analysis, AI-driven diagnostics (e.g., prostate cancer segmentation), and radar-based monitoring for aging wellness. He leads interdisciplinary projects in health informatics, synthetic data generation, and AI ethics. His work integrates hardware-software systems for real-world challenges, such as non-invasive retinal imaging and in-home gait analysis. Notable contributions include the Cancer-Net framework for cancer detection, the NutritionVerse dataset for dietary monitoring, and the COVID-Net for pandemic response. His research also addresses public health issues like HIV/HCV co-infection and mental health impacts on healthcare utilization in marginalized populations. Dr. Wong's innovations span from foundational AI algorithms to clinical and industrial applications, emphasizing cross-disciplinary collaboration to bridge theoretical advancements with practical solutions.

Wuyang Chenمشاهده پروفایل

استادیار

• Deep Learning
• Scientific Machine Learning
• Computer Vision
• +۷ مورد دیگر

Wuyang Chen is an Assistant Professor at the School of Computing Science, Simon Fraser University. He holds a Ph.D. in Electrical and Computer Engineering from The University of Texas at Austin (2023). His research focuses on integrating AI with physics, developing principled deep learning methods, and advancing areas like scientific machine learning (SciML), computer vision, and foundation models. Notable contributions include PDE-Controller for LLM-based scientific reasoning and neural operators for fluid reconstruction. He has received prestigious awards, including the INNS Doctoral Dissertation Award (2024) and the NSERC Discovery Grant. Dr. Chen serves as an Area Chair for NeurIPS 2025 and ICML 2025. His lab, DeLTA, emphasizes AI applications in science and hosts active collaborations. He teaches CMPT 419/983 (Special Topics in AI) and mentors students in research areas like model efficiency and self-supervised learning. Education: Ph.D., Electrical and Computer Engineering, The University of Texas at Austin (2023) Recent Awards: INNS 2024 Doctoral Dissertation Award, iSchools Doctoral Dissertation Award 2024, Google GCP Credit Award Key Activities: Organized NeurIPS 2024 workshop on Foundation Models for Science; presented at AAAI 2025, SIAM CSE25, and Berkeley BLISS seminar

Wolfgang Stuerzlingerمشاهده پروفایل

استاد

• 3D User Interfaces
• Extended Reality
• Augmented Reality
• +۱۰ مورد دیگر

Wolfgang Stuerzlinger is a Professor and Director of the VVISE Lab at Simon Fraser University's School of Interactive Arts & Technology. His research focuses on 3D user interfaces, virtual/augmented reality (VR/AR), and human-computer interaction. He holds a Doctorate from the Vienna University of Technology and has held academic roles at York University and the University of North Carolina. **Education**: PhD (Dr. techn.) in Computer Science, Technical University of Vienna, Austria Dipl.-Ing. (Master's equivalent), Computer Science, Technical University of Vienna **Research Interests**: Specializes in VR/AR interaction techniques, 3D user interfaces, and visual/immersive analytics. Current projects address 3D interaction challenges, occasionally failing systems, and hardware/software innovations for extended reality. Notable contributions include studies on vergence-accommodation conflict, gaze-based text entry, and locomotion techniques in VR. **Awards**: Member of the IEEE VGTC Virtual Reality Academy (2023) and ACM SIGCHI Academy, recognizing leadership in VR and HCI research. **Grants & Labs**: Leads the VVISE Lab, collaborating on projects like RedirectedStepper and Depth3DSketch. Active in grant-funded research on immersive analytics and accessibility in XR navigation. **Teaching**: Teaches courses such as IAT 445 (Immersive Environments) and IAT 848 (Mediated Reality Systems).

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

Jonathan Wu is a distinguished academic and Professor in the Faculty of Engineering at the University of Windsor. He is recognized for his leadership in artificial intelligence, computer vision, and signal processing. His research bridges theoretical advancements with practical applications, including maritime surveillance using High-Frequency Surface Wave Radar (HFSWR) and cybersecurity protocols for vehicular networks. Wu holds notable accolades such as Fellow of the Canadian Academy of Engineering (2019) and a Canada Research Chair (2018). His work spans interdisciplinary areas such as medical imaging, robotics, and data science. Notable contributions include developing novel algorithms for semantic segmentation, authentication protocols, and radar-based target detection systems. Wu has advised graduate students like Ashirbani Saha and Kaushik Ray on projects involving facial recognition and drunk-driving prevention technologies. His research emphasizes real-world impact, with funding for AI integration in manufacturing and contributions to international conferences and journals.

• Authentication
• Brain-Computer/Machine Interface
• Cybersecurity
• +۱ مورد دیگر

Dr. Garima Bajwa is an Assistant Professor in the Department of Computer Science at Lakehead University, where she joined in January 2021. Her research focuses on authentication systems, brain-computer/machine interfaces, cybersecurity, and machine learning applications. She holds a Ph.D. in Computer Science and Engineering from the University of North Texas (2016), an MEng in Electrical and Computer Engineering from the University of Waterloo (2011), and a B.Tech in Electronics and Communication Engineering from Mody Institute of Technology & Science, India (2009). Education: Ph.D., Computer Science & Engineering, University of North Texas (2016) MEng, Electrical & Computer Engineering, University of Waterloo (2011) B.Tech, Electronics & Communication Engineering, Mody Institute (2009) Her research interests span cybersecurity frameworks for neurotechnology, explainable AI in healthcare diagnostics, and multimodal fusion of EEG and image data. Recent work includes EEG signal anonymization, federated learning for medical diagnosis, and reproducibility in BCI research. Her publications explore cutting-edge topics like BCI standardization, driver distraction detection, and quantum computing integration. Grants and lab affiliations are not explicitly mentioned in the provided text.

Reto Achermannمشاهده پروفایل

استادیار

• Resilient Systems
• Efficient Systems
• Operating Systems
• +۵ مورد دیگر

Reto Achermann is an Assistant Professor in the Department of Computer Science at the University of British Columbia (UBC), affiliated with the Systopia Lab. His research focuses on resilient and efficient systems at the intersection of operating systems, applied formal methods, and hardware models. He holds a PhD from ETH Zurich and previously served as a Postdoctoral Research Fellow at UBC under Prof. Margo Seltzer. Education: PhD in Computer Science from ETH Zurich (advised by Prof. Timothy Roscoe), MSc in Computer Science from ETH Zurich. Research interests include memory and storage systems, formal verification, device drivers, and software synthesis. He contributed to the Barrelfish OS project, particularly in memory management and hardware abstractions. Key achievements: Won distinguished artifact awards at ASPLOS'25 and SOSP'24 for 'Velosiraptor' and 'Verus'. Received UBC's Faculty of Science Excellence in Service Award for educational contributions. Served on program committees for ASPLOS, PLDI, EuroSys, and USENIX ATC. Current roles: Actively supervises graduate students in thesis-based MSc/PhD programs. Engages in interdisciplinary research and collaborates on grants. Previously advised undergraduate research projects and directed studies.

Peter D. Lawrenceمشاهده پروفایل

استاد

• Human-Machine Interfaces
• Robotics and Control Systems
• Computer Vision
• +۴ مورد دیگر

Professor Peter D. Lawrence holds a faculty position at the University of British Columbia (UBC) within the Department of Electrical & Computer Engineering, part of the Faculty of Applied Science. He has been a Professor since 1974 and has held visiting research roles at Chalmers University of Technology (1970-1972) and MIT (1972-1974). His educational background includes a B.A.Sc. from the University of Toronto (1965), M.Sc. from the University of Saskatchewan (1967), and Ph.D. from Case Western Reserve University (1970). He is a Professional Engineer (P.Eng.) and Fellow of the Canadian Academy of Engineering (FCAE). Research interests focus on improving human-machine interfaces, sensor technologies for control systems, and medical robotics. Key areas include teleoperation of heavy machinery, vision-based control, EEG-based brain interfaces, and functional approximation methods for complex systems. Collaborations span multiple disciplines at UBC, including Mechanical Engineering, Computer Science, Mining Engineering, and Forestry, with funding from NSERC and PRECARN/IRIS. Medical Robotics: Brain-computer interfaces and ultrasound-guided surgery. Autonomous Systems: Path planning and vision-based tracking for excavators and haul trucks. Sensing Technologies: Eye-tracking, joint-angle sensors, and slip detection for mobile robots. His teaching contributions include coordinating the Project Integrated Program (PIP) for ECE students and co-developing the interdisciplinary New Venture Design course with the Sauder School of Business. He leads the RCL Lab and has authored books on real-time microcomputer systems and contributed to IEEE publications. Awards include recognition as a Fellow of the Canadian Academy of Engineering.

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.